Prediction of heterosis and other traits by transcriptome analysis

This invention relates to methods of producing hybrid plants and hybrid non-human animals having high levels of hybrid vigour or heterosis and/or producing plants and non-human animals (e.g. hybrid, inbred or recombinant plants) having other traits such as desired flowering time, seed oil content and/or seed fatty acid ratios, and plants and non-human animals produced by these methods .

The invention relates to selection of suitable organisms, preferably plants or non-human animals, for use in producing hybrids and/or for use in breeding programmes, e.g. screening of germplasm collections for plants that may be suitable for inclusion in breeding programmes.

Many animal and plant species exhibit increased growth rates, reach larger sizes and, in the cases of crops [1,2] and farm animals [3, 4], have higher yields and productivity when bred as hybrids, produced by crossing genetically dissimilar parents, a phenomenon known as hybrid vigour or heterosis [5] . The term heterosis can be applied to almost any aspect of biology in which a hybrid can be described as outperforming its parents.

The degree of heterosis observed varies a lot between different hybrids. The magnitude of heterosis can be described relative to the mean value of the parents (Mid-Parent Heterosis, MPH) or relative to the "better" of the parents (Best-Parent Heterosis, BPH) .

Heterosis is of great importance in many agricultural crops and in plant and animal breeding, where it is clearly desirable to produce hybrids with high levels of heterosis. However, despite extensive genetic analysis in this area, the molecular mechanisms

underlying heterosis remain poorly understood. Some progress has been made towards understanding the heterosis observed in simple traits controlled by single genes [6] , but the mechanisms controlling more complex forms of heterosis, such as the vegetative vigour of hybrids, remain unknown [7, 8, 9] .

Genetic analyses of heterosis have led to three, non-exclusive, genetic mechanisms being hypothesised to explain heterosis: the "dominance" model, in which heterotic interactions are considered to be the cumulative effect of the phenotypic expression of dispersed dominant alleles, whereby deleterious alleles that are homozygous in the respective parents are complemented in the hybrids [2, 10]; the "overdominance" model, in which heterotic interactions are considered to be the result of heterozygous loci resulting in a phenotypic expression in excess of either parent, so that the heterozygosity per se produces heterosis [5, 11, 12) ; - the "epistatic" model, which includes other types of specific interactions between combinations of alleles at separate loci [13, 14] .

Hypothetical models based on gene regulatory networks have been proposed to explain these types of interaction [15] .

Whilst the hypothesised models attempt to explain in genetic terms at least a proportion of heterosis observed in hybrids, they do not provide a practical indicator that would enable breeders to predict quantitatively the level of heterosis for a given hybrid or to know which hybrid crosses are likely to perform well.

In allogamous crops, such as maize, heterotic groups have been established that enable the selection of inbreds that will show good heterosis when crossed. For example, Iowa Stiff Stalk vs. Non-Stiff Stalk lines [16] . Inter-group hybrids have greater genetic distance and heterosis than hybrids produced by crossing

within an individual heterotic group [17] and it has been proposed that the level of genetic diversity may be a predictor of heterosis and yield [18] . However, this has not proven to be a reliable approach for the prediction of heterosis in crops [17] . Heterosis shows an inconsistent relationship with the degree of relatedness of the two parents, with an absence of correlation reported between heterosis and genetic distance in Arabidopsis thaliana [7, 19] and other species [20, 21, 22] . Thus, in general the level of heterosis observed in a hybrid does not depend solely upon the genetic distance between the two parents from which the hybrid was produced, nor does this variable, genetic distance, necessarily provide a good indicator of likely heterosis of hybrids.

At the gene transcript level, expression of alleles in a hybrid may represent the cumulative level of expression of the alleles inherited from each parent, or expression may be non-additive. Non-additive patterns of gene expression are believed to contribute to hybrid effects and therefore several studies have investigated non-additive gene expression in hybrids compared with their parents. Characteristics of the transcriptome (the contribution to the rriRNA pool of each gene in the genome) have been analysed in heterotic hybrids of crop plants, and extensive differences in gene expression in the hybrids relative to the parents have been reported [23, 24, 25, 26, 27] . Hybrid transcriptomes were shown to be different from the transcriptomes of the parents. Quantitative changes were seen in the contribution to the mRNA pool of a subset of genes, when the transcriptomes of the hybrids were compared with the transcriptomes of their parents. These experiments were conducted with the expectation that differences in the transcriptomes of the hybrids, compared with their parents, contribute to the basis of heterosis.

Using differential display, Sun et al [24] identified differences in gene expression, of approximately 965 genes, between wheat

seedling hybrids and their parents. The hybrids were generated from two single direction crosses, and represented one heterotic and one non-heterotic sample. Differences in gene expression were found between the hybrids and the parents, with some evidence provided of differences in response between the hybrids. In later experiments, Sun et al [28] used differential display techniques to identify changes in transcriptional remodelling for 2800 genes, between nine parental and 20 wheat hybrids. They found that around 30% of these genes showed some degree of remodelling. Broad trends in gene expression were assessed by random amplification. Gene expression differences were observed between the hybrid and both parents, between the hybrid and one parent only, and genes expressed only in the hybrid. The total number of non-additively expressed genes was found to correlate with some traits. The authors concluded that these differences in gene expression must be involved in developing a heterotic phenotype .

Guo et al. [29] reported allele-specific variation in transcript abundance in hybrids. Transcript abundance of 15 genes was analysed in maize hybrids, and transcript levels for the two alleles of each gene were compared. In 11 genes, the two alleles were found to be expressed unequally (bi-allelic expression) , and in 4 genes just one allele was expressed (mono-allelic expression) . Allele-specific differences in expression were observed between genetically different hybrids. Additionally, the two alleles in each hybrid were shown to respond differently to abiotic stress. Allele-specific differences may indicate different functions for the two parental alleles in hybrids, and this functional diversity of the two parental alleles in the hybrid was suggested to have an impact on heterosis.

Auger et al. [27] examined differences in transcript abundance between hybrids relative to their inbred parents. Several genes were found to be expressed at non-additive levels in the hybrids, but relevance to heterosis was not demonstrated.

Vuylsteke et al. [30] measured variations in transcript abundance between three inbred lines and two pairs of reciprocal F ₁ hybrids of Arabidops±s . Non-additive levels of gene expression in the hybrids were used to estimate the proportion of genes expressed in a "dominance" fashion according to a genetic model of heterosis .

Microarray technology has also been used to study differences in transcript abundance across plant populations. For example, Kliebenstein et al. [31] used microarrays to quantify gene expression in seven Arabidopsis accessions, and found an average of 2234 genes to be significantly differentially expressed between any pair of accessions. The differences in gene expression were found to be related to sequence diversity in the accessions. Kirst et al. [32] examined transcript abundance in a pseudobackcross population of eucalyptus in order to compare transcript regulation in different genetic backgrounds of eucalyptus, and concluded that the genetic control of transcript levels was modulated by variation at different regulatory loci in different genetic backgrounds. Paux et al. [33] also conducted transcript profiling of eucalyptus genes, to examine gene expression during tension wood formation.

Another mechanism that has been proposed to explain heterosis is complementation of bottlenecks in metabolic systems [34] . It is possible that several different mechanisms are involved in heterosis, so that any one specific mechanism may only explain a proportion of heterosis observed.

Heterosis has been the subject of intense genetic analysis for almost a century, but no reliable and accurate basis for determining, predicting or influencing the degree of heterosis in a given hybrid has yet been identified. Thus, there has been a long-felt need to identify some basis on which parents may be selected in order to produce hybrids of increased vigour.

Attempts to produce hybrids with high levels of heterosis must currently be undertaken on the basis of trial and error, by experimentally crossing different parents and then waiting for the progeny to grow until it can be seen which of the new hybrids exhibit the most vigour. Breeding for new heterotic hybrids thus necessarily results in the co-production of significant numbers of under-performing hybrids with low hybrid vigour. The desired hybrids may not be obtained, or may only represent a fraction of the total number of hybrids produced overall. Additionally, hybrids must normally reach a certain age before their level of heterosis can be determined, which increases still further the time, cost and resources that must be invested in a breeding program, since it is necessary to continue to grow large numbers of hybrids even though many, or perhaps all, will not have the desired characteristics.

A method that could provide at least some measure of prediction of the level of heterosis likely to be exhibited by a given hybrid could result in significantly more effective breeding programs .

There are comparable needs to determine a basis on which plants or animals may be selected as parents for producing hybrids with further desirable multigenic traits, and for predicting which hybrid, inbred or recombinant plants or animals are likely to exhibit desired traits.

The invention disclosed herein is based on the unexpected finding that transcript abundance of certain genes is predictive of the degree of heterosis in a hybrid. Transcriptome analysis may be used to identify genes whose transcript abundance in hybrids correlates with heterosis. The abundance of those gene transcripts in a new hybrid can then be used to predict the degree of heterosis of the new hybrid. Moreover, transcriptome analysis may be used to identify genes whose transcript abundance in plants or animals correlates with heterosis in hybrids

produced by crossing those plants or animals. Thus, transcriptome data from parents can be used to predict the magnitude of heterosis in hybrids which have yet to be produced.

We show herein that changes in transcript abundance in the transcriptome represent the majority of the basis of heterosis. Importantly, this means that predictions based on transcript abundance are close to the observed magnitude of heterosis, i.e. the invention allows quantitative prediction of the degree of heterosis in a hybrid. Transcriptome characteristics alone may thus be used to predict heterosis in hybrids and as a basis for selection of parents.

Thus, remarkably, we have solved a problem that has been unanswered for almost a century. By demonstrating that the basis of heterosis resides primarily at the level of the regulation of transcript abundance, we have provided a means of predicting heterosis in hybrids and thus selecting which hybrids to maintain. Furthermore, we were able to identify characteristics of parental transcriptomes that could be used successfully as markers to predict the magnitude of heterosis in untested hybrids, and we have thus also provided basis for identifying parents which can be crossed to produce heterotic hybrids.

This invention differs from previous studies involving transcriptome analysis of hybrids, since those earlier studies did not identify any relationship between the transcriptomes of hybrids and the degree of heterosis observed in those hybrids. As discussed above, earlier studies showed that transcript levels of some genes differ in hybrids compared with the parents from which those hybrids were derived, and differences between hybrid and parent transcriptome were suggested to contribute to phenotypic differences including heterosis. However, the previous ^' investigators did not compare transcriptome remodelling in a range of non-heterotic hybrids and heterotic hybrids, and

did not show whether transcriptome remodelling correlates with heterosis .

We have recognised that most differences in the hybrid transcriptome are due to hybrid formation, not heterosis. We found that, in fact, transcriptome remodelling involving transcript abundance fold-changes of 2 or more occurs to a similar extent in all hybrids relative to their parents, regardless of the degree of heterosis observed in the hybrids. Accordingly, the overall degree of transcriptome remodelling in a hybrid is not an indicator of the degree of heterosis in that hybrid.

Therefore, earlier studies involving limited numbers of hybrids were not able to identify genes whose transcript abundance correlated with heterosis. The vast majority of differences in transcript abundance observed in earlier studies would have been due only to hybrid formation itself, and would not show any correlation with heterosis. Nor was any such correlation even looked for in the prior art, since it was not recognised that a correlation might exist.

However, despite showing that the overall degree of transcriptome remodelling in a hybrid is not related to heterosis, we found that transcriptome analysis can nevertheless be used to reveal features of the hybrid transcriptome that are predictive of the degree of heterosis in a hybrid. Through transcriptome analysis of a wide range of hybrids we have unexpectedly shown that transcript abundance of a proportion of genes correlates with heterosis. As described herein, we studied 13 different heterotic hybrids of Arabidopsis thaliana , and identified features of the hybrid transcriptome that are characteristic of heterotic interactions. We identified 70 genes whose transcript abundance in the hybrid transcriptome correlated with the degree of heterosis in the Arabidopsis hybrids. We then successfully used the transcript abundance of that defined set of 70 genes to

quantitatively predict the magnitude of heterosis observed in 3 untested hybrid combinations. Transcript abundance of two additional genes, Atlg67500 and At5g45500, was also shown to have a significant negative correlation with heterosis. Transcript abundance of each of these genes successfully predicted heterosis in further hybrids.

Further, we identified a larger set of genes whose transcript abundance in the transcriptome of Arabidopsis inbred lines correlated with the degree of heterosis in hybrid progeny produced by crossing those lines. We successfully used the transcript abundance of that set of genes to quantitatively predict the magnitude of heterosis in 3 hybrids produced from those lines. Transcript abundance of At3gll220 was found to be negatively correlated with heterosis in a highly significant manner and transcript abundance of this gene in the parental transcriptome was found to be predictive of heterosis in hybrid offspring.

Heterosis in hybrids of Arabidopsis thaliana may be predicted on the basis of the transcript abundance of these identified Arabidopsis genes. Moreover, since heterosis is a widely observed phenomenon, and is not restricted to Arabidopsis or even to plants, but is also observed in animals, it is to be expected that many of the same genes whose transcript abundance correlates with heterosis in Arabidopsis will also correlate with heterosis in other organisms. Transcript abundance of orthologues of those genes in other species may thus correlate with heterosis.

However, prediction of heterosis need not be based on genes selected from the sets of genes disclosed herein, since one aspect of the invention is use of transcriptome analysis to identify the particular genes whose transcript abundance correlates with heterosis in any population of hybrids that is of interest. Once identified, those genes may then be used for prediction of heterosis or other trait in the particular hybrids

of interest. Whilst the identified genes may include at least some genes, or orthologues thereof, from the set of genes identified in Arabidopsis, they need not do so.

The invention enables hybrids likely to exhibit high levels of heterosis to be identified and selected, while hybrids likely to exhibit lower degrees of heterosis may be discarded. Notably, the invention may be used to predict the level of heterosis in a hybrid at an early stage in the life of the hybrid, for example in a seedling, before it would be possible to directly observe differences between heterotic and non-heterotic hybrids. Thus, the invention may be used in a hybrid whose degree of heterosis is not yet determinable from its phenotype . The invention thus provides significant benefits to a breeder, since it allows a breeder to determine which particular hybrids in a potentially vast array of different hybrids should be retained and grown. For example, a breeder may use transcript abundance data from seedlings to decide which plant hybrids to grow or test in yield/performance trials.

Furthermore, we have shown that regulation of transcript abundance underlies not only heterosis but also other traits. These may include all genetically complex traits in hybrid, inbred or recombinant plants and animals, e.g. flowering time or seed composition in plants. Accordingly, the invention also relates to determining features of plant or non-human animal transcriptomes (e.g. transcriptomes of hybrids and/or inbred or recombinant plants or animals) for prediction of other traits in the plant or animal or offspring thereof. Where the invention relates to traits other than heterosis, the plant or animal may be a hybrid or alternatively it may be inbred or recombinant. Examples of traits that may be predicted using the invention are yield, flowering time, seed oil content and seed fatty acid ratios in plants, especially plant hybrids, e.g. accessions of A. thaliana. These and other traits may also be predicted in the plant or non-human animal (e.g. hybrid, inbred or recombinant

plant or animal) before those traits are manifested in the phenotype. Thus, for example, we demonstrate herein that the invention allows seed oil content of inbred plants to be accurately predicted by analysis of plants that have not yet flowered. The invention thus confers significant predictive, cost and workload reductive advantages, particularly for traits manifested at a relatively late stage, since it means that it is not necessary to wait until a plant or animal reaches a particular (often late) stage of development before being able to know the magnitude or properties of the trait that will be exhibited by a given plant or animal.

Other aspects of the invention allow prediction of traits in plants or animals based on characteristics of their parents, and thus traits of plants or animals may be predicted and selected for even before those plants or animals are produced. As noted above, the trait may be heterosis in a plant or animal hybrid. Therefore, in accordance with the invention, features of plant or animal transcriptoπtes may be identified that allow the degree of heterosis of plants or animals produced by crossing those plants or animals to be predicted. The invention can be used to predict one or more traits, such as the degree of heterosis observed in plants or animals produced by crossing different combinations of parental germplasms . This is potentially as valuable or even more valuable than being able to predict heterosis and other traits in plants and animals that have already been produced, since it avoids producing under-performing plants or animals and therefore allows significant savings in logistics, costs and time. Particular plants or animals may thus be selected for breeding, with an increased chance that their progeny will be heterotic hybrids, or possess other traits, compared with if the parents were selected at random. Thus, the methods of the invention allow prediction in terms of the level of heterosis or of other traits produced by any particular cross between different parents, and allow particular parents to be selected accordingly. For example in agricultural crop plant breeding the

invention reduces the need to make large numbers of different crosses in order to obtain new heterotic hybrids, since the invention can be used to identify in advance which particular crosses will be most productive.

Remarkably, methods of the invention may be used to predict traits based on transcript abundance in tissues in which the trait is not exhibited or which have no apparent relevance to the trait. For example, traits such as flowering time or seed composition may be predicted in plants based on transcript abundance data from non-flowering tissue, such as leaf tissue. Thus, the invention allows generation of statistical correlations between one or more traits and abundance of one or more gene transcripts. There is no requirement for the tissue sampled for transcriptome analysis to be the same as that used for trait measurement. It may be preferable that the tissue sampled for transcriptome analysis is, in terms of evolution, be a more ancient origin - hence the transcriptome in leaves can be used to predict more recently evolved characteristics of plants, such as flowering time or seed composition.

Based on the extensive transcriptome remodelling in hybrids of Arabidopsis thaliana disclosed herein, including some combinations that are heterotic for vegetative biomass and some combinations that are non-heterotic, it is evident that the methods of the invention may be applied to advantage in crops of economic importance.

Maize is currently bred as a hybrid crop, with its cultivation in the UK being for silage from the whole plant. Biomass yield is therefore paramount, and heterosis underpins this yield. In the USA maize is primarily grown for corn production, for which kernel weight represents the productive yield, and this yield is also dependent on heterosis. The ability to efficiently select for hybrid performance at an early stage of the hybrid parent breeding process provided by the method of this invention greatly

accelerates the development of hybrid plant lines to increase yields and introduce a range of "sustainability" traits from exotic germplasm without loss of yield. Oilseed rape hybrids hold much potential, but their exploitation is limited as heterosis is often restricted to vegetative vigour, with little improvement in seed dry weight yield. The ability to select for specific performance traits at early stages of growth similarly accelerates the development of more productive and sustainable varieties. There is great potential for hybrid breeding of bread wheat (already a hexaploid, so benefits from some "fixed" heterosis) which, like oilseed rape, is supported by a breeding community based in the UK. In addition, hybrid varieties are important for a large number of vegetable species cultivated in the UK (such as cabbages, onions, carrots, peppers, tomatoes, melons), which are grown for enhancement of crop uniformity, appearance and general quality. Use of the invention to define a predictive marker for heterosis and other performance traits thus has the potential to revolutionise both the breeding process and the performance of crops for the farmer.

As demonstrated in the Examples, we identified relationships between gene expression in glasshouse-grown seedlings of maize inbreds and phenotypes (grain yield) in related plants at a later developmental stage and after growth under different environmental conditions.

In summary, the invention involves use of transcriptome analysis of plants or animals, e.g. hybrids and/or inbred or recombinant plants or animals, for: (i) identifying genes involved in the manifestation of heterosis and other traits; and/or

(ii) predicting and producing plants or animals of improved heterosis and other traits by selecting plants or animals for breeding, wherein the plants or animals which exhibit enhanced transcriptome characteristics with respect to a selected set of

genes relevant to the transcriptional regulatory networks present in potential parental breeding partners; and/or

(iii) predicting a range of trait characteristics for plants and animals based on transcriptome characteristics.

The invention also relates to plant and animal hybrids of improved heterosis, and to hybrids, inbreds or recombinants with improved traits as produced or predicted by the methods of the invention.

The results disclosed herein provide evidence for a link between heterosis and growth repression that is a consequence of stress tolerance mechanisms. We identified a number of genes which are highly predictive of heterosis, and which showed a significant negative correlation between gene expression and heterotic performance. As discussed in the Examples herein, these genes may represent key genetic loci that are downregulated in heterotic hybrids, leading to decreased expression of stress- avoidance genes and thus allowing better hybrid performance under favourable conditions. This raises the possibility that heterosis, at least for vegetative biomass, is at least partly a consequence of genetic interactions that lead to a reduction in repression of growth, rather than direct promotion of growth. However, whatever the molecular mechanism underlying heterosis, we have established that certain genes and sets of genes predictive of heterosis may be identified and successfully used in accordance with the present invention for predicting heterosis .

A hybrid is offspring of two parents of differing genetic composition. Thus, a hybrid is a cross between two differing parental germplasms . The parents may be plants or animals. A hybrid is typically produced by crossing a maternal parent with a different paternal parent. In plants, the maternal parent is usually, though not necessarily, impaired in male fertility and

the paternal parent is a male fertile pollen donor. Parents may for example be inbred or recombinant.

An inbred plant or animal typically lacks heterozygosity. Inbred plants may be produced by recurrent self-pollination. Inbred animals may be produced by breeding between animals of closely related pedigree.

Recombinant plants or animals are neither hybrid nor inbred. Recombinants are themselves derived by the crossing of genetically dissimilar progenitors and may contain extensive heterozygosity and novel combinations of alleles . Most samples in germplasm collections of plant breeding programmes are recombinant .

The invention may be used with plants or animals. In some embodiments the invention preferably relates to plants. For example, the plants may be crop plants. The crop plants may be cotton, sugar beet, cereal plants (e.g. maize, wheat, barley, rice), oil-seed crops (e.g. soybeans, oilseed rape, sunflowers), fruit or vegetable crop plants (e.g. cabbages, onions, carrots, peppers, tomatoes, melons, legumes, leeks, brassicas e.g. broccoli) or salad crop plants e.g. lettuce [35] . The invention may be applied to hardwood timber trees or alder trees [36] . All species grown as crops could benefit from the invention, irrespective of whether they are currently cultivated extensively as hybrids.

Other embodiments relate to non-human animals e.g. mammals, birds and fish, including farm animals for example cattle, pigs, sheep, birds or poultry (e.g. chickens), goats, and farmed fish e.g. salmon, and other animals such as sports animals e.g. racehorses, racing pigeons, greyhounds or camels. Heterosis has been described in a variety of different animals including for example pigs [37], sheep [38, 39], goats [39], alpaca [39], Japanese

quail [40] and salmon [41], and the invention may be applied to these and to other animals.

The invention can most conveniently be used in relation to organisms for which the genome sequence or extensive collections of Expressed Sequence Tags are available and in which microarrays are preferably also available and/or resources for transcriptome analysis have been developed.

In one aspect, the invention is a method comprising: analysing the transcriptomes of plants or animals in a population of plants or animals; measuring a trait of the plants or animals in the population; and identifying a correlation between transcript abundance of one or more, preferably a set of, genes in the plant or animal transcriptomes and the trait in the plants or animals.

Thus the invention provides a method of identifying an indicator of a trait in a plant or animal.

The population may comprise e.g. at least 5, 10, 20, 30, 40, 50 or 100 plants or animals. Use of a large population to obtain trait measurements from many different plants or animals may allow increased accuracy of trait predictions based on correlations identified using the population.

The invention may thus be used to generate a model (e.g. a regression, as described in detail elsewhere herein) for predicting the trait based on transcript abundance of the one or more genes e.g. a set of genes.

One or more traits may be determined or measured, and thus correlations may be identified, and models may be generated, for a plurality of traits.

The plant or animal may be a hybrid, or it may be inbred or recombinant. In a preferred embodiment the plant or animal is a hybrid. A preferred trait is heterosis.

Plants or animals in a population may or may not be related to one another. The population may comprise plants or animals, e.g. hybrids, having different maternal and/or paternal parents. In some embodiments, all plants or animals, e.g. hybrids, in the population have the same maternal parent, but may have different paternal parents. In other embodiments, all plants or animals, e.g. hybrids, in the population have the same paternal parent, but may have different maternal parents. Parents may be inbred or recombinant, as explained elsewhere herein.

Methods for determining heterosis, for transcriptome analysis and for identifying statistical correlations are described in detail elsewhere herein.

Determining or measuring heterosis or other trait can be performed once the relevant phenotype is apparent e.g. once the heterosis can be calculated, or once the trait can be measured.

Transcriptome analysis may be performed at a time when the degree of heterosis or other trait of the plant or animal can be determined. Transcriptome analysis may be performed after, normally directly after, measurements are taken for determining or measuring heterosis or other trait in the plant or animal. This is suitable e.g. when measurements are taken for determining heterosis for fresh weight in hybrids.

However, we have demonstrated herein that it is possible to use transcriptome analysis of plants at a relatively early developmental stage, e.g. before flowering, to identify genes whose transcript abundance correlates with traits that only occur later in development, e.g. traits such as the time of flowering and aspects of the composition of seeds produced by plants.

Accordingly, transcriptome analysis may be performed when the degree of heterosis or other trait is not yet determinable from the phenotype. This is suitable e.g. when measuring aspects of performance other than fresh weight, such as yield, for determining heterosis. For example, transcriptome analysis may be performed when plants are in vegetative phase or when animals are pre-adolescent, in order to predict heterosis for characteristics that are evident later in development, or to predict other traits that are evident later in development. For example, heterosis for seed or crop yields, or traits such as flowering time, seed or crop yields or seed composition, may be predicted using transcriptome data from vegetative phase plants.

Correlations between traits and transcript abundance represent models that may be used to predict traits in further plants or animals by determining transcript abundance in those plants or animals .

Thus, in another aspect, the invention is a method comprising: determining transcript abundance of one or more, preferably a set of, genes in a plant or animal, wherein the transcript abundance of the one or more genes, or set of genes, in the transcriptome of the plant or animal correlates with a trait in the plant or animal; and thereby predicting the trait in the plant or animal.

The analysis of transcript abundance is predictive of the trait in a plant or animal of the same genotype as the plant or animal in which transcript abundance was determined. Thus, in some embodiments the method may be used for the purpose of predicting a trait in the actual plant or animal whose transcript abundance is determined, and in other embodiments the method may be used for the purpose of predicting a trait in another plant or animal that is genetically identical to the plant or animal whose transcript abundance was sampled. For example the method may be used for predicting a trait in a genetically identical plant or

animal that may be grown or produced subsequently, and indeed the decision whether to grow or produce the plant or animal may be informed by the trait prediction.

Methods of the invention may comprise determining transcript abundance of one or more genes, preferably a set of genes, in a plurality of plants or animals, and thus predicting one or. more traits in the plurality of plants or animals. Thus, the invention may be used to predict a rank order for the trait in those plants or animals, which allows selection of plants or animals that are predicted to exhibit the highest or lowest trait- (e.g. longest or shortest time to flowering, highest seed oil content, highest heterosis) .

The plant or animal may be a hybrid, or it may be inbred or recombinant. In a preferred embodiment the plant or animal is a hybrid. A preferred trait is heterosis, and thus the method may be for predicting the magnitude of heterosis in a hybrid.

A method of the invention may comprise: determining transcript abundance of one or more, preferably a set of, genes in a plant or animal, e.g. a hybrid, wherein transcript abundance of the one or more genes, or set of genes, correlates with a trait in a population of plants or animals, e.g. a population of hybrids; and thereby predicting the trait in the plant or animal.

Plants or animals in the population may or may not be related to one another. The population typically comprises plants or animals, e.g. hybrids, having different maternal and/or paternal parents. In some embodiments, all plants or animals in the population have the same maternal parent, but may have different paternal parents. In other embodiments, all plants or animals in the population have the same paternal parent, but may have different maternal parents. Where plants or animals in the population share a common maternal parent or a common paternal

parent, the plant or animal in which the trait is predicted may share the same common maternal or paternal parent, respectively.

The method may comprise, as an earlier step, a method of identifying an indicator of the trait in a plant or animal, as described above.

The plant or animal in which the indicator of the trait is identified may be the same genus and/or species as the plant or animal in which transcript abundance is determined for prediction of the trait. However, as discussed elsewhere herein, predictions of traits in one species may be performed based on correlations between transcript abundance and trait data obtained in other genus and/or species.

Thus, the invention may be used to predict one or more traits in a plant or animal, typically a previously untested plant or animal. As noted above, the method is useful for predicting heterosis or other trait in a plant or animal when heterosis or other trait is not yet determinable from the phenotype of the organism at the time, age or developmental stage at which the transcriptome is sampled. In a preferred embodiment the method comprises analysing the transcriptome of a plant prior to flowering.

Suitable methods of determining transcript abundance and of predicting heterosis or other traits based on transcript abundance are described in more detail elsewhere herein.

Once genes whose levels of transcript abundance are involved in heterosis or other traits have been identified for a given plant or animal species, further aspects of the invention may involve regulation of transcript abundance, regulation of expression of one or more of those genes, or regulation of one or more proteins encoded by those genes, in order to regulate, influence, increase

or decrease heterosis or another trait in a plant or animal organism.

Thus, the invention may involve increasing or decreasing heterosis or other trait in an organism, by upregulating one or more genes or their encoded proteins, wherein transcript abundance of the one or more genes correlates positively with heterosis or other trait in the organism, or by downregulating one or more genes or their encoded proteins in an organism, wherein transcript abundance of the one or more genes correlates negatively with heterosis or other trait in the organism. Thus, heterosis and other desirable traits in the organism may be increased using the invention. The invention also extends to plants and animals in which traits are up- or down-regulated using methods of the invention. The invention may comprise down- regulating one or more genes involved in stress avoidance or stress tolerance, wherein transcript abundance of the one or more genes is negatively correlated with heterosis, e.g. heterosis for biomass .

Examples of genes whose transcript abundance correlates positively with heterosis, and examples of genes whose transcript abundance correlates negatively with heterosis, are shown in Table 1 and Table 19. Additionally, transcript abundance of genes Atlg67500 and At5g45500 correlates negatively with heterosis. In a preferred embodiment the one or more genes are selected from Atlg67500 and At5g45500 and/or those shown in Table 1 and/or Table 19, or are orthologues of Atlg67500 and/or At5g45500 and/or of one or more genes shown in Table 1 and/or Table 19.

The invention may involve increasing or decreasing a trait in an organism, by upregulating one or more genes whose transcript abundance correlates negatively with the trait in the organism, or by downregulating one or more genes whose transcript abundance correlates positively with the trait in hybrids. Thus,

undesirable traits in organisms may be decreased using the invention.

Examples of genes whose transcript abundance correlates with particular traits are shown in Tables 3 to 17, Table 20 and Table 22. Preferred embodiments of the invention relate to one or more of those traits, and preferably to one or more of the listed genes for which transcript abundance is shown to correlate with those traits, as discussed elsewhere herein. Thus, the one or more genes may be selected from the genes shown in the relevant tables, or may be orthologues of those genes. For example, flowering time (e.g. as represented by leaf number at bolting) may be delayed (time to flowering increased, e.g. leaf number at bolting increased) by upregulating expression of one or more genes in Table 3A or Table 4A. Flowering time may be accelarated (time to flowering decreased, e.g. leaf number at bolting decreased) by downregulating expression of one or more genes in Table ^' 3B or Table 4B.

A trait may be increased by upregulating a gene for which transcript abundance correlates positively with the trait or by downregulating a gene for which transcript abundance correlates negatively with the trait. A trait may be decreased by downregulating a gene for which transcript abundance correlates positively with the trait or by upregulating a gene for which transcript abundance correlates positively with the trait.

Upregulation of a gene involves increasing its level of transcription or expression, and thus increasing the transcript abundance of that gene. Upregulation of a gene may comprise expressing the gene from a strong and/or constitutive promoter such as 35S CaMV promoter. Upregulation may comprise increasing expression of an endogenous gene. Alternatively, upregulation may comprise expressing a heterologous gene in a plant or animal, e.g. from a strong and/or constitutive promoter. Heterologous genes may be introduced into plant or animal cells by any

suitable method, and methods of transformation are well known in the art. A plant or animal cell may for example be transformed or transfected with an expression vector comprising the gene operably linked to a promoter e.g. a strong and/or constitutive promoter, for expression in the cell. The vector may integrate into the cell genome, or may remain extra-chromosomal.

By "promoter" is meant a sequence of nucleotides from which transcription may be initiated of DNA operably linked downstream (i.e. in the 3' direction on the sense strand of double-stranded DNA) .

"Operably linked" means joined as part of the same nucleic acid molecule, suitably positioned and oriented for transcription to be initiated from the promoter. DNA operably linked to a promoter is under transcriptional initiation regulation of the promoter.

Downregulation of a gene involves decreasing its level of transcription or expression, and thus decreasing the transcript abundance of that gene. Downregulation may be achieved for example by antisense or RNAi, using RNA complementary to messenger RNA (mRNA) transcribed from the gene.

Anti-sense oligonucleotides may be designed to hybridise to the complementary sequence of nucleic acid, pre-mRNA or mature mRNA, interfering with the production of polypeptide encoded by a given DNA sequence (e.g. either native polypeptide or a mutant form thereof) , so that its expression is reduce or prevented altogether. Anti-sense techniques may be used to target a coding sequence, a control sequence of a gene, e.g. in the 5' flanking sequence, whereby the antisense oligonucleotides can interfere with control sequences. Anti-sense oligonucleotides may be DNA or RNA and may be of around 14-23 nucleotides, particularly around 15-18 nucleotides, in length. The construction of

antisense sequences and their use is described in refs . [42] and [43] .

Small RNA molecules may be employed to regulate gene expression. These include targeted degradation of mRNAs by small interfering RNAs (siRNAs), post transcriptional gene silencing (PTGs), developmentally regulated sequence-specific translational repression of mRNA by micro-RNAs (itdRNAs) and targeted transcriptional gene silencing.

A role for the RNAi machinery and small RNAs in targeting of heterochromatin complexes and epigenetic gene silencing at specific chromosomal loci has also been demonstrated. Double- stranded RNA (dsRNA) -dependent post transcriptional silencing, also known as RNA interference (RNAi), is a phenomenon in which dsRNA complexes can target specific genes of homology for silencing in a short period of time. It acts as a signal to promote degradation of mRNA with sequence identity. A 20-nt siRNA is generally long enough to induce gene-specific silencing, but short enough to evade host response. The decrease in expression of targeted gene products can be extensive with 90% silencing induced by a few molecules of siRNA.

In the art, these RNA sequences are termed "short or small interfering RNAs" (siRNAs) or "microRNAs" (miRNAs) depending in their origin. Both types of sequence may be used to down- regulate gene expression by binding to complimentary RNAs and either triggering mRNA elimination (RNAi) or arresting mRNA translation into protein. siRNA are derived by processing of long double stranded RNAs and when found in nature are typically of exogenous origin. Micro-interfering RNAs (miRNA) are endogenously encoded small non-coding RNAs, derived by processing of short hairpins. Both siRNA and miRNA can inhibit the translation of mRNAs bearing partially complimentary target sequences without RNA cleavage and degrade mRNAs bearing fully complementary sequences.

The siRNA ligands are typically double stranded and, in order to optimise the effectiveness of RNA mediated down-regulation of the function of a target gene, it is preferred that the length of the siRNA molecule is chosen to ensure correct recognition of the siRNA by the RISC complex that mediates the recognition by the siRNA of the rriRNA target and so that the siRNA is short enough to reduce a host response.

iriiRNA ligands are typically single stranded and have regions that are partially complementary enabling the ligands to form a hairpin. miRNAs are RNA genes which are transcribed from DNA, but are not translated into protein. A DNA sequence that codes for a miRNA gene is longer than the miRNA. This DNA sequence includes the miRNA sequence and an approximate reverse complement. When this DNA sequence is transcribed into a single- stranded RNA molecule, the miRNA sequence and its reverse- complement base pair to form a partially double stranded RNA segment. The design of microRNA sequences is discussed in ref. [44] .

Typically, the RNA ligands intended to mimic the effects of siRNA or miRNA have between 10 and 40 ribonucleotides (or synthetic analogues thereof) , more preferably between 17 and 30 ribonucleotides, more preferably between 19 and 25 ribonucleotides and most preferably between 21 and 23 ribonucleotides. In some embodiments of the invention employing double-stranded siRNA, the molecule may have symmetric 3' overhangs, e.g. of one or two (ribo) nucleotides, typically a UU of dTdT 3' overhang. Based on the disclosure provided herein, the skilled person can readily design of suitable siRNA and miRNA sequences, for example using resources such as Ambion's siRNA finder, see http: //www. ambion. com/techlib/misc/siRNA_finder.html . siRNA and miRNA sequences can be synthetically produced and added exogenously to cause gene downregulation or produced using

expression systems (e.g. vectors). In a preferred embodiment the siRNA is synthesized synthetically.

Longer double stranded RNAs may be processed in the cell to produce siRNAs (see for example ref. [45]). The longer dsRNA molecule may have symmetric 3' or 5' overhangs, e.g. of one or two (ribo) nucleotides, or may have blunt ends. The longer dsRNA molecules may be 25 nucleotides or longer. Preferably, the longer dsRNA molecules are between 25 and 30 nucleotides long. More preferably, the longer dsRNA molecules are between 25 and 27 nucleotides long. Most preferably, the longer dsRNA molecules are 27 nucleotides in length. dsRNAs 30 nucleotides or more in length may be expressed using the vector pDECAP [46] .

Another alternative is the expression of a short hairpin RNA molecule (shRNA) in the cell. shRNAs are more stable than synthetic siRNAs. A shRNA consists of short inverted repeats separated by a small loop sequence. One inverted repeat is complimentary to the gene target. In the cell the shRNA is processed by DICER into a siRNA which degrades the target gene mRNA and suppresses expression. In a preferred embodiment the shRNA is produced endogenously (within a cell) by transcription from a vector. shRNAs may be produced within a cell by transfecting the cell with a vector encoding the shRNA sequence under control of a RNA polymerase III promoter such as the human Hl or 7SK promoter or a RNA polymerase II promoter. Alternatively, the shRNA may be synthesised exogenously (in vitro) by transcription from a vector. The shRNA may then be introduced directly into the cell. Preferably, the shRNA molecule comprises a partial sequence of the gene to be downregulated. Preferably, the shRNA sequence is between 40 and 100 bases in length, more preferably between 40 and 70 bases in length. The stem of the hairpin is preferably between 19 and 30 base pairs in length. The stem may contain G-U pairings to stabilise the hairpin structure.

siRNA molecules, longer dsRNA molecules or miRNA molecules may be made recombinantIy by transcription of a nucleic acid sequence, preferably contained within a vector. Preferably, the siRNA molecule, longer dsRNA molecule or miRNA molecule comprises a partial sequence of the gene to be downregulated.

In one embodiment, the siRNA, longer dsRNA or miRNA is produced endogenously (within a cell) by transcription from a vector. The vector may be introduced into the cell in any of the ways known in the art. Optionally, expression of the RNA sequence can be regulated using a tissue specific promoter. In a further embodiment, the siRNA, longer dsRNA or miRNA is produced exogenously (in vitro) by transcription from a vector.

In one embodiment, the vector may comprise a nucleic acid sequence according to the invention in both the sense and antisense orientation, such that when expressed as RNA the sense and antisense sections will associate to form a double stranded RNA. In another embodiment, the sense and antisense sequences are provided on different vectors.

Alternatively, siRNA molecules may be synthesized using standard solid or solution phase synthesis techniques which are known in the art. Linkages between nucleotides may be phosphodiester bonds or alternatives, for example, linking groups of the formula

P(O)S, (thioate); P(S)S, (dithioate) ; P(O)NR'2; P(O)R'; P(O)OR6; CO; or CONR' 2 wherein R is H (or a salt) or alkyl (1-12C) and R6 is alkyl (1-9C) is joined to adjacent nucleotides through-O-or-S-

Modified nucleotide bases can be used in addition to the naturally occurring bases, and may confer advantageous properties on siRNA molecules containing them.

For example, modified bases may increase the stability of the siRNA molecule, thereby reducing the amount required for

silencing. The provision of modified bases may also provide siRNA molecules which are more, or less, stable than unmodified siRNA.

The term ^λ modified nucleotide base' encompasses nucleotides with a covalently modified base and/or sugar. For example, modified nucleotides include nucleotides having sugars which are covalently attached to low molecular weight organic groups other than a hydroxyl group at the 3 'position and other than a phosphate group at the 5 'position. Thus modified nucleotides may also include 2 ' substituted sugars such as 2 ' -O-methyl- ; 2-0- alkyl ; 2-0-allyl ; 2'-S-alkyl; 2'-S-allyl; 2'-fluoro- ; 2 '-halo or 2; azido-ribose, carbocyclic sugar analogues a-anomeric sugars; epimeric sugars such as arabinose, xyloses or lyxoses, pyranose sugars, furanose sugars, and sedoheptulose.

Modified nucleotides are known in the art and include alkylated purines and pyrimidines, acylated purines and pyrimidines, and other heterocycles . These classes of pyrimidines and purines are known in the art and include pseudoisocytosine, N4,N4- ethanocytosine, 8-hydroxy-N6-methyladenine, 4-acetylcytosine, 5-

(carboxyhydroxylmethyl) uracil, 5 fluorouracil, 5-bromouracil, 5- carboxymethylaminomethyl-2-thiouracil, 5-carboxymethylaminomethyl uracil, dihydrouracil, inosine, N6-isopentyl-adenine, 1- methyladenine, 1-methylpseudouracil, 1-methylguanine, 2,2- dimethylguanine, 2methyladenine, 2-methylguanine, 3- methylcytosine, 5-methylcytosine, N6-methyladenine, 7- methylguanine, 5-methylaminomethyl uracil, 5-methoxy amino methyl-2-thiouracil, -D-mannosylqueosine, 5- methoxycarbonylmethyluracil, 5methoxyuracil, 2 methylthio-N6- isopentenyladenine, uracil-5-oxyacetic acid methyl ester, psueouracil, 2-thiocytosine, 5-methyl-2 thiouracil, 2-thiouracil, 4-thiouracil, 5methyluracil, N-uracil-5-oxyacetic acid methylester, uracil 5-oxyacetic acid, queosine, 2-thiocytosine, 5-propyluracil, 5-propylcytosine, 5-ethyluracil, 5ethylcytosine, 5-butyluracil, 5-pentyluracil, 5-pentylcytosine, and

2, 6, diaminopurine, methylpsuedouracil, 1-methylguanine, 1- methylcytosine .

Methods relating to the use of RNAi to silence genes in C. elegans, Drosophila, plants, and mammals are known in the art [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59].

Other approaches to specific down-regulation of genes are well known, including the use of ribozymes designed to cleave specific nucleic acid sequences. Ribozymes are nucleic acid molecules, actually RNA, which specifically cleave single-stranded RNA, such as mRNA, at defined sequences, and their specificity can be engineered. Hammerhead ribozymes may be preferred because they recognise base sequences of about 11-18 bases in length, and so have greater specificity than ribozymes of the Tetrahymena type which recognise sequences of about 4 bases in length, though the latter type of ribozymes are useful in certain circumstances. References on the use of ribozymes include refs. [60] and [61].

The plant or animal in which the gene is upregulated or downregulated may be hybrid, recombinant or inbred. Thus, in some embodiments the invention may involve over-expressing genes correlated with one or more traits, in order to improve vigour or other characteristics of the transformed derivatives of inbred plants and animals.

In a further aspect, the invention is a method comprising: analysing transcriptomes of parental plants or animals in a population of parental plants or animals; measuring heterosis or other trait in a population of hybrids, wherein each hybrid in the population is a cross between a first plant or animal and a plant or animal selected from the population of parental plants or animals; and identifying a correlation between transcript abundance of one or more genes, preferably a set of genes, in the population

of parental plants or animals and heterosis or other trait in the population of hybrids.

Thus, the invention provides a method of identifying an indicator of heterosis or other trait in a hybrid.

The plants or animals in the population whose transcriptomes are analysed are thus parents of the hybrids. These parents may be inbred or recombinant.

All hybrids in the population of hybrids used for developing each predictive model are the result of crossing one common parent with an array of different parents. Normally, all hybrids in the population share one common parent, which may be either the maternal parent or the paternal parent. Thus, the paternal parent of the all the hybrids in the population may be the "first parent plant or animal", or the maternal parent of all the hybrids in the population may be the "first parent plant or animal". For plants, a first female parent is normally crossed to a population of different male parents. For animals, a first male parent may preferably be crossed with a population of different females.

Suitable methods of determining or measuring heterosis in hybrids, of transcriptome analysis and of identifying correlations are discussed elsewhere herein.

Correlations between traits and transcript abundance represent models that may be used to predict traits in further plants or animals by determining transcript abundance in those plants or animals. The invention may thus be used to generate a model (e.g. a regression, as described in detail elsewhere herein) for predicting the trait based on transcript abundance of the one or more genes e.g. a set of genes.

Accordingly, in another aspect, the invention is a method of predicting heterosis or other trait in a hybrid, wherein the hybrid is a cross between a first plant or animal and a second plant or animal; comprising determining the transcript abundance of one or more genes, preferably a set of genes, in the second plant or animal, wherein the transcript abundance of those one or more genes, or of the set of genes, in a population of parental plants or animals correlates with heterosis or other trait in a population of hybrids produced by crossing the first plant or animal with a plant or animal from the population of parental plants and animals; and thereby predicting heterosis or other trait in the hybrid.

The invention may be used to predict one or more traits in hybrid offspring of parental plants or animals, based on transcript abundance in one of the parents. The parental plants or animals may be inbred or recombinant. Plants or animals may be referred to as "parents" or "parental plants or animals" even where they have not yet been crossed to produce a hybrid, since the invention may be used to predict traits in hybrids before those hybrids are produced. This is a particular advantage of the invention, in that methods of the invention may be used to predict heterosis or other trait in a potential hybrid, without needing to produce that hybrid in order to determine its heterosis or traits.

A plurality of plants or animals may be tested by determining transcript abundance using the method of the invention, each plant or animal representing the second parent for crossing to produce a hybrid, in order to identify a suitable plant or animal to use for breeding to produce a hybrid with a desired trait. A parent may then be selected for breeding based on the predicted trait for a hybrid produced by crossing that parent. Thus, in one example a germplasm collection, which may comprise a

population of recombinants, may be screened for plants that may be suitable for inclusion in breeding programmes.

