The present application is concerned with methods for interrogating mixtures of nucleic acids through amplification of short tandem repeat markers (loci) within each nucleic acid, and thereby analysis of the amounts of each allele amplified from each marker, and in particular interrogating mixtures of DNA, such as forensic (trace) samples, to identify the most probable number of contributors of nucleic acid in the mixture, the most probable ratio/proportion of the nucleic acids in the mixture, and thereby the most probable nucleic acid sequence for each marker within a nucleic acid.

Forensic DNA analysis was first developed in about 1985. The development of methods utilising short tahdem repeat markers (loci) began in the early 1990s. An STR locus is a length polymorphism where alleles have different numbers of short DNA units (typically four or five base pairs) that are repeated in tandem. An allele is one of two or more versions of a gene. An individual inherits two alleles for each gene, one from each parent. If the two alleles are the same, the individual is homozygous for that gene. If the alleles are different, the individual is heterozygous. When a polymorphic locus has 15 or more possible alleles it provides for over a hundred possible genotype values, and thus is useful for distinguishing between people in a population.

The analysis of complex DNA mixtures, particularly those containing several DNA profiles remains a challenge, with statistical and mathematical models within software being used to improve the sensitivity and accuracy of analysis.

Generally, the presence of multiple contributors is identified through maximum allele count, based on each person having two alleles per marker (locus), and thus the identification of a maximum of four alleles for any one marker would be indicative of a mixture of DNA from two contributors. This is however based on a potentially dangerous assumption, as the minimum number of alleles for any . mixture is one, since each contributor could potentially have two copies of the same allele. Thus, when≤ 2 alleles are observed at any locus, a sample may still present a DNA mixture. Moreover, mixtures are still currently interpreted by an expert DNA analyst, as opposed to through using objective algorithmic methods.

Accurate statistical interpretation of mixtures of DNA remains a challenge, and there is especially a need in the art for methods which do not rely upon assumptions, such as the number of contributors from maximum allele count. A method that could identify the number of contributors, and DNA sequences for the STR markers therein, without any knowledge of the contributors, or the contributors' genotypes/would be of great benefit. Such a method could avoid biased analysis based on the known identification of one or more potential contributors, such as a victim or suspect.

Currently it is particularly challenging to resolve mixtures comprising nucleic acid from three contributors, especially in an unbiased analysis, and thus not reliant on whether potential nucleic acid sequences are known or not.

The present invention thus generally aims to provide an unbiased means for interrogating mixtures

/

of nucleic acids in a sample, especially mixtures of DNA in a forensic sample, which can in particular interrogate mixtures of nucleic acid from three, or more, contributors.

Thus, in a first aspect, the present invention provides a method for interrogating a mixture of nucleic acids in a sample through analysis of short tandem repeat markers to identify the most probable proportion of each nucleic acid in the sample for a defined number of contributors, and the most probable allele sequences for each marker within each nucleic acid from each contributor comprising i) obtaining a sample which may comprise a mixture of nucleic acids for interrogation; ii) amplifying multiple short tandem repeat markers from nucleic acids in the.sample to enable amplification of a maximum of two alleles per marker per nucleic acid; iii) evaluating data from the amplification such that the number of alleles per marker in the sample, and amounts and relative percentages of each allele per marker in the sample are ascertained;

iv) identifying all possible allele pair combinations per marker in the sample from the data; v) Predicting the amount and relative percentages of each allele for each possible allele pair combination for each marker in the sample for a defined number of contributors in various proportions;

vi) Comparing, and calculating the residual (i.e. difference) between the relative

percentages of each allele per marker in the sample with that predicted for each possible allele pair combination for each marker for a defined number of contributors in various proportions, and using least square analysis to minimise the sum of squared residuals obtaining the probability for each allele combination for each marker for the defined number of contributors being present in the sample at each proportion ;

vii) repeating steps ii to viii numerous times;

viii) multiplying the probabilities from each repetition for each allele combination for each marker for the defined number of contributors at each proportion to identify the most likely allele pair combinations and their most likely proportion in the sample for each marker, and thereby identifying the most likely proportion of nucleic acids in the sample for the defined number of contributors, and the most likely allele sequences for each marker within each nucleic acid from each contributor.