Following prediction of the trait in the hybrid, the inbred or recombinant plant or animal may be selected for breeding to produce a hybrid, e.g. as discussed further below. Alternatively, if the hybrid for which the trait is predicted has already been produced, that hybrid may be selected e.g. for further cultivation.

The method of predicting the trait may comprise, as an earlier step, a method of identifying an indicator of the trait in a hybrid, as described above.

When the method is used for predicting heterosis in hybrids based upon parental transcriptome data, for example data from inbred plants or animals, the one or more genes may comprise At3gll2200 and/or one or more of the genes shown in Table 2, or one or more orthologues thereof.

When the method is used for predicting yield, e.g. grain yield, in hybrids based on parental transcriptome data, for example data from inbred plants or animals, e.g. maize, the one or more genes may comprise one or more of the genes shown in Table 22, or one or more orthologues thereof. For example, transcript abundance of one or more genes, e.g. a set of genes, from Table 22 may be determined in a maize plant and used for predicting yield in a hybrid cross between that maize line and B73.

Genes with transcript abundance correlating with other traits are shown in Tables 3 to 17 and Table 20, and transcript abundance of one or more of those genes in parental plants or animals may be used to predict those traits in accordance with hybrid offspring of those plants or animals, in accordance with this aspect of the invention. Alternatively, the invention may be used to identify

other genes with transcript abundance in parental plants or animals correlating with those traits in their hybrid offspring.

By predicting heterosis and other traits in hybrids produced by crossing parental germplasm, whether they be inbred or recombinant, the invention allows selection of inbred or recombinant plants and animals that can be crossed to produce hybrids with high or improved levels of heterosis and desirable or improved levels of other traits.

Inbred or recombinant plants and animals may thus be selected on the basis of heterosis or other trait predicted in hybrids produced by crossing those plants and animals.

Accordingly, one aspect of the invention is a method comprising: determining transcript abundance of one or more genes, preferably .a set of genes, in parental plants or animals, wherein the transcript abundance of the one or more genes in a population of parental plants or animals correlates with heterosis or other trait in hybrid crosses between a first parental plant or animal and plants or animals from the population of parental plants or animals; selecting one of the parental plants or animals; and producing a hybrid by crossing the selected plant or animal and a different plant or animal, e.g. by crossing the selected plant or animal and the first plant or animal.

Thus, one or more traits may be predicted for hybrid crosses between the parental plants or animals, and then a parental plant or animal predicted to produce a hybrid with a desired trait e.g. late flowering, high heterosis, and/or high yield, and/or with a reduced undesirable trait, may be selected. Methods for predicting traits are discussed in more detail elsewhere herein.

Genes whose transcript abundance correlates with heterosis or other trait in hybrids produced by crossing a first plant or

animal and other plants or animals are referred to elsewhere herein, and may be At3gll2200 and/or one or more genes selected from the genes in Table 2, or orthologues thereof. Genes with transcript abundance correlating with other traits are shown in Tables 3 to 17 and Table 20, as described elsewhere herein.

Hybrids produced by methods of the invention may be raised or cultivated, e.g. to maturity or breeding age. The invention also extends to hybrids produced using methods of the invention.

The invention may be applied to any trait of interest. For example, traits to which the invention applies include, but are not limited to, heterosis, flowering time or time to flowering, seed oil content, seed fatty acid ratios, and yield. Examples genes whose transcript abundance correlates with certain traits are shown in the appended Tables. For animals, preferred traits are heterosis, yield and productivity. Traits such as yield may be underpinned by heterosis, and the invention may relate to modelling and/or predicting yield and other traits, and/or modelling and/or predicting heterosis for yield and other traits, based on transcript abundances of genes.

Genes in Tables shown herein are identified by AGI numbers, Affymetrix Probe identifier numbers and/or GenBank database accession numbers. AGI numbers can be used to identify the gene from TAIR (The Arabidopsis Information Resource) , available online at http://www.arabidopsis.org/index.jsp, or findable by searching for "TAIR" and/or "Arabidopsis information resource" using an internet search engine. Affymetrix Probe identifier numbers can be used to identify sequences from Netaffx, available on-line at http://www.affymetrix.com/analysis/index.affx, or findable by searching for "netaffx" and/or "Affymetrix" using an internet search engine. It is now possible to convert between the two identifier formats using the converter, from Toronto university, currently available at http : //bbc . botany . utoronto . ca/ntools/cgi-

bin/ntools__agi_converter . cgi, or findable by searching for "agi converter" using an internet search engine. GenBank accession numbers can be used to obtain the corresponding sequence from GenBank, available at http://www.ncbi.nlm.nih.gov/Genbank/index.html or findable using any internet search engine.

A set of genes may comprise a set of genes selected from the genes shown in a table herein.

In methods of the invention relating to heterosis, the one or more genes may comprise one or more of the 70 genes listed in Table 1 or one or more orthologues thereof, and/or may comprise one or more of the genes listed in Table 19 or one or more orthologues thereof.

In methods relating to traits other than heterosis, the trait may for example be a trait referred for Tables 3 to 17, Table 20 or Table 22, and the one or more genes may comprise one or more of the genes shown in the relevant tables, or one or more orthologues thereof. Preferably, the genes in Tables 3 to 17, 20 and/or 22 are used for predicting or influencing (increasing or decreasing) traits in inbred plants or animals. However, the genes may also be used for predicting, increasing or decreasing traits in recombinants and/or hybrids.

When the trait is flowering time, or time to flowering, in plants, e.g. as represented by leaf number at bolting, the one or more genes may comprise one or more genes shown in Table 3 or Table 4, or orthologues thereof. Table 3 shows genes for which transcript abundance was shown to correlate with flowering time in vernalised plants, and Table 4 shows genes for which transcript abundance was shown to correlate with flowering time in unvernalised plants. These may be used for predicting flowering time in vernalised or unvernalised plants, respectively. However, as discussed elsewhere herein, transcript

abundance of genes which correlates with a trait in vernalised plants may also correlate (normally according to a different model or equation) with the trait in unvernalised plants. Thus, transcript abundance of genes in either Table 3 or Table 4 may be used to predict flowering time in either vernalised or unvernalised plants, using the appropriate correlation for vernalised or unvernalised plants respectively.

Whilst the transcript abundance data of the genes listed in many of the Tables herein were used in our example for predicting traits in vernalised plants, these data could also be used to predict traits in unvernalised plants. Thus, a first correlation may be identified between transcript abundance and the trait in vernalised plants, and a second correlation may be identified between transcript abundance and the trait in unvernalised plants. The appropriate model may then be used to predict the trait in vernalised or unvernalised plants respectively, based on transcript abundance of one or more of those genes, or orthologues thereof.

Oil content is a useful trait to measure in plants. This is one of the measures used to determine seed quality, e.g. in oilseed rape .

When the trait is oil content of seeds, e.g. as represented by % dry weight, the one or more genes may comprise one or more genes shown in Table 6, or orthologues thereof.

Seed quality may also be represented by the proportion, percentage weight or ratio of certain fatty acids.

Normally, seed traits are predicted for vernalised plants, e.g. oilseed rape in the UK is grown as a Winter crop and will therefore be vernalised at the time of trait expression (seed production in this example) . However, predictions may be for either vernalised or unvernalised plants.

When the trait is ratio of 18:2 / 18:1 fatty acids in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 7, or orthologues thereof.

When the trait is ratio of 18:3 / 18:1 fatty acids in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 8, or orthologues thereof.

When the trait is ratio of 18:3 / 18:2 fatty acids in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 9, or orthologues thereof.

When the trait is ratio of 2OC + 22C / 16C + 18C fatty acids in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 10, or orthologues thereof.

When the trait is ratio of polyunsaturated / monounsaturated + saturated 18C fatty acids in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 12, or orthologues thereof.

When the trait is % 16:0 fatty acid in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 14, or orthologues thereof.

When the trait is % 18:1 fatty acid in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 15, or orthologues thereof.

When the trait is % 18:2 fatty acid in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 16, or orthologues thereof.

When the trait is % 18:3 fatty acid in seed oil, the one or more genes may comprise one or more genes selected from the genes shown in Table 17, or orthologues thereof.

It may be desirable to predict responsiveness of a plant trait to vernalisation, and this may be measured for example as the ratio of a trait measurement in vernalised plants to the trait measurement in unvernalised plants.

For example, responsiveness of flowering time to vernalisation may be measured as the ratio of leaf number at bolting in vernalised plants to leaf number at bolting in unvernalised plants. Genes whose transcript abundance correlates with this ratio are shown in Table 5. Thus, in embodiments of the invention where the trait is responsiveness of plant flowering time to vernalisation, the one or more genes may comprise one or more genes shown in Table 5, or orthologues thereof.

Responsiveness to vernalisation of the ratio of 2OC + 22C / 16C + 18C fatty acids in seed oil may be measured as the ratio of (ratio of 2OC + 22C / 16C + 18C fatty acids in seed oil in vernalised plants) to (ratio of 20C + 22C / 16C + 18C fatty acids in seed oil in unvernalised plants) . Genes whose transcript abundance correlates with this ratio are shown in Table 11. Thus, in embodiments of the invention where the trait is responsiveness of this ratio to vernalisation, the one or more genes may comprise one or more genes shown in Table 11, or orthologues thereof.

Responsiveness to vernalisation of the ratio of polyunsaturated / monounsaturated + saturated 18C fatty acids in seed oil may be measured as the ratio of (ratio of polyunsaturated / monounsaturated + saturated 18C fatty acids in seed oil in vernalised plants) to (ratio of polyunsaturated / monounsaturated + saturated 18C fatty acids in seed oil in unvernalised plants) . Genes whose transcript abundance correlates with this ratio are

shown in Table 13. Thus, in embodiments of the invention where the trait is responsiveness of this ratio to vernalisation, the one or more genes may comprise one or more genes shown in Table 13, or orthologues thereof.

When the trait is yield, the one or more genes may comprise one or more of the genes shown in Table 20 or Table 22, or orthologues thereof.

Genes in Tables 1 to 17 are from Arabidopsis thaliana, and may be used in embodiments of the invention relating to A. thaliana or to another organism, such as for predicting or increasing heterosis in a plant or animal (genes of Tables 1 and 2, or orthologues thereof) , or for predicting, increasing or decreasing another trait in A. thaliana or other plant. Genes in Tables 19, 20 and 22 are from maize, and may be used in embodiments of the invention relating to maize or to another organism, such as for predicting or increasing heterosis in a plant or animal (genes of Table 19 or orthologues thereof) or for predicting, increasing or decreasing another trait in maize or other plant.

We have demonstrated that transcript abundance in plants of genes shown in Tables 1, 3 to 17, 20 and 22 is predictive of the described traits in those plants. In some embodiments of the invention relating to use of parental transcriptome data for prediction of traits in hybrids, transcript abundance in plants of genes shown in Tables 1, 3 to 17, 20 and 22 or orthologues thereof may be used to predict the described traits in hybrid offspring of those plants.

Preferably, in embodiments of the invention relating to use of parental transcriptome data for prediction of heterosis in hybrids, transcript abundance in plants of At3gll2200 and/or of genes shown in Table 2, or orthologues thereof, is used to predict the magnitude of heterosis in hybrid offspring of those plants .

In embodiments of the invention relating to use of parental transcriptome data for prediction of yield, e.g. grain yield, in hybrids, transcript abundance in plants of one or more genes shown in Table 22 is used to predict the yield in hybrid offspring of those plants.

Heterosis or other trait is normally determined quantitatively. As noted above, heterosis may be described relative to the mean value of the parents (Mid-Parent Heterosis, MPH) or relative to the "better" of the parents (Best-Parent Heterosis, BPH) .

Heterosis may be determined on any suitable measurement, e.g. size, fresh or dry weight at a given age, or growth rate over a given time period, or in terms of some measure of yield or quality. Heterosis may be determined using historical data from the parental and/or hybrid lines.

Heterosis may be calculated based on size, for which size measurements may for example be taken of the maximum length and width of the plant or animal, or of a part of the plant or animal, e.g. using electronic callipers. For plants, heterosis may be calculated based on total aerial fresh weight of the plants, which may be determined by cutting off all above soil plant material, quickly removing any soil attached, and weighing.

In preferred embodiments, heterosis is heterosis for yield (e.g. in plants or animals, yield of harvestable product) , or heterosis for fresh weight (e.g. fresh weight of aerial parts of a plant) .

The magnitude of heterosis may thus be determined, and is normally expressed as a % value. For example, mid parent heterosis for fresh weight can be presented as a percentage figure calculated as (weight of the hybrid - mean weight of the parents) / mean weight of the parents. Best parent heterosis for fresh weight can be presented as a percentage figure calculated

as (weight of the hybrid - weight of the heaviest parent) / weight of the heaviest parent.

For other traits, an appropriate measurement can be determined by the skilled person. Some traits can be directly recorded as a magnitude, e.g. seed oil content, weight of plant or animal, or yield. Other traits would be determined with reference to another indicator, e.g. flowering time may be represented by leaf number at bolting. The skilled person is able to select an appropriate way to quantify a particular trait, e.g. as a magnitude, ratio, degree, volume, time or rate, and to measure suitable factors representative of the relevant trait.

A transcript is messenger RNA transcribed from a gene. The transcriptome is the contribution of each gene in the genome to the mRNA pool. The transcriptome may be analysed and/or defined with reference to a particular tissue, as discussed elsewhere herein. Analysis of the transcriptome may thus be determination of transcript abundance of one or more genes, or a set of genes.

Transcriptome analysis or determination of transcript abundance is normally performed on tissue samples from the plants or animals. Any part of the plant or animal containing RNA transcripts may be used for transcriptome analysis. Where an organism is a plant, the tissue is preferably from one or more, preferably all, aerial parts of the plant, preferably when the plant is in the vegetative phase before flowering occurs. In some embodiments, transcriptome analysis may be performed on seeds. Methods of the invention may involve taking tissue samples from the plants or animals. In methods of predicting the heterosis or other trait, the sampled organism may remain viable after the tissue sample has been taken. Where prediction is to be performed for genetically identical plants or animals, which may be grown on a different occasion, tissues may include all parts or all aerial plants or a whole seed (for plants) or the whole embryo (for animals) . Where prediction is to be performed

for the exact plant sampled, a subset of the leaves of the plant may be sampled. However, there is no requirement for the organism to remain viable, since sampling of one or more individuals for transcriptome analysis that results in loss of viability may be used for the prediction of heterosis or other traits in hybrid, inbred or recombinant organisms of similar or identical genetic composition grown on either the same or a different occasion and under the same or different environmental conditions .

Typically, transcriptome analysis is performed on RNA extracted from the plant or animal. The invention may comprise extracting RNA from a tissue sample of the hybrid or inbred plant or animal. Any suitable methods of RNA extraction may be used, e.g. see the protocol set out in the Examples.

Transcriptome analysis comprises determining the abundance of an array of RNA transcripts in the transcriptome. Where oligonucleotide chips are used for transcriptome analysis, the numbers of genes potentially used for model development are the numbers of probes on the GeneChips - ca. 23,000 for Arabidopsis and ca . 18,000 for the present maize Chip. Thus, while in some embodiments, the transcript abundance of each gene in the genome is assessed, normally transcript abundance of a selected array of genes in the genome is assessed.

Various techniques are available for transcriptome analysis, and any suitable technique may be used in the invention. For example, transcriptome analysis may be performed by bringing an RNA sample into contact with an oligonucleotide array or oligonucleotide chip, and detecting hybridisation of RNA transcripts to oligonucleotides on the array or chip. The degree of hybridisation to each oligonucleotide on the chip may be detected. Suitable chips are available for various species, or may be produced. For example, Affymetrix GeneChip array hybridisation may be used, for example using protocols described

in the Affymetrix Expression Analysis Technical Manual II (currently available at http: //www. affymetrix. com/support/technical/iuanuals . affx. or findable using any internet search engine) . For detailed examples of transcriptome analysis, please see the Examples below.

Transcript abundance of one or more genes, e.g. a set of genes, may be determined, and any of the techniques above may be employed. Alternatively, reverse transcriptase may be used to synthesise double stranded DNA from the RNA transcript, and quantitative polymerase chain reaction (PCR) may be used for determining abundance of the transcript.

Transcript abundance of a set of genes may be determined. A set of genes is a plurality of genes, e.g. at least 10, 20, 30, 40, 50, 60, 70, 80, 90 or 100 genes. The set may comprise genes correlating positively with a trait and/or genes correlating negatively with the trait. As noted below, preferably, the set of genes is one for which transcript abundance of that set of genes allows prediction of heterosis or other trait. The skilled person may use methods of the invention to determine which genes are most useful for predicting heterosis or other traits in hybrids, and therefore to determine which genes can most usefully be assessed for transcript abundance in accordance with the invention. Additionally, examples of sets of genes for prediction of heterosis and other traits are shown herein.

Preferably, analysis of transcript abundance is performed in the same way for the plants or animals used to generate a model or correlation with a trait "model organism" as for the plants or animals in which the trait is predicted based on that model "test organism". Preferably, the model and test organisms are raised under identical conditions and transcriptome analysis is performed on both the model and test organisms at the same age, time of day and in the same environment, in order to maximise the

predictive value of the model based on transcriptome data from the model organisms.

Accordingly, predicting a trait in a test plant or animal may comprise determining transcript abundance of one or more genes in the test plant or animal at a particular age, wherein transcript abundance of the one or more genes in the transcriptome of model plants or animals at that age conditions correlates with the trait. Thus, preferably transcript abundance in the organism (i.e. plant or non-human animal) is determined when the organism is at the same age as the organisms in the population on which the correlation between transcript abundance and heterosis or other trait was determined. Thus, predicting the degree of a trait in an organism may comprise determining the abundance of transcripts of one or more genes, preferably a set of genes, in the organism at a selected age, and determining the transcript abundance of one or more genes, preferably a set of genes, wherein the transcript abundance of those one or more genes or set of genes in the transcriptome of organisms at the said age correlates with heterosis or other trait in the organism.

As noted elsewhere herein, the age at which transcript abundance is determined may be earlier than the age at which the trait is expressed, e.g. where the trait is flowering time the transcriptome analysis may be performed when plants are in vegetative phase.

Preferably, transcriptome analysis and determination of transcript abundance is determined on plant or animal material sampled at a particular time of day. For example, plant tissue samples may be taken at the middle of the photoperiod (or as close as practicable) . Thus, when predicting a trait by determining the transcript abundance of one or more genes (e.g. set of genes) whose transcript abundance correlates with that trait, the transcript abundance data for making the prediction

are preferably determined at the same time of day as the transcript abundance data used to generate the correlation.

Some aspects of the invention relate to plants, such as cereals, that require vernalisation before flowering. Vernalisation is a period of exposure to cold, which promotes subsequent flowering. Plants requiring vernalisation do not flower the same year when sown in Spring, but continue to grow vegetatively . Such plants ("winter varieties") require vernalisation over Winter, and so are planted in the Autumn to flower the following year. In the present invention, plants may be vernalised or unvernalised.

Transcriptome data may be obtained from plants when vernalised or unvernalised, and those data may be used to identify a correlation between transcript abundance and a trait measured in vernalised plants and/or a correlation between transcript abundance and the trait measured in unvernalised plants. Thus, surprisingly, we have shown that transcriptome data from vernalised plants can be used to develop a model for predicting traits in unvernalised plants, as well as being useful to develop a model for predicting traits in vernalised plants .

In ^' methods of the invention, comparisons and predictions are preferably between plants or animals of the same genus and/or species. Thus, methods of predicting heterosis or other trait in a plant or animal may be based on correlations obtained in a population of hybrids, inbreds or recombinants of that species of plant or animal. However, as discussed elsewhere herein, correlations obtained in one species may be applied to other species, e.g. to other plants or other animals in general, or to both plants and animals, especially where the other species exhibit similar traits. Thus, the test organism in which the trait is predicted need not be of the same species as the model organisms in which the correlation for prediction of the trait was developed.

Determination of transcript abundance for prediction of a trait is normally performed on the same type of tissue as that in which the correlation between the trait and transcript abundance was determined. Thus, predicting the degree of heterosis in a hybrid may comprise determining transcript abundance in tissue in or from the hybrid, and determining the transcript abundance of one or more genes, preferably a set of genes, wherein the transcript abundance of those one or more genes in the transcriptome of the said tissue in hybrids correlates with heterosis or other trait in hybrids.

Data may be compiled, the data comprising:

(i) a value representing the magnitude of heterosis or other trait in each plant or animal; (ii) transcriptome analysis data in each plant or animal, wherein the transcriptome analysis data represents the abundance of each of an array of gene transcripts.

For determination of a correlation, data should be obtained from a plurality of plants or animals. In methods of the invention it is thus preferable that transcriptome analyses are performed and traits are determined for at least three plants or animals, more preferably at least five, e.g. at least ten. Use of more plants or animals, e.g. in a population, can lead to more reliable correlations and thus increase the quantitative accuracy of predictions according to the invention.

Any suitable statistical analysis may be employed to identify a correlation between transcript abundance of one or more genes in the transcriptomes of the plants or animals and the magnitude of heterosis or other trait. The correlation may be positive or negative. For example, it may be found that some transcripts have an abundance correlating positively with heterosis or other trait, while other transcripts have an abundance correlating negatively with heterosis or other trait.

Data from each plant or animal may be recorded in relation to heterosis and/or multiple other traits. Accordingly, the invention may be used to identify which genes have a transcript abundance correlating with which traits in the organism. Thus, a detailed profile may be compiled for the relationship between transcript abundance and heterosis and other traits in the population of organisms.

Typically, an analysis is performed using linear regression to identify the relationship between transcript abundance and the magnitude of heterosis (MPH and/or BPH) or other trait. An F- value may then be calculated. The F value is a standard statistic for regression. It tests the overall significance of the regression model. Specifically, it tests the null hypothesis that all of the regression coefficients are equal to zero. The F value is the ratio of the mean regression sum of squares divided by the mean error sum of squares with values that range from zero upward. From this we get the F Prob (the probability that the null hypothesis that there is no relationship is true) . A low value implies that at least some of the regression parameters are not zero and that the regression equation does have some validity in fitting the data, indicating that the variables (gene expression level) are not purely random with respect to the dependent variable (trait value at that point) .

Preferably a correlation identified using the invention is a statistically significant correlation. Significance levels may be determined as F statistics from the regression Mean Square in the analysis of variance tables of the linear regression analysis. Statistical significance may be indicated for example by F < 0.05, or < 0.001.

Other potential relationships exist between gene expression and plant phenotype, besides simple linear relationships. For example, relationships may fall on a logistic curve. A computer

model (e.g. GenStat) may be used to fit the data to a logistic curve .

Non-linear modelling covers those expression patterns that form any part of a sigmoidal curve, from exponential-type patterns, to threshold and plateau type patterns. Non-linear methods may also cover many linear patterns, and thus may preferentially be used in some embodiments of the invention.

Normally a computer program is used to identify the correlation or correlations. For example, as described in more detail in the Examples below, linear regression analysis may be performed using GenStat, e.g. Program 3 below is an example of a linear regression programme to identify linear regressions between the hybrid transcriptome and MPH.

More generally, each of the methods of the above aspects may be implemented in whole or in part by a computer program which, when executed by a computer, performs some or all of the method steps involved. The computer program may be capable of performing more than one of the methods of the above aspects .

Another aspect of the invention provides a computer program product containing one or more such computer programs, exemplified by a data carrier such as a compact disk, DVD, memory storage device or other non-volatile storage medium onto which the computer program (s) is/are recorded.

A further aspect of the invention is a computer system having a processor and a display, wherein the processor is operably configured to perform the whole or part of the method of one or more of the above aspects, for example by means of a suitable computer program, and to display one or more results of those methods on the display. Typically the computer will be a general purpose computer and the display will be a monitor. Other output

devices may be used instead of or in addition to the display including, but not limited to, printers.

Preferably, a set of genes, e.g. less than 1000, 500, 250 or 100 genes, is identified for which transcript abundance correlates with heterosis or other trait, wherein transcript abundance of that set of genes allows prediction of heterosis or other trait. A smaller set of genes that remains predictive of the trait may- then be identified by iterative testing of the precision of predictions by progressively reducing the numbers of genes in the models, preferentially retaining those with the best correlation of transcript abundance with heterosis or the other trait, e.g. genes with the most significant (e.g. p<0.001) correlations between transcript abundance and traits. Thus, methods of the invention may comprise identifying a correlation between a trait and transcript abundance of a set of genes in transcriptomes, and then identifying a smaller set or sub-set of genes from within that set, wherein transcript abundance of the smaller set of genes is predictive of the trait. Preferably the smaller set of genes retains most of the predictive power of the set of genes.

The magnitude of heterosis or other trait may be predicted from transcript abundance of one or more genes, preferably of a set of genes as noted above, based on a correlation of the transcript abundance with heterosis or other trait (e.g. a linear regression as described above) .

Thus, the equation of the linear regression line (linear or nonlinear) for each of the gene transcripts showing a correlation with magnitude of heterosis or other trait may be used to calculate the expected magnitude of heterosis or other trait from the transcript abundance of that gene. The aggregate of the predicted contributions for each gene is then used to calculate the trait value (e.g. as the sum of the contribution from each gene transcript, normalised by the coefficient of determination, r ² .

Drawings

Figure 1: Workflows for the analysis of expression data for the investigation of heterosis. a) Standard protocols; b) Recommended Prediction Protocol; c) Alternative ^λ Basic' Prediction Protocol; d) Transcription Remodelling Protocol

List of Tables

Table 1: Genes in Arabidopsis thaliana hybrids, transcripts of which correlate with magnitude of heterosis in the hybrids

Table 2: Genes in Arabidopsis thaliana inbred lines, transcripts of which correlate with magnitude of heterosis in hybrids produced by crossing those lines with Ler msl. (A: positive correlation; B: negative correlation)

Table 3: Genes in Arabidopsis thaliana inbred lines, showing correlation in transcript abundance with leaf number at bolting in vernalised plants (A: positive correlation; B: negative correlation)

Table 4: Genes in Arabidopsis thaliana inbred lines showing correlation in transcript abundance with leaf number at bolting in unvernalised plants (A: positive correlation; B: negative correlation)

Table 5: Genes in Arabidopsis thaliana inbred lines showing correlation in transcript abundance with ratio of leaf number at bolting (vernalised plants) / leaf number at bolting (unvernalised plants) . (A: positive correlation; B: negative correlation)

Table 6: Genes in Arabidopsis ^' thaliana inbred lines showing correlation between transcript abundance and oil content of

seeds, % dry weight in vernalised ^' plants (A: positive correlation; B: negative correlation)

Table 7: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of 18:2 / 18:1 fatty acids in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 8 : Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of 18:3 / 18:1 fatty acids in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 9: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of 18:3 / 18:2 fatty acids in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 10: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of 2OC + 22C / 16C + 18C fatty acids in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 11: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of (ratio of 2OC + 22C / 16C + 18C fatty acids in seed oil (vernalised plants)) / (ratio of 2OC + 22C / 16C + 18C fatty acids in seed oil (unvernalised plants)) (A: positive correlation; B: negative correlation)

Table 12: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of polyunsaturated / monounsaturated + saturated 18C fatty acids in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 13: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and ratio of (ratio of polyunsaturated / monounsaturated + saturated 18C fatty acids in seed oil (vernalised plants) ) / (ratio of polyunsaturated / monounsaturated + saturated 18C fatty acids in seed oil

(unvernalised plants)) (A: positive correlation; B: negative correlation)

Table 14: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and % 16:0 fatty acid in seed oil in vernalised plants (A: positive correlation; B: negative correlation)

Table 15: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and % 18:1 fatty acid in seed oil (vernalised plants) (A: positive correlation; B: negative correlation)

Table 16: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and % 18:2 fatty acid in seed oil (vernalised plants) (A: positive correlation; B: negative correlation)

Table 17: Genes in Arabidopsis thaliana inbred lines showing correlation between transcript abundance and % 18:3 fatty acid in seed oil (vernalised plants) (A: positive correlation; B: negative correlation)

Table 18: Prediction of complex traits in inbred lines (accessions) using models based on accession transcriptome data

Table 19: Genes in maize for prediction of heterosis for plant height. Data were obtained in plants at CLY location only (model from 13 hybrids) . Representative public ID shows GenBank accession numbers. (A: positive correlation; B: negative correlation)

Table 20: Genes in maize for prediction of average yield. Data were obtained in plants across 2 sites, MO and L (model from 12 hybrids to predict 3) . Representative public ID shows GenBank accession numbers. (A: positive correlation; B: negative correlation)

Table 21: Pedigree and seedling growth characteristics of maize inbred lines used in Example 6a

Table 22: Maize genes for which transcript abundance in inbred lines of the training. dataset is correlated (P<0.00001) with plot yield of hybrids with line B73. A negative value for the slope indicates a negative correlation between abundance of the transcript and yield, and a positive value indicates a positive correlation.

Table 23: Maize plot yield data for Example 6a.

Examples

Example 1: Transcriptome remodelling in Arabidopsis hybrids

Our initial studies employed Arabidopsis thaliana . We conducted all of our heterosis analyses in Fl hybrids between accessions of A. thaliana, which can be considered inbred lines due to their lack of heterozygosity. The genome sequence of A. thaliana is available [62] and resources for transcriptome analysis in this species are well developed [63] . A. thaliana also shows a wide range of magnitude of hybrid vigour [7, 64, 65] .

The null hypothesis is that all parental alleles contribute to the transcriptome in an additive manner, .i.e. if alleles differ in their contribution to transcript abundance, the observed value in the hybrid will be the mean of the parent values. There are six patterns of transcript abundance in hybrids that depart from

this expected additive effect of contrasting parental alleles [28] :

(i) transcript abundance in the hybrid is higher than either parent; (ϋ) transcript abundance in the hybrid is lower than either parent;

(iii) transcript abundance in the hybrid is similar to the maternal parent and both are higher than the paternal parent; (iv) transcript abundance in the hybrid is similar to the paternal parent and both are higher than the maternal parent;

(v) transcript abundance in the hybrid is similar to the maternal parent and both are lower than the paternal parent; (vi) transcript abundance in the hybrid is similar to the paternal parent and both are lower than the maternal parent.

When using quantitative analytical methods, the terms "higher than", "lower than" and "similar to" can be defined by specific fold-difference criteria. Although differences in the contributions to the transcriptome of divergent alleles in maize hybrids has been reported as common [29, 66] the lack of absolute quantitative analysis of transcript abundance in parental inbred lines means that it is not possible to determine whether the observed effects are due to allelic interaction in the hybrid or simply the expected additive effects of alleles with differing transcript abundance characteristics. We would not consider such additive effects as components of transcriptome remodelling.

We produced reciprocal hybrids between A. thaliana accessions Kondara and Br-O, and between Landsberg er msl and Kondara, Mz-O, Ag-O, Ct-I and Gy-O, with Landsberg er msl as the maternal parent. Hybrids and parents were grown under identical environmental conditions and heterosis calculated for the fresh weight of the aerial parts of the plants after 3 weeks growth (see Materials and Methods) . The heterosis observed for each combination was recorded (BPH (%) and MPH (%) ) .

RNA was extracted from the same material and the transcriptome was analysed using ATHl GeneChips. Plants were grown in three replicates on three successive occasions. RNA was pooled from the three replicates for analysis of gene expression levels on each occasion.

Transcript abundance values in A. thaliana hybrids were compared over all experimental occasions and genes showing differences, at defined fold-levels from 1.5 to 3.0, corresponding to the six patterns indicative of transcriptome remodelling, were identified. Genes with transcript abundance differing between the parents by the same defined fold-level were also identified. The number of genes that appeared consistently in each of these 8 categories across all 3 experimental occasions was counted. To assess whether the number of genes classified into each category differed from that expected by chance, permutation analysis (bootstrapping) was used to calculate an expected value under the null hypothesis of no remodelling.

The significance of the experimental results was assessed, for each category independently, using Chi square tests. The results of the analysis, summarised in Table 1 for 2-fold differences, show that transcriptome remodelling occurred in all of the hybrids analysed, with most individual observations showing highly significant (p<0.001) divergence from the null hypothesis. Similar analyses were conducted for 1.5- and 3-fold differences, with extensive remodelling also being identified. Based on the analysis of gene ontology information, there were no obvious functional relationships of the remodelled genes in the hybrids.

Further analysis of selected genes from these categories were conducted using additional GeneChip hybridisation experiments and by quantitative RT-PCR, and confirmed the transcript abundance patterns. GeneChip hybridization was also performed using genomic DNA from accessions Kondara, Br-O and Landsberg er msl, to assess the proportion of differences between parental

transcriptomes attributable to sequence polymorphisms that would prevent accurate reporting of transcript abundance by the arrays. We found that ca . 20% of the differences between parental transcriptomes may be attributable to sequence variation. However, this does not affect the remodelling analysis, as additivity of allelic contributions to the mRNA pool in hybrids where one parental allele failed to report accurately on the array would result in intermediate signal strength, so would not be assigned to any of the remodelled classes.

The relationship of transcriptome remodelling with hybrid vigour was assessed by carrying out linear regression of the number of genes remodelled in each hybrid combination, at the 1.5, 2 and 3- fold levels, on the magnitude of heterosis observed. This revealed a strong relationship between heterosis and the transcriptome remodelling at the 1.5-fold level (r = +0.738, coefficient of determination r ² = 0.544 for MPH; r = +0.736, r ² = 0.542 for BPH) . The correlation was more modest between heterosis and the transcriptome remodelling involving higher fold level changes (r ² = 0.213 and 0.270 for MPH and BPH, respectively, for 2-fold changes; r ² = 0.300 and 0.359 for MPH and BPH, respectively, for 3-fold changes) . There was extensive remodelling, at all fold changes, even in the hybrid combinations showing the least heterosis. Consequently, the majority of remodelling events identified that result in transcript abundance changes of 2-fold or greater, even in strongly heterotic hybrids, are likely to be unrelated to heterosis. The most highly enriched class in heterotic hybrids is those genes showing 1.5- fold differential abundance, which is below the threshold usually set in transcriptome analysis experiments.

Heterosis shows an inconsistent relationship with the degree of relatedness of parental lines, with an absence of correlation reported between heterosis and genetic distance in A. thaliana [7] . We estimated the genetic distance between the accessions used in the hybrid combinations we have analysed, and these are

shown in Table 1. To assess the relationship of transcriptome remodelling with genetic distance, we regressed the number of genes classified as having remodelled transcript abundance in each hybrid combination against genetic distance. We found that transcriptome remodelling is associated with genetic distance in the higher-fold remodelling classes (r ² = 0.351 and 0.281 for 2 and 3-fold changes respectively), but not for 1.5-fold remodelling (r ^z = 0.030). We found no relationship between heterosis and genetic distance, in accordance with previous reports in A. thaliana (r ² = 0.024 and 0.005 for MPH and BPH, respectively, against relative genetic distance) . We conclude that the formation of hybrids between divergent inbred lines results in transcriptome remodelling, with the extent of remodelling increasing with the degree of genetic divergence of those lines. This result is consistent with the expected effects of allelic variation on transcriptional regulatory networks. The relationship between transcriptome remodelling and heterosis can be interpreted as meaning that heterosis is likely to require transcriptome remodelling to occur, but that much of this involves low magnitude remodelling of the transcript abundance of a large number of genes.

The results of the above experiments indicate that the conventional approach to the analysis of the transcriptome in the hybrid, i.e. studying one or very few hybrid combinations, is unlikely to result in the identification of genes involved specifically in heterosis.

Example 2: Transcript abundance in hybrid transcriptomes

We carried out an analysis using linear regression to identify the relationship between transcript abundance in a range of hybrids and the strength of heterosis (both MPH and BPH) shown by those hybrids. Significance levels were determined as F statistics from the regression Mean Square in the analysis of variance tables of the linear regression analysis. For this, we used the heterosis measurements and hybrid transcriptome data

from the combinations described above with Landsberg er msl as the maternal parent, and from additional hybrids between Landsberg er msl, as the maternal parent, and Columbia, Wt-I, Cvi-0, Sorbo, Br-O, Ts-5, Nok3 and Ga-O. Transcriptome data from 32 GeneChips, representing between 1 and 3 replicates from each of these 13 hybrid combinations of accessions, were used in this study. Nine genes were identified that showed highly significant (F<0.001) regressions (all positive) of transcript abundance in the hybrid on the magnitude of both MPH and BPH. Thirty-four genes showed highly significant regressions (F<0.001; 22 positive, 12 negative) of transcript abundance in the hybrid on MPH and significant regressions (F<0.05) on BPH. Twenty-seven genes showed highly significant regressions (F<0.001; 23 positive, 4 negative) of transcript abundance in the hybrid on magnitude of BPH and significant (F<0.05) regression on MPH. The genes are shown in Table 1 below. Based on gene ontology information, there are no obvious functional relationships between these 70 genes and no excess representation of genes involved in transcription.

The ability to identify a set of genes that show highly significant correlation of transcript abundance and magnitude of heterosis across 13 hybrids indicates that transcriptome-level events are predominant in the manifestation of heterosis. To confirm that this is correct, and that the genes we have identified are indicative of the transcript abundance characteristics that are important in heterosis, we utilized these discoveries to predict the strength of heterosis in new hybrid combinations based on the transcript abundance of the 70 defined genes. We built a mathematical model using the equations of the linear regression lines recalculated for each of the 70 genes against both MPH and BPH, to calculate the expected heterosis as the sum of the contribution from each gene, normalised by the coefficient of determination, r ² . The model operates as a Microsoft Excel spreadsheet, which is available as supplementary materials on Science Online. The spreadsheet also

contained the normalised transcriptome data for the 70 genes from each of the hybrids studied. The model was validated by "predicting" the heterosis in the training set of 32 hybrids from which transcriptome data were used for its construction. It predicted heterosis across the full range of magnitude observed, for both MPH and BPH, with a very high correlation between predicted and observed values for individual samples (r ² = 0.768 for MPH, r ² = 0.738 for BPH). Three new hybrid combinations were produced, between the maternal parent Landsberg er msl and accessions Shakdara, Kas-1 and Ll-O. These were grown, in a

"blind" experiment, under the same environmental conditions as the training set for the model, heterosis for fresh weight was measured and the transcriptomes analysed. The transcript abundance data for the 70 genes of the model were extracted for each of the new hybrids and entered into the heterosis prediction model. The results, as summarised below, confirmed that the model produced excellent quantitative predictions of heterosis, particularly MPH, confirming that transcriptome-level events were, indeed, predominant in the manifestation of heterosis.

Prediction of heterosis using a model based on hybrid transcriptome data

Hybrid Mid-Parent Heterosis Best-Parent % Heterosis %

Predicted Observed Predicted Observed

Landsberg er msl x 43 34 15 22

Shakdara

Landsberg er msl x 46 57 16 24

Kas-1

Landsberg er msl x 66 69 33 67

Ll-O

Mid parent heterosis for fresh weight is presented as a percentage figure calculated as (weight of the hybrid - mean weight of the parents) / mean weight of the parents.

Best parent heterosis for fresh weight is presented as a percentage figure calculated as (weight of the hybrid - weight of the heaviest parent) / weight of the heaviest parent.

Example 2a: Highly significant and specific correlation between heterosis and transcript abundance of Atlg67500 and At5g45500 in hybrids

In a further experiment to identify specific genes that show transcript abundance (gene expression) patterns in hybrids correlated with heterosis, we conducted an additional analysis based upon linear regression. For this we used a "training" dataset consisting of hybrid combinations between Landsberg er msl and Ct-I, Cvi-0, Ga-O, Gy-O, Kondara, Mz-O, Nok-3, Ts-5, Wt- 5, Br-O, CoI-O and Sorbo. For each individual gene represented on the array, the transcript abundance in hybrids was regressed on the magnitude of heterosis exhibited by those hybrids. Twenty one genes showed highly significant (p<0.001) correlation, but this is no more than is expected by chance, as data for almost 23,000 genes were analysed. However, the exceptionally high significance for the two genes showing the greatest correlation (r ² = 0.457, P = 6.0 x 10 ^"6 for gene Atlg67500; r ² = 0.453, P = 6.9 x 10 ^"6 for gene At5g45500) is highly unlikely to have occurred by chance. In both cases the correlation was negative, i.e. expression is lower in more strongly heterotic hybrids.

We tested whether the expression characteristics of these genes could be used for the prediction of heterosis. This was conducted by removing one hybrid from the dataset, formulating the regression line and using this relationship to predict the expected heterosis corresponding to the gene expression measured for the hybrid that had been removed. The analysis was repeated by the removal and prediction of heterosis in each of the 12 hybrids in turn. Three untested hybrids were developed (Landsberg er msl crossed with Ll-O, Kas-1 and Shakdara) as a "test" dataset, grown and assessed for heterosis as for the lines of the training dataset, and their transcriptomes analysed using

ATHl GeneChips . Using formulae derived by regression using all 12 hybrids in the training dataset, the expression data for genes Atlg67500 and At5g45500 in the hybrids of the test dataset were used to predict the heterosis in these test hybrids. Both showed very high correlation between predicted _, and measured heterosis. Overall, predicted heterosis based on the expression of Atlg67500 are better correlated with measured heterosis (r ² = 0.708) than those based on the expression of At5g45500 (r ² = 0.594) . However, removal of one anomalous prediction in the training dataset (that of the heterosis shown by the hybrid Landsberg er msl x Nok-3) improves the latter to r ² = 0.773. Nevertheless, the predictions of heterosis in all three hybrids of the test dataset based on the expression of At5g45500, in particular, are remarkably accurate .

Hybrids that show greater heterosis tend to be heavier than hybrids that show little heterosis. As expected, we identified such a correlation between the magnitude of heterosis we measured and weight for the 15 hybrids of our training and test datasets (r ² = 0.492). In order to assess whether the expression of genes Atlg67500 and At5g45500 are specifically predicting heterosis, we assessed the possibility of correlation between gene expression and the weight of the plants in which expression is being measured. For this, we used the plant weight and gene expression data from the 12 parental lines in the training dataset. We found the expression of Atlg67500 to show weak negative correlation with the weight of the plants (r ² = 0.321), but there was no correlation for At5g45500 (r ² < 0.001). We conclude that the transcript abundance of At5g45500 is indicative specifically of heterosis, but that of Atlg67500 is likely to be influenced also by the weight of hybrid plants. This conclusion is consistent with the errors in prediction of heterosis in the test dataset using the expression of Atlg67500: the prediction of heterosis in the hybrid Landsberg er msl x Kas-1 (which is unusually heavy for the heterosis it shows) is over-estimated, whereas the prediction of heterosis in the hybrid Landsberg er

msl x Ll-O (which is unusually light for the heterosis it shows) is underestimated.

Gene At5g45500 is annotated as encoding "unknown protein", so its functions in the process of heterosis cannot be deduced based upon homology. The function of gene Atlg67500 is known: it encodes the catalytic subunit of DNA polymerase zeta and the locus has been named AtREV3 due to the homology of the corresponding protein with that of yeast REV3 [67] . REV3 is important in resistance to UV-B and other stresses that result in DNA damage as its function is in translesion synthesis, which is required to repair forms of damage to DNA that blocks replication. Studies have shown no differential expression for Atlg67500 in response to UV-B or other stresses [68] . However, the expression of At5g45500 is increased in aerial parts that were subjected to UV-B, genotoxic and osmotic stresses [68]. Thus both of the genes with expression correlated with heterosis in hybrid plants have potential roles in stress resistance. As the expressions of both are negatively correlated with heterosis, one hypothesis is that greater expression of these genes might be related to increased resilience to specific stresses, but this has a repressive effect on growth under favourable conditions. This resembles the situation where biomass and seed yield penalties were found to be associated with R-gene-mediated pathogen resistance to Pseudomonas syringae [69] . Heterosis, at least for vegetative biomass, may therefore be the consequence of genetic interactions that lead to a reduction in repression of growth, rather than direct promotion of growth.

Example 3: Transcript abundance in transcriptomes of inbred lines

We carried out separate analyses using linear regression to identify the relationship between transcript abundance in the parental lines and the strength of MPH shown by their respective hybrids with Landsberg er msl. Significance levels were

determined as F statistics from the regression Mean Square in the analysis of variance tables of the linear regression analysis.

In total, 272 genes were identified that showed highly significant (F<0.001) regressions of transcript abundance in the parent on the magnitude of MPH. See Table 2 below. Based on gene ontology information, there are no obvious functional relationships between these genes and no excess representation of genes involved in transcription.

The invention permits use of transcriptome characteristics of inbred lines as "markers" to predict the magnitude of heterosis in new hybrid combinations.

We built mathematical models, using the equations of the linear regression lines for each of the genes, to calculate the expected heterosis. These models operate as programmes within the Genstat statistical analysis package [70] . The results, as summarised in the table below, confirmed that the model successfully predicted the heterosis observed in the untested combinations using transcriptome characteristics of the inbred parents as markers.

Prediction of heterosis using a model based on parental transcriptome data

Hybrid Mid-Parent Heterosis % {44)

Predicted Observed

Landsberg er msl x 34 34

Shakdara

Landsberg er msl x Kas-1 46 57

Landsberg er msl x Ll-O 50 69

Example 3a: Highly significant correlation between heterosis and transcript abundance of At3gll220 in inbred parents

We conducted an additional analysis based upon linear regression to identify genes that show expression patterns in inbred parents correlated with heterosis shown by the hybrids. For each individual gene represented on the array, transcript abundance in paternal parent lines was regressed on the magnitude of heterosis exhibited by the corresponding hybrids with accession Landsberg er msl in the training dataset.

The expression of one gene, At3gll220, showed an exceptionally high correlation (r ² = 0.649; P = 2.7 x 10 ^"8 ) . The correlation was negative, i.e. expression is lower in parental lines that produce more strongly heterotic hybrids. We assessed the utility of using the expression of this gene in parental lines to predict the heterosis that would be shown by the corresponding hybrids with accession Landsberg er msl. This was conducted for both training and test datasets, as for the predictions based on the expression of Atlg67500 and At5g45500 in hybrids. The heterosis predicted was well correlated with the measured heterosis (r ² = 0.719) and the predicted values for two of the three hybrids in the test dataset were very accurate. However, heterosis was substantially overestimated for the hybrid Landsberg er msl x Kas-1, despite there being no correlation between the expression of At3gll220 in parental accessions and the weight of those accessions (r ² < 0.001).

Gene At3gll220 is annotated as encoding "unknown protein", so its function in the process of heterosis cannot be deduced based upon homology.

Example 4: Transcriptome analysis for prediction of other traits

We used the methodology as described for the prediction of heterosis using parental transcriptome data to develop models for the prediction of additional traits in accessions. The transcriptome data set used for the construction of the models was that obtained for 11 accessions: Br-O, Kondara, Mz-O, Ag-O, Ct-I, Gy-O, Columbia, Wt-I, Cvi-0, Ts-5 and Nok3, as previously described. Trait data had previously been obtained from these, and accessions Ga-O and Sorbo. Transcriptome data from accessions Ga-O and Sorbo were used for trait prediction in these accessions. The lists of genes incorporated into the models relating to the 15 measured traits are listed in Tables 3 to 17. The predicted trait values for Ga-O and Sorbo were compared with measured trait values for these accessions, to assess the performance of the models.

As the models developed for the prediction of additional traits were developed using only 11 accessions, we expected them to contain some false components. These would tend to shift trait predictions towards the average value of the trait across the set of accessions used for the construction of the models. Therefore, our criterion for success of each model was whether or not it ranked the accessions Ga-O and Sorbo correctly. The results, as summarised in Table 18, show that the models were able to successfully predict flowering time, seed oil content and seed fatty acid ratios. As expected, the values produced by the models were between the measured value for the trait in the respective accessions and the average value of the trait across all accessions. Only the models to predict the absolute seed content of a subset of specific fatty acids were unsuccessful.

This lack of success in the experiment we conducted may have been due to the relative lack of precision of the data for these traits and/or insufficient numbers of genes with transcript abundance correlated with the trait to overcome the effects of false components in the models developed using the data sets available at the time. We believe that models based on more

extensive data sets would be able to successfully predict these traits .

The ability to use transcriptome data from an early stage of plant growth under specific environmental conditions (i.e. aerial parts of vegetative-phase plants after 3 weeks growth in a controlled environment room under 8 hour photoperiod) to predict characteristics that appear later in the development of plants grown in different environmental conditions (flowering time, details of seed composition and vernalisation responses of plants grown in a glasshouse under 16 hour photoperiod) is remarkable. We interpret this as evidence of extensive interconnection and multiplicity of gene function, regulated, as for heterosis, largely at the level of transcript abundance. The results presented here indicate that our methodology will allow the use of specific characteristics of the transcriptomes of organisms, including both plants and animals, early in their life cycle as "markers" to predict many complex traits later in their life cycle, and to increase our understanding of the underlying biological processes.

Example 5: METHODS AND MATERIALS

Accessions used

The accessions used for the studies underlying this disclosure were obtained from the Nottingham Arabidopsis Stock Centre

(NASC) : Kondara, Cvi-0, Sorbo, Ag-O, Br-O, Col-0, Ct-I, Ga-O, Gy- 0, Mz-O, Nok-3, Ts-5, Wt-5 (catalogue numbers N916, N902, N931, N936, N994, N1092, N1094, N1180, N1216, N1382, N1404, N1558 and N1612, respectively) . A male sterile mutant of Landsberg erecta (Ler msl) was also obtained from NASC (catalogue number N75) .

Growth conditions

Seeds of parental accessions and hybrids were sown into pots containing A. thaliana soil mix (as described in O'Neill et al

[71] ) and Intercept (Intercept 5GR) . The pot was then watered, and sealed to retain moisture, before being placed at 4 ⁰ C for 6 weeks to partially normalize flowering time. At the end of this time period the pot was placed in a controlled environment room (heated at 22°C and lit for 8 hours per day) . Gradually the seal was removed in order to acclimatise the plants to the reduced air moisture. When the first true leaves appeared the plants were transplanted to individual pots, which were again sealed and returned to the controlled environment rooms. Again the seal was gradually removed over the next few days. The positions of A. thaliana plants in controlled environment rooms was determined using a complete randomised block design, with the trays of plants being regularly rotated and moved in order to reduce environmental effects.

The production of hybrid seeds

Hybrids were produced by crossing accessions Kondara and Br-O by selecting a raceme of the maternal plant, removing all branches and siliques, leaving only the inflorescence. All immature and open buds were removed, along with the apical meristem, leaving 5-6 mature closed buds. From these buds the sepals, petals, and stamens were removed leaving only a complete pistil. For crosses involving Ler msl as the maternal parent, only enough tissue was removed, from unopened buds, to allow access to the stigma. Buds of all plants were then pollinated by removing a stamen from the pollen donor plant, and rubbing the anther against the stigma. This was repeated until the stigma was well coated with pollen when viewed under the microscope. The pollinated buds were then protected from additional pollination by being enclosed in a ^bubble' of Clingfilm, which was removed after 2-3 days.

Trait measurements

The total aerial fresh weight of the plants was determined by cutting off all above soil plant material, quickly removing any soil attached, and weighing on electronic scales (Ohaus Corp. New Jersey. USA) . The plant material was then frozen in liquid

nitrogen. All plant harvesting and weight measurements were taken as close as practicable to the middle of the photoperiod. Where trait data were combined for replicate sets of plants grown at different time, the data were weighted to correct for differences in absolute growth rates between the replicates caused ^' by environmental effects. The mean weight for each of the 14 parent accessions and 13 hybrids was calculated for each of the three growth replicates. These were then normalised to the first replicate mean, to take account of any between-occasion variation in the growth conditions. This was done by dividing each replicate mean by the first replicate mean and then multiplying by itself (for example [a/b] *b) in order to obtain the adjusted mean.

RNA extraction and hybridisation

200mg of plant tissue were ground to a fine powder using liquid nitrogen in a baked pre-cooled mortar, and using a chilled spatula, transferred to labelled chilled 1.5ml tube. To these tubes ImI of TRI Reagent (Sigma-Aldrich, Saint Louis USA) was added, then shaken to suspend the tissue. After a 5 minute incubation at room temperature 0.2ml of chloroform was added, and thoroughly mixed with the TRI Reagent by inverting the tubes for around 15 seconds, followed by 2-3 minutes incubation at room temperature. The tubes were centrifuged at 12000rpm for 15 minutes and the upper aqueous phase transferred to a clean, labelled tube. 0.5ml of isopropanol was then added to the tubes, which were inverted repeatedly for 30 seconds to precipitate the RNA, followed by alO minutes incubation at room temperature. The tubes were then were centrifuged at 12000rpm for 10 minutes at 4°C, revealing a white pellet on the side of the tube. The supernatant was poured off of the pellet, and the lip of the tube gently blotted with tissue paper. ImI 75% ethanol was added and the tubes shaken to detach the pellet from the side of the tube, followed by centrifugation at 7500rpm for 5 minutes. Again the supernatant was poured off of the pellet, which was quickly spun down again and any remaining liquid removed using a pipette. The

pellet was then dried in a laminar flow hood, before 50μl DEPC treated water (Severn Biotech Ltd. Kidderminster, UK) was added to dissolve the pellet.

Sample concentrations were determined using an Eppendorf

BioPhotometer (Eppendorf UK Limited. Cambridge. UK), and RNA quality was determined by running out lμl on a 1% agarose gel for 1 hour. RNA from replicated plants were then pooled according concentration in order to ensure an equal contribution of each replicate.

The pooled samples were then cleaned using Qiagen Rneasy columns (Qiagen Sciences. Maryland. USA) following the protocol on page ^■ 79 of the Rneasy Mini Handbook (06/2001), before again determining the concentrations using an Eppendorf BioPhotometer, and running out lμl on a 1% agarose gel.

Affymetrix GeneChip array hybridisation was carried out at the John Innes Genome Lab (http://www.jicgenomelab.co.uk). All protocols described can be found in the Affymetrix Expression Analysis Technical Manual II (Affymetrix Manual II http: //www. affymetrix. com/support/technical/manuals . affx. )

Following clean up, RNA samples, with a minimum concentration of Iμg, μl-1, were assessed by running lμl of each RNA sample on Agilent RNAβOOOnano LabChips® (Agilent Technology 2100 Bioanalyzer Version A.01.20 SI211) . First strand cDNA synthesis was performed according to the Affymetrix Manual II, using 10 μg of total RNA. Second strand cDNA synthesis was performed according to the Affymetrix Manual II with the following minor modifications: cDNA termini were not blunt ended and the reaction was not terminated using EDTA. Instead Double-stranded cDNA products were immediately purified following the "Cleanup of Double-Stranded cDNA" protocol (Affymetrix Manual II) . cDNA was resuspended in 22μl of RNase free water.

cRNA production was performed according to the Affymetrix Manual II with the following modifications: llμl of cDNA was used as a template to produce biotinylated cRNA using half the recommended volumes of the ENZO BioArray High Yield RNA Transcript Labelling Kit. Labelled cRNAs were purified following the "Cleanup and Quantification of Biotin-Labelled cRNA" protocol (Affymetrix Manual II) . cRNA quality was assessed by on Agilent RNAβOOOnano LabChips® (Agilent Technology 2100 Bioanalyzer Version A.01.20 SI211) . 20μg of cRNA was fragmented according to the Affymetrix Manual II.

High-density oligonucleotide arrays (either Arabidopsis ATHl arrays, or AT Genomel arrays, Affymetrix, Santa Clara, CA) were used for gene expression detection. Hybridisation overnight at 45oC and 60RPM (Hybridisation Oven 640) , washing and staining (GeneChip® Fluidics Station 450, using the EukGEws2_450 Antibody amplification protocol) and scanning (GeneArray® 2500) was carried out according to the Affymetrix Manual II.

Microarray suite 5.0 (Affymetrix) was used for image analysis and to determine probe signal levels. The average intensity of all probe sets was used for normalization and scaled to 100 in the absolute analysis for each probe array. Data from MAS 5.0 was analysed in GeneSpring® software version 5.1 (Silicon Genetics, Redwood City, CA) .

Identification of genes with non-additive transcript abundance in hybrids

Analysis of the normalised transcript abundance data was performed using GenStat [70] . This was undertaken using a script of directives programmed in the GenStat command language (see below) , and used to identify the set of defined patterns of transcript abundance. Briefly, each hybrid transcript abundance data set was compared to its appropriate parental data sets, for each gene, for each of the particular expression patterns of

interest. Those genes showing a particular pattern in each data set were given a test value. ^" Once completed all of these values were added together and only those data sets with a combined test value equal to a given a critical value (equivalent to the value if all data sets displayed that pattern) were counted. Once this had been completed for the experimental data, the results were checked by hand against the source data.

Program 1 below is an example of the pattern recognition programme. This example identifies patterns in the KoBr hybrid and its parents, for three replicates of each at the two-fold threshold criteria.

Permutation analysis to calculate expected values for non- additive transcript abundance in hybrids

Due to the relatively limited replication within the experiment and the large number of genes assayed on the GeneChips it is expected that a proportion of the genes displaying defined patterns will have occurred by chance. It is therefore essential to use appropriate statistical analysis of the data to determine the significance of the results. In order to determine this, random permutation analysis (bootstrapping) was used to generate expected values for random occurrences of defined abundance patterns of the data. Pseudoreplicate data sets were generated by randomly sampling the original data within individual arrays, and using a rotating ^λ seed number' in order to create random data sets of the same size, and variance, as the original. The same pattern recognition directives were then used for this random data set as were used on the original data and the resulting numbers of probes were recorded.

In order to get a statistically significant number of randomized replicates, this randomization and analysis of the data was repeated 250 times. The average numbers of probes identified for each pattern were then used as the value that would be expected to arise by random chance for that pattern. It was determined

that 250 cycles was a sufficiently large random data set, for this experiment by comparing the expected random averages of the defined patterns at 1.5 fold, at 50 cycles and at 250 cycles. Comparisons between higher numbers of cycles (500-1000 cycles) exhibited very little difference between the means except that the longer runs served to reduce the standard errors. A Wilcoxon matched-pairs two-tailed t-test on the means of the two repetition levels (50 cycles and 250 cycles) gave a P-value of 0.674, suggesting very strongly that the means are not statistically different from each other. Based on this it was assumed that the average random values will not change significantly with increased replication, and that 250 cycles is a significantly large number of replicates to generate this mean random value in this case.

Program 2 below is an example of the bootstrapping programme. This example bootstraps the KoBr hybrid at the two-fold threshold criteria, for 250 repetitions.

Chi2 tests for significance of transcriptome remodelling

Fold changes in themselves are not statistical tests, and cannot be used alone to designate a confidence level of the reported differences in expression. The average numbers of probes identified for each pattern after permutation analysis represent the number expected to arise by random chance for that pattern. Once this expected value has been determined it can be used in a maximum likelihood Chi square test, under the null hypothesis of no difference between observed and expected, in order to determine whether the observed patterns differ significantly from random chance. This was undertaken using the "Chi-Square goodness of fit" option of GenStat, and testing the difference between the mean number of genes observed fitting a given expression pattern, and the mean number of genes expected to fit that same pattern (as calculated above) , with a single degree of freedom. Significant relationships, fitting the alternative hypotheses of

significant differences between the two mean values, were considered to be those exhibiting P values of 0.05 or less.

Normalisation of transcriptome remodelling:

Transcriptoitie remodelling was calculated, normalised for the divergence of the transcriptomes of the parental accessions, using the equation:

Where NT = normalised level of transcriptome remodelling of a ^' cross

R _τ = total number of genes summed across all 6 classes indicative of remodelling for the specific hybrid, at the appropriate fold- level Rp = total number of genes with transcript abundance differing between the parental accessions of the specific hybrid, at the appropriate fold-level.

R _pm = Mean number of genes with transcript abundance differing between the parental accessions across all combinations analysed, at the appropriate fold-level.

Estimation of Relative Genetic Distance

In order to develop a measure of the Relative Genetic Distance (RGD) between accession Ler and the 13 accessions crossed with it to produce hybrids the following method was used. A set of 216 loci were selected that were polymorphic for the 14 main accessions studied in this thesis. These were downloaded from the web site of the NSF 2010 project DEB-0115062

(http: //walnut .usc.edu/2010/) . Loci were selected to cover the genome by defining 500 kb intervals throughout the genome, starting at base pair 1 on each chromosome, and selecting the polymorphic locus with the lowest base pair coordinate that has a complete set of sequence data for all 14 accessions, if any, in each interval. The number of polymorphisms across these 216 loci between each accession and Ler were determined and normalised

relative to the polymorphism rate observed between Ler and Columbia (with 45 polymorphisms, the most similar to Ler) to give the RGD.

Regression analysis to identify genes with transcript abundance in hybrid lines correlated with the strength of heterosis

In order to identify genes showing a significant linear relationship between strength of heterosis and transcript abundance in hybrid lines, regression analysis was undertaken using a script of directives programmed in the GenStat command language. This programme conducted a linear regression, for the transcript abundance of each probe, against the phenotypic value for 32 GeneChips . There were three replicate GeneChips for each of the hybrids LaAg, LaCt, LaCv, LaGy, LaKo, and LaMz, and two replicates each for LaBr, LaCo, LaGa, LaNo, LaSo, LaTs, and LaWt, each representing the pooled RNA of three individual hybrid plants. The results of these regressions were presented as F- values. Once this had been completed for the experimental data, significant results were checked by hand against the source data.

Program 3 below is an example of the linear regression programme. This example identifies linear regressions between the hybrid transcriptome and MPH.

Once this had been completed for the transcription data, permutation analysis was used to determine how often particular regression line would arise by random chance. The data was randomised within individual arrays, using a rotating ^λ seed number' and the regression analyses were repeated for this random data, using the same directives used for the original data. In order to get a statistically significant number of random replicates, this randomisation and analysis of the data was repeated 1000 times. Following this, the 1000 regression values for each gene were ranked according to the probability of a relationship between the phenotypic values and random expression values, and the F values of the first, tenth and fiftieth values

(corresponding to the 0.1%, 1% and 5% significance values) were recorded. The probabilities of the actual and randomised samples were then compared and only those genes where the probability of occurring randomly is less than in the actual data at one of the three significance values were counted as showing a significant relationship .

Program 4 below is an example of the linear regression bootstrapping programme. This example randomises linear regressions between the hybrid transcriptome and MPH. Due to the size of the outputs, the files are saved into intermediary files that can be read by the computer but not opened visually.

Program 5 below is an example of the programme written to extract the significant values out of the bootstrapping intermediary data files, into a file that can be manipulated in excel. Again this example handles linear regression data between the hybrid transcriptome and MPH.

Regression analysis to identify genes with transcript abundance in parental lines correlated with the strength of heterosis

In order to identify genes showing a significant linear relationship between strength of heterosis and transcript abundance in parental lines, regression analysis was undertaken as described for the identification of genes with transcript abundance in hybrids correlated with the strength of heterosis.

Example 6: A transcriptomic approach to modelling and prediction of hybrid vigour and other complex traits in maize

Modelling and prediction of heterosis in maize

The experimental design uses a series of 15 different hybrid maize lines, all with line B73 as the maternal parent. The hybrids and parental lines were grown in replicated trials at three locations (two in North Carolina and one in Missouri) in

2005, and data were collected for heterosis and a range of other traits, as listed below. All 31 lines (15 hybrids and 16 parents) were grown for 3 weeks and aerial tissues cut, weighed and frozen in liquid nitrogen. RNA was prepared and Affymetrix maize GeneChips were used to analyse the transcriptome in 2 replicates of each. The methods successfully developed in Arabidopsis, as described above, were used to (i) identify genes with transcript abundance correlated with the magnitude of heterosis, (ii) develop predictive models using the transcriptome data from 12 or 13 hybrids and the corresponding parents and

(iii) test the ability of the models to "predict" the performance of additional hybrids, based only upon their transcriptome characteristics .

Genes whose transcript abundance was shown to correlate with heterosis in maize are shown in Table 19. Heterosis was calculated for plant height, for plants at CLY location (Clayton, North Carolina) only (model from 13 hybrids) .

These data were used to develop a model for prediction of heterosis in two further hybrids. All of the genes used in producing the calibration line were have been used in the prediction, both for the model development and the further "test" plants .

Prediction of heterosis for plant height, CLY location only (model from 13 hybrids to predict 2) :

MPH PH CLY Location Hybrids

CLY B73 x Ki3 B73 x OH43

Actual Value 149.19 134 .88 Predicted 144.59 141.45

No. of correlated genes: 370

The same procedures can be used to develop predictive models for each of the additional traits for which complete data sets are available. For maize, the data from 14 inbred lines (used as parents of the hybrids described above) can be used to develop models for prediction of traits in further inbred lines.

The following traits may be measured in maize: yield; grain moisture; plant height; flowering time; ear height; ear length; ear diameter; cob diameter; seed length; seed width; 50 kernel weight; 50 kernel volume.

Genes with transcript abundance correlating with yield, measured as harvestable product, are shown in Table 20. Average yield was calculated for 12 plants across 2 sites, MO and L.

These genes were used to develop a model for prediction of yield in three further hybrids. All of the genes used in producing the calibration line were have been used in the prediction, both for the model development and the further "test" plants.

Rank order of yield was successfully predicted in these hybrids, and the magnitude was accurate for 2 out of the 3 hybrids, shown below. With improved trait data, accurate predictions would be expected for all hybrids.

Prediction of average yield across 2 sites , MO and L (model from 12 hybrids to predict 3)

Weight

Mo&L

Location Hybrids

B73 x

MO & L M37W B73 X CML247 B73 x M0I8W

Actual

Value 9.70 11.87 11.81

Predicted 9.63 11.38 10.90

No. of correlated genes: 419

Example 6a: Prediction of plot yield in maize hybrids using parental transcriptome data

We used linear regression to identify genes for which expression levels in a training dataset of 20 genetically diverse inbred lines (B97, CML52, CML69, CML228, CML247, CML277, CML322, CML333, IL14H, Kill, Ky21, M37W, Mol7, MolδW, NC350, NC358, Oh43, P39, Tx303, Tzi8) was correlated with the plot yield of the corresponding hybrids with line B73. Pedigrees and phylogenetic grouping 72 of the maize lines used in our studies are summarised - in Table 21.

Using a stringent cut-off for significance (P < 0.00001), correlations (0.288 < r ² < 0.648) were identified for 186 genes. These are listed in Table 22. In the majority of cases (129), gene expression in the inbred lines was negatively correlated with yield of the hybrids. We were able to discount the possibility that these correlations were artefacts of differing proportions of cell types in different sizes of plants, which may have arisen if the sizes of the inbred seedlings were indicative of the performance of the corresponding hybrids, as we found no correlation between plot yield and either the weight (r ² = 0.039) or the height (r ² = 0.001) of the sampled seedlings of the corresponding parental lines.

To assess whether gene expression characteristics may be used successfully for the prediction of yield, each hybrid in turn was removed from the training dataset and models developed based upon a regression conducted with the remaining lines. This was conducted as for A. thaliana, except that the mean of the predictions for all of the genes with highly significant correlation (P < 0.00001) was used as the overall prediction of

heterosis for the excluded line. The numbers of genes exceeding this significance threshold varied from 84 {with P39 excluded) to 262 (with NC350 excluded) . Gene expression data for a test dataset of four additional inbred lines (CML103, Hp301, Ki3, OH7B) was then used to predict the heterosis that would be shown by the corresponding hybrids with B73, by averaging the predictions from each of the 186 genes identified by regression analysis using the complete training dataset. The results showed that the predicted plot yield is strongly correlated with the measured plot yield (r ² = 0.707), demonstrating that gene expression characteristics can, indeed, be used for the prediction of heterosis, as quantified by yield. Although the • relationship was non-linear, with reduced ability to quantitatively predict yields at the higher end of the range studied, the method was able to correctly resolve the two highest yielding hybrids in the test dataset from the two lowest yielding hybrids. The poor yield performance of hybrids including the popcorn (HP301) and the two sweet corns (IL14H and P39) were correctly predicted, but the exceptionally high yield of the hybrid NC350 x B73 was not predicted. We conclude that maternal effects are minor, as the analysis was based on a mixture of crosses with B73 as the maternal parent (15 hybrids) and as the paternal parent (9 hybrids) .

Growth and trait analysis of maize plants

Plants used for transcriptome analysis were grown from seeds for 2 weeks. Maize seeds were first imbibed in distilled water for 2 days in glasshouse conditions to break dormancy, before transfer to peat and sand P7 pots. They were grown in long day glass house conditions (16 hours photoperiod) at 22°C. Aerial parts above the coleoptiles were excised, weighed and frozen in liquid nitrogen. All plant harvesting and weight measurements were taken as close as practicable to the middle of the photoperiod. Plants for yield trials were grown in field conditions in Clayton, NC in 2005. Forty plants of each hybrid were grown in duplicate 0.0007

hectare plots. Yield was calculated as pounds of grain harvested per plot, corrected to 15% moisture, as shown in Table 23.

Example 7: A transcriptomic approach to modelling and prediction of hybrid vigour and other complex traits in oilseed rape

Modelling and prediction of heterosis in oilseed rape

The experimental design uses a series of 14 different hybrid oilseed rape restorer lines, all with line MSL 007 C (which is a male sterile winter line and has been used for commercial hybrid production) as the maternal parent. The hybrids and parental lines were grown in Hohenlieth and Hovedissen in Germany and Wuhan in China in 2004/5, and data for heterosis and a range of other traits, as listed below, were collected. All 29 lines (14 hybrids and 15 parents) are grown for 3 weeks and aerial tissues cut, weighed and frozen in liquid nitrogen. RNA is prepared and Affymetrix Brassica GeneChips are used to analyse the transcriptome in 3 replicates of each. The methods successfully developed in Arabidopsis are used to (i) identify genes with transcript abundance correlated with the magnitude of heterosis, (ii) predictive models are developed using the transcriptome data from 12 hybrids and the corresponding parents and (iii) the ability of the models to "predict" the performance of the 2 additional hybrids, based only upon their transcriptome characteristics, is demonstrated.

Traits measured in oilseed rape: Seed yield, seed weight, seed oil content, seed protein content; seed glucosinolates; establishment; Winter hardiness; Spring development; flowering time; plant height; standing ability.

Modelling and prediction of additional traits

Upon completion of heterosis modelling, the same procedures are used to develop predictive models for each of the additional

traits for which complete data sets are available. For oilseed rape, the data ' from 12 inbred lines (used as parents of the hybrids described above) is used to develop models, which is used to "predict" the traits in 2 further inbred lines. The performance of the models is validated.

Example 8 : Further data modelling techniques

Improvement of the models

The models developed in Arabidopsis utilize linear regression approaches. However, non-linear approaches may enable the identification of more comprehensive gene sets and, hence, more precise models. Non-linear approaches are therefore incorporated into the model development protocols. Additional opportunities for refinement include weighting of the contribution of individual genes and data transformations.

Development of reduced representation models

Although approaches based on the use of GeneChips or microarrays may continue to be the preferred analytical platform for commercialization, there are other methods available for the quantitative determination of transcript abundance. Quantitative PCR methods can be reliable and are amenable to some automation. However, when such approaches are to be used, it is desirable to identify a subset of genes (ideally under 10) that retain most of the predictive power of the sets of genes used to date in the models (70 for prediction of heterosis based on hybrid transcriptom.es, typically >150 for prediction of heterosis or other traits based on inbred transcriptomes) . Therefore, a limited set of genes is identified by iterative testing of the precision of predictions by progressively reducing the numbers of genes in the models, preferentially retaining those with the best correlation of transcript abundance with the trait.

Example 9 : Standard Operating Instruction for the Analysis of

Gene Expression Data

This section provides detailed guidance for development and use of predictive models using the program GenStat [70] .

List of programmes

The following GenStat programmes may be used in accordance with the invention and are suitable for analysing any Affymetrix based expression data.

GenStat Programme l~Basic Regression Programme ~ Method 4

GenStat Programme 2- Basic Prediction Regression Programme ~ Method 5

GenStat Programme 3~ Prediction Extraction Programme ~ Method 5 GenStat Programme 4- Basic Best Predictor Programme ~ Method 7 GenStat Programme 5- Basic Linear Regression Bootstrapping Programme ~ Method 9

GenStat Programme 6- Basic Linear Regression Bootstrapping Data Extraction Programme ~ Method 9 GenStat Programme 7~ Basic Transcriptome Remodelling Programme -Method 10

GenStat Programme 8~ Dominance Pattern Programme -Method 11 GenStat Programme 9~ Dominance Permutation Programme -Method 11 GenStat Programme 10- Transcriptome Remodelling Bootstrap Programme -Method 12

Introduction

These standard operating procedures are designed to enable the undertaking of gene expression analysis studies, from RNA extraction through to advanced prediction.

The procedures are divided into 4 workflows, depending on the type of analyses you wish to undertake. See Figure 1.

Workflow a) follows the basic first steps, common to all analyses (methods 1-3) , to the stage of predicting traits based upon transcription profiles.

Workflow b) follows the recommended analysis procedure (based on the latest analysis developments) . It culminates in the prediction of traits based on a subset of best predictor genes.

Workflow c) follows an alternative analysis procedure, used to generate the prediction reported in my thesis, and includes a bootstrapping step.

Workflow d) describes to methods for analysing the degree of transcriptome remodelling between hybrids and their parent lines.

All of these workflows are designed to be ^λ worked through' and contain step-by-step instruction on how to complete the analysis.

a) Standard Protocols

Method 1, Extract RNA

This stage results in the production of good quality total RNA at a concentration of between 0.2 - lμg μl ^"1 for hybridisation to Affymetrix GeneChips . These methods are the same for both Arabidopsis and Maize chips, for other species, contact Affymetrix for their recommended methods .

1.1 Trizol RNA extraction

200mg of plant tissue were ground to a fine powder using liquid nitrogen in a baked pre-cooled mortar, and using a chilled spatula, transferred to labelled chilled capped tube. To these tubes ImI of TRI REAGENT (Sigma-Aldrich, Saint-Louis USA) was added and shaken to suspend the tissue. After a 5 minute incubation at room temperature 0.2ml of chloroform was added, and thoroughly mixed with the TRI REAGENT by inverting the tubes for around 15 seconds, followed by 2-3 minutes ^' incubation at room

temperature. The tubes were centrifuged at 12000rpm for 15 minutes and the upper aqueous phase transferred to a clean, labelled tube.

0.5ml of isopropanol was then added to the tubes, which were inverted repeatedly for 30 seconds to precipitate the RNA, followed by 10 minutes incubation at room temperature. The tubes were then centrifuged at 12000rpm for 10 minutes at 4 ⁰ C, revealing a white pellet on the side of the tube. The supernatant was poured off the pellet, and the lip of the tube gently blotted with tissue paper. ImI 75% ethanol was added and the tubes shaken to detach the pellet from the side of the tube, followed by centrifugation at 7500rpm for 5 minutes. Again the supernatant was poured off the pellet, which was quickly spun down again and any remaining liquid removed using a pipette. The pellet was then dried in a laminar flow-hood; before 50μl DEPC treated water (Severn Biotech Ltd. Kidderminster, UK) was added to dissolve the pellet.

1.2 RNA Clean-up

RNA samples were cleaned up using RNeasy ^® mini columns (Qiagen

Ltd, Crawly, UK) , according to the protocol given in the RNeasy ^® Mini Handbook (3 ^rd edition 06/2001 pages 79-81) . Due to the maximum binding capacity, no more than lOOμg of RNA could be loaded on to each column. In order to obtain as high a concentration as possible during the elution step, 40μl was used and the elute run through the column twice. This was followed by a second 40μl volume of DEPC treated water in order to remove any remaining RNA, which could be used to increase the amount of clean RNA available, should further concentration be required.

1.3 Concentration of RNA Samples

If the concentration of the clean RNA was less than lμg μl ^"1 a further precipitation and dissolution can be performed using an Affymetrix recommended method which can be found in the

Affymetrix Expression Analysis Technical Manual II (http: //www. affymetrix. com/support/technical/manuals . affx) .

5μl 3 M NaOAc, pH 5.2 (or one tenth of the volume of the RNA sample) was added to the RNA sample requiring concentrating, together with 250μl of 100% ethanol (or two and a half volumes of the RNA sample) . These were mixed and incubated at -2O ⁰ C for at least 1 hour. The samples were centrifuged at 12000 rpm in a micro-centrifuge (MSE, Montana, USA) for 20 minutes at 4 ⁰ C, and the supernatant poured off leaving a white pellet. This pellet was washed twice with 80% ethanol (made up with DEPC treated water) , and air-dried in a laminar flow hood. Finally the pellet was re-suspended in DEPC treated water, to a volume appropriate to the required concentration.

Method 2, RNA Hybridisation

2.1 Hybridisation to GeneChips

Affymetrix GeneChip array hybridisation was carried out at the John Innes Genome Lab (http: //www. jicgenomelab. co.uk) . All protocols described can be found in the Affymetrix Expression Analysis Technical Manual II (Affymetrix Manual II http: //www. affymetrix. com/support/technical/manuals .affx. )

Following clean up, RNA samples, with a concentration of between 0.2-lμg, μl ^"1 , were assessed by running lμl of each RNA sample on Agilent RNA ^β OOOnano LabChips ^® (Agilent Technology 2100 Bioanalyzer Version A.01.20 SI211). First strand cDNA synthesis was performed according to the Affymetrix Manual II, using 10 μg of total RNA. Second strand cDNA synthesis was performed according to the Affymetrix Manual II with the following minor modifications:

cDNA termini were not blunt ended and the reaction was not terminated using EDTA. Instead Double-stranded cDNA products were immediately purified following the "Cleanup of Double-Stranded

cDNA" protocol (Affymetrix Manual II) . cDNA was re-suspended in 22μl of RNase free water.

cRNA production was performed according to the Affymetrix Manual II with the following modifications:

llμl of cDNA was used as a template to produce biotinylated cRNA using half the recommended volumes of the ENZO BioArray High Yield RNA Transcript Labelling Kit. Labelled cRNAs were purified following the "Cleanup and Quantification of Biotin-Labelled cRNA" protocol (Affymetrix Manual II) . cRNA quality was assessed by on Agilent RNA6000nano LabChips ^® (Agilent Technology 2100 Bioanalyzer Version A.01.20 SI211) . 20μg of cRNA was fragmented according to the Affymetrix Manual II.

High-density oligonucleotide arrays were used for gene expression detection. Hybridisation overnight at 45°C and 60RPM (Hybridisation Oven 640) , washing and staining (GeneChip® Fluidics Station 450, using the EukGEws2_450 Antibody amplification protocol) and scanning (GeneArray® 2500) was carried out according to the Affymetrix Manual II.

Microarray suite 5.0 (Affymetrix) was used for image analysis and to determine probe signal levels. The average intensity of all probe sets was used for normalization and scaled to 100 in the absolute analysis for each probe array. Data from MAS 5.0 was analysed in GeneSpring® software version 5.1 (Silicon Genetics, Redwood City, CA) .

Files were saved as . txt files, for further analysis.

Method 3, Data Loading

This section describes the methods used to load the expression data into GeneSpring, how to normalise the data, and how to save it in excel for further analysis. These instructions are best

followed while carrying out the analysis. A GeneSpring course is recommended if further analysis is required using this programme.

3.1 Loading Data into GeneSpring

Open GeneSpring, > File > Import data > select the first of the data files you wish to load > click Open

Choose file format - Affy pivot table

(Create new genome - if you don' t want to go into an existing one) Select genome - Arabidopsis, Maize, etc, or create a new genome following instructions on screen

Import data: selected files - select any remaining files you want to analyse

Import data: sample attributes - this is where you can enter the MIAME info

Import data: create experiment - yes. Save new experiment - give it a name, it will appear in the experiment folder in the navigator toolbar.

3.2 New experiment checklist

These 4 factors should be completed in turn, to ensure that the data is properly normalised. This will impact upon all of the subsequent analyses. Generally the defaults or recommended orders should be used.

Define Normalisations

Click on ^λ use recommended order' and check that the following is included:

Data transformation: measurements less than 0.01 to 0.01 Per chip: 50th % Per gene: normalise to median, cut off = 10 in raw signal

Define Parameters

Here we define the names of the expression data. Depending upon the labelling of the expression files, changes may not be required here. If changes are required: Click on 'New custom' Type the name of each sample. Delete other parameters to avoid confusion. Save

Define Default interpretation

No changes needed for this experiment

Define Error model

No changes needed for this experiment

3.3 Transfer Data in to Excel

Once the data is normalised it can be transferred into an excel spreadsheet.

To do this, click on the relevant data in the experiment tree (on the far left of the main GeneSpring screen)

Click View > view as spreadsheet select all > copy all > paste into Excel spreadsheet. Save. This forms the master Excel chart.

Method 4, Regression Analysis

These instructions describe the basic regression method. This regression forms the basis of the subsequent prediction methods.

4.1 Create Data File

To create a data file for use in GenStat. Open the master Excel file (with normalised expression data from GeneSpring) > Copy the relevant data columns (the data for those accessions that will

form the ^training data set' from which significant predictive genes will be selected) into a new chart> add a column of " : " at the far end > save chart as .txt file>close file

Open the text file in GenStat> Enclose any title names in speech marks (""), this should have the effect of turning the titles green> Find and replace (ctrl R) * with blanks> Replace all> Save file again

4.2 Regression Programme

Open ^λ basic regression programme' (GenStat Programme l~Basic

Regression Programme) in GenStat

Check that the input data filename is correct, and is opening to channel 2

Check that the output data file is going to the correct destination and is opening to channel 3. These input and output file names should be RED

Check that the phenotypic trait data are correct for the trait under investigation. Use "\" to go on to new lines, these backslashes will turn GREEN. Check that the number of genes to be investigated is set to the correct value (usually 22810 for Arabidopsis, or 17734 for

Maize) .

If the R ² , Slope, and Intercept are required remove the "" from the appropriate analysis section, and from the print command, both will turn BLACK from green.

4.3 Running the Programme

To run the programme, ensure that both the programme window and output windows are open (to tile horizontally Alt+Shift+F4) . Select the programme window and press Ctrl+W. This will set the programme running, check that the GenStat server icon (histogram symbol, in taskbar at bottom right-hand corner of the screen) has changed colour to red.

To cancel the programme right click on the server icon and choose interrupt

Once complete the GenStat icon will change colour back to green

4.4 Analysing the Output

To analyse the data, first open it in Excel, select "delimited"> next> tick the "Tab" and "Space"> Finish .

Add a new row at the far left-hand side of the sheet, and label the appropriate columns "P value" "Df" and "R square" "Slope" and

"Intercept" if these were included in the analysis Add a new column to the beginning and label it "ID"

Fill the remaining cells of the ID column with a series 1-22810 for Arabidopsis or 1-17734 for Maize (edit>fill>series>OK)

Delete the column "Df"

Select all of the data columns> Data> Sort> P value ascending Select all of the rows where the P value are less than or equal to 0.05. Colour these cells using the "paint" option, and record the number in this list. These are the genes significant at the

5% level

Select all of the rows where the P value' are less than or equal to 0.01. Colour these cells an alternative colour using the

"paint" option, and record the number in this list. These are the genes significant at the 1% level

Select all of the rows where the P value are less than or equal to 0.001. Colour these cells a third colour using the "paint" option, and record the number in this list. These are the genes significant at the 0.1% level

These three values are the number of OBSERVED significant probes in the data set

These observed significant probes, can be used as ^prediction probes' for the prediction of traits in other accessions, or hybrid combinations .

Method 5, Prediction

These instructions describe the basic prediction method. All subsequent prediction methods are a variation on this.

5.1 Producing the Prediction Calibration Lines

Using the list of identified prediction probes; create a specific prediction sub-set gene list. This can be done by copying your ID and P-value columns (sorted by ID to return the data to its original order) in to a new excel sheet along with the expression data of your training line accessions. You can then sort by P- value and delete those genes that do not appear in the relevant significance (usually 0.1%) list. Remember to sort by ID again to return the file to its correct order, then delete the ID and SigO.1% columns you added. Save this file under a new file name as a . txt file (for example trainingsetdata.txt).

Open the ^λ Basic Prediction Regression Programme' (GenStat

Programme 2)

Check that the input file is the one that you have just created Check that the output file is named correctly (calibration output file)

Check that the number of genes is correct (for example the 0.1% significant genes)

Check that the bin values are appropriate for the trait data. These values should cover the range of the data and a little way either side.

Save the file and run the programme (Ctrl+W)

5.2 Making the Test Expression File

To make the predictions use the identified prediction probes, and the expression data of the ^λ unknown lines' for which we are making the prediction of heterosis .Using the list of identified prediction probes, create a specific prediction sub-set gene list, as was done when generating the file for the calibration curves (section 5.1) . This can be done by copying your ID and P- value columns (sorted by ID to return the data to its original order) in to a new excel sheet along with the expression data of your training line accessions. You can then sort by P-value and delete those genes that do not appear in the relevant

significance (usually 0.1%) list. Remember to sort by ID again to return the file to its correct order, then delete the ID and SigO.1% columns you added. Save this file under a new file name as an Excel spread sheet.

In this file add two blank columns between each of the data columns. In the first column, next to the first unknown line's expression measurement, insert a number series from 1 to however long the list on gene measurements is. In the next column, list the identifier for those measurements (the best identifier would be the parent name, for instance Kas, B73 etc.).

In the first column next to the second data list type the command "=B2+0.01" Then copy this down the column. This will have the effect of giving a number series that is 0.01 greater than its equivalent for the first parent. In the next column, list the identifier for those measurements again

Repeat this process for any remaining parent data sets. Each number series should always be 0.01 greater than its equivalent in the previous series.

Starting with the second set of data columns, cut all of the genes, number series and identifies, and add them to the bottom of first set of data columns. Be sure to use Edit> Paste Special> Values so as not to upset your commands. Repeat this for the remaining columns. You should now have three long columns with all of the data in.

Select all of the data. Click Data>Soft>Column B (or whichever is the column with the number sequence in) . After sorting, you should have all of your parental data mixed together, with all of the same genes next to each other (for example, with three parents your number sequence should read 1, 1.1, 1.2,2,2.1,2.2 etc. and the identifier column should read Kas, Sha, Ll-O, Kas,

Sha, Ll-O etc. or equivalent) save the file. This is your identifier file.

Copy only the column with the expression data into a new work book. Delete all headings and add a column of colons ":". Save the file as a . txt file. This is your ^Tester' data file. Ensure that you close this file, as GenStat will not recognise the file if open in Excel.

Open this file in GenStat press Ctrl+R and in the 'Find What' box type * leave the 'Replace With ¹ box blank. Click 'Replace All' then save this file. This is your test expression file.

5.3 Running the Prediction File

Open the ^λ Prediction Extraction Programme' (GenStat Programme 3

Check the variate "mpadv" these are the X-axis values for the calibration lines. Ensure that these are the same as the bin values entered earlier (section 5.1).

Check the first input file. This should be the expression data of your Tester lines (section 5.2).

Check the second input file. This should be the output file from your calibration line (calibration output file- section 5.1).

Check that the "ntimes" command is the number of test genes multiplied by the number of parents, therefore the total number of genes in your test expression file.

Check that the "calc Z=Z+3" command is correct for your number of Tester lines, for example, for four Tester lines this should read "calc Z=Z+4".

Check that your "if (estimate) " commands are appropriate for the range of your trait data. This is for the ^capped' prediction.

These should be set at 2 ^λ bin sizes' beyond and below the bin range, if appropriate.

Run the programme (Ctrl+W) . This programme prints to the output window, which should be saved as an output (.out) file.

Note it is normal for there to be error messages, if all of the previous steps have been followed ignore these.

5.4 Analysing the Output

Open your saved output file in Excel. Choose Delimited > Next and tick the Tab and Space buttons.

Delete the writing found in the file until you reach the first data point. Usually the first 60 lines.

Name the columns "No." "Cap" "Raw" Scroll to the bottom and delete all of the messages you see there .

Select all and sort by "No" ascending. Check that you have the correct number of rows remaining. This should equal the ntimes value from the Prediction Extraction Programme (the number of prediction genes you have generated, multiplied by the number of Tester lines you are predicting for) .

Scroll to the bottom and delete all of the non-relevant information you see there (for example "regvr=regms/resms" "code

CA" etc) Delete any remaining warning messages, to the left and right of the ^useful data.'

Open the identifier . xls file you generated earlier. Copy the

Number series and Identifier columns in to your output file.

Select all (Ctrl+A) and sort by Identifier, this should separate the data by parent name.

Cut and paste all of the parents into neighbouring columns (so that they are next to each other) .

Scroll to the bottom of the list under the cap column enter the command "=AVERAGE (B2 :B203) " (Note, this command is based on 202

predictive genes, you should adjust this command to cover the number of predictions for your gene set) .

Copy this command to the bottom of all of your lists. You should now have two predictions for each of your Tester lines, the

CAPPED and RAW prediction values .

These predictions can be used individually, or they can be averaged between replicates of the same accessions.

b) Recommended Prediction Protocol

Method 6, N-I Model

These instructions describe the first steps of the recommended prediction protocol. The N-I model is a modification to the basic regression method, and using the same GenStat programme, however this regression is repeated for each accession in the training set.

6.1 Running the N-I model

To undertake the N-I model, prepare an expression file containing all of the accessions you wish to use in your training set.

Run a basic regression (GenStat Programme l~Basic Regression

Programme) using all but one of these accessions. If you have multiple replicates of the same accession, ensure that all are removed.

Using the genes identified from this experiment, undertake a prediction as described in Method 5, using the removed accession as the tester line. Record the ID list of the predictive genes (section 4.4), and the results of the RAW prediction for each gene (as listed in section 5.4) for each replicate.

Repeat this process for all of the accession in the training set, until you have predicted each accession against a training set containing all of the other accessions. These data can be used to

asses the overall accuracy of these predictions by plotting the ACTUAL trait values against the predicted, or they can be used for the later ^λ Best Predictor' prediction method.

Method 7, Best Predictor

This programme calculates which genes consistently predict well over a wide range of accessions and phenotypes. You can also use the output to investigate the frequency of genes appearing in the predictive lists, and thereby identify many noise genes.

7.1 Creating- the data file

To create the data file first open a new Excel spreadsheet. In the first column, paste the list of predictive gene IDs (the numbers assigned at the regressions stage) from the first of the N-I accessions (section 6.1). In the next column paste the list of predictions for these genes for this accession, as generated in the prediction stage for that accession in the N-I model. In the third column at each stage paste the accession name, repeated next to each gene in the list. In the fourth column type the replicate number for that accession, if there is only one replicate type 1. In the fifth type the actual trait value for that accession.

7.2 Running the Prediction File

Open the ^λ Basic Best Predictor Programme' (GenStat Programme 4) Check that the names of the accessions are correctly listed.

Check that the number of replicates is correct (note these should be written [values='chip 1 ' , ' chip 2'] and so on for however many replicates there are) .

Check that the Input file name is correct.

Run the programme (Ctrl+W) . This programme prints to the output window, which should be saved as an output (.out) file.

1.3 Generating a Best Predictor File

Open your saved output file in Excel. Choose Delimited > Next and tick the Tab and Space buttons.

Delete the copy of the programme in the output (first 31 lines or so) at the top of the file, and the programme information at the bottom of the file (last 8 lines) .

Only the first 4 columns (gene, number, Delta, and se_delta) are at the top of the file. Scroll half way down the sheet; there are 3 further columns (a repeat of gene, Ratio, and se_ratio) copy these columns next to the 4 columns at the top of the sheet.

Ensure that the column names are gene, number, Delta, and se_delta, gene, Ratio, se_ratio; respectively.

Delete the second ^λ gene' column.

Save the file. This file is your Best Predictor file

1.4 Using the Best Predictor File

The information in the Best Predictor file is:

Gene Gene is the gene ID list of the predictive genes (section 4.4) .

Number The number of occasions that each gene occurs in the predictive gene lists of the N-I model. Using this we can quickly understand the distribution of this gene between gene lists from the N-I model (section 6.1). This information can be used to quickly identify ^λ noise genes' by their low frequency in gene lists .