The Applicant has created a method for interrogating a mixture of nucleic acid in a sample through analysis of short tandem repeat (STR) markers (loci) to identify the most probable proportion of each nucleic acid in the sample based solely on the amount of each allele with no knowledge of the contributors, or the contributors' genotypes. This method does not rely upon assumptions, such as the number of contributors in a sample. This method does not require allelic frequency tables or population statistics. It does not require the number of contributors to the mixture sample to be known.

The method is preferably undertaken a number of times for different defined number of

contributors to identify the most likely proportion of nucleic acids in the sample and thereby the most likely number of contributors. For example the method may be undertaken with the defined number of contributors being, one, two, three, and four, to statistically identify the most likely number of contributors and the most likely proportion of nucleic acids in the sample.

The method relies upon minimising the residuals between the predicted/estimated amount and the observed amount for each allele value across all markers.

The method is designed to identify the, proportion of nucleic acid for each contributor in the mixture, and the most likely allele sequences for each marker for each contributor. The method allows for an unbiased analysis of nucleic acid mixtures, which is advantageous since genotypic information is often not available for the potential contributors.

Once a potential contributor's genotypes are known, we can compare them to those produced from the unbiased analysis of the mixture and produce a statement such as ^" the evidence supports the contention that genotype combination AB, CD is the most likely ¹ .

Background allele frequencies can also be incorporated to produce a Likelihood Ratio, by following the methods of Evett et a 1, 1991, Journal of the Forensic Science Society, Volume 31, Issue 1, pages 41-47, that someone contributed to the mixture.

Preferably the sample is a forensic sample, such as a trace forensic sample.

Differentiation is in particular directed to identifying the most probable number of contributors of nucleic acid in the sample (i.e. sources of nucleic acid), and the most probable allele sequences for each marker within each nucleic acid. Although step ii is directed to amplification of two alleles per marker per nucleic acid because each contributor will have one allele from each parent, it may be that the two alleles are the same. If the alleles are the same for a particular marker, the individual is homozygous for that marker. If the alleles are different, the individual is heterozygous.

Multiple short tandem repeat (STR) markers are at least two, but most likely at least ten, such as between 10 and 16 STR markers.

Evaluating data may be enabled through the production of an electropherogram, such that the number of alleles per marker amplified in the sample, and the respective peak area and/or peak height of each allele, can be ascertained/calculated. The amounts of each allele are thus preferably represented by peak height and/or peak area for each alelle in an electropherogram, and the establishment of the relative amount of each allele per marker by dividing each peak height and/or peak area by the sum of the peak heights and/or peak areas of each allele per marker. An electropherogram is a plot of results from an analysis done by electrophoresis based sequencing. An advantage of the method is that it can utilise not only the peak height but the peak area of the allelic signature produced via an electropherogram.

The step of comparing the relative percentages of each allele per marker with that predicted for each possible allele pair combination for each marker for a defined number of contributors in various proportions, may involve comparing the percentages with that predicted for two, three, four or five contributors, thus the defined number of people may be two, three, four or five, or more. This step of the method may also be repeated for different defined numbers of contributors.

Alternatively, the method could interrogate the sample based on a number of possible contributors, such as two or three contributors, to enable identification of the most likely number of contributors to a mixture of nucleic acid, together with the probable proportion of each nucleic acid in the sample, and the most probable allele nucleic acid sequences for each marker within each nucleic acid for each contributor. An advantage of the method is its ability to determine the number of contributors to a mixture. The analysis based on numerous defined numbers of contributors may require the method to be performed successively or sequentially with each defined number of contributors.

The term various proportions relates to the possible ratios of concentration of nucleic acids in the sample. For two contributors the various proportions may differ from between 99:1 (or 1:99) between the two contributors, to an equal proportion of 50:50, with proportions varying in increments of 1, or 5, or 10, for example the various proportions could range from 5:95 (or 95:5) to 50:50, in increments of 5. For three contributors, the various proportions may range from 1:1:98 (or 1:98:1, or 98:1:1) through to equal proportions from each contributor. The increments may again vary in increments of 1, or 5, or 10. For example, the ranges may vary from 5:5:90 to 30:30:35 in increments of 5.