Delta The Absolute Difference (AD) is the mean of the differences between actual trait values and the values predicted for each

line in the model. The closer the AD to 0 the closer the predictions are, on average, to the actual value. This value gives a good ^λ feel ^f for how close a prediction is to the actual, in relation to the trait of interest. For example, an AD of 4 might seem good if the trait was height in cm, and seem a fair tolerance- for a prediction, however if the trait was plot yield in Kg, this value might be rather large.

se_delta The standard error of the Absolute Difference (seAD) . This value gives a measure of the variability of the prediction, the smaller this value is the smaller the variability of the AD. An ideal predictive gene will have a small AD and seAD.

Ratio Ratio of the Difference (RD) . This is the mean of the Ratio between actual trait values and the values predicted for each line in the model. This value is a more universal measure of AD, as all values are normalised to 1 (1 being a perfect match between prediction and actual) , and the closer to 1 a gene is the better the gene appears to be for prediction. In theory this should allow the predictive ability of a gene can be assigned, independently of the trait value. For example, a particular gene might have an AD of -0.12 for yield weight, but an RD of 0.98. Saying that the gene is on average a 98% accurate predictor is perhaps an easier concept to understand.

se_ratio The standard error of the Ratio of the Difference (seRD) . This value gives a measure of the variability of the ratio of the prediction, the smaller this value is the smaller the variability of the RD. An ideal predictive gene will have an RD close to 1 and a small seRD.

Using these parameters it is possible to generate more accurate gene list for the prediction of heterosis. This is a trial and error process at present, experimenting with different combinations of parameters will identify the best combination of genes for that trait. At present the most consistent combination

of parameters for a good analysis has been a gene frequency of ALL MODELS (the predictive gene must appear in all N-I models), and a Ratio (or RD) of >0.98 and <l..O2.

In order to the gene combination with the parameters of gene frequency of all models, and an RD of >0.98 and <1.02, firstly sort (data> sort) the Best Predictor file by 'number' with the data descending. Before pressing ^λ 0K' use the 'THEN BY' function to sort the data by Ratio ascending. Press OK.

This will bring all of the most consistent genes to the top of the worksheet. Select all of the genes that display an RD of between 0.98 and 1.02.

To test whether this is a good predictor list, calculate the average prediction for each accession and replicate for this best predictor gene list, and plot these predictions against the actual values for that trait.

An R ² value between 0.5 and 1 suggests that gene list contains genes that are good markers for predictions of that trait.

Method 8, Best Predictor-Prediction

8.1 Best Predictor Prediction

This method is a variation on the standard predictive method (method 5), and uses the same GenStat programmes.

The only variation of this programme is to use the best predictor gene list in place of the 0.1% P-valve list, for generating the training and tester files.

c) Alternative "Basic" Prediction Protocol

Method 9, Bootstrapping

These instructions describe the first steps of the alternative prediction protocol. These methods are an addition to the basic

regression method, and using the same GenStat programmes for the early stages. This Bootstrapping follows on directly from the basic regression (method 4), but prior to the prediction, and acts as an alternative method for identifying significant 'marker' genes. It works by generating a 'customised T-table' that is specific for the experiment in question.

9.1 Regression Bootstrapping

Open the 'Basic Linear Regression Bootstrapping Programme' (GenStat Programme 5) in GenStat Check that the input data filename is correct, and is opening to channel 2. This input file will be the same expression data file used for the initial regression (section 4.1) Check that the output data files are going to the correct destinations and are opening to channels 2,3,4, and 5 Check that the numbers of genes to be analysed are correct for each output file (for Arabidopsis ATH-I GeneChips this will be three files with 6000 genes and one with 4810) , and that the print directives are pointing to the correct channels

-To run the programme, ensure that both the programme window and output windows are open. Select the programme window and press Ctrl+W. This will set the programme running, check that the GenStat server icon (bottom right-hand corner of the screen) has changed colour to red.

To cancel the programme right click on the server icon and choose interrupt.

Once complete the GenStat icon will change colour back to green. This programme can take many days to run due to the large number calculations, and produces output files totalling up to 430Mb, so plenty of disk space would be required. Once generated, the data for this programme needs to be extracted.

9.2 Data Extraction Programme

Open the 'Basic Linear Regression Bootstrapping Data Extraction Programme' (GenStat Programme 6) in GenStat

Check that the input files are correct (the output files from the bootstrapping programme) Run the programme (Ctrl-W)

This programme prints to the Output window. Save this window as an .out file.

9.3 Analysing the Output

To analyse the data, first open it in Excel, select "delimited"> next> tick the "Tab" and "Space"> Finish

Delete the first 32 rows, all of the gaps (after 6000, 12000, and 18000 probes), and all the text at the end of the data file. The data should be the same length as the regression file (for Arabidopsis 22810 lines long) .

Add a new row, and label the columns "boot@5%" "boot@l%" and

"boot@0.1%"

Add a new column to the beginning and label it "ID"

Fill the remaining cells of the ID column with a series 1-22810 (edit>fill>series>OK)

Copy all of these columns into the same sheet as the Observed significant probes data set, generated from the initial regression (section 4.4) with a one column gap Leaving another single column gap label three further columns "sig@5%" "sig@l%" and "sig@0.1%". In the first cell in the column "sig@5%" type "=E2-$B2". Copy this to all of the cells in the three new columns.

9. 4 Cal cula ting Significance

Select all of the data columns> Data> Sort> Sig@5% descending Select all of the cells in this row where the value is positive. Colour these cells using the "paint" option, and record the number in this list. These are the genes significant at the 5% level

Select all of the data colurans> Data> Sort> Sig@l% descending Select all of the cells in this row where the value is positive. Colour these cells using the "paint" option, and record the number in this list. These are the genes significant at the 1% level

Select all of the data columns> Data> Sort> Sig@0.1% descending Select all of the cells in this row where the value is positive. Colour these cells using the "paint" option, and record the number in this list. These are the genes significant at the 0.1% level

These results indicate whether or not the OBSERVED values differ significantly from random chance. These lists of significant genes can be used as markers, for the prediction of this trait as described in Method 5.

d) Transcription Remodelling Protocol

These analyses are designed to investigate the degree of difference in the transcriptome profiles between the hybrid and parental lines. There are two methods, investigating the transcriptome remodelling, and investigating the degree of dominance.

Method 10, Transcriptome Remodelling Fold-Change Experiments

This analysis is designed to investigating the transcriptome remodelling between hybrid and parental transcriptomes .

10.1 Create Data File

To create a data file for use in GenStat. Open master normalised expression Excel file > Copy the relevant data columns (in the order 3 hybrid files, 3 paternal files, 3 maternal files) into a new chart> add a colon " : " at the very end of the last row > save chart as .txt file>close file Open the text file in GenStat> Enclose any title names in speech marks (""), this should have the effect of turning the titles green> Find and replace (Ctrl+R) * with blanks> Save file again

10.2 Fold Change Analysis Programme

Open the ^λ Basic Transcriptome Remodelling Programme' (GenStat

Programme 7) in GenStat

Check that the input data filename is correct, and is opening to channel 2

Check that the output data file is going to the correct destination and is opening to channel 3

Check that the ratios are set correctly for the ratio comparison under investigation. For example, for

"if ( (elem(i;k) .gt.0.5) .and. (elem(i;k) .It.2) ) "

This is set for a 2-fold ratio

For 3 fold the values would be 0.33 and 3

For 1.5 fold the values would be 0.66 and 1.5 The values are entered 3 times in the programme

Check that the ratios are set correctly for the fold change comparison under investigation. This is undertaken for all of the sections and should be set simply to the relevant fold level

To run the programme, ensure that both the programme window and output windows are open. Select the programme window and press ctrl>W. This will set the programme running, check that the GenStat server icon (bottom right-hand corner of the screen) has changed colour to red.

To cancel the programme right click on the server icon and choose interrupt

Once complete the GenStat icon will change colour back to green

10.3 Analysing the Output

To analyse the data, first open it in Excel, select "delimited"> next> tick the "Tab" and "Space"> Finish

Delete the first 266 rows in Excel, until you reach the column headers. Then delete bottom line beyond the data output At the bottom of each column calculate the total number of significant patterns in that list. This can be done by using the directive "=SUM (C2 :C22811) " in the first column and copying this into the remaining columns, ensuring that the correct data is selected.

The initial analysis is now complete. These values represent the OBSERVED data in the further analysis, following bootstrapping to generate the expected values.

Method 11, Transcriptome Remodelling Dominance Experiments

This analysis is designed to investigating dominance type transcriptome remodelling between hybrid and parental transcriptomes . Significance is calculated by comparing observed values to the expected generated from random data. Note, this programme is in its early stages, and is not easy to modify.

11.1 Create Data File

This experiment compares the expression of the profile of the hybrid against the mean of it parents. To do this we must first calculate these mean values.

Open a new Excel worksheet. Paste in the parent expression data

(both maternal and paternal) for the first replicate of the first accession.

Calculate the mean value for each gene. This can be done using typing the equation =AVERAGE (A2 : B2) into the next cell along. Copy this equation all the way down this column. Open another worksheet and paste in the expression data of the first hybrid, copy the newly generated mean parental expression value and Edit>Paste Special >Values in to the next column. Repeat this for all of the replicates and accessions. Note that this programme is designed to analyse 3 replicates of each hybrid, a total of 6 columns per accession.

Once this is complete, save the file as . txt. Open the file in GenStat> enclose the titles in "" which should change their colour to green. Save the file again. This is the input file.

11.2 Running the Dominance Pattern Recognition Programme

Open the 'Dominance Pattern Programme' (GenStat Programme 8) in

GenStat

Check the accession names (first scalar command) are correct. If you are investigating less than 8 accessions, you will need to change the numbers of these identifiers throughout the programme. Should you not wish to do this, running ^λ pseudo-data' in the remaining columns will not affect the output and can be ignored at the analysis stage.

Check the number of columns (second scalar command) is correct.

It should be a 6x the number of accessions used (default is 48) . Check that the out put file is correctly named and addressed.

Check that the input file is correct.

Check that the fold level is correct for the analysis you wish to under take. These values a recorded for 2 fold as

if (ratio. ge.0.5) .and. (ratio. Ie.2) "calculates flags" calc heqmp=l elsif (ratio. gt.2) calc hgtmp=l elsif (ratio. It.0.5) calc hltmp=l

For other fold levels change the 0.5 and 2 values to the appropriate value for that fold level. For 3 fold the values would be 0.33 and 3 For 1.5 fold the values would be 0.66 and 1.5 Run the file by pressing Ctrl+W.

11.3 Analysing the Pattern Recognition Output

To analyse the output file, first open it in Excel, select "delimited"> next> tick the "Tab" and "Space"> Finish

You will see a file filled with Us' and ^λ 0s.' Scroll to the bottom of this file. Underneath the first filled column write the equation "=SUM (Bl .-B22810) " (ensuring that all of the data in that column is filled) . Copy this equation to all of the columns. Each set of three 'sum values' represent the data output for a single accession (3 replicates) , in the order that the data was loaded into the programme. These values represent Column 1= The number of genes who' s hybrid expression falls within the fold level criterion of the mid-parent value, for ALL 3 replicates.

Column 2- The number of genes who's hybrid expression is greater than that of the mid-parent value, by at least the fold level criterion, for ALL 3 replicates. Column 3= The number of genes who' s hybrid expression is lower than that of the mid-parent value, by at least the fold level criterion, for ALL 3 replicates. Record these values, as the OBSERVED for these data.

11.4 Generating the EXPECTED value.

The expected data set is generated using the *Dominance

Permutation Programme' (GenStat Programme 9)

Check the number of columns (second scalar command) is correct.

It should be a 6x the number of accessions used (default is 48) .

Check that the out put file is correctly named and addressed. Check that the input file is correct. This is the same input file as generated previously. <

Check that the fold level is correct for the analysis you wish to under take. These values a recorded for 2 fold as before (section

11.1) Check the number in the permutation loop is correct for then number of permutations you require. A minimum of 100 is recommended (although 1000 is ideal) .

Run the file by pressing Ctrl+W.

This programme may take a few days to run, depending upon how many permutations are added.

11.5 Analysing the Pattern Recognition Permutation Output

To analyse the output file, first open it in Excel, select "delimited"> next> tick the "Tab" and "Space"> Finish You will see a file filled with numbers. Scroll to the bottom of this file. Underneath the first filled column write the equation "=SUM(B1:B123) " (ensuring that all of the data in that column is filled). Copy this equation to all of the columns.

Each set of three ^λ sum values' represent the permuted data output for a single accession (3 replicates) , in the order that the data was loaded into the programme. The three values represent the ^λ expected by random chance' versions of the values calculated in section 11.3.

The calculated values at the bottom of the columns are the

EXPECTED values required for this analysis. As these data are effectively random it is acceptable to combine these for comparison, if time is limiting.

11.6 Analysing the Significance

The level of significance is calculated by chi square analysis, using the observed and expected data generated previously, and 1 degree of freedom.

Method 12, Transcriptome Remodelling Fold-Change Bootstrapping

This analysis is designed to assess the significance of fold change experiments described in Method 10 . Significance is calculated by comparing observed values to expected generated from random data

12.1 Fold Change Bootstrapping

Open ^λ Transcriptome Remodelling Bootstrap Programme' (GenStat Programme 10) in GenStat

Check that the input data ^' filename is correct, and is opening to ' channel 2. This will be the same input file as created in section

10.1.

Check that the output data files is going to the correct destinations and is opening to channels 3

Check that the number of randomisations is set to the desired value. As few as 50 randomisations are sufficient to give valid estimates of random chance, however 1000 would be ideal, but this can take many days to obtain. Check that the ratios are set correctly for the ratio comparison under investigation.

For example :

"if ( (elem(i;k) .gt.0.5) .and. (elem(i;k) .It.2) ) "

This is set for a 2-fold ratio For 3 fold the values would be 0.33 and 3

For 1.5 fold the values would be 0.66 and 1.

To run the programme, ensure that both the programme window and output windows are open. Select the programme window and press

Ctrl>W. This will set the programme running, check that the GenStat server icon (bottom right-hand corner of the screen) has changed colour to red.

To cancel the programme right click on the server icon and choose interrupt

Once complete the GenStat icon will change colour back to green

12.2 Analysing the Output

To analyse the data, first open it in Excel, select "delimited"> next> tick the "Tab" and "Space"> Finish

Delete the first 281 rows in Excel, until you reach the first row of data. Then delete bottom line beyond the data output

Select the whole sheet and go to data>sort>sort by "Column B" .

This will remove the empty rows from the data.

At the bottom of each column calculate the mean number of significant patterns in that list. This can be done by using the directive "=AVERAGE (B2 :B22811) " in the first column and copying

this into the remaining columns, ensuring that the correct data is selected.

This will give the EXPECTED mean value, expected by random chance in the data

12.3 Calculating Significance

Calculating the significance of the observed patterns requires the use of a maximum likelihood chi square test

Firstly open GenStat> Stats> Statistical Tests> Chi-Square

Goodness of Fit Click on "Observed data create table"> Spreadsheet

Name the table OBS> Change rows and columns to 1> OK and ignore the error message

In the new table cell type the number of the first OBSERVED column sum value Click on "expected frequencies create table"> Spreadsheet

Name the table EXP> leave rows and columns as 1> OK and ignore the error message

In the new table cell type the number of the first Expected mean column mean value On the Chi-Square window put 1 into the degrees of freedom box and click Run

Record the Chi-Square and P value that appears in the Output window.

Type the next OBSERVED value into the OBS box and click onto the output window

Type the next EXPECTED value into the EXP box and click onto the output window

On the Chi-Square window click Run, and record the new Chi-Square and P value that appears in the Output window This should then be undertaken for all of the remaining OBSERVED and EXPECTED values.

These results indicate whether or not the OBSERVED values differ significantly from random chance.

Troubleshooting

This section describes some of the most common problems that can occur while running these programmes. Many of these problems/solutions apply to most of the programmes and as a result this section has not been divided up along programme lines. This list is not exhaustive, but should cover the majority of problems encountered. It should be noted that the ^λ fault codes' given are only for illustration, often many fault codes can result from the same root problem.

General GenStat problems

One common method of solving general problems is to ensure that all of the input files are closed prior to running the programme. This is achieved by typing (to close channel 2) "close ch=2" and then running this directive. By repeating this for channels 3-5, you can ensure that all of the channels are closed before running your programme, and thus avoiding conflicts.

Fault 16, code VA 11, statement 4 in for loop Command: fit [print=*]mpadv Invalid or incompatible type(s) Structure mpadv is not of the required type.

Remove comma from the end of the variate list.

Fault 29, code VA 11, statement 4 in for loop Command: fit [print=*]mpadv Invalid or incompatible type(s) Structure mpadv is not of the required type

Problem with the trait-data identifier. Possibly a different or missing identifier following the trait data variates (X-axis data)

Failure to run problems

- Too many values

Fault # code VA 5, statement 2 in for loop Command: read [ch=2/print-*;serial=n]exp Too many values

1) Ensure that the width parameter is large enough, set to a large enough value (400 is standard)

2) Ensure that if titles are included in the data file, that they are 'greened out' and not being read as data

3) Ensure that the "Unit" number (at the beginning of the programme) and the number of trait . "variate"s are the same

Too Few values

Fault 13, code VA 6, statement 4 in for loop Command: fit [print=*]mpadv Too few values (including null subset from RESTRICT) Structure mpadv has 37 values, whereas it should have 38

Ensure that the "Unit" number (at the beginning of the programme) and the number of trait "variate" are the same

Warning 6, code VA 6, statement 2 in for loop Command: read [ch=2/print=*/serial=n]exp Too few values (including null subset from RESTRICT)

Ensure that the "ntimes=" number and the number of probes in the data file are the same

File Opening Failure

Fault #, code IO 25, statement 2 in for loop Command: read [ch=2;print=*;ser±al=n]exp Channel for input or output has not been opened, or has been terminated Input File on Channel 2

1) Input file name is incorrect

2) Input file address is incorrect

Fault 32, code IO 25, statement 12 in for loop Command: print [ch=3;iprint=*;clprint=*;rlprint=*]bin Channel for input or output has not been opened, or has been terminated Output File on Channel 3

Output file address is incorrect.

Very slow running of bootstrapping

Check that the programme is not having conflicts with anti-virus software. This should be solved by the computing department, but results from anti-virus software scanning the file each time it makes a write-to-disk operation. This can often be easily changed by modifying the scanning settings.

If All Else Fails

Check that the file C:\Temp\Genstat is not filled. This can result from too many temp (.tmp) files being generated as a result of bootstrapping programmes. Deleting these files may improve the running of the programme.

Finally VSN (GenStat providers) can be contacted at ^λ support@vsn- intl . com'

Data Analysis problems

Missing or very high F-problems

Ensure that the data has not ^λ shifted' at very low f- probabilities . At the regression stage (section 4.4), before creating the ID column, add an extra column to the beginning of the file. Insert the ID column, and sort by DF, if the data has shifted, this should become apparent here.

Table 1. Genes showing correlation of transcript abundance in hybrids with the magnitude of heterosis exhibited by those hybrids

Affymetrix AG! Code Description

Genes with transcript abundance in hybrids correlated with strength of heterosis F < 0.001 MPH and F < 0.001 BPH

Positive correlation

251222_at AT3G62580 expressed protein

257635_at AT3G26280 cytochrome P450 family protein

250900_at AT5G03470 serine/threonine protein phosphatase 2A (PP2A) regulatory

252637_at AT3G44530 transducin family protein / WD-40 repeat family protein

253415_at AT4G33060 peptidyl-prolyl cis-trans isomerase cyclophilin-type family protein

265226_at AT2G28430 expressed protein

259770_s_at AT1 G07780 phosphoribosylanthranilate isomerase 1 (PAH)

261075_at AT1G07280 expressed protein

252501_at AT3G46880 expressed protein

Genes with transcript abundance in hybrids correlated with strength of heterosis F < 0.001 MPH and F < 0.01 BPH

Positive correlation

265217_s_at AT4G20720 dentin sialophosphoprotein-related

253236_at AT4G34370 IBR domain-containing protein

246592_at AT5G14890 NHL repeat-containing protein

266018_at AT2G18710 preprotein translocase secY subunit, chloroplast (CpSecY)

250755_at AT5G05750 DNAJ heat shock N-terminal domain-containing protein

261555_s_at AT1G63230 pentatricopeptide (PPR) repeat-containing protein

262321_at AT1G27570 phosphatidylinositol 3- and 4-kinase family protein

246649_at AT5G35150 CACTA-like transposase family (Ptta/En/Spm)

264214_s_at AT1 G65330 MADS-box family protein

261326_s_at AT1 G44180 aminoacylase, putative / N-acyl-L-amino-acid amidohydrolase,

255007_at AT4G10020 short-chain dehydrogenase/reductase (SDR) family protein

246450_at AT5G16820 heat shock factor protein 3 (HSF3) / heat shock transcription factor

Negative correlation 251608_at AT3G57860 expressed protein 260595_at AT1 G55890 pentatricopeptide (PPR) repeat-containing protein 248940_at AT5G45400 replication protein, putative 254958_at AT4G11010 nucleoside diphosphate kinase 3, mitochondrial (NDK3) 257020 at AT3G 19590 WD-40 repeat family protein / mitotic checkpoint protein, putative

Genes with transcript abundance in hybrids correlated with strength of heterosis F < 0.001 MPH and F < 0.05 BPH

Positive correlation

254431_at AT4G20840 FAD-binding domain-containing protein 248941_s_at AT5G45460 expressed protein 256770_at AT3G13710 prenylated rab acceptor (PRA1) family protein 247443_at AT5G62720 integral membrane HPP family protein 258059_at AT3G29035 no apical meristem (NAM) family protein 246259_at AT1 G31830 amino acid permease family protein 262844_at AT1 G14890 invertase/pectin methyiesterase inhibitor family protein 246602_at AT1 G31710 copper amine oxidase, putative 247092_at AT5G66380 mitochondrial substrate carrier family protein 264986_at AT1G27130 glutathione S-transferase, putative

Table 1, continued

Negative correlation 258747_at AT3G05810 expressed protein 266427_at AT2G07170 expressed protein 263908_at AT2G36480 zinc finger (C2H2-type) family protein 250924_at AT5G03440 expressed protein 249690_at AT5G36210 expressed protein 245447_at AT4G 16820 lipase class 3 family protein 260383 s at AT1G74060 60S ribosomal protein L6 (RPL6B)

Genes with transcript abundance in hybrids correlated with strength of heterosis F < 0.001 BPH and F < 0.01 MPH

Positive correlation

260260_at AT1G68540 oxidoreductase family protein

252502_at AT3G46900 copper transporter, putative

256680_at AT3G52230 expressed protein

254651_at AT4G18160 outward rectifying potassium channel, putative (KCO6)

264973_at AT1G27040 nitrate transporter, putative

256813_at AT3G21360 expressed protein

248697_at AT5G48370 thioesterase family protein

267071_at AT2G40980 expressed protein

246835_at AT5G26640 hypothetical protein

252205_at AT3G50350 expressed protein

Genes with transcript abundance in hybrids correlated with strength of heterosis F < 0.001 BPH and F < 0.05 MPH

Positive correlation

266879_at AT2G44590 dynamin-like protein D (DL1 D) 253999_at AT4G26200 1-aminocyclopropane-i -carboxylate synthase, putative / ACC 266268_at AT2G29510 expressed protein 264565_at AT1 G05280 fringe-related protein 255408_at AT4G03490 ankyrin repeat family protein 261166_s_at AT1G34570 expressed protein 252375_at AT3G48040 Rac-like.GTP-binding protein (ARAC8) 264192_at AT1 G54710 expressed protein 259886_at AT1 G76370 protein kinase, putative 251255_at AT3G62280 GDSL-motif lipase/hydrolase family protein 260197_at AT1 G67623 F-box family protein 253645_at AT4G29830 transducin family protein / WD-40 repeat family protein 245621_at AT4G14070 AMP-binding protein, putative

Negative correlation 246053_at AT5G08340 riboflavin biosynthesis protein-related 264341 _at At1G70270 unknown protein 250349_at AT5G12000 protein kinase family protein 256412- at AT3G11220 Paxneb protein-related

Table 2. List of genes showing a correlation between transcript abundance in parents with the magnitude of MPH exhibited by their hybrids with Landsberg er msl.

2A: Genes showing positive correlation between transcript abundance and trait value

AT5G 10140 AT2G32340 AT4G04960 AT3G58010 AT1G03710 AT2G07717 AT3G06640 AT5G65520 AT3G29035 AT1G03620 AT1G02180 AT3G03590 AT5G24480 AT2G41650 AT4G25280 AT5G46770 AT3G47750 AT1G13980 AT5G20410 AT1G68540 AT1G65370 AT1G22090 AT4G01897 AT2G26500 AT5G66310 AT1G65310 AT1G31360 AT5G53540 AT1G70890 AT2G39680 AT2G21195 AT5G18150 AT2G06460 AT3G28750 AT5G 13730 AT5G54095 AT4G 19470 AT2G47780 AT5G43720 AT1G54780 AT1G54923 AT4G 11760 AT3G59680 AT5G55190 AT5G60610 AT3G51000 AT2G27490 AT1G80600 AT5G46750 AT1 G09540 AT2G 16860 AT3G57040 AT1 G27030 AT5G63080 AT2G20350 AT5G59400 AT4G 18330 AT4G14410 AT2G13610 AT5G58960 AT5G61290 AT1G51360 AT4G00530 AT2G41890 AT3G23760 AT1G44180 AT1G14150 AT1G78790 AT3G47220 AT3G51530 AT2G 14520 AT1G70760 AT3G05540 AT4G20720 AT1G72650 AT2G32400 AT3G47250 AT3G27400 AT1G64810 AT2G36440 AT3G22940 AT5G48340 AT4G24660 AT5G16610 AT3G23570 AT1G34460 AT5G38360 AT5G05700 AT5G25220 AT5G38790 AT5G03010 AT2G31820 AT5G28560 AT1 G15000 AT3G21360 AT1 G05190 AT1 G14890 AT1G58080 AT3G56140 AT5G64350 AT5G27270 AT3G26130 AT3G17880 AT2G35795 AT4G 10380 AT1 G67910 AT1G60830 AT4G00420 AT2G07671 AT1G80130 AT1G79880 AT1G04830 AT2G 16980 AT4G16170 AT2G42450 AT5G04410 AT2G45830 AT2G44480 AT2G36350 AT1G68550 AT3G09160 orf107f AT5G04900 AT2G29710 AT1G21770 AT4G 15545 AT5G 17790 AT5G58130 AT4G21280 AT4G20860 AT2G35690 AT2G22905 AT1G04660 AT2G24040 AT2G32650 AT5G66380 AT1G18990 AT4G 16470 nad9 AT4G 10030 AT1G70480

AT5G56870 AT3G20270 AT2G36370 AT5G24310 ycf9 AT5G64280 AT5G06530 AT4G20830 AT3G 10750 AT1G29410 AT1G71480 AT3G61070 AT1G67600 AT3G 14560 AT5G11840 AT3G44120 AT5G66960 AT5G40960 AT3G58350 AT1G26230 AT1G76080 AT4G 10410 AT4G28100 AT3G23540 AT1G70870 AT3G50810 AT1G34620 psbl AT5G37540 AT3G12010

AT1 G33910 AT1G03300 AT1G45050 AT3G 10450 AT1G65070 AT4G 17740

2B: Genes showing negative correlation between transcript abundance and trait value

AT1G50120 AT4G22753 AT4G30890 AT5G66750 AT5G11560 AT3G53170 AT3G07170 AT5G28460 AT3G50000 AT3G22310 AT5G26100 AT3G47530 AT1G12310 AT3G02230 AT3G03070 AT4G37870 AT5G63220 AT3G30867 AT2G 14835 AT1G25230 AT1G61770 AT2G 14890 AT1G74050 AT1G47210 AT1 G42480 AT4G 19040 AT5G50000 AT5G 10390 AT1G13900 AT1G71880 AT2G40290 AT3G52500 AT2G03220 AT1G04040 AT5G57870 AT5G06265 AT2G26140 AT4G34710 AT4G04910 AT3G60450 AT1G48140 AT4G21480 AT2G38970 AT3G23560 AT5G63400 AT5G45270 AT2G42910 AT2G34840 AT4G03550 AT5G11580 AT2G41110 AT3G23080 AT2G33845 AT3G09270 AT2G30530 AT5G40370 AT3G55360 AT4G23570 AT3G45770 AT5G53940 AT5G20280 AT4G36680 AT3G51550 AT1 G64450 AT4G00860 AT3G 19590 AT5G27120 AT5G45550 AT3G49310 AT2G32190 AT4G27430 AT2G37340 AT5G 19320 AT3G 11220 AT1G21830 AT2G32190 AT2G 17440 AT4G27590 AT5G54100 AT2G22470 AT2G 15000 AT1G31550 AT4G 13270 AT2G22200 AT1 G55890 AT5G45510 AT5G40890 AT5G45500 AT3G62960 AT1G59930 AT3G58180 AT4G21650 AT4G31630 AT3G57550 AT4G24370

Table 3 . Genes used for prediction of leaf number at bolting in vernalised plants ; Transcript ID (AGI code )

3A: Genes showing positive correlation between transcript abundance and trait value

At1 g02620 At2g03760 At3g13120 At4g08680 At5g16800

At1 gO9575 At2g06220 At3g 13222 At4g 10550 At5g17210

AU g 10740 At2g07050 At3g 14000 At4g 10925 At5g 17570

AtIg 16460 At2g 15810 At3g 14250 At4g12510 At5g38310

At1g27210 At2g 16650 At3g 14440 At4g 13800 At5g40290

At1g27590 At2g19010 At3g15190 At4g 14920 At5g41870

At1 g29440 At2g20550 At3g 18050 At4g 17240 At5g44860

At1 g29610 At2g22440 At3g19170 At4g 17260 At5g45320

At1 g30970 At2g23180 At3g 19850 At4g 17560 At5g45390

At1g32150 At2g23480 At3g20020 At4g 18460 At5g47390

At1g32740 At2g23560 At3g21210 At4g18820 At5g48900

At1g35660 At2g24660 At3g22710 At4g19140 At5g49730

At1g36160 At2g24790 At3g27020 At4g 19240 At5g51080

At1 g43730 At2g25850 At3g27325 At4g19985 At5g51230

At1 g45474 At2g27190 At3g27770 At4g23290 At5g52780

At1g52870 At2g27220 At3g30220 At4g23300 At5g52900

At1 g52990 At2g30990 At3g44410 At4g27050 At5g53130

At1 g53170 At2g31800 At3g44720 At4g27990 At5g55750

At1 g55130 At2g32020 At3g45580 At4g29420 At5g56520

At1 g55300 At2g34020 At3g45780 At4g31030 At5g57345

At1 g57760 At2g40420 At3g45840 At4g32000 At5g59650

At1g58470 At2g40940 At3g48730 At4g32250 At5g63360

At1 g67690 At2g42380 At3g51560 At4g32410 At5g63800

At1g67960 At2g42590 At3g53680 At4g32810 At5g67430

At1 g68330 At2g43320 At3g55560 At4g35760 ndhA

At1 g68840 At2g44800 At3g57780 At4g35930 ndhH

At1g70730 At3gO2180 At3g60260 At4g39390 psbM

At1g70830 At3g05750 At3g60290 At4g39560 rpl33

At1g75490 At3g09470 At3g60430 At5g04190

At1 g77490 At3g 10810 At3g61530 At5g 14340

At2g02750 At3g11100 At3g62430 At5g 14800

At2g03330 At3g 11750 At4g02610 At5g16010

Table 3, continued

3B: Genes showing negative correlation between transcript abundance and trait value

At1 g01230 At1 g64900 At2g29070 At3g52590 At5g15800

At1 g03710 At1 g68990 At2g34570 At3g53140 At5g16040

At1 g03820 At1 g69440 At2g35150 At3g56900 At5g 17370

At1 g03960 At1g69750 At2g36170 At4g02290 At5g 17420

At1g07070 At1g69760 At2g37020 At4gO3156 At5g20740

AtI g 13090 At1g74660 At2g40435 At4gO8150 At5g22460

AtI g13680 At1g75390 At2g41140 At4g11160 At5g22630

AtIg 14930 At1g77540 ^' At2g45660 At4g14010 At5g37260

AtI g 15200 At1g77600 At2g45930 At4g14350 At5g40380

AtI g 18250 At1g78050 At2g47640 At4g14850 At5g42180

AtI g 18850 At1 g78780 At3g02310 At4g15910 At5g43860

AtI g 19340 At1 g79520 At3g02800 At4g17770 At5g44620

At1 g20070 At1 g80170 At3g03610 At4g18470 At5g45010

At1 g22340 At2g01520 At3g05230 At4g 18780 At5g47540

At1 g24070 At2g01610 At3g09310 At4g19850 At5g50110

At1 g24100 At2g04740 At3g09720 At4g21090 At5g50350

At1g24260 At2g14120 At3g 12520 At4g29230 At5g50915

At1 g29050 At2g 17670 At3g 13570 At4g29550 At5g52040

At1 g29310 At2g 18040 At3g 14120 At4g35940 At5g53770

At1 g29850 At2g 18600 At3g 15270 At4g39320 At5g54250

At1g32770 At2g 18740 At3g 16080 At5g01730 At5g55560

At1 g51380 At2g 19480 At3g 18280 At5g01890 At5g57920

At1 g51460 At2g 19750 At3g 19370 At5g02030 At5g58710

At1 g52040 At2g 19850 At3g20100 At5g03840 At5g59305

AtI g52760 At2g20450 At3g20430 At5g04850 At5g59310

At1 g52930 At2g22240 At3g22370 At5g04950 At5g59460

At1 g53160 At2g22920 At3g22540 At5g05280 At5g60490

At1 g59670 At2g23700 At3g25220 At5g06190 At5g60690

At1g61570 At2g25670 At3g28500 At5g07370 At5g60910

At1g62560 At2g27360 At3g49600 At5g08370 At5g61310

At1g63540 At2g28450 At3g51780 At5g11630 At5g62290

Table 4. Genes used for prediction of leaf number at bolting in unvernalised plants; Transcript ID (AGI code)

4A. Genes showing positive correlation between transcript abundance and trait value

At1 gO2813 At1 g63680 At2g42120 At3g51680 At5g 10250

At1 g02910 At1 g66070 At2g44820 At3g55510 At5g 10950

At1 g03840 At1 g66850 At3g01040 At3g59780 At5g11240

At1 g08750 At1 g68600 At3g01110 At4g00640 At5g 11270

AtI g13810 At1 g69680 At3g01250 ^• At4g01970 At5g 16690

AtI g 15530 At1 g70870 At3g01440 At4g02820 At5g20680

AtI g 16280 At1 g74700 At3g01790 At4g04790 At5g25070

AtIg 18530 At1g74800 At3g02350 At4g05640 At5g26780

At1g20370 At1g76380 At3gO323O At4g08140 At5g27330

At1g21070 At1g76880 At3g03780 At4g08250 At5g36120

At1g24390 At1 g77140 At3g07040 At4g 12460 At5g40830

At1 g24735 At1 g77870 At3g11980 At4g 14605 At5g41480

At1 g28430 At1 g78070 At3g 13280 At4g16120 At5g42700

At1g28610 At1 g78720 At3g 15400 At4g17615 At5g46330

At1 g31500 At1 g78930 At3g16100 At4g 18030 At5g46690

At1 g31660 At2gO1860 At3g17170 At4g 18070 At5g47435

At1g33265 At2g01890 At3g17710 At4g 18720 At5g51050

At1 g34480 At2g02050 At3g 17840 At4g21890 At5g51100

At1 g42690 At2g03420 At3g 17990 At4g22040 At5g53070

At1g45616 At2g03460 At3g 18000 ^l At4g22800 At5g56280

At1g47230 At2g03480 At3g18130 At4g23740 At5g57310

At1g47980 At2g04840 At3g18700 At4g26310 At5g59350

At1g48040 At2gO7734 At3g20140 At4g26360 At5g59530

At1g50230 At2g12400 At3g20320 At4g30720 At5g63040

At1 g51340 At2g 13690 At3g21950 At4g31590 At5g63150

At1g52290 At2g 17250 At3g23310 At4g33070 At5g63440

At1g52600 At2g 17870 At3g24150 At4g33770 At5g64480

At1 g53500 At2g20200 At3g25140 At4g38050 ace D

At1g55370 At2g23610 At3g25805 At4g38760 nad4L

At1 g56500 At2g28620 At3g25960 At5g05450 orf121 b

At1g59510 At2g30390 At3g27240 At5gO584O orf294

At1g59720 At2g30460 At3g27360 At5g07630 rps12.1

At1g61280 At2g35400 At3g27780 At5g07720 rps2

At1g62630 At2g38650 At3g28007 At5gO8180 ycf4

At1 g63150 At2g41770 At3g29660 At5g 10020

Table 4, continued.

4B. Genes showing negative correlation between transcript abundance and trait value

At1g02360 At1 g70090 At2g48020 At3g60980 At5g22450

At1 g04300 At1 g70590 At3g01650 At3g62590 At5g24450

At1 g04810 At1g72300 At3g01770 At4g02470 At5g25120

At1 g04850 At1 g72890 At3g04070 At4g07950 At5g25440

At1 g06200 At1 g75400 At3g06130 At4g09800 At5g25490

At1 g08450 At1 g78420 At3g07690 At4g 15420 At5g25560

Att g 10290 At1 g78870 At3g08650 At4g 15620 At5g25880

AtI g 12360 At1g78970 At3gO9735 At4g 16760 At5g38850

AtI g 15920 At1g79380 At3g09840 At4g 16830 At5g39610

AtI g 18700 At1 g79840 At3g 10500 At4g 16845 At5g39950

At1 g18880 At1 g80630 At3g11410 At4g 16990 At5g40250

At1g21000 At2g01060 At3g 12480 At4g 17040 At5g40330

At1 g22190 At2g02390 At3g 13062 At4g 17340 At5g42310

At1 g22930 At2g05070 At3g 15900 At4g 17600 At5g42560

At1 g23050 At2g 15080 At3g 17770 At4g 18260 At5g43460

At1 g23950 At2g21180 At3g18370 At4g20110 At5g44390

At1 g24340 At2g22800 At3g20250 At4g22190 At5g45050

At1 g30720 At2g25080 At3g21640 At4g23880 At5g45420

At1 g33990 At2g26300 At3g23600 At4g28160 At5g45430

At1 g34300 At2g28070 At3g26520 At4g29735 At5g45500

At1 g34370 At2g29120 At3g29180 At4g29900 At5g45510

At1 g48090 At2g30140 At3g43520 At4g31985 At5g48180

At1 g50570 At2g31350 At3g44880 At4g33300 At5g49000

At1 g54250 At2g32850 At3g46960 At4g35060 At5g49500

At1 g54360 At2g35900 At3g48410 At5g01650 At5g52240

At1 g59590 At2g41640 At3g48760 At5gO3455 At5g57160

At1 g59960 At2g41870 At3g51010 At5g05680 At5g57340

At1 g60710 At2g42270 At3g51890 At5g06960 At5g58220

At1 g60940 At2g43000 At3g52550 At5g 12250 At5g58350

At1 g61560 At2g44130 At3g55005 At5g 14240 At5g59150

At1g65980 At2g45600 At3g56310 At5g 15880 At5g66810

At1 g66080 At2g47250 At3g59950 At5g 18900 At5g67380

At1 g68920 At2g47800 At3g60245 At5g21070

Table 5 . Genes used for prediction of ratio of leaf number at bolting (vernalised plants ) / leaf number at bolting

(unvernalised plants ) ; Transcript ID (AGI code )

5A. Genes showing positive correlation between transcript abundance and trait value

At1 g01550 At1g50420 At2g18690 At3g08690 At3g50290 At4g16950 At5g38850

At1 g02360 At1g50430 At2g20145 At3g08940 At3g50770 At4g16990 At5g38900

At1g02390 At1g50570 At2g22170 At3g09020 At3g50930 At4g17250 At5g39030

At1g02740 At1 g51280 At2g22690 At3gO9735 At3g51010 At4g17270 At5g39520

At1g02930 At1 g51890 At2g22800 At3g09940 At3g51330 At4g17900 At5g39670

At1g03210 At1 g53170 At2g23810 At3g10640 At3g51430 At4g19660 At5g40170

At1 g03430 At1 g54320 At2g24160 At3g10720 At3g51440 At4g21830 At5g40780

At1 g07000 At1 g54360 At2g24850 At3g11010 At3g51890 At4g22560 At5g40910

At1 g07090 At1 g55730 At2g25625 At3g11820 At3g52240 At4g22670 At5g41150

At1 g08050 At1 g57650 At2g26240 At3g11840 At3g52400 At4g23140 At5g42050

At1 g08450 At1 g57790 At2g26400 At3g12040 At3g52430 At4g23150 At5g42090

At1 g09560 At1 g58470 At2g26600 At3g13100 At3g53410 At4g23180 At5g42250

At1 g10340 At1 g61740 At2g26630 At3g13270 At3g56310 At4g23220 At5g42560

At1g10660 At1 g62763 At2g28210 At3g13370 At3g56400 At4g23260 At5g43440

At1g12360 At1 g66090 At2g28940 At3g13610 At3g56710 At4g23310 At5g43460

At1g13100 At1 g66100 At2g29350 At3g13772 At3g57260 At4g25900 At5g43750

At1g13340 At1 g66240 At2g29470 At3g13950 At3g57330 At4g26070 At5g44570

At1 g14070 At1 g66880 At2g30500 At3g13980 At3g60420 At4g26410 At5g44980

At1 g14870 At1 g67330 At2g30520 At3g14210 At3g60980 At4g27280 At5g45050

At1 g15520 At1 g67850 At2g30550 At3g14470 At3g61010 At4g29050 At5g45110

At1 g15790 At1 g68300 At2g30750 At3g16990 At3g61540 At4g29740 At5g45420

At1 g15880 At1 g68920 At2g30770 At3g18250 At4g00330 At4g29900 At5g45500

At1 g15890 At1 g69930 At2g31880 At3g18490 At4g00355 At4g33300 At5g45510

At1 g18570 At1g71070 At2g31945 At3g18860 At4g00700 At4g34135 At5g48810

AtI g 19250 At1g71090 At2g32140 At3g 18870 At4g00955 At4g34215 At5g51640

At1g19960 At1g72060 At2g33220 At3g20250 At4g01010 At4g35750 At5g51740

At1 g21240 At1g72280 At2g33770 At3g22060 At4g01700 At4g36990 At5g52240

At1g21570 At1g72900 At2g34500 At3g22231 At4g02380 At4g37010 At5g52760

At1 g22890 At1 g73260 At2g35980 At3g22240 At4g02420 At5g04720 At5g53050

At1 g22930 At1 g73805 At2g39210 At3g22600 At4g02540 At5g05460 At5g53130

At1 g22985 At1 g75130 At2g39310 At3g22970 At4g03450 At5g06330 At5g53870

At1 g23780 At1 g75400 At2g40410 At3g23050 At4g04220 At5g06960 At5g54290

At1 g23830 At1 g78410 At2g40600 At3g23080 At4g05040 At5g07150 At5g54610

At1 g23840 At1 g79840 At2g40610 At3g23110 At4g05050 At5g08240 At5g55450

At1 g26380 At1 g80460 At2g41100 At3g25070 At4g08480 At5g10380 At5g55640

At1 g26390 At2g02390 At2g42390 At3g25610 At4g10500 At5g10740 At5g57220

At1 g28130 At2g02930 At2g43000 At3g26170 At4g11890 At5g10760 At5g58220

At1g28280 At2g03070 At2g43570 At3g26210 At4g11960 At5g11910 At5g59420

At1 g28340 At2g03870 At2g44380 At3g26220 At4g12010 At5g11920 At5g60280

At1 g28670 At2g03980 At2g45760 At3g26230 At4g12510 At5g13320 At5g60950

At1 g30900 At2g05520 At2g46020 At3g26450 . At4g12720 At5g14430 At5g61900

At1 g32700 At2g06470 At2g46150 At3g26470 At4g13560 At5g18060 At5g62150

At1 g32740 At2g11520 At2g46330 At3g28180 At4g14365 At5g18780 At5g62950

At1g32940 At2g13810 At2g46400 At3g28450 At4g14610 At5g21070 At5g63180

At1 g34300 At2g14560 At2g46450 At3g28510 At4g15420 At5g22570 At5g64000

At1 g34540 At2g14610 At2g46600 At3g43210 At4g15620 At5g24530 At5g66590

At1 g35230 At2g15390 At2g47710 At3g44630 At4g16260 At5g25260 At5g67340

At1 g35320 At2g16790 At3gO1O8O At3g45240 At4g16750 At5g25440 At5g67590

At1g35560 At2g17040 At3g03560 At3g45780 At4g16845 At5g26920

At1 g43910 At2g17120 At3g04070 At3g47050 At4g16850 At5g27420

At1g45145 At2g17650 At3g04210 At3g47480 At4g16870 At5g35200

At1 g48320 At2g17790 At3g04720 At3g48090 At4g16880 At5g37070

At1 g49050 At2g18680 At3g08650 At3g48640 At4g16890 At5g37930

5B. Genes showing negative correlation between transcript abundance and trait value

At1 g03820 At1 g76270 At3g 10840 At4g 10320 At5g15050 At1 g05480 At1 g77680 At3g 13560 At4g 12430 At5g19920 At1 g06020 At1 g78720 At3g 13640 At4g 14420 At5g20240 At1 g06470 At1 g78930 At3g 15400 At4g 16700 At5g22430 At1 g07370 At2g01890 At3g 17990 At4g17180 At5g22790 At1 g18100 At2g03480 At3g 18000 At4g19100 At5g23570 At1g20750 At2g 13920 At3g 18070 At4g23720 At5g27330 At1g28610 At2g 14530 At3g 19790 At4g23750 At5g27660 At1g31660 At2g 17280 At3g20240 At4g24670 At5g41480 At1 g44790 At2g18890 At3g21510 At4g26140 At5g43880 At1 g47230 At2g20470 At3g24470 At4g31210 At5g49555 At1g49740 At2g22870 At3g27180 At4g31540 At5g51050 At1 g51340 At2g33330 At3g28270 At4g34740 At5g51350 At1 g52290 At2g36230 At3g45930 At4g35990 At5g53760 At1 g61280 At2g36930 At3g47510 At4g38050 At5g53770 Atl g63130 At2g37860 At3g49750 At4g38760 At5g55400 At1 g63680 At2g39220 At3g50810 At5g02050 At5g55710 At1g64100 At2g39830 At3g52370 At5g02180 At5g56620 At1 g66140 At2g40160 At3g54250 At5g02590 At5g57960 At1g67720 At2g44310 At3g54820 At5g02740 At5g59350 At1g69420 At3g05030 At3g57000 At5g06050 At5g61770 At1 g69700 At3g05940 At4g04790 At5g07800 At5g62575 At1 g71920 At3g06200 At4g08140 At5g08180 orf121 b At1 g74800 At3g 10450 At4g 10280 At5g 14370

Table 6. Genes for prediction of oil content of seeds, % dry weight (vernalised plants) ; Transcript ID (AGI code)

6A. Genes showing positive correlation between transcript abundance and trait value

At1 g02640 At1 g67350 At2g42300 At4g01460 At5g25180 At1 g02750 At1 g69690 At2g42590 At4g02440 At5g25760 At1g02890 At1 g70730 At2g42740 At4g02700 At5g26270 At1 g04170 At1 g71970 At2g44130 At4g03050 At5g27360 At1g05550 At1g74670 At2g44530 At4g03070 At5g32470 At1g05720 At1 g74690 At2g45190 At4g07400 At5g36210 At1 gO811O At2g01090 At3g02500 At4g11790 At5g36900 At1gO856O At2g 14890 At3g03310 At4g 12600 At5g37510 At1g09200 At2g 17650 At3g03380 At4g12880 At5g38140 At1gO9575 At2g 18400 At3g05410 At4g 14550 At5g40150 At1 g10170 At2g 18550 At3g06470 At4g 15780 At5g41650 AtIg 10590 At2g 18990 At3g07080 At4g 16490 At5g44860 AtI g 13250 At2g20210 At3g 14240 At4g 17560 At5g45260 AtI g 15260 At2g20220 At3g 15550 At4g20070 At5g45270 AtIg 17590 At2g20840 At3g 17850 At4g21650 At5g46160 AtIg 18650 At2g21860 At3g 18390 At4g27830 At5g47030 At1g23370 At2g25170 At3g19170 At4g29750 At5g47760 At1g27590 At2g25900 At3g24660 At4g32760 At5g48900 At1 g29180 At2g27260 At3g28345 At4g34250 At5g50230 At1 g31020 At2g29550 At3g51150 At4g38670 At5g51660 At1 g34030 At2g30050 At3g53110 At5g02770 At5g52110 At1 g42480 At2g30530 At3g53170 At5g04600 At5g52250 At1 g48140 At2g31120 At3g55480 At5g07000 At5g54190 At1 g49660 At2g31640 At3g55610 At5g07030 At5g54580 At1 g51950 At2g31955 At3g57340 At5g07300 At5g55670 At1 g52800 At2g32440 At3g57490 At5gO764O At5g55900 At1g54850 At2g36490 At3g57860 At5g07840 At5g57660 At1 g55300 At2g37050 At3g60390 At5g08330 At5g58600 At1 g60010 At2g37410 At3g60520 At5g08500 At5g60850 At1 g60230 At2g38120 At3g61180 At5g09330 At5g62530 At1 g61810 At2g38720 At3g62720 At5g 10390 At5g62550 At1g63780 At2g39850 At3g63000 At5g 15390 At5g63860 At1 g64105 At2g39870 At4g00180 At5g17100 At5g65650 At1g64450 At2g39990 At4g00600 At5g 19530 At1 g65260 At2g40040 At4g00860 At5g22290 At1 g66130 At2g40570 At4g00930 At5g23420 At1 g66180 At2g41370 At4g01120 At5g24210

Table 6 , continued .

6B. Genes showing negative correlation between transcript abundance and trait value

At1g01790 At1g70250 At3g09480 At4g03260 At5g23010

At1 g03710 At1g70270 At3g14395 At4g03400 At5g24510

At1 g04220 At1 g72800 At3g 14720 At4g03500 At5g24850

At1 g04960 At1 g73177 At3g 16520 At4g03640 At5g25640

At1 gO4985 At1 g74590 At3g 17800 At4g04900 At5g25830

At1 g06550 At1 g74650 At3g18980 At4g09680 At5g26665

At1 g06780 At1 g75690 At3g19320 At4g10150 At5g28560

AtI g 10550 At1 g77000 At3g19710 At4g 12020 At5g35400

At1 g11070 At1g77380 At3g20270 At4g 13050 At5g35520

At1 g11280 At1 g78450 At3g22370 At4g13180 At5g37300

At1 g11630 At1g78740 At3g22740 At4g 14040 At5g38780

AtI g 12550 At1g78750 At3g23170 At4g 17390 At5g38980

At1 g15310 At1 g79950 At3g24400 At4g18210 At5g39550

AtI g 16060 At1g80130 At3g25120 At4g 18780 At5g39940

AtIg 16540 At1 g80170 At3g26130 At4g19980 At5g42180

At1 g16880 At2g02960 At3g27960 At4g20840 At5g43480

AtI g 18830 At2g11690 At3g28050 At4g21400 At5g43500

At1 g22480 At2g 13770 At3g29787 At4g22790 At5g44030

At1 g23120 At2g 19570 At3g30720 At4g24130 At5g44740

At1g27440 At2g19850 At3g42840 At4g24940 At5g45170

At1 g29700 At2g20410 At3g43240 At4g25040 At5g46490

At1 g31580 At2g20500 At3g45070 At4g25890 At5g47050

At1 g34040 At2g21630 At3g45270 At4g26610 At5g47630

At1 g34210 At2g22920 At3g46500 At4g28350 At5g481 10

At1g47410 At2g23340 At3g47320 At4g32240 At5g48340

At1g47960 At2g26170 At3g49360 At4g32690 At5g49530

At1g49710 At2g27760 At3g50810 At4g33040 At5g49540

At1 g50580 At2g30020 At3g51030 At4g34240 ^■ At5g52380

At1 g51070 At2g31450 At3g51580 At4g37150 At5g53090

At1g51440 At2g31820 At3g53690 At4g39780 At5g53350

At1g51580 At2g32490 At3g57630 At5g02820 At5g54660

At1 g51805 At2g33480 At3g57680 At5g05420 At5g54690

At1g53690 At2g37970 At3g57760 At5g08600 At5g56030

At1 g54560 At2g37975 At3g60170 At5g08750 At5g56700

At1 g55850 At2g44850 At3g62390 At5g10180 At5g58980

At1 g61667 At2g47570 At3g62400 At5g 11600 At5g59305

At1g62860 At2g47640 At3g62410 At5g 15600 At5g59690

At1g63320 At3g01720 At4g00960 At5g 16520 At5g60160

At1g64950 At3g01970 At4g01070 At5g 17060 At5g61640

At1g65480 At3g05210 At4g01080 At5g 17420 At5g63590

At1 g66930 At3g05540 At4g02450 At5g 17790 At5g64816

At1 g69750 At3g09410 At4g03060 At5g20180

Table 7. Genes with transcript abundance correlating with ratio of 18:2 / 18:1 fatty acids in seed oil (vernalised plants); Transcript ID (AGI code)

7A. Genes showing positive correlation between transcript abundance and trait value

At1 g01730 At1 g77590 At2g44910 At4g02450 At5g 19560

AtI g 15490 At1 g78450 At3g01720 At4g03060 At5g20180

AtI g 16060 At1 g78750 At3g05210 At4g04650 At5g23010

AtI g 16540 At1 g79950 At3g05270 At4g10150 At5g28500

At1 g23120 At1 g80170 At3g05320 At4g 12020 At5g28560

At1 g26730 At2g01120 At3g11880 At4g 13050 At5g38980

At1g34220 At2g02960 At3g 13840 At4g13180 At5g43330

At1g35260 At2g03680 At3g 14450 At4g 15260 At5g44740

At1g50580 At2g 13770 At3g 16520 At4g 17390 At5g47050

At1g54560 At2g 17220 At3g 19930 At4g24920 At5g49540

At1 g59620 At2g20410 At3g22690 At4g24940 - At5g56910

At1 g61400 At2g21630 At3g24400 At4g32240 At5g60160

At1 g62860 At2g27090 At3g42840 At5g06730 At5g64816

At1g67550 At2g34440 At3g45640 At5g06810

At1 g74650 At2g37975 At3g48580 At5g08750

At1 g76690 At2g38010 At3g49360 At5g 13890

At1 g77380 At2g44850 At3g57760 At5g 17060

18:2 = linoleic acid

18:1 = oleic acid

Table 7 , continued .

7B. Genes showing negative correlation between transcript abundance and trait value

At1 g02050 At1 g63780 At2g38120 At3g60530 At5g17100

At1 g04170 At1 g64105 At2g39450 At3g61830 At5g17220

At1 g04790 At1 g66180 At2g39870 At3g62430 At5g18070

At1 g06580 At1 g66250 At2g40040 At3g62460 At5g25590

At1 g08110 At1 g66900 At2g40570 At4g00600 At5g26270

AtI g 13250 At1 g67590 At2g42740 At4g00930 At5g37510

AtI g 14700 At1 g67830 At2g44860 At4g03050 At5g40150

AtI g 15280 At1g69690 At3g02500 At4g03070 At5g43280

AtI g 18650 At1g75710 At3g07200 At4g 12600 At5g46160

At1 g26920 At1g76320 At3g08000 At4g 13980 At5g47760

At1 g29180 At2g04700 At3g 11420 At4g 14550 At5g51080

At1 g29950 At2g 14900 At3g 11760 At4g 15780 At5g51660

At1 g33055 At2g16800 At3g 14240 At4g16920 At5g52230

At1 g35720 At2g 18990 At3g24660 At4g 17560 At5g54190

At1 g49660 At2g20210 At3g26310 At4g22160 At5g55670

At1 g51950 At2g20220 At3g27420 At4g25150 At5g57660

At1 g52800 At2g20360 At3g44010 At4g26555 At5g63860

At1 g52810 At2g21860 At3g47060 At4g36140 At5g65390

At1 g54450 At2g25900 At3g53230 At4g36740 At5g65650

At1 g60190 At2g27970 At3g55480 At5g07000 At5g65880

At1 g60390 At2g31120 At3g55610 At5g07030

At1 g60800 At2g34560 At3g56060 At5g 10390

At1g62500 At2g36490 At3g57860 At5,g15120

At1g62510 At2g37410 At3g60520 At5g17020

18:2 = linoleic acid

18:1 = oleic acid

Table 8. Genes for prediction of ratio of 18:3 / 18:1 fatty acids in seed oil (vernalised plants) ; Transcript ID (AGl code)

8A. Genes showing positive correlation between transcript abundance and trait value

AtI g11940 At1 g71140 At4g01690 At5g 11270 At5g44290

AtI g 15490 At1 g78210 At4g08240 At5g 13890 At5g44520

At1g22200 At2g07050 At4g11900 At5g 14700 At5g46630

At1g23890 At2g31770 At4g 12300 At5g 16250 At5g47410

At1g28030 At2g35736 At4g 18593 At5g 17880 At5g49540

At1 g33560 At2g46640 At4g23300 At5g18400 At5g49630

At1g49030 At3g 14780 At4g24940 At5g20180 At5g54970

At1 g51430 At3g 16700 At4g38930 At5g22860 At5g55760

At1 g59265 At3g26430 At4g39390 At5g23510 At5g55930

At1 g62610 At3g46540 At5g03290 At5g27760 At5g64110

At1g64190 At3g49360 At5g05750 At5g28940

At1 g69450 At3g51580 At5g08590 At5g44240

18:3 = linolenic acid

18:1 = oleic acid

8B. Genes showing negative correlation between transcript abundance and trait value

At1 gO555O At1 g70430 At3g18940 At4g05450 At5g19830

At1 g06500 At1 g72260 At3g22210 At4g 10320 At5g22290

At1 g06580 At1 g76720 At3g23325 At4g 14870 At5g23330

AtI g 10320 At2g01090 At3g24660 At4g 14890 At5g25120

AtI g 10980 At2g 17550 At3g26240 At4g 14960 At5g25180

AtI g16170 At2g18100 At3g44600 At4g 16830 At5g26270

At1g21080 At2g20490 At3g44890 At4g17410 At5g41970

At1 g24070 At2g20515 At3g50380 At4g 18975 At5g47550

At1 g29180 At2g20585 At3g51780 At4g23870 At5g47760

At1 g30880 At2g21090 At3g52090 At4g26170 At5g48580

At1 g32310 At2g21860 At3g53110 At4g35240 At5g48760

At1g33055 At2g31840 At3g53390 At4g35880 At5g49190

At1g59900 At2g32160 At3g54290 At4g36380 At5g49500

At1 g61810 At2g36570 At3g57860 At5g07640 At5g50950

At1 g63780 At3g06470 At3g62080 At5g08540 At5g51660

At1 g63850 At3g07080 At3g62860 At5g11310 At5g64650

At1 g65560 At3g11410 At4g01330 At5g 13970 At5g65010

At1 g66130 At3g14150 At4g02210 At5g 17010

At1g67830 At3g 15900 At4g03070 At5g17100

18:3 = linolenic acid

18:1 = oleic acid

Table 9. Genes with transcript abundance correla ting wi th ra tio of 18 : 3 / 18 : 2 fatty acids in seed oil (vernalised plants) ; Transcript ID (AGI code)

9A. Genes showing positive correlation between transcript abundance and trait value

At1 gO137O At1 g62770 At2g45920 At4g07420 At5g26180

At1 g01530 At1 g66520 At2g46640 At4g11835 At5g28620

At1 g02300 At1 g66620 At2g47600 At4g 12300 At5g28940

At1 g02710 At1 g70830 At3g05520 At4g12510 At5g35490

At1 g03420 At1 g71690 At3gO914O At4g 17650 At5g38120

At1 g05650 At1g77490 At3g 10810 At4g 18460 At5g40230

At1 g08170 At1g79000 At3g11090 At4g 18593 At5g43070

At1 g11940 At1 g79060 At3g 12920 At4g 18820 At5g45120

AtIg 13280 At2g02590 At3g14780 At4g20140 At5g45320

At1 g13810 At2g02770 At3g16370 At4g23300 At5g46630

AtI g 15050 At2g07050 At3g 18060 At4g25570 At5g47400

At1 g20810 At2g07702 At3g 18270 At4g31870 At5g49630

At1 g20980 At2g11270 At3g22710 At4g32960 At5g51080

At1 g21710 At2g 15790 At3g22850 At4g33160 At5g51230

At1 g22200 At2g18115 At3g22880 At4g35530 At5g51960

At1 g23670 At2g19310 At3g27325 At4g37220 At5g56370

At1 g23890 At2g28100 At3g28090 At4g39390 At5g57345

At1 g27210 At2g28160 At3g29770 At5g03730 At5g59660

At1 g33880 At2g32330 At3g31415 At5g05840 At5g62030

At1g44960 At2g34310 At3g43960 At5g05890 At5g64110

AtI g51430 At2g35890 At3g45440 At5g07250 At5g64970

At1 g51980 At2g38140 At3g46670 At5g08280 At5g65100

At1 g57760 At2g39700 At3g48730 At5g17210 At5g66985

At1 g57780 At2g41600 At3g59860 At5g 18390 cox1

At1 g59740 At2g43320 At3g61160 At5g20590 orf154

At1 g60300 At2g44100 At3g61170 At5g22500

At1 g60560 At2g45150 At3g62430 At5g22860

At1g62630 At2g45710 At4g01350 At5g26140

18:3 = linolenic acid

18:2 = linoleic ε icid

Table 9 , continued .

9B. Genes showing negative correlation between transcript abundance and trait value

At1 g02500 At1 g74880 At3g06790 At3g62040 At5g07370

At1g02780 At1 g76260 At3g07230 At4g02075 At5g07690

At1 g03710 At1 g76560 At3g09480 At4g03240 At5gO8535

At1 g06500 At1g76890 At3g11410 At4g04620 At5g08540

At1 g06520 At1 g77540 At3g 12090 At4g05450 At5g13970

AtIg 12750 At1g77600 At3g 13490 At4g10120 At5g 16040

AtIg 13090 At1g78080 At3g 13800 At4g13195 At5g 17930

AtIg 14930 At1 g78750 At3g 15900 At4g 14020 At5g25120

At1g14990 At1 g78780 At3g 16080 At4g 14350 At5g28080

AtIg 15200 At1 g79430 At3g 17770 At4g14615 At5g28500

AtIg 19340 At1 g80170 At3g 18940 At4g 15230 At5g39550

At1g22500 At2g 15630 At3g21250 At4g17410 At5g40540

At1g22630 At2g 19740 At3g22210 At4g 18330 - At5g45840

At1g26170 At2g 19850 At3g23325 At4g 18780 At5g47050

At1g28060 At2g20490 At3g25220 At4g 19850 At5g47540

At1g29850 At2g21640 At3g25740 At4g21090 At5g48110

At1g30530 At2g22920 At3g28700 At4g22380 At5g48580

At1g31340 At2g25670 At3g31910 At4g25890 At5g49530

At1 g32310 At2g25970 At3g44890 At4g29230 At5g50915

At1 g47480 At2g27360 At3g46490 At4g29550 At5g50940

At1g50140 At2g28200 At3g47320 At4g30220 At5g50950

At1 g52040 At2g28450 At3g48860 At4g30290 At5g51010

At1g53590 At2g29070 At3g51780 At4g30760 At5g51820

At1g54250 At2g29120 At3g53390 At4g31310 At5g55560

At1g59670 At2g30000 At3g53500 At4g31985 At5g57160

At1g59900 At2g36750 At3g53630 At4g32240 At5g58520

At1 g60710 At2g37585 At3g53890 At4g35240 At5g59460

At1 g62560 At2g39910 At3g54260 At4g37150 At5g61450

At1g63540 At2g40010 At3g55005 At5g02610 At5g61830

At1g64140 At2g45930 At3g55630 At5g02670 At5g62290

At1g64900 At2g47250 At3g56900 At5gO3455 At5g63590

At1g66690 At2g48020 At3g57180 At5g03540 At5g64140

At1g67860 At3g01860 At3g59810 At5g04420 At5g64190

At1g72510 At3g03610 At3g61100 At5g04850 At5g66530

At1g73177 At3g06110 At3g61980 At5g05680

18:3 = linolenic acid

18:2 = linoleic acid

Table 10. Genes with transcript abundance correlating with ratio of 2OC + 22C / 16C + 18C fatty acids in seed oil (vernalised plants); Transcript ID (AGI code)

10A. Genes showing positive correlation between transcript abundance and trait value

At1 g01370 At1 g55120 At2g46710 At3g57880 At5g24280

At1g03420 At1 g60390 At2g47380 At4g13360 At5g24520

At1g04790 At1g62150 At3g04680 At4g14090 At5g25940

At1g06730 At1g69670 At3g09710 At4g24390 At5g37290

At1g09850 At1 g79060 At3g10650 At4g26555 At5g38630

At1g11800 At1g79460 At3g14240 At4g31570 At5g40880

At1 g21690 Atl g79970 At3g26090 At4g35900 At5g47320

At1g43650 At2g25450 At3g26310 At5g05230 At5g52410

At1 g49200 At2g35155 At3g26380 At5g05370 At5g54860

At1 g50660 At2g40070 At3g29770 At5g10400 At5g55810

At1 g53460 At2g40480 At3g44500 At5g 17210

At1 g53850 At2g45710 At3g56060 At5g23940

16C fatty acid = palmitic 18C fatty acids = oleic, stearic, linoleic, linolenic 2OC fatty acids = eicosenoic 22C fatty acids = erucic

Table 10 , continued .

10B. Genes showing negative correlation between transcript abundance and trait value

At1g02410 At1 g64150 At2g32160 At3g48860 At4g38980

At1 gO2475 At1 g66540 At2g34690 At3g50050 At5g01970

At1 g02500 At1 g66645 At2g35520 At3g55005 At5g02010

At1g05350 At1 g72920 At2g38220 At3g59180 At5g02610

At1g05360 At1 g73120 At2g40010 At3g61950 At5g03090

At1 g07260 AU g73250 At2g41830 At3g63310 At5g03220

At1 g17310 At1 g73940 At2g45740 At3g63330 At5g05060

AtI g 17970 At1 g74620 At2g46730 At4g00030 At5gO8535

At1 g21110 At1 g77590 At3g01520 At4g00234 At5g08540

At1 g21190 At1 g77960 At3g01860 At4g00950 At5g 14680

At1g21350 At1 g77970 At3g04610 At4g01410 At5g 16980

At1 g22520 At1g78750 At3g06100 At4g02500 At5g25530

At1g22910 At1 g79890 At3g06110 At4g02790 At5g27410

At1 g27000 At1 g80640 At3g08990 At4g02850 At5g33250

At1 g32050 At1 g80700 At3g09530 At4g02960 At5g35260

At1g32070 At2g02500 At3g11400 At4g04110 At5g35740

At1g32310 At2g02960 At3g11500 At4g05460 At5g36890

At1 g33330 At2g05950 At3g11780 At4g11820 At5g37330

At1 g33600 At2g14170 At3g 13450 At4g 12310 At5g42310

At1 g34580 At2g15560 At3g15150 At4g14100 At5g43330

At1 g35650 At2g 15930 At3g 17690 At4g19100 At5g44880

At1g44750 At2g 16750 At3g19515 At4g 19490 At5g44910

At1g47480 At2g 17265 At3g22690 At4g 19500 At5g45490

At1 g47920 At2g 19800 At3g24030 At4g 19520 At5g45550

At1g49240 At2g 19950 At3g27050 At4g 19550 At5g45680

At1 g50630 At2g21070 At3g27920 At4g21410 At5g46540

AfI g51940 At2g22570 At3g42120 At4g22330 At5g49080

At1 g53650 At2g23360 At3g44020 At4g24950 At5g50130

At1 g58300 At2g24610 At3g44890 At4g29380 At5g51010

At1 g59900 At2g28850 At3g45430 At4g31720 At5g51820

At1 g60810 At2g28930 At3g46370 At4g32240 At5g52070

At1 g60970 At2g29680 At3g46770 At4g33330 At5g52430

At1 g61400 At2g30000 At3g46840 At4g34265 At5g58120

At1g62090 At2g30270 At3g48720 At4g38240 At5g60710

16C fatty acid = palmitic

18C fatty acids = = oleic, stearic, linoleic, linolenic

2OC fatty acids = = eicosenoic

22C fatty acids = = erucic

Table 11. Genes with transcript abundance showing correlation with ratio of (ratio of 2OC + 22C / 16C + 18C fatty acids in seed oil (vernalised plants)) / (ratio of 2OC + 22C / 16C + 18C fatty acids in seed oil (unvernalised plants) ) ; transcript ID (AGI code)

11A. Genes showing positive correlation between transcript abundance and trait value

At1g01230 At1 g64270 At2g33990 At3g26130 At4g 15230 At5g 13970

At1g02190 At1g64360 At2g36130 At3g28700 At4g 15490 At5g 16040

At1 g02500 At1 g64370 At2g36750 At3g29180 At4g 15660 At5g 17420

At1g02780 At1 g64900 At2g36850 At3g29787 At4g17410 At5g 17930

At1g02840 At1 g66690 At2g37430 At3g31910 At4g18330 ^ At5g18880

At1 g03710 At1 g67860 At2g37585 At3g44890 At4g 18780 At5g20740

At1 g06500 At1 g68440 At2g38080 At3g45270 At4g19850 At5g24290

At1g06520 At1 g69510 At2g38600 At3g46490 At4g21090 At5g25120

At1g06530 At1 g69750 At2g39910 At3g46590 At4g21590 At5g28080

AtIg 10360 At1g70480 At2g40010 At3g47320 At4g22350 At5g28500

At1g11070 At1 g72510 At2g44850 At3g47990 At4g22380 At5g28910

AtIg 12750 At1g73177 At2g45930 At3g48860 At4g22760 At5g29090

AtIg 13090 At1 g73640 At2g47250 At3g49600 At4g24130 At5g39550

AtI g 13680 At1 g74590 At2g47640 At3g50380 At4g25890 At5g40540

AtIg 14930 At1 g74880 At2g48020 At3g51780 At4g27580 At5g40930

At1g15200 At1 g76260 At3g01860 At3g52590 At4g29230 At5g42180

At1g17100 At1 g76560 At3g02800 At3g53390 At4g29550 At5g42980

AtI g 19340 At1 g76890 At3g03610 At3g53630 At4g30110 At5g43860

At1g22160 At1 g77540 At3g04630 At3g53890 At4g30220 At5g45010

At1 g22480 At1 g77590 At3g06110 At3g54260 At4g30290 At5g45840

At1 g22500 At1g77600 At3g06720 At3g54290 At4g31310 At5g47050

At1g23390 At1 g78080 At3g06790 At3g55005 At4g31985 At5g47540

At1g26170 At1 g78750 At3g07230 At3g55630 At4g32240 At5g48110

At1g27980 At1 g78780 At3g07590 At3g56730 At4g32710 At5g48870

At1g28060 At1 g79430 At3g08030 At3g56900 At4g35240 At5g49250

At1 g29050 At1g80020 At3g09310 At3g57180 At4g35940 At5g49530

At1 g29850 At1 g80170 At3g09410 At3g57320 At4g36190 At5g50915

At1 g30490 At2g01520 At3g09480 At3g59810 At4g37150 At5g50940

At1 g30530 At2g01610 At3g10340 At3g60170 At4g37470 At5g50950

At1g31340 At2g06480 At3g11410 At3g60245 At4g37970 At5g51010

At1 g31580 At2g14120 At3g 12090 At3g60650 At4g39320 At5g51820

At1 g32310 At2g 14730 At3g13490 At3g61100 At5gO136O At5g52040

At1 g32770 At2g 15630 At3g 13800 At3g61980 At5g02610 At5g53460

At1g37826 At2g 18600 At3g14120 At3g62040 At5gO3455 At5g54250

At1g52040 At2g 19850 At3g15352 At4g00390 At5g03540 At5g55560

At1g52690 At2g19930 At3g15900 At4g02020 At5g03590 At5g57160

At1g52760 At2g20490 At3g16080 At4g02075 At5g04420 At5g58520

At1g53280 At2g21290 At3g16920 At4gO3156 At5g04850 At5g58710

At1g53590 At2g21640 . At3g17770 At4g04620 At5g05680 At5g59460

At1g54250 At2g21890 At3g18940 At4g04900 At5g06710 At5g59780

At1g55950 At2g22920 At3g20100 At4g05450 At5g07370 At5g60490

At1g56075 At2g25670 At3g20430 At4gO948O At5g07690 At5g61310

At1g59660 At2g25970 At3g21250 At4g10120 At5g08100 At5g61830

At1g59670 At2g27360 At3g22210 At4g12470 At5gO8535 At5g62290

At1 g59900 At2g28110 At3g22220 At4g13180 At5gO854O At5g63320

At1g60710 At2g28200 At3g22370 At4g13195 At5g08600 At5g63590

At1g62250 At2g28450 At3g22540 At4g 14020 At5g09480 At5g64190

At1g62560 At2g29070 At3g22740 At4g 14060 At5g 10210 At5g65530

At1g63540 At2g29120 At3g25220 At4g 14350 At5g 10550 At5g66530

At1 g64140 At2g32860 At3g25740 At4g14615 At5g 11630

16C fatty acid = palmitic; 18C fatty acids = oleic, stearic, linoleic, linolenic; 2OC fatty acids = eicosenoic; 22C fatty acids = erucic

11 B. Genes showing negative correlation between transcript abundance and trait value

At1g02300 At1 g69450 At2g45150 At3g61170 At5g 14800

At1 g02710 At1 g70830 At2g45710 At3g62430 At5g 17210

At1 g03420 At1g71690 At2g46640 At4g00860 At5g 17570

At1g05650 At1g77490 At2g47600 At4g01350 At5g 18390

At1gO817O At1 g79000 At3g02290 At4g02610 At5g20590

At1g08770 At1 g79060 At3g05520 At4g04750 At5g22860

At1 g11940 At2g02770 At3g05750 At4g10780 At5g26180

At1 gi3280 At2g07050 At3g06710 At4g11835 At5g28940

AtI g13810 At2g07702 At3g10810 At4g11900 At5g35490

At1g15050 At2g 15790 At3g11090 At4g12300 At5g38120

At1 g20810 At2g15810 At3g12920 At4g12510 At5g38310

At1 g20980 At2g19310 At3g14780 At4g 17650 At5g40230

At1g21710 At2g23180 At3g 16370 At4g 18460 At5g43070

At1g22200 At2g23560 At3g 18060 At4g 18593 At5g45320

At1g27210 At2g28100 At3g 18270 At4g 18820 At5g46630

At1g33880 At2g28160 At3g22710 At4g20140 At5g47400

At1g44960 At2g32330 At3g22850 At4g23300 At5g49630

At1g51430 At2g33540 At3g22880 At4g25570 At5g51080

At1g51980 At2g34310 At3g22990 At4g28740 At5g51230

At1g55130 At2g35780 At3g27325 At4g31870 At5g51960

At1 g57760 At2g35890 At3g28090 At4g32960 At5g53580

At1 g57780 At2g38140 At3g29770 At4g35530 At5g57345

At1g59520 At2g39700 At3g43510 At4g39390 At5g59660

At1g59740 At2g41600 At3g43960 At5g03730 At5g62030

At1g60560 At2g42590 At3g46510 At5gO584O At5g64110

At1g62050 At2g43130 At3g46670 At5g05890 orf154

At1g62630 At2g43320 At3g48730 At5g07250

At1g66620 At2g44100 At3g61160 At5g08280 16C fatty acid = palmitic 18C fatty acids = oleic, stearic, linoleic, linolenic 2OC fatty acids = eicosenoic 22C fatty acids = erucic

Table 12. Genes with transcript abundance correla ting wi th ra tio of polyunsa tura ted / monoun satura ted + sa turated 18C fatty acids in seed oil (vernalised plants)

12A. Genes showing positive correlation between transcript abundance and trait value

AtI g 15490 At2g03680 At3g 16520 At4g 12020 At5g18400

At1g33560 At2g27090 At3g 19930 At4g13050 At5g20180

At1g34220 At2g35736 At3g49360 At4g 17390 At5g38980

At1g49030 At2g38010 At3g51580 At4g22840 At5g49540

At1 g59620 At3g01720 At3g59660 At4g24940 At5g58910

At1 g74650 At3g05210 At4g02450 At5g13890

At1 g78210 At3g13840 At4g10150 At5g 17060

Polyunsaturated 18C fatty acids = linoleic, linolenic Monounsaturated 18C fatty acid = oleic Saturated 18C fatty acid = stearic

12B. Genes showing negative correlation between transcript abundance and trait value

At1 g02050 At1 g62500 At2g39870 At3g57860 At5g07030

At1 g05550 At1 g63780 At2g40570 At3g60520 At5g09630

At1 gO658O At1g64105 At2g41370 At4g00600 At5g17100

At1 g08560 At1g65560 At2g44860 At4g00930 At5g 18070

AfIg 10980 At1g66180 At3g02500 At4g03050 At5g25180

AtIg 13250 At1g66900 At3g07200 At4g03070 At5g25590

At1g15280 At1 g67590 At3g07270 At4g12600 At5g26230

At1 g29180 At1 g67830 At3g11420 At4g 12880 At5g26270

At1 g33055 At1 g69690 At3g14150 At4g 15780 At5g40150

At1g34030 At1 g76320 At3g 14240 At4g 17560 At5g46160

At1 g51950 At2g20360 At3g24660 At4g20070 At5g47760

At1 g52800 At2g20585 At3g27420 At4g21650 At5g48760

At1 g52810 At2g21860 At3g44010 At4g22160 At5g49190

At1g60190 At2g25900 At3g44600 At4g26170 At5g51660

At1g60390 At2g27970 At3g531 10 At4g36380 At5g52230

At1g60800 At2g36490 At3g53230 At4g36740 At5g54190

At1g61810 At2g39450 At3g55610 At5g07000 At5g63860

Polyunsaturated 18C fatty acids = linoleic, linolenic

Monounsaturated 18C fatty acid = oleic

Saturated 18C fatty acid = stearic

Table 13. Genes with transcript abundance showing correlation with ratio of (ratio of polyunsaturated / monounsaturated + saturated 18C fatty acids in seed oil (vernalised plants) ) / (ratio of polyunsaturated / monounsaturated + saturated 18C fatty acids in seed oil (unvernalised plants) ); Transcript ID (AGI code)

13A. Genes showing positive correlation between transcript abundance ! and trait value

At1 g05040 At1 g64190 At2g40313 At4g 10470 At5g17210

At1 gO6225 At1g65330 At2g40980 At4g 10920 At5g24230

At1 g06650 At1 g67910 At2g44740 At4g11560 At5g28410

At1g07640 At1 g70870 At2g47300 At4g 13050 At5g38360

At1 g09740 At1 g71140 At2g47340 At4g 15440 At5g39080

AtI g 14340 At1 g73630 At3g01510 At4g17180 At5g40670

AtI g 15410 At1 g77070 At3g03780 At4g 18810 At5g43830

At1 g23130 At1 g77310 At3gO5165 At4g19470 At5g46030

At1g23880 At1 g78720 At3g06060 At4g19770 At5g48800

At1g24490 At1 g79460 At3g16190 At4g 19985 At5g50250

At1 g24530 At1g79640 At3g16500 At4g23920 At5g50970

At1 g29410 At1 g80190 At3g 19490 At4g24940 At5g54095

At1g31240 At2g01350 At3g20390 At4g31920 At5g56185

At1g33265 At2g02080 At3g20950 At4g34480 At5g63020

At1g33790 At2g04520 At3g22850 At4g39560 At5g63150

At1g33900 At2g07550 At3g23570 At4g39660 At5g63370

At1g34400 At2g 13570 At3g47750 At5g01690 At5g64630

At1g45180 At2g15040 At3g48730 At5g04740 At5g64830

At1g52590 At2g 17600 At3g52750 At5g04750 At5g67060

At1 g56270 At2g19110 At3g58830 At5g07580 orf107g

At1g61090 At2g23560 At3g61160 At5g07630

At1g61180 At2g30695 At3g62580 At5g10140

At1g62540 At2g39750 At4g07960 At5g16140

Polyunsaturated 18C fatty acids = = linoleic, linolenic

Monounsaturated 18C fattv acid = s oleic

Saturated 18C fatty acid = stearic

Table 13, continued.

13B. Genes showing negative correlation between transcript abundance and trait value

At1 g02500 At2g29120 At3g27340 At4g02420 At5g24450

At1 g03430 At2g29320 At3g44890 At4g02500 At5g25020

AtI g 18570 At2g29570 At3g45240 At4g02530 At5g25120

At1 g23750 At2g35950 At3g46590 At4g05460 At5g40450

At1 g28670 At3g01560 At3g47990 At4g08470 At5g42310

At1 g30530 At3g01740 At3g50000 At4g10710 At5g42720

At1g32310 At3g01850 At3g50380 At4g 14350 At5g44450

At1 g52550 At3g04670 At3g51610 At4g 15420 At5g45490

At1 g59840 At3g09310 At3g52310 At4g 15620 At5g45800

At1 g59900 At3g 10930 At3g53390 At4g 16760 At5g49500

At1 g66970 At3g17890 At3g55005 At4g 18260 At5g50350

At1 g68560 At3g 17940 At3g58460 At4g19530 At5g57160

At1 g78970 At3g 19520 At3g61100 At4g23880

At2g04550 At3g20480 At3g62860 At5g01650

At2g21830 At3g23880 At4g01330 At5g04380

At2g22425 At3g26470 At4g01400 At5g23420

Polyunsaturated 18C fatty acids = linoleic, linolenic

Monounsaturated 18C fatty acid = oleic

Saturated 18C fatty acid = stearic

Table 14. Genes with transcript abundance showing correlation with % 16: 0 fatty a cid in seed oil (vernalised plants) ; Transcript ID (AGI code)

14A. Genes showing positive correlation between transcript abundance and trait value

At1g03300 At1g74170 At2g41760 At3g60350 At5g 10820

At1 g03420 At1g74180 • At2g42750 At3g60980 At5g 13740

At1g04640 At1 g75490 At2g43180 At3g61160 At5g 15680

At1 g08170 At1g78460 At2g45050 At3g61200 At5g17210

AtI g 13980 At1 g79000 At2g48100 At3g61600 At5g 19050

At1 g20640 At1g80600 At3g01330 At3g63440 At5g20150

At1 g22200 At1g80660 At3g02700 At4g00500 At5g22000

At1 g24420 At1 g80920 At3g04350 At4g00730 At5g22700

At1 g25260 At2g05540 At3g04800 At4g02970 At5g24410

At1 g27210 At2g05980 At3g05250 At4g03970 At5g25040

At1 g28960 At2g07240 At3g11210 At4g04870 At5g27400

At1 g33170 At2gO7675 At3g 11760 At4g 10020 At5g35330

At1g33880 At2gO7687 At3g 12820 At4g11530 At5g38080

At1g34110 At2g07702 At3g 14750 At4g 12300 At5g38310

At1 g35340 At2gO7741 At3g 15095 At4g 13800 At5g38895

At1 g35420 At2g 11270 At3g15120 At4g 16960 At5g38930

At1 g36060 At2g 15040 At3g 15290 At4g 18593 At5g39020

At1 g47330 At2g 15230 At3g 15840 At4g 18600 At5g41850

At1g47750 At2g15880 At3g 16750 At4g20360 At5g41870

At1g48380 At2g18115 At3g 17280 At4g26200 At5g42030

At1 g52420 At2g18190 At3g18215 At4g28130 At5g44240

At1 g52920 At2g 19310 At3g20090 At4g30993 At5g47410

At1 g52990 At2g 19340 At3g20930 At4g32960 At5g50565

At1 g53290 At2g22170 At3g21420 At4g33500 At5g50600

At1 g54710 At2g23170 At3g22880 . At4g33570 At5g51080

At1g56150 At2g23560 At3g25900 At4g35530 At5g51980

At1 g61730 At2g25850 At3g26040 At4g37590 At5g53430

At1g63690 At2g27190 At3g26380 At4g40050 At5g54730

At1 g64230 At2g27620 At3g27990 At5g01670 At5g55540

At1 g65950 At2g29860 At3g29650 At5g02540 At5g55870

At1 g66570 At2g35155 At3g46900 At5g03730 At5g65250

At1 g66980 At2g35690 At3g49210 At5g05080 At5g65380

At1 g67960 At2g37120 At3g53800 At5g05290 At5g66040

At1 g70300 At2g38180 At3g55850 At5g05690 ndhG

At1 g71000 At2g40070 , At3g57270 At5g05700 ndhJ

At1 g72650 At2g40970 At3g57470 At5g05750 orf111 d

At1 g73480 At2g41340 At3g60040 At5g05890 orf262

At1 g73680 At2g41430 At3g60290 At5g06130 petD

16:0 = palmitic . acid

Table 14 , continued.

14B. Genes showing negative correlation between transcript abundance and trait value

At1g02500 At1g66200 At2g36880 At3g48130 At5g20110

At1 g04040 At1g69250 At2g37020 At3g48720 At5g22630

At1gO5760 At1 g69700 At2g37110 At3g49720 At5g23540

At1 g06410 At1g72450 At2g37400 At3g51780 At5g23750

At1g08580 At1g75390 At2g39560 At3g52500 At5g25920

At1g12310 At1 g75590 At2g40010 At3g52900 At5g26330

AtIg 14780 At1g75780 At2g40230 At3g54430 At5g27990

AtIg 17620 At1 g75840 At2g40660 At3g54980 At5g36890

At1g22710 At1g76260 At2g41830 At3g63200 At5g37330

At1 g27000 At1 g76550 At2g43290 At4g01100 At5g40770

At1g27700 At1g77970 At2g44745 At4g05530 At5g42150

At1 g29310 At1g77990 At2g46730 At4g 14350 At5g45550

At1g30510 At1 g78090 At3g05020 At4g 18570 At5g45650

At1 g30690 At2g04780 At3g05230 At4g20120 At5g46280

At1 g31340 At2g 15860 At3g05490 At4g20410 At5g47210

At1g31660 At2g 16280 At3g06160 At4g21090 At5g47540

At1 g32050 At2g17670 At3g06510 At4g28780 At5g49510

At1g32450 At2g 19540 At3g06930 At4g31480 At5g50740

At1 g35670 At2g20270 At3g08990 At4g34870 At5g54900

At1g44800 At2g21580 At3g 12370 At4g35510 At5g56350

At1 g48830 At2g22470 At3g15150 At4g37190 At5g56950

At1 g50010 At2g22475 At3g 15260 At4g39280 At5g58030

At1 g50500 At2g28510 At3g16340 At5g02740 At5g59290

At1g52040 At2g28760 At3g 16760 At5g06160 At5g61660

At1 g52910 At2g29070 At3g 17780 At5g06190 At5g62165

At1 g54830 At2g29540 At3g 19590 At5g11630 At5g65710

At1 g56170 At2g33430 At3g21020 At5g 14680

At1g57620 At2g33620 At3g23620 At5g 18280

At1 g63000 At2g35120 At3g25220 At5g18690

At1 g65010 At2g36620 At3g27200 At5g19910

16:0 = palmitic acid

Table 15. Genes with transcript abundance correlating with % 18:1 fatty acid in seed oil (vernalised plants) ; Transcript ID (AGI code)

15A. Genes showing positive correlation between transcript abundance and trait value

At1 g05550 At1 g67830 At3g14150 At4g20030 At5g 18070 At1 g06580 At1g69690 At3g 19590 At4g20070 At5g 19830 At1 gO856O At1g70430 At3g24450 At4g21650 At5g23420 AtI g10320 At1 g72260 At3g24660 At4g22620 At5g25180 AH g10980 At1 g74690 At3g26240 At4g23870 At5g25920 AtI g 13250 At1 g75110 At3g28345 At4g28040 At5g26230 AtI g 15280 At2g01090 At3g44010 At4g30910 At5g26270 At1 g21080 At2g 17550 At3g44600 At4g32130 At5g40150 At1 g23750 At2g 19370 At3g48130 At4g35880 At5g41970 At1g29180 At2g20360 At3g53110 At4g36380 At5g47550 At1g33055 At2g20585 At3g53170 At4g36740 At5g47760 At1g34030 At2g21860 At3g54680 At5g06160 At5g48470 At1 g51950 At2g25900 At3g57860 At5gO6190 At5g48760 At1g52800 At2g32160 At3g60880 At5g07000 At5g49190 At1g52810 At2g36490 At3g62860 At5g07030 At5g49500 At1g61810 At2g37050 At4g00600 At5g07640 At5g50950 At1 g62500 At2g39870 At4g01330 At5g08540 At5g51660 At1 g63780 At2g41370 At4g03050 At5g 10390 At5g54190 At1 g64105 At2g44230 At4g03070 At5g11310 At5g58300 At1 g65560 At3g02500 At4g12600 At5g 13970 At5g63860 At1g66130 At3g06470 At4g 12880 At5g 14070 At5g64650 At1 g67590 At3g08680 At4g15070 At5g17100 At5g65010 18:1 = oleic acid

15B. Genes showing negative correlation between transcript abundance and trait value

At1gO4985 At2g27090 At3g51580 At5g05750 At5g39940 AtIg 15490 At2g35736 At3g59660 At5g08590 At5g44290 At1g26530 At2g38010 At4g02450 At5g 11270 At5g47580 At1g28030 At3g01930 At4g12020 At5g 13890 At5g49540 At1g33560 At3g05210 At4g 12300 At5g 16250 At5g55760 At1 g49030 At3g 16520 At4g 13050 At5g 18400 At1g59620 At3g 17300 At4g 17390 At5g20180 At1g76520 At3g20900 At4g24940 At5g23010 At1 g78210 At3g49360 At4g32870 At5g27760 18:1 = oleic acid

Table 1 6. Genes wi th transcript abundance correla ting with % 18 : 2 fatty acid in seed oil (vernalised plants) ; Transcript ID (AGI code)

16A. Genes showing positive correlation between transcript abundance ! and trait value

At1 g02500 At1 g65000 At2g44850 At3g54260 At5g04420

At1 g06500 At1 g67860 At2g46730 At3g54420 At5g06730

At! g10460 At1 g72510 At3g01860 At3g55005 At5g07370

AtI g11880 At1 g73177 At3g02800 At3g55630 At5gO8535

AtI g 13090 At1 g73940 At3g03360 At3g57180 At5g08540

AtIg 13750 At1 g74590 At3g05320 At3g61980 At5g08600

At1 g14780 At1 g76890 At3g06110 At4g00030 At5gO948O

AtI g 14990 At1g77590 At3g07230 At4gO119O At5g11600

At1g19340 At1 g77600 At3g08990 At4gO141O At5g 16040

At1 g21100 At1 g78750 At3g09410 At4g02960 At5g16980

At1g21110 At1 g79890 At3g09870 At4g03240 At5g 19560

At1g21190 At1 g79950 At3g 10525 At4g04620 At5g27410

At1 g22520 At1 g80170 At3g11400 At4g09900 At5g28500

At1 g23120 At1 g80700 At3g15150 At4g10120 At5g38530

At1 g26170 At2g01120 _r At3g15352 At4g 10955 At5g38980

At1g30530 At2g02500 At3g 17690 At4g11820 At5g39550

At1g32050 At2g02960 At3g19515 At4g 12310 At5g42310

At1 g32450 At2g05950 At3g20430 At4g13180 At5g43330

At1g33600 At2g 13750 At3g22690 At4g14615 At5g45190

At1 g34210 At2g 13770 At3g22930 At4g 15230 At5g47050

At1g34740 At2g 15560 At3g24050 At4g 15260 At5g47540

At1g35143 At2g 15650 At3g27610 At4g 18780 At5g48110

At1g35650 At2g 17265 At3g27920 At4g19100 At5g50940

At1g42705 At2g21640 At3g28700 At4g 19850 At5g51010

At1g47480 At2g22920 At3g30720 At4g21090 At5g51820

At1g47870 At2g27360 At3g30810 At4g25890 At5g53360

At1g50630 At2g28200 At3g31910 At4g27580 At5g55560

At1g52040 At2g28450 At3g44890 At4g29230 At5g56700

At1g52760 At2g29070 At3g46840 At4g32240 At5g57160

At1g54250 At2g30000 At3g48720 At4g34120 At5g57300

At1 g55850 At2g35585 At3g48860 At4g37150 At5g61450

At1g59670 At2g37585 At3g48920 At5g01360 At5g61830

At1g59900 At2g37970 At3g50050 At5g02010 At5g64816

At1g60710 At2g37975 At3g53630 At5g02610 At5g66380

At1g62860 At2g40010 At3g53650 At5g03090 At5g66530

At1g63540 At2g41830 At3g53720 At5g03540

18:2 = linoleic acid

Table 16, continued.

16B. Genes showing negative correlation between transcript abundance and trait value

At1 g01370 At1 g66250 At2g34560 At3g56060 At5g05370

At1 g02300 At1 g66520 At2g39700 At3g57830 At5g08280

At1 g02710 At1g68810 At2g40070 At3g57880 At5g 17210

At1g03420 At1g70830 At2g41600 At3g60350 At5g 17220

At1g04790 At1g71690 At2g43130 At3g61160 At5g 18390

At1g06730 At1 g79000 At2g44740 At3g62430 At5g22700

At1g11800 At1 g79060 At2g44760 At4g00340 At5g24280

At1g12250 At1 g79460 At2g45710 At4g01350 At5g24760

At1g15050 At1 g80530 At3g05520 At4g 12300 At5g26110

At1g20930 At2g04700 At3g07200 At4g12510 At5g26180

At1g20980 At2gO6255 At3g11090 At4g 13360 At5g28940

At1g21690 At2g07702 At3g11760 At4g 13980 At5g35490

At1g21710 At2g 15790 At3g 14240 At4g 17560 At5g38120

At1g22200 At2g 17450 At3g 18060 At4g 17650 At5g45320

At1 g28440 At2g 18990 At3g22850 At4g24390 At5g51080

At1g47750 At2g23560 At3g26070 At4g26555 At5g52230

At1g50660 At2g28100 At3g26310 At4g31870 At5g55810

At1g53460 At2g29995 At3g26990 At4g32960 At5g59130

At1g55130 At2g32990 At3g29770 At4g35900 At5g59330

At1g57760 At2g33540 At3g48040 At4g39230. At5g63180

At1g62050 At2g34310 At3g55480 At5g05230 At5g64110

18:2 = linoleic acid

Table 27. Genes with transcript abundance correlating with % 18:3 fatty acid in seed oil (vernalised plants) ; Transcript ID ^■ (AGI code)

17A. Genes showing positive correlation between transcript abundance and trait value

At1g05060 At1 g64230 At3g11090 At4g 15960 At5g28940

At1 g08170 At1g69450 At3g 14780 At4g 18460 At5g35350

AtI g 13280 At1 g71800 At3g 17840 At4g 18593 At5g38310

AtIg 13580 At1g74290 At3g 18270 At4g 18820 At5g38460

AtI g13810 At1 g77140 At3g 18650 At4g23300 At5g39790

AtI g 14660 At1 g77490 At3g20230 At4g25570 At5g40230

AtI g 15330 At1 g79000 At3g22710 At4g26870 At5g44240

At1 g20370 At2g02360 At3g22850 At4g27900 At5g44290

At1 g20810 At2g02770 At3g22880 At4g31150 At5g44520

At1g20980 At2g07050 At3g26430 At4g31870 At5g46270

At1 g21710 At2g 16090 At3g30140 At4g39390 At5g46630

At1g22200 At2g181 15 At3g43790 At4g39920 At5g47400

At1g23890 At2g32330 At3g48730 At4g39930 At5g47410

At1g33265 At2g35890 At3g53680 At5g03290 At5g49630

At1g33880 At2g41600 At3g53900 At5g05840 At5g51960

At1g51430 At2g43180 At3g56590 At5g05890 At5g55760

At1g51980 At2g43320 At3g61480 At5g07250 At5g59660

At1 g57780 At2g44690 At4gO169O At5g08280 At5g63370

At1 g59780 At2g45150 At4g01970 At5g 17210 At5g63740

At1g61830 At2g45560 At4g11835 At5g 17520 At5g64110

At1g63200 At2g46640 At4g11900 At5g 18400 orf114

At1 g64190 At3g05520 At4g 12300 At5g22860 ycf4

18:3 = linolenic acid

Table 1 1, con tin ued.

17B. Genes showing negative correlation between transcript abundance ! and trait value

At1 g02500 At1g76560 At3g09310 At4g02290 At5g07640

At1 g05550 At1g76720 At3g 10340 At4gO3156 At5g08540

At1 g06500 At1g77600 At3g11410 At4g04620 At5g09760

At1 g06520 At1g78080 At3g12110 At4g05450 At5g 13970

At1 g06530 At1 g78780 At3g 12520 At4g09760 At5g 16040

At1 g07470 At1 g78970 At3g13490 At4g10120 At5g 16470

At1g09660 At1g79430 At3g14150 At4g 10320 At5g 18790

At1 g10980 At2g01520 At3g 15900 At4g 12490 At5g 19830

At1 g13090 At2g 15620 At3g 16080 At4g13195 At5g24740

At1 g13680 At2g18100 At3g20100 At4g14010 At5g25120

At1 g14930 At2g 18650 At3g21250 At4g 14020 At5g25180

AtI g 15200 At2g 19740 At3g22210 At4g 14320 At5g27720

At1 g18810 At2g20450 At3g22230 At4g 14350 At5g35240

AtI g18880 At2g20490 At3g23325 At4g14615 At5g40250

At1g21080 At2g20515 At3g25220 At4g 16830 At5g42720

At1g23950 At2g20820 At3g25740 At4g17410 At5g45010

At1g24070 At2g21290 At3g26240 At4g 18750 At5g45840

At1 g26170 At2g21640 At3g29180 At4g21590 At5g47540

At1 g28060 At2g21890 At3g46490 At4g22380 At5g47550

At1g29180 At2g23090 At3g47370 At4g23870 At5g47760

At1 g29850 At2g25670 At3g47990 At4g25890 At5g48580

At1 g30530 At2g25970 At3g48130 At4g26230 At5g49190

At1 g33055 At2g26460 At3g49600 At4g26790 At5g49500

At1 g50140 At2g27360 At3g50380 At4g29230 At5g49970

At1 g52040 At2g28450 At3g51780 At4g29550 At5g50915

At1g52690 At2g29070 At3g52590 At4g30220 At5g50950

At1g53030 At2g29120 At3g53260 At4g30290 At5g51390

At1g54250 At2g36170 At3g53390 At4g31985 At5g51660

At1g59840 At2g36570 At3g53500 At4g35240 At5g52040

At1g59900 At2g41560 At3g53630 At4g35880 At5g53460

At1g61570 At2g41790 At3g53890 At4g35940 At5g57160

At1g61810 At2g47250 At3g54290 At4g36190 At5g58520

At1g63020 At2g47790 At3g55005 At4g36380 At5g59460

At1 g63540 At3g03610 At3g56900 At4g37250 At5g61830

At1g64900 At3g04670 At3g58840 At4g39200 At5g64190

At1g66080 At3g05530 At3g59540 At5g01890 At5g64650

At1g66920 At3g06110 At3g62080 At5gO3455 At5g65050

At1g72260 At3g06130 At3g62860 At5g04420 At5g65530

At1g74250 At3g06310 At4g02075 At5g04850 At5g65890

At1g74270 At3g06790 At4g02210 At5g05680

At1g74880 At3g08030 At4g02230 At5g06710

18:3 = linolenic acid

Table 18. Prediction of complex traits using- models based on accession transcriptome data

Table 19. Maize genes wi th transcript abundance in hybrids correlating with heterosis

Probe Set ID Representative Public ID

19A. Positive Correlation

Zm.18469.1.S1_at BM378527 ZmAffx.448.1.S1_at AI677105

Zm.5324.1.A1_at AI619250

Zm.886.5.S1_a_at BU499802

Zm.5494.1.A1_at AI622241

Zm.17363.1. S1_at CK370960

Zm.1234.1.A1_at BM073436

Zm.11688.1.A1_at CK347476

Zm.695.1.A1_at U37285.1

Zm.12561.1.A1_at AI834417

Zm.17443.1.A1_at CK347379

Zm.11579.2.S1_a_at CF629377

Zm.342.2.A1_at U65948.1

Zm.8950.1.A1_at AY109015.1

Zm.18417.1.A1_at CO528437

Zm.2553.1.A1_a_at BQ619023

Zm.13487.1.A1_at AY108830.1

Zm.13746.1. S 1_at CD998898

Zm.8742.1.A1_at BM075443

Zm.17701.1.S1_at CK370965

Zm.2147.1.A1_a_at BM380613

Zm.10826.1. S1_at BQ619411 ZmAffx.501.1.S1_at AI691747

Zm.17970.1.A1_at CK827393

Zm.12592.1.S1_at CA830809

Zm.13810.1.S1_at AB042267.1

Zm.4669.1.S1_at AI737897 ZmAffx.351.1.S1_at AI670538

Zm.5233.1.A1_at CF626276

Zm.9738.1.S1_at BM337426

Zm.8102.1.A1_at CF005906

Zm.6393.4.A1_at BQ048072

Zm.15120.1.A1_at BM078520

Zm.17342.1. S1_at CK370507

Zm.2674.1.A1_at CF045775

Zm.4191.2.S1_a_at BQ547780

Zm.14504.1,A1_at AY107583.1

Zm.6049.3.A1_a_at AI734480

Zm.2100.1.A1_at CDO01187

Zm.13795.2.S1_a_at CF042915

Zm.5351.1.S1_at AI619365

Zm.5939.1.A1_s_at AI738346

Zm.2626.1.S1_at AY112337.1

Zm.15454.1. A1_at CD448347

Zm.4692.1.A1 at AI738236

Zm.5502.1.A1_at BM378399

Zm.2758.1.A1_at AW067110 ZmAffx.752.1.S1_at AI712129

Zm.14994.1. A1_at BQ538997

Zm.12748.1. S 1_at AW066809

Zm.18006.1.A1_at AW400144 ZmAffx.601.1.A1_at AI715029

Zm.6045.7.A1_at CK347781

Zm.81.1.S1_at AY106090.1 ZmAffx.292.1.S1_at AI670425

Zm.17917.1.A1_at CF629332 ZmAffx.424.1.S1_at AI676856

Zm.6371.1.A1_at AY122273.1

Zm.1125.1.A1_at BI993208

Zm.4758.1.S1_at AY111436.1

Zm.17779.1.S1_at CK370643

Zm.2964.1.S1_s_at AY106674.1

Zm.17937.1.A1_at CO529646

Zm.7162.1.A1_at BM074641

Zm.13402.1.S1_at AF457950.1

Zm.18189.1.S1_at CN844773

Zm.4312.1.A1_at BM266520

Zm.2141.1.A1_at BM347927

Zm.19317.1. S1_at CO521190

Zm.4164.2.A1_at CF627018

Zm.8307.2.A1_a_at CF635305

Zm.16805.2.A1_at CF635679

Zm.19080.1.A1_at CO522397

Zm.1489.1.A1_at CO519381

Zm.13462.1.A1_at CO522224 ZmAffx.191.1.S1_at AI668423

Zm.19037.1. S 1_at CA404446

Zm.4109.1.A1_at CD441071

Zm.2588.1.S1_at AI714899

Zm.10920,1.A1_at CA399553

Zm.1710.1.S1_at AY106827.1

Zm.16301.1.S1_at CK787019

Zm.4665.1.A1_at CK370646

Zm.733S.1.A1_at AF371263.1

Zm.16501.1.S1_at AY108566.1

Zm.10223.1.S1_at BM078528

Zm.3030.1.A1_at CA402193

Zm.14027.1.A1_at AW499409

Zm.8796.1.A1_at BG841012

Zm.13732.1.S1_at AY106236.1

Zm.4870.1.A1_a_at CK985786 ZmAffx.555.1.A1_x_at AI714437

Zm.7327.1.A1_at AF289256.1

Zm.2933.1.A1_at AW091233

Zm.949.1.A1_s_at CF624182

Zm.15510.1.A1_at CD441066

Zm.8375.1.A1_at BM080176

Zm.4824.6.S1_a_at AI665566

Zm.612.1.A1_at AF326500.1

Zm. 12881.1.A1_at CA401025

Zm.7687.1.A1_at BM072867

Zm.10587.1.A1_at AY107155.1

Zm.17807.1.S1_at CK371584

Zm.3947.1.S1_at BE510702

Zm.6626.1.A1_at AI491257

Zm.1527.2.A1_a_at BM078218

Zm.6856.1.A1_at AI065480 ZmAffx.1477.1.S1_at 40794996-104

Zm. 12588.1.S1_at CO530559

Zm.15817.1.A1_at D87044.1

Zm. 16278.1.A1_at CO532740

Zm.18877.1.A1_at CO529651

Zm.2090.1.A1_at AI691653

Zm.5160.1.A1_at CD995815

Zm. 17651.1.A1_at CF043781

Zm.15722.2.A1_at CA404232

Zm.5456.1.A1_at AI622004

Zm.13992.1.A1_at CK827024

Zm.3105.1.S1_at AY108981.1 ZmAffx.941.1.S1_at AI820356

Zm.3913.1.A1_at CF000034

Zm.1657.1.A1_at BG842419

Zm. 13200.1.A1_at CF635119

Zm.18789.1.S1_at CO525842

Zm.10090.1.A1_at BM382713

Zm.312.1.A1_at S72425.1

Zm.9118.1.A1_at BM336433

Zm.9117.1.A1_at CF636944

Zm.610.1.A1_at AF326498.1

Zm.5725.1.A1_at CK986059

Zm.6805.1.S1_a_at BG266504

Zm.1621.1.S1_at AY107628.1

Zm.1997.1.A1_at BM075855 ZmAffx.1086.1.S1_at AW018229

Zm. 17377.1.A1_at CK144565

Zm.15822.1. S1_at AY313901.1

Zm.5486.1.A1_at AI629867

Zm.4469.1.S1_at AI734281

Zm.8620.1.S1_at BM073355

Zm. 18031.1.A1_at CK985574

Zm. 13597.1.A1_at CF630886

Zm.75.2.S1_at CK371662

Zm.4327.1.S1_at BI993026

Zm.17157.1.A1_at BM074525

Zm.7342.1.A1_at AF371279.1

Zm.2781.1.S1_at CF007960

Zm.3944.1.S1_at M29411.1

Zm.98.1.S1_at AY106729.1

Zm.3892.6.A1_x_at CD441708

Zm.12051.1.A1_at AI947869

Zm.4193.1.A1 at AY106195.1

Zm.2197.1.S1_a_at AF007785.1

Zm.12164.1.A1_at CO521714

2m.15998.1.A1_at CA403811 ZmAffx.1186.1.A1_at AY110093.1

Zm.19149.1.S1_at CO526376

Zm.14820.1. S1_at AY106101.1

Zm.15789.1.A1_a_at CD440056 ZmAffx.655.1.A1_at AI715083

Zm.19077.1.A1_at CO526103

Zm.698.1.A1_at AY112103.1

Zm.10332.1.A1_at BQ048110

Zm.10642.1.A1_at BQ539388

Zm.11901.1.A1_at BM381636 ZmAffx.1494.1.S1_s_at 40794996-111 ZmAffx.871.1.A1_at AI770769

Zm.13463.1. S 1_at AY109103.1

Zm.18502.1. A1_at CF623953

Zm.2171.1.A1_at BG841205

Zm.14069.2.A1_at AY110342.1

Ztn.6036.1.S1_at AY110222.1

Zm.17638.1.S1_at CK368502

Zm.813.1.S1_at AF244683.1

Zm.8376.1.S1_at BM073880

Zm.16922.1.A1_a_af CD998944

Zm.16913.1.S1_at BQ619268

Zm.12851.1.A1_at CA400703

Zm.3225.1.S1_at BE512131

Zm.13628.1.S1_at CD437947

Zm.9998.1.A1_at BM335619

Zm.15967.1.S1_at CA404149

Zm.6366.2.A1_at CA398774

Zm.1784.1.S1_at BF728627

Zm.19031.1.A1_at BU051425

Zm.6170.1.A1_a_at AY107283.1

Zm.3789.1.S1_at AW438148

Zm.4310.1.A1_at BM078907

Zm.3892.10.A1_at AI691846

RPTR-Zm-U47295-1_at RPTR-Zm-U47295-1

Zm. 15469.1. S 1_at CD438450

Zm.7515.1.A1_at BM078765

Zm.6728.1.A1_at CN844413

Zm.16798.2.A1_a_at CF633780

Zm.455.1.S1_a_at AF135014.1

Zm.10134.1.A1 at BQ619055

B. Negative Correlation

Zm.10492.1.S1_at CA826941 2m.5113.2.A1_a_at CF633388

Zm.3533.1.A1_at AY110439.1 ZmAffx.674.1.S1_at AI734487 ZmAffx.1060.1.S1_at AI881420 ZmAffx.361.1.A1_at AI670571

Zm.10190.1.S1_at CF041516

Zm.12256.1. S1_at BU049042 ZmAffx.1529.1. S 1_at 40794996-124

Zm.19120.1.A1_at CO523709

Zm.2614.2.A1_at CD436098

Zm.10429.1.S1_at BQ528642

Zm.13457.1. S1_at AY109190.1

Zm.4040.1.A1_at AI834032

Zm.5083.2.S1_at AY109962.1

Zm.5704.1.A1_at AI637031

Zm.3934.1.S1_at AI947382

Zm.6478.1.S1_at AI692059

Zm.1161.1.S1_at BE511616

Zm.12135.1. A1_at BM334402

Zm.4878.1.A1_at AW288995

Zm.18825.1.A1_at CO527281

Zm.4087.1.A1_at AI834529

Zm.9321.1.A1_at AY108492.1

Zm.9121.1.A1_at CF631233

Zm.7797.1.A1_at BM079946

Zm.1228.1. S1_at CF006184

Zm.1118.1.S1_at CF631214

Zm.3612.1.A1_at AY103746.1

Zm.17612.1.S1_at CK368134

Zm.7082.1.S1_at CF637101

Zm.6188.2.A1_at AY108898.1

Zm.6798.1.A1_a( CA400889

Zm.6205.1.A1_at CK985870

Zm.582.1. S1_at AF186234.2

Zm.5798.1.A1_at BM072971

Zm.8598.1.A1_at BM075029

Zm.15207.1.A1_at BM268677

Zm.4164.3.A1_s_at CF636517

Zm.1802.1.A1_at BM078736

Zm.13583.1.S1_at AY108161.1 ZmAffx.513.1.A1_at AI692067 ZmAffx.853.1.A1_at AI770653

Zm.2128.1.S1_at AY105930.1

Zm.18488.1.A1_at BM269253

Zm.10471.1.A1_at CA399504 ZmAffx.716.1.S1_at AI739804

Zm.10756.1.S1_at CD97S109

Zm.1482.5.S1_at AI714961 ZmAffx.494.1.S1_at AI770346

Zm.5688.1.A1_at AY105372.1

Zm.4673.2.A1_a_at CA400524

Zm.9542.1.A1_at CF624708

Zm.10557.2.A1_at BQ538273 ZmAffx.1051.1.A1_at AI881809

Zm.3724.1.A1_x_at CF627032

Zm.6575.1.A1_at AI737943

Zm.18046.1.A1_at BI993031

Zm.4990.1.A1_at AI586885 ZmAffx.891.1.A1_at AI770848

Zm.10750.1.A1_at AY104853.1

Zm.6358.1.S1_at CA402045

Zm.2150.1.A1_a_at CD977294

Zm.4068.2.A1_at BQ619512

Zm.1327.1.A1_at BE643637

Zm.3699.1.S1_at U92045.1 ZmAffx.175.1.S1_ai A1668276

Zm.311.1.A1_at BM268583

Zm.19326.1. A1_at CO530193

Zm,728.1.A1_at BM338202 ZmAffx.963.1.A1_at AI833792

Zm.5155.1.S1_at CD433333

Zm.3186.1.S1_a_at CK827152 ZmAffx.1164.1.A1_at AW455679

Zm.10069.1.A1_at AY108373.1

Zm.17869.1.S1_at CK701080

Zm.1670.1.A1_at AY109012.1

Zm.737.1.A1_at D45403.1

Zm.9947.1.A1_at BM349454

Zm.3553.1.S1_at AY112170.1

Zm.1i794.1.A1_at BM380817 ZmAffx.139.1.S1_at AI667769

Zm.5328.2.A1_at AW258090

Zm.534.1.A1_x_at AF276086.1

Zm.17724.3.S1_x_at CK370253

Zm.13806.1. S1_at AY104790.1

Zm.8710.1.A1_at BM333560

Zm.14397.1.A1_at BM351246

Zm.5495.1.S1_at AY103870.1

Zm.4338.3.S1_at AW000126

Zm.9199.1.A1_at CO522770

Zm.15839.1.A1_at AY109200.1

Zm.12386.1.A1_at CF630849

Zm.7495.1.A1_at CF636496

Zm.2181.1.S1_at BF727788 ZmAffx.144.1.S1_at AI667795

Zm.4449.1.A1_at BM074466

Zm.8111.1.S1_at CD972041

Zm.17784.1.S1_at CK370703

Zm.16247.1.S1_at AY181209.1

Zm.3699.5.S1_a_at AY107222.1

Zm.7823.1.S1__at BM078187

Zm.5866.1.Si_at CF044154

Zm.6469.1.S1_at BE345306

Zm.10434.1.S1_at BQ577392

Zm.16929.1.S1_at AW055615

Zm.7572.1.S1_at CO521006

Zm.6726.1.S1_x_at A1395973 ZmAflx.387.1.S1_at AI673971

Zm.9543.1.A1_at CK370330

Zm. 1632.1.S1_at AY104990.1

Zm.8897.1.S1_at BM079371

Zm. 14869.1.A1_at AI586666

Zm. 1059.2.A1_a_at CO518029

Zm.4611.1.A1_s_at BG842817 ZmAffx.1172.1.S1_at AW787638

Zm.8751.1.A1_at BM348137

Zm. 1066.1.S1_a_at AY104986.1

Zm. 13931.1.S1_x_at Z35302.1

Zm.9916.1.A1_at BM348997 ZmAffx.1203.1.A1_at BE128869

Zm.9468.1.S1_at AY108678.1

Zm.4049.1.A1_at AI834098

Zm.14325.1.S1_at AY104177.1

Zm.9281.1.A1_at BM267756

Zm.229.1.S1_at L33912.1

Zm .2244.1. S1_a_at CF348841

Zm.4587.1.A1_at CO528135

Zm.9604.1.A1_at BM333654

Zm.7831.1.A1_at BM080062

Zm.648.1.S1_at AF144079.1

Zm.5018.3.A1_at AI668145 ZmAffx.962.1.A1_at AI833777

Zm.11663.1.A1_at CO531620

Zm. 19167.2.A1_x_ai CF636656 ZmAffx.776.1.A1_ai AI746212

Zm.4736.1.A1_at AY108189.1 ZmAffx.1053.1.A1_at AI881846

Zm.4248.1.A1_at AY110118.1 ZmAffx,1523.1.S1_at 40794996-120

Zm.4922.1.A1_at AI586404

Zm.6601.2.A1_a_at BM078978

Zm.18355.1.A1_at CO532040

Zm.16351.1.A1_at CF623648

Zm.12150.1.S1_at AY106576.1 ZmAffx.1428.1.S1_at 11990232-13

Zm.11468.1.A1_at BM382262

Zm.11550.1.A1_at BG320003

Zm.12235.1.A1_at CF972364

Zm.10911.1.A1_x_at BM340657

Zm.1497.1,S1_at AF050631.1

Zm.2440.1.A1_a_at BM347886

Zm.6638.1.A1_at AI619165 ZmAffx.840.1.S1_at AI770592

Zm.15800.2.A1_at CD998623

Zm.2220.4.S1_at AY110053.1

Zm.5791.1.A1_at AY103953.1

Zm.9435.1.A1_at BM268868

Zm.2565.1.S1_at AY112147.1 ZmAffx.964.1.A1_ai AI833796

Zm.3134.1.A1_at AY112040.1

Zm.8549.1.A1_at BM339103

Zm.10807.2.A1_at CD970321

Zm.3286.1.A1_at BG265986

Zm.11983.1.A1_at BM382368 ZmAffx.841.1.A1_at AI770596

Zm.2950.1.A1_at AI649878

Zm.900.1.S1_at BF728342

Zm.8147.1.A1_at BM073080

Zm.18430.1.S1_at CO524429

Zm.15859.1.A1_at D14578.1

Zm.17164.1.S1_at AY188756.1

Zm.1204.1.S1_at BE519063

Zm.17968.1.A1_at CK827143

Table 20: Maize genes with transcript abundance in hybrids used for prediction of average yield in hybrids

Probe Set ID Representative Public ID

2OA. Positive Correlation

Zm.4900.2.A1_at AY105715.1

Zm.6390.1.S1_at BU098381 2m.17314.1.S1_at CK369303

Zm.8720.1.S1_at AY303682.1 ZmAffx.435.1.A1_at AI676952

Zm.4807.1.A1_at CO518291

Zm.16794.1.A1_at AF330034.1 2m.19357.1.A1_at CO533449 2m.13190.1.A1_at CD433968 2m.16025.1.A1_at BM340438 AFFX-r2-TagC_at AFFX-r2-TagC ZmAffx.844.1.S1_at AI770609

Zm.6342.1.S1_at AW052791

Zm.9453.1.A1_at CO521132 2m.13708.1.A1_at AY 106587.1

Zm.10609.1.A1_at BQ538614

Zm.6589.1.A1_at AI622544 ZmAffx.1308.1.S1_s_at 11990232-76

Zm.4024.1.S1_at AY105692.1

Zm.16805.4.A1_at AI795617

Zm.10032.1.S1_at CN844905

Zm.4943.1.A1_at BG320867

Zm.6970.1.A1_a_at AY111674.1

Zm.8150.1.A1_at BM073089

Zm.4696.1.S1_at BG266403 ZmAffx.994.1.A1_at AI855283

Zm.11585.1.A1_at BM379130 2mAflx.45.1.S1_at AI664925

2m.6214.1.A1_a_at BQ538548

Zm.9102.1.A1_at BM333481

Zm.4909.1.A1_at AY111633.1

Zm.13916.1.S1_at AF037027.1

Zm.17317.1. S1_at CK370700

Zm.5684.1.A1_at BWI334571

AFFX-r2-TagJ-3_at AFFX-r2-TagJ-3

Zm.2232.1.S1_at BM380334

Zm.15667.1. S1_at CD437700

Zm.1996.1.S1_at CK347826

Zm.9642.1.A1_at BM338826

Zm.12716.1.S1_at AY112283.1

Zm.6556.1.A1_at AY109683.1 2mAffx.54.1.S1_at AI665038

Zm.5099.1.S1_at AI600819

Zm.5550.1.S1_at AI622648

Zm.1352.1.A1_at AY106566.1

Zm.4312.3.S1_at CF075294

Zm.2202.1.A1_at AY105037.1

Zm.14089.1.S1_at AW324724

Zm.13601.1.S1_at AY107674.1

Zm.4.1.S1_a_at CD434423 ZmAffx.219.1.S1_at AI670227 ZmAffx.122.1.S1_at AI665696 ZmAffx.109.1.S1_at AI665560 ZmAffx.331.1.A1_at AI670513

Zm.4118.1.A1_at AY105314.1

Zm.6369.3.A1_at AI881634

Zm.15323.1.A1_at BM349667

Zm.3050.3.A1_at CF630494

Zm.2957.1.A1_at CK371564 ZmAffx.439.1.A1_at AI676966

Zm.4860.2.A1_at AI770577

Zm.19141.1.A1_at CF625022

Zm.5268.1.S1_at CF626642

Zm.5791.2.A1_a_at AW438331

Zm.4616.1.A1_x_at BQ538201

Zm.12940.1.S1_at AY104675.1

Zm.4265.1.A1_at CA402796

Zm.8412.1.A1_at AY108596.1

Zm.18041.1.A1_at BQ620926

Zm.13365.1.A1_at CK827054

Zm.2734.2.S1_at BF727671

Zm.16299.2.A1_a_at BM336250

Zm.13007.1.S1_at CO532826

Zm.12716.1.A1_at AY112283.1

Zm.11827.1.A1_at BM381077

Zm.14824.1.S1_at AJ430693.1

Zm.15083.2.A1_at AY107613.1

Zm.445.2.A1_at AF457968.1

Zm.5834.1.A1_a_at BM335098 ZmAffx.823.1.S1_at AI770503

Zm.8924.1.A1_at BM381215

Zm.722.1.A1_at AW288498

Zm.13341.1.S1_at CF044863

Zm.12037.1.S1_at BI894209

Zm.2557.1.S1_at CF649649 ZmAffx.1152.1.A1_at AW424633

Zm.5423.1.S1_at CD997936 ZmAffx.243.1.S1_at A1670255

Zm.17696.1.A1_at BM073027

Zm.13194.2.A1_at AY108895.1

Zm.13059.1.S1_at AB112938.1

Zm.3255.2.A1_a_at BM073865 ZmAffx.57.1.A1_at AI665066

Zm.18764.1.A1 at GO519979

Table 20 , continued .

2OB. Negative AI691556 Correlation Zm.4875.1.S1_at AI666161

Zm.5980.2.Ai_a_ ^at BM337093

Zm.6045.2.A1_a_at CF016873 _Zm .- _| 4497.15.Ai_x_at U06831.1

Zm.281.1.Si_at AF001634.1

Zm.2376.1AUO* A1666154

Zm.6007.1.S1_at AI670498 ZmAffx.316.1A1_at CF623596 _Zm .17786.1.S1_ at CF631047

Zm.18419.1.1i_at CF624893

Zm.16237.1.A1_at CF972362

Zm.6594.1.A1_at BF727820

Zm.18998.1.S1_at AI676853 ZmAffx.421.1-S1_at CN844169

Zm.3198.2.A1_a_at BM1339714

Zm.155i .1.A1_at CF052340

Zm.936.1.A1_at AW519914

Zm.6194.1-A1_at AFFχ_ThrX-M AFFX-ThrX-M_at AI834719

Zm.4304.1.S1-,at BM380107

Zm.3616.1.A1_at AW355980

Zm.16207.1.A1 _-- at BM379236

Zm.5917.2.A1_at AI770970 ZmAffx.914.1-A1_at CF602623

Zm.18260.1.A1_at CF645954

Zm.16879.1.A1_at CO520849

Zm.19203.1.Si_at CK371009

Zm.17500.1.A1 _-- at A1637038

Zm.5705.1.S1_at CO520489

Zm.7892.1.A1_at A1715014 ZmAffx.586.1.A1_at BM380733

Zm.11783.1.A1_at CF632979

Zm.18254.2.Ai_at BM348441

Zm.4258.1A1_at AY105115.1

Zm.13790.1.S1_at AY106109.1

Zm.14428.1.S1_at A1737859

Zm.13947.2.A1_at CF624446 Ztn.125i7.1.A1_at CN071496

Zm.δ507.1.S1_at BM336314

Zm.11055.1.A1_at CA400681

Zm.13417.1.A1_at AI833552

Zm.12iO1.2.S1_at AY112463.1

Zm.10202.1.A1_at A1670401 ZmAffx.273.1.A1_at CF005849

Zm.784.1.A1_at AY108500.1

Zm.7858.1.A1_at BM339393

Zm.9839.1.A1_at BE056195 ZmAffx.1198- ¹ - ^s1 - ^at

Zm.4326.1.A1_at AI711615

Zm.9735.1.A1_at BM336891

Zm.3634.1.A1_at CF638013

Zm.1408.1.A1_at CN845023

Zm.16848.1.A1_at CK369421

Zm.8114.1.A1_at BM072985 ZmAffx.138.1.A1_at AI667759

Zm.5803.1.A1_at AI691266

Zm.10681.1.A1_at BQ538977

Zm.9867.1.A1_at AY106142.1

Zm.1511.1.S1_at CO532736

Zm.7150.1.A1_x_at AY103659.1

Zm.9614.1.At_at BM335440

Zm.1338.1.S1_at W49442

Zm.8900.1.A1_at CK827399 ZmAflx.721.1.A1_at AI665110

Zm.7596.1.A1_at BM079087

Zm.19034.1.S1_at BQ833817

Zm.8959.1.A1_at BM335622 Zm .2243.1. A1_at BM349368

Zm.13403.1.S1_x_at AF457949.1 AFFX-Zm-r2-Ec-bioB-3_at AFFX-Zm-r2-Ec-bioB-3

Zm.3633.1.A1_at U33816.1 Ztη.17529.1.S1_at CK394827

Zm.18275.1.A1_at CO526155

Zm.7056.6.A1_at CF051906

Zm.5796.1.A1_at BM332299 ZmAffx.1106.1.S1_at AW216267

Zm.12965.1.A1_at CA402509

Zm.13845.1.A1_at AY103950.1

Zm.12765.1.A1_at AI745814 ZmAffx.1500.1.S1_at 40794996-117

Zm.10867.1.A1_at BM073190

Zm.19144.1.A1_at CO518283 ZmAffx.262.1.A1_s_at AI670379

Zm.7012.9.A1_at BE123180 ZmAffx.1295.1.S1_s_at 40794996-25

Zm.4682.1.S1_at AI737946

Zm.2367.1.S1_at AW497505

Zm.8847.1.A1_at BM075896

Zm.2813.1.A1_at BM381379 ZmAffx.53δ.1.S1_at A1715014

Zm.14450.1.A1_at AI391911

Zm.1454.1.A1_at BG841866

Zm.18933.2.S1_at AI734652

Zm.1118.1.S1_at CF631214

Zm.18416.1.A1_at CO524'449 ZmAffx.939.1.S1_at AI820322

Zm.16251.1.A1_at AI711812

Zm.18427.1.S1_at CO523584

Zm.10053.1.A1_at CO523900

Zm.18439.1.A1_at BM267666

Zm.12356.1.S1 at BQ547740

ZmAffx.507.1.A1_at AI691932

Zm.10718.1.A1_ai BM339638

Zm.15796.1.S1_at BE640285 ZmAffx.270.1.A1_at AI670398

Zm.54.1.S1_at L25805.1

Zm.8391.1.A1_at BM347365

Zm.9238.1.A1_at CO533275

Zm.3633.2.S1_x_at CF634876

Zm.4505.1.S1_at AY111153.1

Zm.12070.1.A1_at BM418472

Zm.17977.1.A1_s_at CK827616

Zm.5789.3.S1_at X83696.1 ZmAffx.771.1.A1_at AI746147

Zm.11620.1.A1_at BM379366

Zm.5571.2.A1_a_at AY107402.1

Zm.12192.1.A1_at BM380585

Zm.19243.1.A1_at AW181224

Zm.12382.1.S1_at BU097491

Zm.7538.1.A1_at BM337034

Zm.1738.2.A1_at CF630684

Zm.1313.1.A1_s_at BM078737

Zm.9389.2.A1_x_at BQ538340 ZmAffx.678.1.A1_at AI734611

Zm.18105.1.S1_at CO527288

Zm.19042.1.A1_at CO521963 ZmAffx.782.1.A1_at AI759014

Zm.5957.1.S1_at AY105442.1

Zm.18908.1. S1_at CO531963

Zm.1004.1.S1_at BE511241

Zm.6743.1.S1_at AF494284.1

Zm.8118.1.A1_at AY107915.1 ZmAffx.960.1.S1_at AI833639

Zm.17425.1.S1_at CK145186

Zm.8106.1.S1_at BM079856 ZmAffx.277.1.S1_at AI670405

Zm.13686.1.A1_at AY106861.1

Zm.1068.1. S1_at BM381276

Zm.778.1.A1_a_at CO529433

Zm.11834.1.S1_at BM381120

Zm.16324.1.A1_at CF032268

Zm.18774.1. S1_at CO524725

Zm.14811.1.S1_at CF629330

Zm.6654.1.A1_at CF038689

Zm.17243.1.S1_at CK786707

Zm.6000.1.S1_at BG265807

Zm.17212.1.A1_at CO529021

Zm.8233.2.S1_a_at BM381462

Zm.13884.2.A1_at AF099414.1 ZmAffx.1362.1.S1_at 11990232-90

Zm.7904.1.A1_at BM080363

Zm.16742.1.A1_at AW499330

Zm.5119.1.A1_a_at CF634150

Zm.152.1.S1_at J04550.1

Zm.15451.1 S 1_at CD439729 Zm.5492.1.A1_at AI622235 Zm.2710.1.S1_at CO520765 Zm.8937.1.A1_at BM080734 Zm.14283.4.S1_at BG841525 Zm.6437.1.A1_a_at CA402215 Zm.10175.1.A1_at BM379420 Zm.6228.1.A1_at AI739920 Zm.5558.1.A1_at AY072298.1 Zm.10269.1. S1_at BM660878 Zm.1894.2.S1_at CK371174 Zm.12875.1. A1_at CA400938 Zm.3138.1.A1_a_at AI621861 Zm.15984.1.A1_at CD441218 ZmAffx.1073.1.A1_at A1947671 Zm.8489.1.A1_at BQ538173 Zm.14962.1.A1_at BM268018 Zm.9799.1.A1_at AY111917.1 Zm.3833.1.A1_at AW288806 Zm.15467.1.A1_at CD219385 Zm.4316.1.S1_a_at AI881448 Zm.4246.1.A1_at AI438854 Zm.9521.1.A1_x_at CF624102 Zm.17356.1. A1_at CF634567 Zm.17913.1.S1_at CF625344 Zm.17630.1.A1_at CK348094 Zm.3350.1.A1_x_at BM266649 Zm.2031.1.S1_at AY103664.1 Zm.5623.1.A1_at BG840990 Zm.16338.1. A1_at CF348862 Zm.6430.1.A1_at AY111839.1 Zm.10210.1.A1_at CF627510 Zm.4418.1.A1_at BM378152 ZmAffx.791.1.A1_at AI759133 Zm.9048.1.A1_at CF024226 Zm.2542.1.A1_at CF636373 Zm.19011.2.A1_at AY108328.1 Zm.9650.1.S1_at BM380250 Zm.7804.1.S1_at AF453836.1 Zm.17656.1.S1_at CK369512 Zm.7860.1.A1_at BM333940 Zm.3395.1.A1_at AY103867.1 Zm.14505.2.A1_at CF059379 Zm.3099.1.S1_at CO522746 Zm.12133.1.si_at CF636936 Zm.4999.1.S1_at AI600285 Zm.16080.1. A1_at AY108583.1 Zm.2715.1.A1_at AW066985 Zm.5797.1.S1_at CFO 12679 ZmAffx.844.1.A1_at AI770609 Zm.13263.1.A1_at AY109418.1 Zm.3852.1.S1_at CD998914 Zm.12391.1.S1 at CF349132

Zm.6624.1.S1_at AI491254

Zm.13961.1.S1_at AY540745.1

Zm.8632.1.A1_at BM268513

Zm.15102.1.A1_at AI065586

Zm.11831.1.S1_a_at CA401860

Zm.4460.1.A1_at AI714963

Zm.4546.1.A1_at BG266283

RPTR-Zm-U55943-1_at RPTR-Zm-U55943-1

Zm.7915.1.A1_at BM080414 ZmAffx.188.1.S1_at AI668391

Zm.3889.5.A1_x_at AI737901

Zm.2078.1.A1_at CF675000

Zm.7648.1.A1_at CO517814

Zm.3167.1.S1_s_at U89342.1

Zm.19347.1.S1_at AI902024

Zm.1881.1.A1_at AY110751.1

Zm.6982.1.S1_at AY105052.1

Zm.4187.1.S1_at AY 105088.1

Zm.6298.1.A1_at CD444675

Zm.9529.1.A1_at CA399003

Zm.1383.1.A1_at BG873830

Zm.9339.1.A1_at BM332063

Zm.6318.1.A1_at BM073937

Zm.16926.1. S1_at CO522465 ZmAffx.485.1.S1_at AI691349

Zm.3795.1.A1_at BM335144

Zm.5367.1.A1_at CF638282

Zm.2040.2.S1_a_at CB331475

Zm.7056.12.S1_at AI746152

Zm.5656.1.A1_at BG837879

Zm.1212.1.S1_at CF011510

Zm.9098.1.A1_a_at BM336161

Zm.3805.1.S1_at AY112434.1

Zm.6645.1.S1_at CF637989

Zm.9250.1.S1_at CF016507

Zm.2656.2.S1_s_at AY111594.1

Zm.13585.1. S1_at AY107846.1 ZmAffx.261.1.S1_at AI670366

Zm.1056.1.S1_a_af AW120162 ZmAffx.474.1.S1_at AI677507

Zm.2225.1.S1_at BF728179

Zm.8292.1.S1_at AY106611.1

Zm.6569.9.A1_x_at AW091447

Zm.4230.1.S1_at CO523811

RPTR-Zm-JOI 636-4_at RPTR-Zm-JOI 636-4

Zm.13326.1.S1_at CF042397 ZmAffx.728.1.A1_at AI740010

Zm.6048.2.S1_at AI745933

Zm.9513.1.A1_at BM349310

Zm.5944.1.A1_at BG874229 ZmAffx.1059.1.A1_at AI881930

Zm.14352.2.S1_at AY104356.1 ZmAffx.607.1.S1 at AI715035

Zm.2199.2.S1_at CA404051

Zm.9169.2.S1_at CO521754 ZmAffx.630.1.S1_at AI715058

Zm.16285.1.S1_at CD970925

Zm.9747.1.S1_at BM337726

Zm.9783.1.A1_at BM347856 ZmAffx.827.1.A1_at AI770520

Zm.3133.1.S1_at CK371248

Zm.15512.1.S1_at CD436002

Zm.453i .1.A1_at AI734623

Zm.12810.1.A1_at CA399348

Zm.17498.1.A1_at CK144816 ZmAffx.821.1.A1_at AI770497

Zm.5723.1.A1_at BM079835

Zm.16535.2.A1_s_at CF062633

Zm.14502.1.S1_at CO531791

Zm.10792.1.A1_at AY106092.1

Zm.14170.1.A1_a_at BG841910 ZmAffx.1005.1.A1_at AI881362

Zm.5048.6.A1_at BM380925

Zm.8270.1.A1_at AY649984.1

Zm.1899.1.A1_at BM333426

Zm.17843.1.A1_at BM380806

Zm.7005.1.A1_at BM333037

Zm.15576.1.A1_a_at CK827910

Zm.13930.1.A1_x_at Z35298.1

Zm.12433.1.S1_at AY105016.1 ZmAffx.1031.1.A1_at AI881675 ZmAffx.237.1.S1_at AI670249

Zm.13103.1.S1_at CO534624

Zm.16538.1.S1_at BM337996

Zm.10271.1.S1_at CA452443

Zm.6625.2.S1_at BM347999

Zm.8756.1.A1_at BM333012

Zm.885.1.S1_at BM080781 ZmAffx.1077.1.A1_at AI948123

Zm.14463.1.A1_at BM336602 ZmAffx.58.1.S1_at AI665082

Zm.5112.1.A1_at AI600906

Zm.14076.2.A1_a_at CO526265

Zm.3077.2.S1_x_at CF061929

Zm.9814.1.A1_at BM351590

Zm.161.2.S1_x_at X70153.1

Zm.16266.1.S1_at CF243553

Zm.17657.1.A1_at CK369553

Zm.19019.1.A1_at BM080703

Zm.10514.1.S1_at BQ485919

Zm.2473.1.S1_at AY104610.1

Zm.13720.1.S1_s_at AY106348.1

Zm.2266.1.A1_at AW330883

Zm.5228.1.A1_at AW051845

AFFX-Zm-r2-Ec-bioC-3_at AFFX-Zm-r2-Ec-bioC-3

Zm.13858.1.S1 at CO524282

2m.5847.1.A1_at BM078382

Zm.9056.1.A1_af BM334642

Zm.4894.1.A1_at BM076024 ZmAffx.1032.1.S1_at AI881679

Zm.9757.1.A1_at BM338070

Zm.4616.1.A1_a_at BQ538201

Zm.4287.1,A1_at BG266567

Zm.5988.1.A1_at AI666062

Zm.4187.1.A1_at AY105088.1

Zm.8665.1.A1_at BM075117

Zm.5080.1.A1_at AI600750

Zm.5930.1.S1_at CF018694

Table 21 : Pedigree and seedling growth chara cteristics of the maize inbred lines used in Example 6a

Table 22 : Maize genes for which transcript abundance in inbred lines of the training dataset is correlated (P<0. 00001) with plot yield of hybrids with line B73

Systematic Name P value R2 Slope Intercept GenBank entry gb:L81162.2

Zm.3907.1.S1_at 0 0.648 -0.1182 1.773 DB_XREF=gi:50957230 gb:CN844890

Zm.18118.1. S1_at 0 0.5906 -0.3374 5.653 DB_XREF=gi:47962181 gb:CB603857

Zm.2741.1.A1_at 1.13E-12 0.585 -0.3268 5.597 DB_XREF=gi:29543461 gb:CA403748

Zm.13075.1.A1_at 4.58E-12 0.5647 -0.8445 12.26 DB_XREF=gi:24768619 gb:CO530711

Zm.11896.1.A1_at 4.62E-12 0.5646 -0.523 7.705 DB_XREF=gi:50335585 gb:CF005102

Zm.8790.1.A1_at 3.76E-11 0.5324 -0.1699 3.336 DB_XREF=gi:32865420 gb:BG840169

Zm.14547.1. S1_a_at 4.19E-11 0.5307 -0.2015 2.891 DB_XREF=gi:14243004 gb:CK368635

Zm.17578.1.A1_at 5.68E-11 0.5258 -3.303 48.37 DB_XREF=gi:40334565 gb:AI881726

ZmAffx.1036.1.S1_at 8.13E-11 0.52 -0.1258 1.934 DB_XREF=gi:5566710 gb:BE345306

Zm.6469.1.S1_at 8.45E-11 0.5194 0.0888 -0.1612 DB_XREF=gi:9254838 gb:BG842238

ZmAffx.1211.1.A1_at 9.65E-11 0.5172 -0.5151 8.386 DB_XREF=gi: 14244259 gb:CK370833

Zm.17743.1.S1_at 1.06E-10 0.5156 -0.8687 12.7 DB_XREF=gi:40336763 gb:AA979835

Zm.11126.1.S1_at 3.41 E-10 0.496 0.103 -0.3613 DB_XREF=gi:3157213 gb:CN844978

Zm.17115.1.S1_at 4.19E-10 0.4925 -0.395 6.294 DB_XREF=gi:47962269 gb:BG840947

Zm.1465.1. A1_at 1.08E-09 0.476 -1.141 17.41 DB_XREF=gi:14243198 gb:AI668276

ZmAffx.175.1.A1_at 1.58E-09 0.4692 -0.7394 11.35 DB_XREF=gi:4827584 gb:BM074289

Zm.7407.1.A1_a_at 1.77E-09 0.4672 -0.1588 3.222 DB_XREF=gi:16919636 gb:BM417375

Zm.12072.1.S1_at 1.86E-09 0.4663 -0.2694 3.894 DB_XREF=gi:18384175 gb:BM073068

Zm.17209.1. A1_at 2.01 E-09 0.4648 0.07619 -0.06023 DB_XREF=gi:16916971 gb:AY106014.1

Zm.1615.1.S1_at 2.37E-09 0.4618 -0.1839 3.377 DB_XREF=gi:21209092 gb:CK985959

Zm.1835.2.A1_at 2.76E-09 0.459 -0.1609 2.806 DB_XREF=gi:45568216 gb:CO528780

Zm.5605.1.S1 at 3.21 E-09 0.4563 -0.1728 3.327 DB_XREF=gi:50333654

Table 22 _r continued

gb:AY110526.1

Zm.17923.1.A1_at 3.99E-09 0.4523 -0.2692 4.808 DB_XREF=gi:21214935 gb:BM074289

Zm.7407.1.A1_x_at 4.46E-09 0.4502 -0.1987 3.798 DB_XREF=gi:16919636 gb:CD443909

Zm.1143.1.S1_at 4.54E-09 0.4499 -0.166 3.287 DB_XREF=gi:31359552 gb:BG837879

Zm.5656.1.A1_at 5.20E-09 0.4473 0.1137 -0.4548 DB_XREF=gi: 14204202 gb:BQ539216

Zm.7397.1.A1_at 5.31 E-09 0.4469 0.168 -1.328 DB_XREF=gi:28984830 gb:AY106810.1

Zm.11141.1.S1_at 7.30E-09 0.441 -0.1185 2.511 DB_XREF=gi:21209888 gb:AW585256

Zm.6221.1.S1_at 7.80E-09 0.4397 -0.06997 1.969 DB_XREF=gi:7262313 gb:AI600480

Zm.4741.1.A1_a_at 8.01 E-09 0.4392 -0.2734 4.707 DB_XREF=gi:4609641 gb:AY104401.1

Zm.8535.1.A1__at 1.06E-08 0.4338 -0.1364 2.904 DB_XREF=gi:21207479 gb:BG840169

Zm.14547.1.S1_at 1.39E-08 0.4287 -0.2202 3.814 DB_XREF=gi: 14243004 gb:CF630748

Zm.16839.1.A1_at 1.67E-08 0.4251 0.0764 0.004757 DB_XREF=gi:37387111 gb:CO528850

Zm.19172.1.A1_at 1.90E-08 0.4226 -0.1808 3.45 DB_XREF=gi:50333724 gb:CF349172

Zm.5170.1.S1_at 2.20E-08 0.4197 0.11 -0.4471 DB_XREF=gi:33942572 gb:CO527835

Zm.5851.11.A1_x_at 2.71 E-08 0.4156 -0.7137 11.37 DB_XREF=gi:50332709 gb:AW225324

Zm.7006.2.A1_at 2.84E-08 0.4147 0.07037 0.09825 DB_XREF=gi:6540662 gb:BM073720

Zm.8914.1.S1_at 2.95E-08 0.414 0.0947 -0.2888 DB_XREF=gi:16918380 gb:CF920129

Zm.1974.1.A1_at 3.19E-08 0.4124 -0.3785 6.334 DB_XREF=gi:38229816 gb:CK368613

Zm.13497.1.S1_at 3.62E-08 0.4099 0.08851 -0.1197 DB_XREF=gi:40334543 gb:AY107547.1

Zm.10640.1.S1_at 3.96E-08 0.4081 -0.08601 2.231 DB_XREF=gi:21210625 gb:CO531568

Zm.19062.1.S1_at 4.74E-08 0.4045 -0.08075 2.065 DB_XREF=gi:50336442 gb:CK985812

Zm.18060.1.A1_at 4.79E-08 0.4043 -0.2694 4.583 DB_XREF=gi:45567918 gb:AI855310

Zm.878.1.S1_x_at 5.24E-08 0.4025 0.1231 -0.4754 DB_XREF=gi:5499443 gb:CA403363

Zm.5159.1.A1 at 6.20E-08 0.3991 0.0685 0.06159 DB_XREF=gi:24768234

Table 22, con tin ued.

gb:AI737439

Zm.4632.1.A1_at 6.24E-08 0.399 -0.1062 2.425 DB_XREF=gi:5058963 gb:BM339882

Zm.11189.1.A1_at 6.86E-08 0.3971 -0.08985 1.381 DB_XREF=gi: 18170042 gb:CF650678

Zm.1541.2.S1_at 8.18E-08 0.3935 0.09864 -0.363 DB_XREF=gi:37425858 gb:CF014037

Zm.15307.1.A1_at 8.20E-08 0.3934 -4.65 68.91 DB_XREF=gi:32909225 gb:CA398576

Zm.12775.1.A1_x_at 8.37E-08 0.393 -0.1098 1.876 DB_XREF=gi:24763400 gb:CF625592

Zm.5086.1.A1_at 1.03E-07 0.3887 0.05381 0.329 DB_XREF=gi:37377894 gb:AY105349.1

Zm.5851.9.S1_at 1.15E-07 0.3865 -0.2305 3.44 DB_XREF=gi:21208427 gb:CK827062

Zm.3182.1.A1_at 1.31 E-07 0.3838 -0.06838 1.868 DB_XREF=gi:44900517 gb:BM074945

Zm.5415.1. A1__at 1.32E-07 0.3837 -0.3297 5.269 DB_XREF=gi: 16921022 gb:AF036949.1

Zm.16855.1.A1_at 1.34E-07 0.3833 -0.1675 2.758 DB_XREF=gi:2865393 gb:CO527835

Zm.5851.11.A1_a_at 1.35 E-07 0.3832 -2.667 40.08 DB_XREF=gi:50332709 gb:AI665540

ZmAffx.106.1.A1_at 1.42E-07 0.3822 -0.317 5.565 DB_XREF=gi:4776537 gb:BM338540

Zm.5688.2.A1_at 1.73E-07 0.3781 -0.733 12.07 DB_XREF=gi:18168700 gb:BM335301

Zm.9294.1.A1_at 1.99E-07 0.3751 -0.4105 6.62 DB_XREF=gi: 18165462 gb:BM339882

Zm.11189.1.A1_x_at 2.14E-07 0.3736 -0.1475 2.193 DB_XREF=gi: 18170042 gb:CK371274

Zm.8904.1.A1_at 2.24E-07 0.3726 -0.2324 3.566 DB_XREF=gi:40337204 gb:BM336220

Zm.9631.1.A1_at 2.37E-07 0.3714 -0.1776 2.7 DB_XREF=gi:18166381 gb:CK786800

Zm.2106.1.S1_at 2.38E-07 0.3713 -0.2349 4.515 DB_XREF=gi:44681752 gb:AF244691.1

Zm.552.1.A1_at 2.74E-07 0.3683 0.1283 -0.6816 DB_XREF=gi: 11385502 gb:BM350310

Zm.9371.1.A1_x_at 3.1 E-07 0.3657 -0.1302 2.806 DB_XREF=gi: 18174922 gb:BM335125

Zm.16747.1.A1_at 3.18E-07 0.3652 0.06149 0.2381 DB_XREF=gi:18165286 gb:AI855310

Zm.878.1.S1_at 3.2E-07 0.365 0.2286 -1.663 DB_XREF=gi:5499443 gb:BM382754

Zm.12188.1.A1_at 3.43E-07 0.3636 -0.08906 1.631 DB_XREF=gi:18181544 gb:AI691174

Zm.4452.1.A1 at 3.5E-07 0.3631 -0.1109 2.573 DB_XREF=gi:4938761

Table 22, continued.

gb:CK370971

Zm.17790.1.S1_at 3.51 E-07 0.363 0.1348 -0.6063 DB_XREF=gi:40336901 gb:AY104026.1

Zm.13843.1. A1_at 3.79E-07 0.3614 0.06967 0.1099 DB_XREF=gi:21207104 gb:BG316519

Zm.4271.4.A1_at 3.88E-07 0.3609 0.05597 0.2215 DB_XREF=gi:13126069 gb:BM080861

Zm.8922.1.S1_at 3.95E-07 0.3605 -0.1195 2.683 DB_XREF=gϊ.16927792 gb:CB885460

Zm.6092.1.S1_at 4.22E-07 0.3591 0.07163 0.03375 DB_XREF=gi:30087252 gb:L46399.1

Zm.5851.6.S1_x_at 4.64E-07 0.3571 -1.814 27.33 DB_XREF=gi:939782 gb:CF626421

Zm.3467.1.A1_at 4.7E-07 0.3568 -0.11 2.537 DB_XREF=gi:37379355 gb:AF236369.1

Zm.495.1.A1_at 5.15E-07 0.3548 0.05399 0.3248 DB_XREF=gi:7716457 gb:AF529266.1

Zm.446.1.S1_at 5.28E-07 0.3543 -0.764 12.28 DB_XREF=gi:27544873 gb:AI665953

Zm.5960.1.A1_at 5.32E-07 0.3541 -0.215 3.564 DB_XREF=gi:4804087 gb:BG841480

Zm.4213.1.A1_at 5.5E-07 0.3534 -0.1478 3.071 DB_XREF=gi:14243777 gb:A!855200

Zm.4728.1.A1_at 5.59E-07 0.3531 -0.1074 2.592 DB_XREF=gi:5499333 gb:BM332976

Zm.9580.1.A1_at 5.62E-07 0.3529 -0.2372 4.381 DB_XREF=gi:18163137 gb:AY104740.1

Zm.13808.1.S1_at 5.75E-07 0.3524 -0.105 2.492 DB_XREF=gi:21207818 gb:AY112337.1

Zm.2626.1.A1_at 6.12E-07 0.3511 -0.05262 1.708 DB_XREF=gi:21216927 gb:BM336226

Zm.15868.1.A1_at 6.23E-07 0.3507 0.1032 -0.2451 DB_XREF=gi:18166387 gb:CD964540

Zm.4180.1.S1_at 6.88E-07 0.3485 0.1176 -0.5887 DB_XREF=gi:32824818 gb:AI759130

Zm.5851.15.A1_x_at 7.11 E-07 0.3478 -0.3181 5.392 DB_XREF=gi:5152832 gb:BM337820

Zm.1739.1.A1_at 7.48E-07 0.3467 0.1393 -0.8398 DB_XREF=gi:18167980 gb:BM078263

Zm.5390.1.A1_at 7-.81 E-07 0.3458 -0.1602 3.31 DB_XREF=gi: 16925195 gb:AY103827.1

Zm.3097.1.A1_at 7.87E-07 0.3456 0.1663 -0.8862 DB_XREF=gi:21206905 gb:AY108079.1

Zm.6736.1.S1_at 8.55E-07 0.3438 -0.1797 3.458 DB_XREF=gi:21211157 gb:CK145276

Zm.2910.1.S1 at 8.67E-07 0.3435 0.09427 -0.2644 DB_XREF=gi:38688245

Table 22 _r continued.

gb:BM079294

Zm.8697,1.A1_ at 8.83E-07 0.3431 -0.1124 2.472 DB_XREF=gi:16926226 gb:CA400292

Zm.4046.1.S1_ at 8.85E-07 0.343 0.1288 -0.7911 DB_XREF=gi:24765132 gb:AY111542.1

Zm.1285.1.A1_ at 9.43E-07 0.3416 0.05565 0.2897 DB_XREF=gi:21216132 gb:BE638571

Zm.2563.1.A1_ at 9.52E-07 0.3414 -0.05074 1.192 DB_XREF=gi:9951988 gb:CF632730

Zm.17952.1.A1 _at 9.87E-07 0.3406 -0.6734 10.55 DB_XREF=gi:37390982 gb:BG840404

Zm.5766.1.S1_ x_at 1 E-06 0.3403 -0.3844 5.842 DB_XREF=gi: 14242680 gb:AY108613.1

Zm.15977.1. S1 _at 1.17E-06 0.3368 0.08845 -0.8911 DB_XREF=gi:21211748 gb:CF000034

Zm.3913.1.A1_ at 1.24E-06 0.3355 0.1163 -0.4099 DB_XREF=gi:32860352 gb:AF236373.1

Zm.303.1.S1_at 1.3E-06 0.3346 -0.07128 2.002 DB_XREF=gi:7716465 gb:AI711854

Zm.4332.1.A1_ at 1.36E-06 0.3336 -0.3654 6.262 DB_XREF=gi:5005792 gb:BM332576

Zm.9376.1.A1_ at 1.41 E-06 0.3326 0.09554 -0.3578 DB_XREF=gi:18162737 gb:CF047935

Zm.1423.1.A1_ at 1.46E-06 0.3319 -0.0643 1.871 DB_XREF=gi:32943116 gb:AY107188.1

Zm.1792.1.A1_ .at 1.49E-06 0.3314 0.06852 0.04595 DB_XREF=gi:21210266 gb:CO525036

Zm.17540.1.A1 _at 1.51 E-06 0.3311 -0.07019 1.93 DB_XREF=gi:50329910 gb:CK826673

Zm.3561.1.A1_ .at 1.52E-06 0.3311 -0.6223 9.644 DB_XREF=gi:44900128 gb:AI714636

ZmAffx.566.1.A1_at 1.62E-06 0.3297 -0.07933 1.337 DB_XREF=gi:5018443 gb:AI629497

Zm.5597.1.A1_ .at 1.63E-06 0.3295 -0.2103 3.985 DB_XREF=gi:4680827 gb:CD438478

Zm.13082.1.S1 l_a_at 1.68 E-06 0.3288 -0.2151 3.969 DB_XREF=gi:31354121 gb:CO531189

Zm.6216.1.S1_ .at 1.69E-06 0.3287 -0.04754 1.586 DB_XREF=gi:50336063 gb:AY111235.1

Zm.2742.1.A1_ .at 1.72E-06 0.3283 -0.1419 3.028 DB_XREF=gi:21215825 gb:BF729152

Zm.1559.1.S1_ .at 1.72 E-06 0.3282 -0.07846 1.413 DB_XREF=gi:12058302 gb:BM333548

Zm.3154.1.A1_ .at 1.74E-06 0.328 -0.03944 1.529 DB_XREF=gi:18163709 gb:BM347858

Zm.3357.1.A1 at 1.75E-06 0.3279 0.08751 -0.1318 DB_XREF=gi: 18172470

Table 22 , continued.

gb:BM349722

Zm.2924.1.A1_a_at 1.8E-06 0.3273 -0.05843 1.786 DB_XREF=gi: 18174334 gb:BU050993

Zm.10301.1.A1_ at 1.86E-06 0.3265 0.1287 -0.5513 DB_XREF=gi:22491070 gb:AY108021.1

Zm.5992.1.A1_at 1.87E-06 0.3264 0.07232 0.08961 DB_XREF=gi:21211099 gb:AY106770.1

Zm.13693.1.S1_at 1.87E-06 0.3264 -0.1718 3.323 DB_XREF=gi:21209848 gb:BM074413

Zm.6117.1.A1_at 1.89E-06 0.3262 -0.05436 1.737 DB_XREF=gi:16919905 gb:BM350783

Zm.8911.1.A1_at 2.03E-06 0.3246 -0.2179 4.077 DB_XREF=gi: 18175488 gb:CD437071

Zm.7595.1.A1_at 2.11 E-06 0.3237 -0.05045 1.648 DB_XREF=gi:31352714 gb:BG841655

Zm.2424.1.A1_at 2.28E-06 0.3219 -0.3084 5.458 DB_XREF=gi:14243883 gb:CK826632

Zm.2391.1.A1_at 2.44E-06 0.3204 -0.3225 5.482 DB_XREF=gi:44900087 gb:BM416746

Zm.2455.1.A1_at 2.47E-06 0.3201 -0.09311 2.332 DB_XREF=gi: 18383546 gb:AY106367.1

Zm.12934.1.A1_a_at 2.55E-06 0.3194 -0.3145 4.903 DB_XREF=gi:21209445 gb:CO533594

Zm.13266.2.S1_at 2.6E-06 0.3189 -0.2755 4.818 DB_XREF=gi:50338468 gb:BM334062

Zm.9364.1. A1_at 2.63E-06 0.3187 0.1468 -0.7177 DB_XREF=gi: 18164223 gb:CF038760

Zm.6293.1.A1_at 2.68E-06 0.3182 -0.08441 2.061 DB_XREF=gi:32933948 gb:CF637153

Zm.2530.1.A1_at 2.71 E-06 0.318 -0.1539 3.168 DB_XREF=gi:37399642 gb:BM073273

Zm.8204.1.A1_at 2.8E-06 0.3172 -0.07345 2.051 DB_XREF=gi: 16917409 gb:AY111573.1

Zm.843.1.A1_a_at 2.81 E-06 0.3172 0.06446 0.1415 DB_XREF=gi:21216163 gb:CA826847

Zm.13288.1.S1_at 2.82E-06 0.3171 -0.07191 1.268 DB_XREF=gi:26455264 gb:CO532922

Zm.19018.1.A1_at 2.87 E-06 0.3167 -0.05674 1.775 DB_XREF=gi:50337796 gb:X55388.1

Zm.14036.1.S1_at 2.89E-06 0.3165 -0.05461 0.846 DB_XREF=gi:22270 gb:Y09301.1

Zm.13248.1.S1_at 2.98E-06 0.3158 -0.04989 0.7365 DB_XREF=gi:3851330 gb:D10622.1

Zm.14272.2.A1_at 3.07E-06 0.3151 0.1132 -0.5078 DB_XREF=gi:217961 gb:AY104313.1

Zm.14318.1.A1_at 3.33E-06 0.3133 0.1184 -0.4017 DB_XREF=gi:21207391 gb:CA829102

Zm.19303.1.S1_at 3.4E-06 0.3128 0.04973 0.3873 DB_XREF=gi:26457519 gb:AI770947

ZmAffx.909.1.S1_at 3.54E-06 0.3119 -0.1389 2.793 DB_XREF=gi:5268983 gb:AW331208

Zm.2293.1.A1_at 3.65E-06 0.3112 -0.3914 5.735 DB_XREF=gi:6827565 gb:BG836961

Zm.3796.1.A1 at 3.66E-06 0.3111 -0.1047 2.305 DB_XREF=gi: 14203284

Table 22 , continued .

gb:Z29518.1

Zm.6560.1.S1_a_at 3.95E-06 0.3094 -0.1021 2.428 DB_XREF=gi:575959 gb:Z29518.1

Zm.6560.1.S1_at 4.13E-06 0.3083 -0.5382 9:188 DB_XREF=gi:575959 gb:AI734359

ZmAffx.667.1.A1_at 4.19E-06 0.308 -0.1973 3.638 DB_XREF=gi:5055472 gb:BM339241

Zm.9931.1.A1_at 4.36E-06 0.3071 -0.2746 4.617 DB_XREF=gi:18169401 gb:CF013366

Zm.11852.1. A1_x_at 4.54E-06 0.3062 0.1797 -1.23 DB_XREF=gi:32908553 gb:AF200528.1

Zm.520.1.S1_x_at 4.74E-06 0.3052 0.1057 -0.5001 DB_XREF=gi:9622879 gb:AB102956.1

Zm.16977.1. S1_at 4.76E-06 0.3051 -0.04535 1.634 DB_XREF=gi:38347685 gb:BI180294

Zm.16227.1. A1_at 4.77E-06 0.305 -0.2137 4.017 DB_XREF=gi:14646105 gb:AI621513

Zm.5379.1.S1_at 4.91 E-06 0.3043 0.4236 -3.132 DB_XREF=gi:4630639 gb:BM340967

Zm.17720.1. A1_at 4.93E-06 0.3042 -0.08202 1.488 DB_XREF=gi:18171127 gb:AF142322.1

Zm.588.1.S1_at 5.14E-06 0.3033 0.06464 0.1791 DB_XREF=gi:4927258 gb:BM080835

Zm.18033.1. A1_at 5.17E-06 0.3031 -0.08471 2.06 DB__XREF=gi: 16927766 gb:AF318075.1

Zm.663.1.S1_at 5.22E-06 0.3029 -0.178 3.527 DB_XREF=gi: 14091009 gb:CF634462

Zm.16513.1.A1_at 5.27E-06 0.3027 -0.07343 1.845 DB__XREF=gi:37394377 gb:CK367910

Zm.17307.1.S1_at 5.53E-06 0.3016 0.06901 -0.101 DB_XREF=gi:40333840 gb:AY106357.1

Zm.13719.1.A1_at 5.64E-06 0.3011 -0.04963 1.62 DB_XREF=gi:21209435 gb:AW787466

Zm.1611.1.A1_at 5.7E-06 0.3009 -0.09719 2.327 DB_XREF=gi:7844244 gb:CD434479

Zm.6251.1.A1_at 5.77E-06 0.3006 -0.05725 1.778 DB_XREF=gi:31350122 gb:CF674957

Zm.16854.1. S1_at 6.1 E-06 0.2993 -0.08796 2.166 DB_XREF=gi:37621904 gb:AI612464

Zm.7731.1.A1_at 6.19E-06 0.299 0.0859 -0.1337 DB_XREF=gi:4621631 gb:CF634632

Zm.7074.1.A1_at 6.21 E-06 0.2989 0.09015 -0.1237 DB_XREF=gi:37394712 gb:BM073880

Zm.8376.1.S1_at 6.34E-06 0.2984 -0.07696 1.936 DB_XREF=gi:16918753 gb:CO527469

Zm.14497.8.A1_x_at 6.36E-06 0.2983 0.06997 0.1062 DB_XREF=gi:50332343 gb:AY110683.1

Zm.14590.1. A1_x_at 6.39E-06 0.2982 -0.1306 2.728 DB_XREF=gi:21215273 gb:AF232008.2

Zm.15293.1. S 1_a_at 6.49E-06 0.2978 -0.1162 2.534 DB_XREF=gi:9313026 gb:BM382478

Zm.15282.1. A1_at 6.52E-06 0.2977 -0.1326 2.786 DB_XREF=gi:18181268 gb:AF200528.1

Zm.520.1.S1_at 6.67E-06 0.2972 0.1149 -0.623 DB_XREF=gi:9622879 gb:CD441187

Zm.10553.1. A1 at 6.93E-06 0.2963 -0.2323 4.09 DB_XREF=gi:31356830

Table 22, continued.

gb:AI964613

Zm.3428.1. A1_at 7.38E-06 0.2948 -0.1968 3.706 DB_XREF=gi:5757326 gb:AI974922

ZmAffx.1083.1.A1_at 7.6E-06 0.2942 -0.09468 2.276 DB_XREF=gi:5777303 gb:BG874061

Zm.6997.1.A1_at 7.72E-06 0.2938 0.045 0.4419 DB_XREF=gi: 14245479 gb:CF637893

Zm.16489.1.S1_at 7.76E-06 0.2937 0.06034 0.2686 DB_XREF=gi:37401062 gb:AY104012.1

Zm.5851.3.A1_at 7.91 E-06 0.2932 -0.4542 7.864 DB_XREF=gi:21207090 gb:BM080703

Zm.19019.1.A1_at 8.06E-06 0.2928 -0.06012 1.716 DB_XREF=gi: 16927634 gb:CF627543

Zm.4880.1.S1_at 8.19E-06 0.2924 -0.0599 1.721 DB_XREF=gi:37381330 gb:AY105697.1

Zm.3243.1.A1_at 8.21 E-06 0.2924 0.08508 -0.1167 DB_XREF=gi:21208775 gb:CO526898

Zm.19022.1.S1_at 8.43E-06 0.2917 -0.246 3.664 DB_XREF=gi:50331772 gb:AW424608

Zm.13991.1.S1_at 8.5E-06 0.2915 0.07005 0.1974 DB_XREF=gi:6952540 gb:AY106142.1

Zm.9867.1.A1_at 8.51 E-06 0.2915 0.3098 -3.067 DB_XREF=gi:21209220 gb:AI065715

Zm.6480.2.S1_a_at 8.6E-06 0.2912 0.04572 0.403 DB_XREF=gi:30052426 gb:AY588275.1

Zm.6931.1.S1_a_at 9.14E-06 0.2898 -0.09601 2.355 DB_XREF=gi:46560601 gb:CA402151

Zm.12942.1. A1_at 9.16E-06 0.2898 -0.5247 7.489 DB_XREF=gi:24767006 gb:CD439290

Zm.889.2.S1_at 9.29E-06 0.2894 -0.6597 10.97 DB_XREF=gi:31354933 gb:AY104584.1

Zm.6816.1.A1 at 9.86E-06 0.288 0.0469 0.3894 DB_XREF=gi:21207662

Table 23: Maize Plot Yield Data

Program 1

job ' kondara br-0 heterosis work' output [width=132]l variate [nvalues=22810] Secl,sec2,sec3,sec4,sec5,sec6,sec7,sec8,sec9,\

DK22, DKLD, DKSD, DB22, DBLD, DBSD, DBH22, DBHLD, DBHSD, DKH22, DKHLD, DKHSD, \

HBK22 , HBKLD, HBKSD, KBH22 , KBHLD, KBHSD, D_K22, DJCLD, D_KSD, H22 , HLD, HSD, \ BDK22 , BDKLD, BDKSD, HB22 , HBLD, HBSD, HK22 , HKLD, HKSD, B_K22 , B_KLD, BJCSD, \ r22kb, rldkb, rsdkb, r22bk, rldbk, rsdbk, KHB22, KHBLD, KHBSD, BHK22, BHKLD, BHKSD, \

KDB22 , KDBLD, KDBSD, BDK22 , BDKLD, BDKSD, H22h, HLDh, HSDh, H221 , HLDl, HSDl, A,B,C,\

D_k22,b_kLD,b_kSD, K_H22h, K_HLDh,K_HSDh, B_H22h, B_HLDh, BJiSDh, \ HB221, HBLDl, HBSDl, HB22h, HBLDh, HBSDh, \ HK221, HKLDl, HKSDl, HK22h, HKLDh, HKSDh

variate [values=l ...22810] gene

open 'x: \\daves\\reciprocals\\hk 22k. txt ' ; ch=2 read [ch=2;print=e, s; serial=n] h22, hid, hsd, k22, kid, ksd, b22,bld,bsd close ch=2

" INITIAL SEED FOR RANDOM NUMBER GENERATION scalar int,x,y scalar [value=54321] a & [value=78656]b & [value=17345]c output [width=132]l

" OPEN OUTPUT FILE " open 'x: \\daves\\reciprocals\\hk 22k. out ' ; ch=3; width=132; filetype=o scalar [value=12345] a scalar [value=*]miss scalar [value=l]int

" CALCULATES COMPARISONS FOR THREEOFOLD DIFFERENCES "

& rldkb=kld/bld & rsdkb=ksd/bsd

& r22bk=b22/k22

& rldbk=bld/kld

& rsdbk=bsd/ksd

5 & r22hk=h22/k22 & rldhk=hld/kld & rsdhk=hsd/ksd

]_0

& r22hb=h22/b22

& rldhb=hld/bld

& rsdhb=hsd/bsd 15 for k=l...22810

20 for i=r22hb,rldhb,rsdhb; j=A, B, C;m=b22,bld, bsd;n=h22, hid, hsd; o=HB221, HBLDl, HB SDl;p=HB22h, HBLDh, HBSDh

25 if ( (elem(i;k) .gt.0.5) .and. (elem(i;k) .It.2) ) calc elem(j ; k) =int else calc elem (j ; k) =miss endif 30 calc x=elem(m; k)

& y=elem(n;k)

" LOWEST VALUE OF B OR H " if (y.gt.x) .and. (elem(j;k) .eq.l) calc elem(o;k)=x 35 elsif (x.gt .y) .and. (elem(j ; k) .eq.1) calc elem(o; k) =y else calc elem(o; k) =miss endif 40

HIGHEST VALUE OF B OR H if (x.gt.y) .and. (elem(j;k) .eq.l) calc elem(p; k) =x elsif (y.gt.x) .and. (elem(j;k) .eq.l) 45 calc elem(p;k)=y else calc elem(p; k) =miss endif endfor 5

for

55 i=r22hk,rldhk,rsdhk; j=A,B,C;m=k22, kid, ksd;n=h22,hld, hsd;o=HK221,HKLDl,HK SDl;p=HK22h, HKLDh, HKSDh if ( (elem(i;k) .gt.0.5) .and. (elem{i;k) .It.2) ) calc elem(j ;k) =int else 60 calc elem{j ;k) =miss

endif calc x=elem(m; k) & y=elem(n;k)

5 " LOWEST VALUE OF K OR H " if (x. It . y) . and. (elem(j ; k) ,eq.1) calc eleπι(o; k) =x elsif (y.lt.x) .and. (elem(j;k) .eq.l) calc elem{o;k)=y 10 else calc elem(o; k) =miss endif

" HIGHEST VALUE OF K OR H "

15 if (x.gt.y) .and. (elem(j ;k) .eq.l) calc elem(p; k) =x elsif (y.gt.x) .and. (elem(j;k) .eq.l) calc elem(p;k)=y else 20 calc elem(p; k) =miss endif endfor

for i=r22kb, rldkb, rsdkb; j=A, B, C;m=k22, kid, ksd;n=b22,bld,bsd if ( (elem(i;k) .gt.0.5) .and. (elem(i;k) .It.2) ) calc elem (j ; k) =int 30 else calc elem (j ; k) =miss endif endfor

35 for i=r22kb, rldkb, rsdkb; j=A, B, C;m=k22, kid, ksd;n=b22, bid, bsd;o=B_K22, B_KLD, B_ 40 KSD;p=b_k22,b_kLD,b_kSD calc x=elem(m; k) & y=elem(n; k) if (x.gt.y) calc elem(o;k) =x 45 else calc elem(o;k)=y endif if (x.lt.y) calc elerα(p;k) =x 50 else calc elerα(p;k)=y endif endfor endfor ^' 55

.calc H22h=h22 /B_K22 60 & HLDh=hld/B KLD

3 K AND H > B (BUT K = H)

5 if

(elem(KHB22;k) .gt.2) .and. (elem (KHBLD; k) .gt.2) .and. (elem (KHBSD; k) .gt.2) calc elem(sec3; k) =int else calc elem(sec3; k) =miss 10 AND H > K (BUT B = H) if

15 (elem(BHK22;k) .gt.2) .and. (elem (BHKLD; k) .gt.2) .and. (elem (BHKSD; k) .gt.2) calc elem(sec4 ; k) =int else calc elem(sec4 ; k) =miss 20 > B and H (BUT B = H) if

(elem(KDB22;k) .gt.2) .and. (elem(KDBLD; k) .gt.2) .and. (elem (KDBSD; k) .gt.2) 25 calc elem(sec5; k) =int else calc elem(sec5; k) =miss > K and H (BUT K = H) if (elem(BDK22;k) .gt.2) .and. (elem (BDKLD; k) .gt.2) .and. (elem(BDKSD; k) .gt.2) calc elem(sec6; k) =int 35 else calc elem(sec6; k) =miss endif > B and

40 if

(elem(H22h;k) .gt.2) .and. (elem(HLDh;k) .gt.2) .and. (elem(HSDh; k) .gt.2) calc elem(sec7 ; k) =int else

45 calc elem(sec7 ; k) =miss

50 if

(elem(H221;k) .It.0.5) .and. (elem (HLDl; k) .It.0.5) .and. (elem (HSDl; k) . It .0.5 ) calc elem(sec8; k)=int else 55 calc elem(sec8; k)=miss endif endfor

60

j=Nol , No2 , No3 , No4 , No5 , N06, No7 , N08 ; \ k=Nl,N2,N3,N4,N5,N6,N7,N8;\ l=iϊivl , mv2 , mv3 , mv4 , mv5 , mvβ, mv7 , mvδ calc k=nvalues (i) & l=nmv(i)

& j=k-l endfor print NoI , No2 , No3 , No4 , No5 , No6, No7 , No8 print [ch=3 ; iprint=* ; rlprint=* ; clprint=* ] NoI , No2 , No3 , No4 , No5 , No6, No7 , No8 endfor stop

Program 2

job ' kondara br-0 heterosis work ¹ output [width=132]l variate [nvalues=22810] seel, sec2, sec3, sec4, sec5, sec6, sec7, secδ, sec9, \

DK22 , DKLD, DKSD, DB22 , DBLD, DBSD, DBH22 , DBHLD, DBHSD, DKH22 , DKHLD, DKHSD, \

HBK22 , HBKLD, HBKSD, KBH22 , KBHLD, KBHSD, D_K22, D_KLD, D_KSD, H22, HLD, HSD,

\ BDK22, BDKLD, BDKSD, HB22, HBLD, HBSD, HK22, HKLD, HKSD, B_K22 , B_KLD, B_KSD, \ r22kb, rldkb, rsdkb, r22bk, rldbk, rsdbk, KHB22, KHBLD, KHBSD, BHK22, BHKLD, BHKSD, \

KDB22, KDBLD, KDBSD, BDK22, BDKLD, BDKSD, H22h, HLDh, HSDh, H221, HLDl, HSDl, A,B,C,\ b_k22, b_kLD, b_kSD, K_H22h, K_HLDh, K_HSDh, B_H22h, B_HLDh, B_HSDh, \ HB221, HBLDl, HBSDl, HB22h, HBLDh, HBSDh, \ HK221, HKLDl, HKSDl, HK22h, HKLDh, HKSDh variate [values=l ...22810] gene

open 'x: \\daves\\reciprocals\\hk 22k. txt ' ; ch=2 read [ch=2;print=e, s; serial=n]h22,hld,hsd, k22, kid, ksd,b22,bld,bsd close ch=2

" INITIAL SEED FOR RANDOM NUMBER GENERATION

TT scalar int,x,y scalar [value=54321] a & [value=78656]b & [value=17345] c output [width=132]l

" OPEN OUTPUT FILE " open 'x: \\daves\\reciprocals\\hk 22k. out ' ; ch=3; width=132; filetype=o scalar [value=16598] a scalar [value=*]miss scalar [value=l] int for [ntimes=250] ' "START OF LOOP FOR BOOTSTRAPPING" " RANDOMISES ALL NINE VARIATES " for i=b22,h22,k22,bld,hld,hsd,bsd,kld,ksd;\ j=b22,h22,k22,bld,hld,hsd,bsd,kld,ksd calc a=a+l calc xx=urand(a;22810) calc j=sort(i;xx) endfor "

& rldkb=kld/bld & rsdkb=ksd/bsd

& rldbk=bld/kld & rsdbk=bsd/ksd

& rldhk=hld/kld & rsdhk=hsd/ksd & r22hb=h22/b22 & rldhb=hld/bld & rsdhb=hsd/bsd for k=l...22810

for i=r22hb,rldhb,rsdhb; j=A, B, C;m=b22,bld,bsd;n=h22, hid, hsd; o=HB221, HBLDl, HB SDl;p=HB22h, HBLDh, HBSDh if ( (elem(i;k) .gt.0.5) .and. (elem(i;k) .It.2) ) calc elem (j ; k) =int else calc elerα(j ; k) =miss endif calc x=elem(m; k)

& y=elem(n;k) " LOWEST VALUE OF B OR H " if (y.gt.x) .and. (elem(j ;k) .eq.1) calc elem(o; k) =x elsif (x.gt.y) .and. (elem(j;k) .eq.l) calc elem(o;k)=y else calc elem(o; k) =miss endif

" HIGHEST VALUE OF B OR H " if (x.gt.y) .and. (elem(j;k) .eq.l) calc elem(p;k)=x elsif (y . gt .x) . and. (elem(j ; k) . eq.1) calc elem(p;k)=y else calc elem(p; k) =miss endif endfor for i=r22hk,rldhk,rsdhk; J=A, B, C;m=k22, kid, ksd;n=h22, hld,hsd;o=HK221,HKLDl, HK SDl;p=HK22h, HKLDh, HKSDh if ((elem(i;k) .gt .0.5) .and. (elem(i;k) .It.2) ) calc elem(j ;k)=int else calc elem( j ;k) =miss endif calc x=elem(m; k) & y=elem(n;k) LOWEST VALUE OF K OR H " if (x.lt.y) .and. (elem(j;k) .eq.l) calc elem(o; k) =x

elsif (y.lt.x) .and. (elem(j;k) .eq.l) calc elem(o; k) =y else calc elem(o; k) =miss~ endif

HIGHEST VALUE OF K OR H " if (x.gt.y) .and. (elem(j;k) .eq.l) calc elem (p; k) =x elsif (y.gt.x) .and. (elem(j;k) .eq.l) calc elem(p;k)=y else calc elem(p; k) =miss endif

b22, bld,bsd if ( (elem(i;k) .gt.0.5) .and. (elem(i;k) .It.2) ) calc elem (j ; k) =int else calc elem (j ; k) =miss endif endfor for i=r22kb, rldkb, rsdkb; j=A, B, C;m=k22, kid, ksd;n=b22, bld,bsd; o=B_K22, B_KLD, : KSD;p=b_k22,b_kLD,b__kSD calc x=elem(m;k) & y=elem(n;k) if (x.gt.y) calc elem(o;k)=x else calc elem(o;k)=y endif if (x.lt.y) calc elem(p;k)=x else calc eleπι(p; k) =y endif endfor endfor

calc H22h=h22/B_K22 & HLDh=hld/B_KLD

calc H221=h22/b_k22 & HLDl=hld/b_kLD

& KDBLD=kld/HBLDh & KDBSD=ksd/HBSDh

if

(elem(BDK22;k) .gt.2) .and. (elem (BDKLD; k) .gt.2) .and. (elem(BDKSD; k) .gt.2) calc elem(sec6; k) =int else calc elem(5ec6; k) =miss endif

(elem(H22h;k) .gt.2) .and. (elem (HLDh; k) .gt.2) .and. (elem(HSDh; k) .gt.2) calc elem (sec7 ; k) =int else calc elem (sec7 ; k) =miss endif

(elem(H221;k) .It.0.5) .and. (elem (HLDl; k) .It.0.5) .and. (elem(HSDl; k) . It .0.5 ) calc elem(sec8; k) =int else calc elem(sec8; k) =miss endif endfor for i=secl, Sec2,sec3,sec4,sec5,sec6,sec7,sec8;\ J=NoI ,No2,No3,No4,No5,No6,No7,No8;\ k=Nl,N2,N3,N4,N5,N6,N7,N8;\ I=mvl,mv2 , mv3,mv4,mv5,mv6,mv7 ,mv8 calc k=nvalues(i) & l=nmv(i) & j=k-l endfor print Nol,No2,No3,No4,No5,No6,No7,No8 endfor stop

Program 3

job 'correlation & linear regression analysis of expression data for 30

22k chips hybrid'

" MID PARENT ADVANTAGE " set [diagnostic=fault] unit [32] output [width=132]l open 'x: \\daves\\linreg\\all 32 hybs data . txt ' ; channel=2; width=250 open 'x: \\daves\\linreg\\fprob 32 hybs lin midp. out ' ; channel=3; filetype=o variate values=220.29, 147.22, 242.86, 188.79, 125.42, 97.38, 123.46, 76.92, 104.48, 103.

61,

270.27,200.00,137.50,184.62,127.50,66.10,110.53, 97.50,121.26,138.4 6,63.53,124.56,103.23,108.33,128.74,122.89,94.38,158.14,230. 95,143 .75,248.10,186.21] mpadv scalar [value=45454 ] a for [ntimes=22810] read [ch=2;print=* ; serial=n] exp model exp fit [print=*] mpadv rkeep exp;meandev=resms; tmeandev=totms; tdf=df calc totss=totms*31 "= number of genotypes-1"

& resss=resms*30 "= number of genotypes-2"

& regms= (totss-resss) /1 & regvr=regms /resins & fprob=l-(clf (regvr; 1; 30) ) print [ch=3; iprint=*; squash=y] fprob,df endfor close ch=2 stop

Program 4

job 'correlation & linear regression analysis of expression data for 30 22k chips hybrid' " MID PARENT 'ADVANTAGE " set [diagnostic=fault] unit [32] output [width=132]l open 'x: \\daves\\linreg\\all 32 hybs data . txt ' ; channel=2 ; width=250 open 'x: \\daves\\linreg\\fprob 32 hybs lin midpA boot . out ' ; channel=2 ; filetype=o

& . 'x: \\daves\\linreg\\fprob 32 hybs lin midpB boot . out ' ; channel=3 ; filetype=o

& 'x: \\daves\\linreg\\fprob 32 hybs lin midpC boot . out ' ; channel=4 ; filetype=o

& 'x: \\daves\\linreg\\fprob 32 hybs lin midpD boot . out ' ; channel=5; filetype=o variate values=220.29, 147.22, 242.86, 188.79, 125.42, 97.38, 123.46, 76.92, 104.48, 103. 61,

270.27,200.00,137.50,184.62,127.50,66.10,110.53, 97.50,121.26,138.4 6,63.53,124.56,103.23,108.33,128.74,122.89,94.38,158.14,230. 95,143 .75,248.10,186.21]mpadv scalar [value=89849] a for [ntimes=6000] read [ch=2;print=*; serial=n] exp for [ntimes=1000] calc a=a+l calc y=urand (a; 32)

& pex=sort (exp; y) model pex fit [print=*]mpadv rkeep pex;meandev=resms;tmeandev=totms calc totss=totms*31 "= number of genotypes-1"

& resss=resms*30 "= number of genotypes-2" & regms= (totss-resss) /1

& regvr=regms/resms & fprob=l-(clf (regvr;l;30) ) print [ch=2;iprint=*; squash=yfprob endfor print [ch=2;iprint=*; squash=y] ' : ' endfor for [ntimes=6000] read [ch=2;print=*; serial=n] exp for [ntimes=1000] calc a=a+l calc y=urand(a; 32) & pex=sort (exp; y) model pex fit [print=*]mpadv rkeep pex;meandev=resms;tmeandev=totms calc totss=totms*31 "= number of genotypes-1"

& resss=resms*30 ^' "= number of genotypes-2"

& regms= (totss-resss) /1 & regvr=regms/resins & fprob=l-(clf (regvr; 1; 30) ) print [ch=3; iprint=*; squash=y] fprob endfor print [ch=3; iprint=*; squash=y] ' : ' endfor for [ntimes=6000] read [ch=2;print=*; serial=n] exp for [ntimes=1000] calc a=a+l calc y=urand (a; 32) & pex=sort (exp;y) model pex fit [print=*]mpadv rkeep pex;meandev=resms ; tmeandev=totms calc totss=totms*31 "= number of genotypes-1"

& resss=resms*30 "= number of genotypes-2"

& regms= (totss-resss) /1 & regvr=regrαs/resms & ^■ fprob=l-(clf (regvr;l;30) ) print [ch=4 ; iprint=*; squash=y] fprob endfor print [ch=4; iprint=*; squash=y] ' : ' endfor for [ntimes=4810] read [ch=2 ;print=*; serial=n] exp for [ntimes=1000] calc a=a+l calc y=urand (a; 32) & pex=sort (exp; y) model pex fit [print=*]mpadv rkeep pex;meandev=resms ; tmeandev=totms calc totss=totms*31 "= number of genotypes-1"

& resss=resms*30 "= number of genotypes-2"

& regms= (totss-resss) /1 & regvr=regms/resms & fprob=l- (elf (regvr; 1; 30) ) print [ch=5; iprint=*; squash=y] fprob endfor print [ch=5; iprint=*; squash=y] ' : ' endfor close ch=2 close ch=3 close ch=4 close ch=5 stop

Program 5

job 'BOOTSTRAP of linear regression analysis of expression data for 32 hybrid 22k chips ' " MID PARENT ADVANTAGE open 'x: \\daves\\linreg\\fprob 32 hybs lin midpA boot .out '; channel=2 & 'x: \\daves\\linreg\\fprob 32 hybs lin midpB boot . out '; channel=3 & 'x: \\daves\\linreg\\fprob 32 hybs lin midpC boot . out ' ; channel=4 & ' x: \\daves\\linreg\\fprob 32 hybs lin midpD boot . out '; channel=5 for [ntimes=6000] sort [dir=d] coeff;bootstrap calc pO5minus=elem (bootstrap; 950 ) & pθlminus=elem (bootstrap; 990) & pθθlminus=elem (bootstrap; 999) print [iprint=*; squash=y] p05minus,p01minus, pOOlminus endfor close ch=2 for [ntimes=6000] read [ch=3;print=* ; serial=y] coeff sort [dir=d] coeff;bootstrap calc pO5minus=elem (bootstrap; 950) & pθlminus=elem (bootstrap; 990) & pθθlminus=elem (bootstrap; 999) print [iprint=*; squash=y]p05minus,p01minus, pOOlminus endfor close ch=3 for [ntimes=6000] read [ch=4;print=*; serial=y] coeff sort [dir=d] coeff;bootstrap calc pO5minus=elem (bootstrap; 950) & pθlminus=elem (bootstrap; 990) & pθθlminus=elem (bootstrap; 999) print [iprint=*; squash=y]p05minus,p01minus,p001minus endfor close ch=4 for [ntimes=4810] read [ch=5;print=*; serial=y] coeff sort [dir=d] coeff;bootstrap calc pO5minus=elem (bootstrap; 950) & pθlminus=elem (bootstrap; 990) & p001minus=elem(bootstrap;999) print [ iprint=* ; squash=y] pO5minus , pOlrninus , pOOlminus endfor close ch=5 stop

GenStat Programme l~Basic Regression Programme

job 'Basic Regression Programme'

" ORDER OF ORIGINAL DATA Ag-O Pl Ag-O P2 Ag-O P3 BR-O Pl Br-O P2 Br-O P3 CoI-O Pl Ct-I

Pl Ct-I P2 Ct-I P3 Cvi-O ^p Pl Cvi-0 P2 Cvi-0 P3

Ga-O Pl Gy-O Pl Gy-O P2 Gy-O P3 Kondara Pl Kondara P2 Kondara P3 Mz-O PlMz-O P2 Mz-O P3 Nok-2 Pl

Sorbo Pl Ts-5 Pl Wt-5 Pl msl 1 msl 2 msl 3 msl 4 msl 5 " "DATA ORDER IS OPTIONAL"

" Data Input Files " set [diagnostic=fault] unit [32] "NUMBER OF GENECHIPS" output [width=132]l open 'x: \\daves\\linreg\\all 32 hybs data . txt ' ; channel=2; width=250 "FILE WITH EXPRESSION DATA " open 'x: \\daves\\linreg\\fprob 32 hybs lin midp.out' ;channel=3;filetype=o "OUTPUT FILE" variate [values=220.29, 147.22, 242.86, 188.79, 125.42, 97.38,123.46, 76.92, 104.48, 103.61, 270.27, 200.00, 137.50, 184.62, \

127.50,66.10,110.53,97.50,121.26,138.46, 63.53,124.56,103.23,108.33 ,128.74, 122.89, 94.38, 158.14, \

230.95,143.75,248.10,186.21]mpadv "TRAIT DATA" scalar [value=45454] a for [ntimes=22810] "NUMBER OF GENES" read [ch=2;print=*; serial=n] exp

model exp fit [print=*]mpadv rkeep exp;meandev=resrrts; tmeandev=totms; tdf=df; "est=fd"

"Use to calculate Rsq Slope and Intercept" "scalar intcpt, slope equate [oldform= ! ( 1 , -1 ) ] fd; intcpt & [oldform=! (-1,1) ]fd; slope"

"Regression Model" calc totss=totms*31 "= number of GeneChips -1"

& resss=resms*30 "= number of GeneChips -2" & regms= (totss-resss) /1 & regvr=regms/resms & fprob=l-(clf (regvr;l;30) ) "= number of GeneChips -2" print [ch=3; iprint=* ; squash=y] "resms, totitis, regms, resss, totss, regvr, "fprob, df, " rsq, slope, intcpt" "OUTPUT OPTIONS"

endfor close ch=2 stop

GenStat Programme 2~ Basic Prediction Regression Programme

job 'Basic Prediction Regression Programme' set [diagnostic=fault] unit [33] output [width=250 ] 1 open 'x:\\Heterosis\\daves\\Predict\\MPH septO5\\BPH pred\\maleparhet 0.1% genes.txt' ;channel=2;width=250 "INPUT FILE " open 'x: \\Heterosis\\daves\\Predict\\MPH septO5\\BPH predWmaleparhet 0.1% genes. out ' ; channel=3; filetype=o "OUTPUT FILE " variate

[values=97.70, 97.70,97.70,130.90,130.90,130.90,103.44,103.44,103.44,138. 89, \ 138.89,138.89,96.18,96.18,141.41,141.41,156.36,156.36,145.77 ,145.7 7, 150.80, \

150.80,150.80,282.42,282.42,385.39,385.39,430.10,430.10,4 30.10,205 .71,205.71, \ 205.71]mpadv "TRAIT DATA" scalar [value=68342] a for [ntimes=706] "Number of Genes" read [ch=2;print=*; serial=n] exp model exp fit [print=*]mpadv rkeep exp;meandev=resms; tmeandev=totms; tdf=df calc totss=totms*32 "= number of genotypes-1"

& resss=resms*31 "= number of genotypes-2"

& regms= (totss-resss) /1 & regvr=regms/resins

& fprob=l- (elf (regvr; 1; 31) ) "= number of genotypes-2" predict

[print=*;prediction=bin]mpadv;levels=! (95,105,115,125,135,145,155,165,17 5,185,195,250,350,450 ) "BINS, COVERING RANGE OF DATA" print [ch=3 ; iprint=*; clprint=*; rlprint=* ] bin & [ch=3;iprint=*;clprint=*] ' : '

endfor close ch=2 stop

GenStat Programme 3~ Prediction Extraction Programme

job 'Prediction Extraction Programme '

" MID PARENT ADVANTAGE " set [diagnostic=fault] variate

[values=95,105,115,125,135,145,155,165,175, 185, 195, 250, 350, 450] mpadv "BIN DATA FROM PREDICTION REGRESSION PROGRAMME" variate [values=*]miss scalar [value=0] gene, Estimate

output [width=200]l open 'x: \\Heterosis\\daves\\predict\\MPH septO5\\BPH pred\\KasLLSha

MalepredprobesSept05_0.1%.txt' ;channel=2;width=500 "file with test parent data" open 'x: \\Heterosis\\daves\\Predict\\MPH septO5\\BPH predWmaleparhet 0.1% genes .out' ; channel=3"file with calibration data" calc y=0 & z=l for [ntimes=2118] "Number of test genes X Number of Parents" calc y=y+l if y . eq. z read [ch=3;print=*; serial=n]bin " 11 bins = 11 values" calc z=z+3 "No of test parents" print ' : ' endif read [ch=2;print=*; serial=n] exp model mpadv fit [print=*]bin rkeep mpadv;meandev=resms ; tmeandev=totms ; tdf=df calc totss=totms*10 "= number of genotypes- 1"

& resss=resms*9 "= number of genotypes-

2"

& regms= (totss-resss) /1 & regvr=regms/resms & fprob=l- (elf (regvr;l; 9) ) "= number of genotypes-2" predict [print=*;prediction=estimate]bin; levels=exp "should be scalar == or restricted variate" if (estimate. It.50) "FOR CAPPED PREDICTION, THIS IS THE LOWER CAP" calc Estimate=miss elsif (estimate.gt.455) "FOR CAPPED PREDICTION, THIS IS THE UPPER CAP" calc Estimate=miss else calc Estimate=estimate endif

calc gene=gene+l print [iprint=*;rlprint=*; squash=y] gene, Estimate, estimate

endfor close ch=2 stop

GenStat Programme 4~ Basic Best Predictor Programme

job 'Basic Best Predictor Programme' text [values=B73xB97,CML103,CML228,CML247,CML277,CML322,CML333,CM L52, IL14H, \

Kill,Ky21,M37W,Mol8W _# NC350,NC358,Oh43,P39,Tx303,Tzi8]l "Name of Accessions"

& [values=' chip I ¹ ,,'chip 2']c "Number of Replicates" factor [labels=l] line & [labels=c] chip factor gene open 'X : \\Heterosis\\daves\\Predictive gene id\\prediction data.dat';ch=2 "Input File" read [ch=2;print=*; serial=n] gene, raw, line, chip, actual; frep=l, *, 1, 1, * calc delta=raw-actual & ratio=raw/actual tabulate [class=gene;print=*] delta;means=Delta;nobs=number; var=t3 calc se_delta=sqrt (t3) /sqrt (number) tabulate [class=gene;print=*] ratio;means=Ratio; var=t7 calc se_ratio=sqrt (t7 ) /sqrt (number) print number, Delta, se_delta, Ratio, se_ratio; fieldwidth=20;dec=0, 2,2,3,4 stop

GenStat Programme 5~ Basic Linear Regression Bootstrapping Programme

j ob ' Basic Linear Regression Bootstrapping Programme '

" Data Input Files " set [diagnostic=fault] unit [32] "NUMBER OF GENECHIPS" output [width=132]l open 'x: \\daves\\linreg\\all 32 hybs data. txt ' ; channel=2; width=250 "FILE WITH EXPRESSION DATA " open 'x : \\daves\\linreg\\fprob 32 hybs lin midpA boot. out ' ;channel=2;filetype=o "OUTPUT FILES "

& ' x: WdavesWlinregWfprob 32 hybs lin midpB boot . out ' ; channel=3 ; filetype=o

& 'x: \\daves\\linreg\\fprob 32 hybs lin midpC boot . out ' ; channel=4 ; filetype=o

& 'x: WdavesWlinregWfprob 32 hybs lin midpD boot . out ' ; channel=5 ; filetype=o variate

[values=220.29, 147.22, 242.86, 188.79, 125.42, 97.38, 123.46, 76.92, 104.48, 103

.61, 270.27, 200.00, 137.50, 184.62, \

127.50, 66.10,110.53,97.50,121.26,138.46,63.53,124.56,103.23,108.33 ,128.74, 122.89, 94.38, 158.14, \

230.95,143.75,248.10,186.21]mpadv "TRAIT DATA" scalar [value=89849] a "SEED NUMBER" for [ntimes=6000] "NUMBER OF GENES TO ANALYSE IN THIS SECTION" read [ch=2;print=*; serial=n] exp for [ntimes=1000] "NUMBER OF RANDOMISATIONS" calc a=a+l calc y=urand(a; 32) "NUMBER OF GENECHIPS TO RANDOMISE" & pex=sort (exp; y) model pex fit [print=*]mpadv rkeep pex;meandev=resms ; tmeandev=totms calc totss=totms*31 "= -number of genotypes-1" & resss=resms*30 "= number of genotypes-2"

& regms= (totss-resss) /1 & regvr=regms/resms

& fprob=l- (elf (regvr; 1; 30) ) "= number of genotypes-2"

print

[ch=2;iprint=*; squash=y] "resms, totms, regms, resss, totss, regvr, "fprob endfor

print [ch=2 ; iprint=* ; squash=y] ' : ' endf or for [ ntimes=6000 ] "NUMBER OF GENES TO ANALYSE IN THIS SECTION" read [ ch=2 ; print=* ; serial=n] exp for [ntimes=1000 ] "NUMBER OF RANDOMISATIONS " calc a=a+l calc y=urand(a;32) "NUMBER OF GENECHIPS TO RANDOMISE" & pex=sort (exp; y) model pex fit [print=*]mpadv rkeep pex;meandev=resms ; tmeandev=totms calc totss=totms*31 "= number of genotypes-1"

& resss=resms*30 "= number of genotypes-2"

& regms= (totss-resss) /1 & regvr=regms/resms

& fprob=l- (elf (regvr; 1; 30) ) "= number of genotypes-2"

print

[ch=3; iprint=*;squash=y] "resms, totms, regms, resss, totss, regvr, "fprob endfor print [ch=3; iprint=*; squash=y] ' : '

endfor for [ntimes=6000] "NUMBER OF GENES TO ANALYSE IN THIS SECTION" read [ch=2;print=*; serial=n] exp for [ntimes=1000] "NUMBER OF RANDOMISATIONS" calc a=a+l calc y=urand(a;32) "NUMBER OF GENECHIPS TO RANDOMISE"

& pex=sort (exp;y) model pex fit [print=*]mpadv rkeep pex;meandev=resms; tmeandev=totms calc totss=totms*31 "= number of genotypes-1"

& resss=resms*30 "= number of genotypes-2"

& regms= (totss-resss) /1 & regvr=regms/resms & fprob=l- (elf (regvr; 1; 30) )"= number of genotypes-2"

print

[ch=4 ; iprint=*; squash=y] "resms, totms, regms, resss, totss, regvr, "fprob endfor print [ch=4; iprint=*; squash=y] ' : ' endfor for [ntimes=4810] "NUMBER OF GENES TO ANALYSE IN THIS SECTION" read [ch=2;print=*; serial=n] exp for [ntimes=1000] "NUMBER OF RANDOMISATIONS" calc a=a+l calc y=urand(a;32) "NUMBER OF GENECHIPS TO RANDOMISE" & pex=sort (exp; y) model pex fit [print=*]mpadv rkeep pex;meandev=resms; tmeandev=totms calc totss=totms*31 "= number of genotypes-1"

& resss=resms*30 "= number of genotypes-2"

& regms= (totss-resss) /1 • & regvr=regms/resms

& fprob=l- (elf (regvr; 1; 30) )"= number of genotypes-2"

print [ch=5; iprint=*;squash=y] "resms, totms, regms, resss, totss, regvr, "fprob endfor print [ch=5; iprint=*;squash=y] ' : '

endfor close ch=2 close ch=3 close ch=4 close ch=5 stop

GenStat Programme 6~ Basic Linear Regression Bootstrapping Data Extraction Programme

job 'Basic Linear Regression Bootstrapping Data Extraction Programme ' " DATA INPUT FILES " open 'x: \\daves\\linreg\\fprob 32 hybs lin midpA boot .out ' ;channel=2 "INPUT FILES"

& 'x: \\daves\\linreg\\fprob 32 hybs lin midpB boot. out ';channel=3 & ' x: \\daves\\linreg\\fprob 32 hybs lin midpC boot . out ' ;channel=4 & 'x: \\daves\\linreg\\fprob 32 hybs lin midpD boot . out ' ;channel=5 for [ntimes=6000] "FIRST INPUT FILE NUMBER OF GENES"

read [ch=2;print=*; serial=y] coeff sort [dir=a] coeff/bootstrap calc pO5plus=elem (bootstrap; 50) & p01plus=elem(bootstrap;10) & p001plus=elem(bootstrap; 1)

print [iprint=*; squash=y]p05plus,p01plus,p001plus "Extracts 5, 1 and 0.1% Significance levels" endfor close ch=2 for [ntimes=6000] "SECOND INPUT FILE NUMBER OF GENES"

read [ch=3;print=* _/ serial=y] coeff sort [dir=a] coeff _/ bootstrap calc pO5plus=elem (bootstrap; 50) & p01plus=elem(bootstrap;10)

& p001plus=elem(bootstrap; 1)

print [iprint=*;squash=y]p05ρlus,p01plus,p001plus

endfor close ch=3 for [ntimes=6000] "THIRD INPUT FILE NUMBER OF GENES"

read [ch=4 ;print=*; serial=y] coeff sort [dir=a] coeff/bootstrap

calc p05plus=elem(bootstrap;50)

& pθlplus=elem (bootstrap; 10)

& pθθlplus=elem (bootstrap; 1)

print [iprint=*; squash=y]p05plus,p01plus,p001plus

print

[iprint=*; squash=y] "pO5plus, pθlplus,pθθlplus, "pO5minus, pOlminus, pOOlminu s endfor close ch=4 for [ntimes=4810] "FOURTH INPUT FILE NUMBER OF GENES"

read [ch=5;print=*; serial=y] coeff sort [dir=a] coeff;bootstrap calc pO5plus=elem (bootstrap; 50) & pθlplus=elem (bootstrap; 10) & pθθlplus=elem (bootstrap; 1)

print [iprint=* ; squash=y] pO5plus, pθlplus,pθθlplus

endfor close ch=5 stop

GenStat Programme 7~ Basic Transcriptome Reiuodelling Programme

job 'Basic Transcriptome Remodelling Programme ' output [width=132]l variate [nvalues=22810] seel, sec2, sec3, sec4, sec5, secβ, sec7, sec8, sec9, \

DK22, DKLD, DKSD, DB22, DBLD, DBSD, DBH22, DBHLD, DBHSD, DKH22, DKHLD, DKHSD, \

HBK22, HBKLD, HBKSD, KBH22, KBHLD, KBHSD, D_K22 , D_KLD, D_KSD, H22, HLD, HSD, \

BDK22 , BDKLD, BDKSD, HB22 , HBLD, HBSD, HK22 , HKLD, HKSD, B__K22 , B_KLD, BJKSD, \ r22kb, rldkb, rsdkb, r22bk, rldbk, rsdbk, KHB22, KHBLD, KHBSD, BHK22, BHKLD, BHKSD, \ KDB22 , KDBLD, KDBSD, BDK22 , BDKLD, BDKSD, H22h, HLDh, HSDh, H221, HLDl, HSDl, A,B,C,\ b_k22 , b_kLD, b_kSD, K_H22h, K_HLDh, K_HSDh, B_H22h, B_HLDh, B_HSDh, \ HB221, HBLDl, HBSDl, HB22h, HBLDh, HBSDh, \

HK221, HKLDl, HKSDl, HK22h, HKLDh, HKSDh "FILE IDENTIFIERS-IGNORE"

variate [values=l ...22810] gene

open 'x:\\daves\\reciprocals\\hb 22k. txt ' ; ch=2 "INPUT FILE" read [ch=2;print=e, s; serial=n] h22, hid, hsd, k22, kid, ksd, b22,bld, bsd close ch=2

" INITIAL SEED FOR RANDOM NUMBER GENERATION

Ir scalar int,x,y scalar [value=54321] a & [value=78656]b

& [value=17345]c output [width=132]l " OPEN OUTPUT FILE open 'x: \\daves\\reciprocals\\hk 22k. out ' ;ch=3; width=132; filetype=o "OUTPUT FILE" scalar [value=12345] a scalar [value=*]miss scalar [value=l]int

" CALCULATES COMPARISONS FOR THREEOFOLD DIFFERENCES *****************************" calc r22kb=k22/b22

& rldkb=kld/bld & rsdkb=ksd/bsd

& r22bk=b22/k22 & rldbk=bld/kld

& rsdbk=bsd/ksd

& r22hk=h22/k22

& rldhk=hld/kld

& rsdhk=hsd/ksd

& r22hb=h22/b22

& rldhb=hld/bld & rsdhb=hsd/bsd

for k=l...22810

for i=r22hb, rldhb, rsdhb; j=A, B, C;m=b22, bid, bsd; n=h22 , hid, hsd; o=HB221, HBLDl, HB SDl;p=HB22h, HBLDh, HBSDh if ( (elem(i;k) .gt.0.5) .and. (elem(i;k) .It.2) ) "SETS FOLD LEVELS" calc elem( j ; k) =int else calc elem ( j ; k) =miss endif calc x=elem(in; k)

& y=elem(n; k) " LOWEST VALUE OF B OR H if (y.gt.x) .and. (elem(j ;k) .eq.1) calc elem(o; k) =x elsif (x.gt.y) .and. (elem(j;k) .eq.l) calc elem(o; k) =y else calc elem (o ; k) =itιiss endif

" HIGHEST VALUE OF B OR H " if (x.gt.y) .and. (elem(j;k) .eq.l) calc elem(p;k)=x elsif (y.gt.x) .and. (elem(j;k) .eq.l) calc elem(p; k) =y else calc elem(p; k) =miss endif endfor

for i=r22hk, rldhk, rsdhk; j=A, B, C;m=k22 , kid, ksd; n=h22, hid, hsd; o=HK221, HKLDl, HK SDl;p=HK22h, HKLDh, HKSDh if ( (elem(i;k) .gt.0.5) .and. (elem(i;k) .It.2) ) calc elem(j ; k) =int else calc elem( j ; k) =miss endif calc x=elem(m; k) ^' & y=elem(n; k)

" LOWEST VALUE OF K OR H " if (x. It .y) .and. (elerα(j ; k) . eq.1) calc elem(o; k) =x elsif (y . It .x) . and. (elem (j ; k) . eq.1) calc elem(o;k)=y else calc elem (o; k) =miss endif

" HIGHEST VALUE OF K OR H " if (x.gt.y) .and. (elem(j;k) .eq.l) calc elem(p;k)=x elsif (y.gt.x) .and. (elem(j ; k) .eq.l) calc elem(p;k)=y else calc elem(p;k) =miss endif endfor

K = B (within 2)

'for i=r22kb,rldkb,rsdkb; j=A, B, C;m=k22, kid, ksd;n=b22, bid, bsd if ( (elem(i;k) .gt.0.5) .and. (elem(i;k) .It.2) ) calc elem(j ;k) =int else calc elem(j ; k) =miss endif endfor

for i=r22kb,rldkb,rsdkb; j=A, B, C;m=k22, kid, ksd;n=b22,bld,bsd;o=B_K22,B_KLD, B_ KSD;p=b_k22,b_kLD,b_kSD ^' calc x=elem(m;k) & y=elem(n;k) if (x.gt.y) calc elem(o; k) =x else calc elem(o;k)=y endif if (x.lt.y) calc elem(p;k)=χ else calc elem(p;k)=y

endif endfor endfor ratio of H : (K = B) high calc H22h=h22/B_K22 & HLDh=hld/B_KLD & HSDh=hsd/B KSD ratio of H : (K = B) low calc H221=h22/b_k22 & HLDl=hld/b__kLD & HSDl=hsd/b kSD tio of K : (B = H) calc KDB22=k22/HB22h & KDBLD=kld/HBLDh & KDBSD=ksd/HBSDh ratio of B (K = H) calc BDK22=b22/HK22h & BDKLD=bld/HKLDh

& BDKSD=bsd/HKSDh ratio of (K = H - low values) : calc KHB22=HK221/b22 & KHBLD=HKLDI/bid & KHBSD=HKSDl/bsd ratio of (B = H) : K calc BHK22=HB221/k22

& BHKLD=HBLDl/kld & BHKSD=HBSDl/ksd

for k=l...22810

if

(elem(r22kb;k) .gt.2) .and. (elem(rldkb; k) .gt.2) .and. (elem(rsdkb; k) .gt.2) calc elem(secl; k)=int else calc elem(secl; k)=miss endif

"*********************** SEC 2 BR— 0>K

if

(elem(r22bk;k) .gt.2) .and. (elem(rldbk; k) .gt.2) .and. (elem(rsdbk; k) .gt.2) calc elem (sec2; k) =int else calc elem (sec2; k) =miss endif π*********************** g _EC 3 K ^ND H > B (BUT K = H)

****************** _* if

(elem(KHB22;k) .gt.2) .and. (elem (KHBLD; k) .gt.2) .and. (elem (KHBSD; k) .gt.2) calc elem (sec3; k) =int else calc elem (sec3; k) =miss endif

SEC 4 B AND H > K (BUT B = H) t****************" if

(elem(BHK22;k) .gt.2) . and..(elem(BHKLD; k) .gt.2) .and. (elem (BHKSD; k) .gt.2) calc elem (sec4 ; k) =int else ' calc elem (sec4 ; k) =miss endif

if

(elem(KDB22;k) .gt.2) .and. (elem(KDBLD; k) .gt.2) .and. (elem(KDBSD; k) .gt.2) calc elem (sec5; k) =int else calc elem(sec5; k) =miss endif

SEC 6 B > K and H (BUT K = H)

if

(elem(BDK22;k) .gt.2) .and. (elem (BDKLD; k) .gt.2) .and. (elem(BDKSD; k) .gt.2) calc elem(sec6; k) =int else calc elem(sec6; k)=miss endif

SEC 7 H > B and K

if (elem(H22h;k) .gt.2) .and. (elem(HLDh; k) .gt.2) .and. (elem (HSDh; k) .gt.2) calc elem(sec7; k) =int else

calc elem(sec7 ; k) =miss endif

if

(eleπι(H221;k) .It.0.5) .and. (elem(HLDl; k) .It.0.5) .and. (elem (HSDl; k) .It.0.5 ) calc elem(sec8 ; k) =int else calc elem(sec8; k) =miss endif endfor

print gene, seel, sec2, sec3, sec4, sec5, secβ, sec7, sec8 for i=secl,sec2,sec3,sec4,sec5,sec6,sec7 _f sec8;\ j=Nol , No2 , No3, No4 , No5 , No6, No7 , No8 ; \ k=Nl , N2 , N3 , N4 , N5 , N6 , N7 , N8 ; \ l=mvl , mv2 , mv3 , mv4 , mv5 , rnvβ, mv7 ,mv8 calc k=nvalues(i) & l=nmv(i) & j=k-l endfor print Nol,No2,No3,No4,No5,No6, No7,No8

stop

GenStat Programme 8~ Dominance Pattern Programme

job 'Dominance Pattern Programme' scalar AGlM, AGl, AG2M,AG2,AG3M, AG3, CTlM, CTl, CT2M, CT2, CT3M, CT3, \ CVlM, CVl , CV2M, CV2 , CV3M, CV3, GYlM, GYl , GY2M, GY2 , GY3M, GY3 , KlM, \ Kl, K2M,K2,K3M,K3, MZlM, MZl, MZ2M, MZ2 , MZ3M, MZ3, BKlM, BKl, BK2M, \ BK2 , BK3M, BK3, KBlM, KBl , KB2M, KB2 , KB3M, KB3 "genotypes names /bins for calculations" scalar [value=48]a "starting value for equate directive"

& [value=12345] seed "seed value for randomisation"

& [value=*]miss "missing value"

& [value=0] AGEQ, AGGT, AGLT, CTEQ, CTGT, CTLT, CVEQ, CVGT, CVLT, GYEQ, GYGT, GYLT, \

KEQ, KGT, KLT, MZEQ, MZGT, MZLT, BKEQ, BKGT, BKLT, KBEQ, KBGT, KBLT "scalars for total signifiant genes"

variate [nvalues=48] gene

& [nvalues=22810] AG, CT, CV, GY, K, MZ, BK, KB & [nvalues=3] eqAG, gtAG, ItAG, eqCT, gtCT, ItCT, eqCV, gtCV, ItCV, eqGY, gtGY, ItGY, \ eqK, gtK, ItK, eqMZ, gtMZ, ItMZ, eqBK, gtBK, ItBK, eqKB, gtKB, ItKB output [width=400]l

" OPEN OUTPUT FILE open 'x: \\daves\\Dominance methodWdom 2 fσld.out';ch=3;width=300;filetype=o "OUTPUT FILE" open ' x: \\daves\\Dominance method\\Expression datab.txt ' ; ch=2; width=500 "INPUT FILE" read [ch=2;print=e, s;serial=n] EXP close ch=2 for i=l...22810 "reads through data gene by gene" calc a=a-48 "incremnets data" equate [oldformat=! (a, 48) ] EXP;gene "puts data in one variate per gene"

"randomises variate for subsequent calculations calc nege=rand (gene; seed) "

"places data for 1 gene at a time into variate bins" for geno=AGlM, AGl , AG2M, AG2 , AG3M, AG3 , CTlM, CTl , CT2M, CT2 , CT3M, CT3 , CVlM, CVl, CV2M ,CV2,CV3M,CV3,\

GYlM, GYl, GY2M,GY2,GY3M,GY3, KlM, Kl, K2M,K2,K3M,K3, MZlM, MZl, MZ2M,MZ2, MZ3M, MZ3, BKlM, BKl, \

BK2M, BK2 , BK3M, BK3 , KBlM, KBl, KB2M, KB2 , KB3M, KB3 ; \ j=l...48 calc geno=elem(gene; j) endfor

"calculation of ratios" for genom=AGlM, AG2M, AG3M, CTlM, CT2M, CT3M, CVlM, CV2M, CV3M, GYlM, GY2M, GY3M, KlM, \ K2M, K3M, MZlM, MZ2M, MZ3M, BKlM, BK2M, BK3M, KBlM, KB2M, KB3M; \ genoh=AGl , AG2 , AG3 , CTl , CT2 , CT3 , CVl , CV2 , CV3 , GYl , GY2, GY3 , \ Kl , K2 , K3, MZl , MZ2 , MZ3, BKl , BK2 , BK3 , KBl , KB2 , KB3 ;\ ratio=rAGl , rAG2 , rAG3 , rCTl , rCT2 , rCT3, rCVl , rCV2 , rCV3 , rGYl , rGY2 , rGY3 , \ rKl , rK2 , rK3 , rMZl , rMZ2 , rMZ3 , rBKl , rBK2 , rBK3 , rKBl , rKB2 , rKB3 ; \ hEQmp=eqAG, eqAG, eqAG, eqCT, eqCT, eqCT, eqCV, eqCV, eqCV, eqGY, eqGY, eqGY, \ eqK, eqK, eqK, eqMZ, eqMZ, eqMZ, eqBK, eqBK, eqBK, eqKB, eqKB, eqKB; \ hGTmp=gtAG, gtAG, gtAG, gtCT, gtCT, gtCT, gtCV, gtCV, gtCV, gtGY, gtGY, gtGY, \ gtK, gtK, gtK, gtMZ, gtMZ, gtMZ, gtBK, gtBK, gtBK, gtKB, gtKB, gtKB; \ hLTmp=ltAG, ItAG, ItAG, ItCT, ItCT, ItCT, ItCV, ItCV, ItCV, ItGY, ItGY, ItGY, \

ItK, ItK, ItK, ItMZ, ItMZ, ItMZ, ItBK, ItBK, ItBK, ItKB, ItKB, ItKB; \ k=l, 2, 3, 1,2, 3, 1,2, 3, 1,2, 3, 1,2, 3, 1,2, 3, 1,2, 3, 1,2, 3 calc ratio=genoh/genom "calculates ratios" calc heqmp=miss

& hgtπvp=miss "sets default flag values"

& hltmp=miss if (ratio. ge.0.5) .and. (ratio. Ie.2) "SETS FOLD LEVEL" calc heqmp=l elsif (ratio. gt.2) "SETS UPPER FOLD LEVEL" calc hgtmp=l elsif (ratio. It.0.5) "SETS LOWER FOLD LEVEL" calc hltmp=l else calc heqmp=miss

& hgtmp=miss & hltmp=miss endif calc elem(hEQmp; k)=heqmp

& elem(hGTmρ; k) =hgtmp & elem(hLTmρ; k) =hltmp endfor for

X=eqAG, gtAG, ItAG, eqCT, gtCT, ItCT, eqCV, gtCV, ItCV, eqGY, gtGY, ItGY, \ eqK, gtK, ItK, eqMZ, gtMZ, ItMZ, eqBK, gtBK, ItBK, eqKB, gtKB, ItKB; \ Y=AGeq, AGgt, AGIt, CTeq, CTgt, CTIt, CVeq, CVgt, CVIt, GYeq, GYgt, GYIt, \ Keq, Kgt, Kit , MZeq, MZgt, MZIt, BKeq, BKgt, BKIt , KBeq, KBgt, KBIt; \

Z=AGEQ, AGGT, AGLT, CTEQ, CTGT, CTLT, CVEQ, CVGT , CVLT, GYEQ, GYGT, GYLT, \

KEQ, KGT, KLT, MZEQ, MZGT, MZLT, BKEQ, BKGT, BKLT, KBEQ, KBGT, KBLT calc Y=sura(X) if Y.eq.3 calc Y=I else

calc Y=O endif calc Z=Z+Y endfor print

[ch=3;iprint=*;squash=y]AGeq,AGgt, AGIt, CTeq,CTgt, CTIt, CVeq,CVgt, CVIt, GYe q,GYgt,GYlt,\ Keq, Kgt, Klt,MZeq, MZgt, MZIt, BKeq, BKgt, BKIt, KBeq, KBgt, KBIt; fieldwidt h=8;dec=0 endfor

stop

GenStat Programme 9~ Dominance Permutation Programme

job 'Dominance Permutation Programme' scalar AGlM, AGl , AG2M, AG2 , AG3M, AG3, CTlM, CTl , CT2M, CT2 , CT3M, CT3 , \ CVlM, CVl, CV2M, CV2 , CV3M, CV3, GYlM, GYl , GY2M, GY2 , GY3M, GY3, KlM, \ Kl, K2M,K2,K3M,K3, MZlM, MZl, MZ2M, MZ2 , MZ3M, MZ3, BKlM, BKl, BK2M, \ BK2, BK3M, BK3, KBlM, KBl , KB2M, KB2 , KB3M, KB3 "genotypes names/bins for calculations" scalar [value=48]a "starting value for equate directive"

& [value=12345] seed "seed value for randomisation"

&

[value=0] AGEQ, AGGT, AGLT, CTEQ, CTGT, CTLT, CVEQ, CVGT, CVLT, GYEQ, GYGT, GYLT, \ KEQ, KGT, KLT, MZEQ, MZGT, MZLT, BKEQ, BKGT, BKLT, KBEQ, KBGT, KBLT

"scalars for total signifiant genes"

variate [nvalues=48] gene & [nvalues=22810]AG,CT,CV,GY,K,MZ,BK,KB

&

[nvalues=3] eqAG, gtAG, ItAG, eqCT, gtCT, ItCT, eqCV, gtCV, ItCV, eqGY, gtGY, ItGY, \ eqK, gtK, ItK, eqMZ, gtMZ, ItMZ, eqBK, gtBK, ItBK, eqKB, gtKB, ItKB output [width=400]l

" OPEN OUTPUT FILE open 'x: \\daves\\Dominance methodWdomperm.out' ; ch=3; width=300; filetype=o "OUTPUT FILE" open 'x: \\daves\\Dominance method\\Expression datab . txt ' ; ch=2; width=500 "INPUT FILE" read [ch=2;print=e, s; serial=n] EXP close ch=2 for [ntimes=1000] "NUMBER OF

PERMUTATIONS" calc seed=seed+l for [ntimes=22810] "NUMBER OF GENES"

calc a=a-48 equate [oldformat=! (a, 48) ] EXP; gene "puts data in one variate per gene"

"randomises variate for subsequent calculations" calc y=urand (seed; 48)

& nege=sort (gene;y)

"places data for 1 gene at a time into variate bins" for geno=AGlM, AGl , AG2M, AG2 , AG3M, AG3 , CTlM, CTl , CT2M, CT2 , CT3M, CT3, CVlM, CVl , CV2M ,CV2,CV3M,CV3,\

GYlM, GYl, GY2M,GY2,GY3M,GY3, KlM, Kl, K2M, K2 , K3M, K3, MZlM, MZl, MZ2M,MZ2, MZ3M,MZ3, BKlM, BKl, \

BK2M, BK2 , BK3M, BK3, KBlM, KBl, KB2M, KB2, KB3M, KB3; \ j=l ...48 calc geno=elem (nege; j ) endfor

******** _*

"calculation of ratios" for genoπι=AGlM, AG2M, AG3M, CTlM, CT2M, CT3M, CVlM, CV2M, CV3M, GYlM, GY2M, GY3M, KlM, \

K2M, K3M, MZlM, MZ2M, MZ3M, BKlM, BK2M, BK3M, KBlM, KB2M, KB3M; \ genoh=AGl , AG2 , AG3, CTl , CT2 , CT3, CVl, CV2 , CV3, GYl, GY2 , GY3, \

Kl, K2 , K3 , MZl , MZ2 , MZ3 , BKl , BK2 , BK3 , KBl , KB2 , KB3 ; \ ratio=rAGl, rAG2 , rAG3, rCTl, rCT2, rCT3, rCVl, rCV2 , rCV3, rGYl, rGY2, rGY3, \ rKl , rK2 , rK3 , rMZl , rMZ2 , rMZ3, rBKl , rBK2 , rBK3 , rKBl , rKB2 , rKB3 ; \ hEQmp=eqAG, eqAG,eqAG, eqCT, eqCT,eqCT, eqCV, eqCV, eqCV, eqGY, eqGY, eqGY, \ eqK,eqK, eqK,eqMZ, eqMZ, eqMZ, eqBK, eqBK, eqBK,eqKB, eqKB, eqKB; \ hGTmp=gtAG, gtAG, gtAG, gtCT, gtCT, gtCT, gtCV, gtCV, gtCV, gtGY, gtGY, gtGY, \ gtK, gtK, gtK, gtMZ, gtMZ, gtMZ, gtBK, gtBK, gtBK, gtKB, gtKB, gtKB; \ hLTmp=ltAG, ItAG, ItAG, ItCT, ItCT, ItCT, ItCV, ItCV, ItCV, ItGY, ItGY, ItGY, \ ItK, ItK, ItK, ItMZ, ItMZ, ItMZ, ItBK, ItBK, ItBK, ItKB, ItKB, ItKB; \ k=l, 2, 3, 1,2, 3, 1,2, 3, 1,2, 3, 1,2, 3, 1,2, 3, 1,2, 3, 1,2, 3 calc ratio=genoh/genom "calculates ratios" calc heqmp=0 & hgtmp=0 . "sets default flag values"

& hltmp=0 if (ratio. Ie.2.0) .and. (ratio. ge.0.5) "SETS FOLD LEVEL" calc heqmp=l elsif (ratio. gt.2.0) "SETS UPPER

FOLD LEVEL" calc hgtmp=l elsif (ratio. It.0.5) "SETS LOWER

FOLD LEVEL" calc hltmp=l else calc heqmp=0 & hgtmp=0

& hltmp=0 endif calc elem(hEQmp; k) =heqmp & elem(hGTmp; k) =hgtmp

& elem (hLTmp ; k) =hltmp endfor for X=eqAG, gtAG, ItAG, eqCT, gtCT, ItCT, eqCV, gtCV, ItCV, eqGY, gtGY, ItGY, \ eqK, gtK, ItK, eqMZ, gtMZ, ItMZ, eqBK, gtBK, ItBK, eqKB, gtKB, ItKB; \

Y=AGeq,AGgt, AGIt, CTeq,CTgt, CTIt, CVeq,CVgt, CVIt, GYeq,GYgt, GYIt, \

Keq, Kgt , Kit, MZeq, MZgt, MZIt, BKeq, BKgt , BKIt, KBeq, KBgt, KBIt; \

Z=AGEQ, AGGT , AGLT, CTEQ, CTGT, CTLT, CVEQ, CVGT, CVLT, GYEQ, GYGT, GYLT, \ KEQ, KGT, KLT, MZEQ, MZGT, MZLT, BKEQ, BKGT, BKLT, KBEQ, KBGT, KBLT calc Y=sum(X) if Y.eq.3 calc Y=I else calc Y=O endif calc Z=Z+Y endfor endfor print

[ch=3; iprint=*; squash=y] AGEQ, AGGT, AGLT, CTEQ, CTGT, CTLT, CVEQ, CVGT, CVLT, GYE Q, GYGT, GYLT, \

KEQ, KGT , KLT , MZEQ, MZGT, MZLT, BKEQ, BKGT, BKLT, KBEQ, KBGT, KBLT; fieldwidt h=8;dec=0 for list=AGEQ, AGGT, AGLT, CTEQ, CTGT, CTLT, CVEQ, CVGT, CVLT, GYEQ, GYGT, GYLT, \ KEQ, KGT, KLT, MZEQ, MZGT, MZLT, BKEQ, BKGT, BKLT, KBEQ, KBGT, KBLT calc list=0 endfor endfor stop

GenStat Programme 10~ Transcriptome Remodelling Bootstrap Programme

job 'Transcriptome Remodelling Bootstrap Programme' output [width=132]l variate [nvalues=22810] Secl,sec2,sec3,sec4,sec5,sec6,sec7,sec8,sec9,\

DK22 , DKLD, DKSD, DB22 , DBLD, DBSD, DBH22 , DBHLD, DBHSD, DKH22 , DKHLD, DKHSD,

\ HBK22 , HBKLD, HBKSD, KBH22 , KBHLD, KBHSD, D_K22 , D_KLD, D_KSD, H22 , HLD, HSD, \

BDK22 , BDKLD, BDKSD, HB22 , HBLD, HBSD, HK22 , HKLD, HKSD, B_K22, B_KLD, BJfCSD, \ r22kb, rldkb, rsdkb, r22bk, rldbk, rsdbk, KHB22, KHBLD, KHBSD, BHK22, BHKLD, BHKSD, \

KDB22, KDBLD, KDBSD, BDK22, BDKLD, BDKSD, H22h, HLDh, HSDh, H221, HLDl, HSDl, A,B,C,\ b_k22, b_kLD, b_kSD, K_H22h, K_HLDh, K_HSDh, B_H22h, B_HLDh, B_HSDh, \ HB221, HBLDl, HBSDl, HB22h, HBLDh, HBSDh, \ HK221, HKLDl, HKSDl, HK22h, HKLDh, HKSDh "FILE IDENTIFIERS-IGNORE"

variate [values=l ...22810] gene

open 'x:\\daves\\reciprocals\\hb 22k. txt ' ; ch=2 "INPUT FILE" read [ch=2;print=e, s ; serial=n] h22, hid, hsd, k22, kid, ksd,b22, bid, bsd close ch=2

" INITIAL SEED FOR RANDOM NUMBER GENERATION scalar int,x,y scalar [value=54321] a

& [value=78656]b

& [value=17345]c output [width=132]l

" OPEN OUTPUT FILE π open 'x: \\daves\\reciprocals\\hb 22k. out ' ; ch=3; width=132; filetype=o "OUTPUT FILE" scalar [value=17589] a scalar [value=*]miss scalar [value=l]int

"START OF LOOP FOR BOOTSTRAPPING" for [ntimes=1000] "NUMBER OF RANDOMISATIONS"

" RANDOMISES ALL NINE VARIATES " for i=b22,h22,k22,bld,hld,hsd,bsd,kld,ksd;\ j=b22,h22,k22,bld,hld,hsd,bsd,kld,ksd

calc a=a+l calc xx=urand ( a ; 22810 ) "NUMBER OF GENES " calc j =s ort ( i ; xx ) endfor

" CALCULATES COMPARI SONS FOR THREEOFOLD DI FFERENCES

& rldkb=kld/bld & rsdkb=ksd/bsd

& r22bk=b22/k22 & rldbk=bld/kld & rsdbk=bsd/ksd

& r22hk=h22/k22 & rldhk=hld/kld & rsdhk=hsd/ksd

& r22hb=h22/b22 & rldhb=hld/bld & rsdhb=hsd/bsd

for k=l...22810

for i=r22hb,rldhb,rsdhb; j=A, B, C;m=b22,bld,bsd; n=h22, hid, hsd; o=HB221, HBLDl, HB SDl;p=HB22h, HBLDh, HBSDh if ( (elem(i;k) .gt.0.5) .and. (elem(i;k) .It.2) ) "SETS FOLD

LEVELS" calc elem(j ;k) =int else calc elem(j ;k) =miss endif calc x=elem(m;k)

& y=elem(n;k)

LOWEST VALUE OF B OR H " if (y.gt.x) .and. (elem(j;k) .eq.l) calc elem(o;k) =x elsif (x.gt.y) .and. (elem(j;k) .eq.l) calc elem(o;k) =y else calc elem(o;k)=miss

endif

" HIGHEST VALUE OF B OR H " if (x.gt.y) .and. (elem(j;k) .eq.l) calc elem(p;k)=x elsif (y.gt.x) .and. (elem(j;k) .eq.l) calc elem(p;k)=y else calc elem(p; k) =miss endif endfor

for i=r22hk, rldhk, rsdhk; j=A, B, C;m=k22, kid, ksd;n=h22, hid, hsd; o=HK221, HKLDl, HK SDl;p=HK22h, HKLDh, HKSDh if ( (elem(i;k) . gt.0.5) .and. (elem(i;k) .It.2) ) calc elem(j ; k) =int else calc elem(j ; k) =miss endif calc x=elem (m; k) & y=elem(n;k)

" LOWEST VALUE OF K OR H " if (x.lt.y) .and. (eleiti ( j ; k) .eq.l) calc elem(o;k)=x elsif (y. It .x) . and. (elem(j ; k) . eq.1) calc elem(o; k) =y else calc elem(o; k) =miss endif

" HIGHEST VALUE OF K OR H " if (x.gt.y) .and. (elem(j;k) .eq.l) calc elem(p; k) =x elsif (y.gt.x) .and. (elem(j;k) .eq.l) calc elem(p;k)=y else calc elem(p; k) =miss endif endfor

for i=r22kb,rldkb,rsdkb; j=A, B, C;m=k22, kid, ksd;n=b22, bld,bsd if ( (elem(i;k) .gt.0.5) .and. (elem(i;k) .lt.2) ) calc elem(j ; k) =int else calc elem(j ; k) =miss endif endfor

& BHKSD=HBSDl/ksd

for k=l...22810

if

(elem(r22kb;k) .gt.2) .and. (elem(rldkb; k) .gt.2) .and. (elem(rsdkb; k) .gt.2) calc elem (seel; k) =int else calc elem (seel; k) =miss endif

if

(elem(r22bk;k) .gt.2) .and. (elem(rldbk; k) .gt.2) .and. (elem(rsdbk;k) .gt.2) calc elem(sec2; k) =int else calc elem(sec2; k) =miss endif

if

(elem(KHB22;k) .gt.2) .and. (elem (KHBLD; k) .gt.2) .and. (elem(KHBSD; k) .gt.2) calc elem(sec3; k) =int else calc elem(sec3; k) =miss endif

if

(elem(BHK22;k) .gt.2) .and. (elem (BHKLD; k) .gt.2) .and. (elem(BHKSD; k) .gt.2) calc elem(sec4;k) =int else calc elem(sec4 ; k) =miss endif

if

(elem(KDB22;k) .gt.2) .and. (elem(KDBLD; k) .gt.2) .and. (elem (KDBSD; k) .gt.2) calc elem(sec5; k) =int else calc elem(sec5;k) =miss endif

if

(elem(BDK22;k) .gt.2) .and. (elem (BDKLD; k) .gt.2) .and. (elem (BDKSD; k) .gt.2) calc elem(sec6; k) =int else calc elem(secβ; k) =miss endif

SEC 7 H > B and K

if

(elem(H22h;k) .gt.2) .and. (elem (HLDh; k) .gt.2) .and. (elem(HSDh; k) .gt.2) calc elem(sec7 ; k) =int else calc elem(sec7; k) =miss endif

"*********************** SEC 8 H < B and K

if (elem(H221;k) .It.0.5) .and. ( elem (HLDl ; k) .It.0.5) .and. (elem(HSDl; k) .It.0.5 ) calc elem(sec8; k) =int else calc elem(sec8; k) =miss endif endfor

"print gene, Secl,sec2,sec3,sec4,sec5,sec6,sec7,sec8" for i=secl, sec2,sec3,sec4, sec5, sec6, sec7, sec8; \

J=NoI , No2 , No3 , No4 , No5 , No6, No7 , No8 ; \ k=Nl,N2,N3,N4,N5,N6,N7,N8;\ l=mvl, mv2 , mv3 , mv4 , mv5 , mv6, mv7 , mvδ calc k=nvalues (i) & l=nmv(i) & j=k-l endfor print NoI , No2 , No3 , No4 , No5 , No6, No7 , No8

endfor stop

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