The step of calculating the residual between the actual relative percentage of alleles per marker and that predicted for each allele pair combination for each marker for a defined number of contributors in the various proportions searches for a consistent mixture proportion across all markers, searching for a low residual for at least some combinations of allele pairs.

Step Vi of the method may be achieved by creating a Chi-square test statistic based on the residual difference between the predicted amount (or percentage) and the actual amount (or percentage) for each allele, considering each allele pair combination and mixture proportion such as described in Curran et al, 2008, Science and Justice, Volume 48, Issue 4, pages 168-177.

The analysis may comprise incorporating a normalised threshold for each marker, where any residuals within a of the minimum residual at that marker (locus) are used to determine a possible mixture proportion. The value for a may be 0.05 (thus 5% around the minimum difference), or 0.1 (thus 10% around the minimum difference), which could be displayed for example as a Gaussian distribution plot to enable identification of the most probable proportion of each allele combination per marker in the sample. The Applicant has observed that low residuals tend to cluster around the 'true' mixture proportion, and a Gaussian shaped distribution is observed over the 'true' mixture proportion.

Optionally, parameters such as mode, median and mean can be calculated to check for a consistent mixture proportion, and ensure minimal residuals, for each data set, and particularly use of combinations of parameters, such as calculating both mode and median.

The numerous times recited in step vii may be zero, however the value of the data and probability will be more robust the more repetitions that can be undertaken. The numerous times may be at least 10, or at least 100, though more likely at least 500 or at least 1000. The number of times undertaken may depend on the amount of data to be processed, and thus more times could be possible where the defined number of contributors is two, rather than three. For an analysis based on two potential contributors the numerous times may be 10,000, whereas for three potential contributors the numerous times may be 1,000.

For step viii if the product of all probabilities is >0.5 for specific allele pair combinations at a particular proportion then that is most likely the correct proportion for that marker, and thereby the most likely proportion of nucleic acids in the sample.

The most likely allele sequences for each marker within each nucleic acid are consequently inferred from the most likely proportion of each allele combination for each marker. The mixture proportion with highest likelihood can be inferred when the residuals for all markers simultaneously minimise.

The method enables a user to search for a consistent mixture proportion across all markers with a low residual for at least some combination of allele pairs.

The advantage of using this approach to calculate the minimum residuals is that the analysis can support the original inference of the expert by considering all possible mixture combinations without any prior conditioning on a genotype combination or mixture proportion. The present invention will now be described with inference to the following non-limiting examples and drawings in which

Figure 1 illustrates contour plots of the residuals produced from the Curran et al data for the first 6 loci (a) and the last seven loci (b);

Figure 2 displays the user prompt in the tool to adjust the threshold parameter, and also a graphical representation of the multinomial distribution produced, with peaks above 0.3 and 0.7;

Figure 3 is a graphical representation of the Gaussian distributions produced from the Curran data, where a standard deviation of 0.05 was used;

Figure 4 is a graphical representation of the probabilities attributed to the most likely genotypes that created the mixture from the Curran et al data;

Figure 5 displays the user prompt in the tool to adjust the threshold parameter, and also a graphical representation of the multivariate normal distribution produced, with peaks above 0.3 and 0.7; Figure 6 is a graphical representation of the Gaussian distributions produced for the Perlin et al data, with a standard deviation of 0.05;

Figure 7 is a graphical representation of the probabilities attributed to the most likely genotypes that created the mixture from the Perlin et al data;

Figure 8 is a graphical representation of the pre-amplification mixture proportion estimation for Example 3 if two people were to be represented in the mixture;

Figure 9 is a graphical representation of the pre-amplification mixture proportion estimation for Example 3 if three people were to be represented in the mixture; and

Figure 10 is a graphical representation of the probabilities attributed to the most likely genotypes that created the mixture from the data in Example 3.

Examples

Two Person Mixtures Example 1

The Applicant illustrates the method using possible allele pair combinations taken from Curran et al (2008, Science and Justice, Volume 48, Issue 4, pages 168-177) at locus (marker) D3S1359.

Table 1. Data from Curran et al pertaining to a 2 person mixture.

Locus Alleles in the mixture Allele Peak Area True Genotype Combination

Victim Offender

D3S1358 15 1989 15 15

16 739 16

18 1550 18 vWA 15 1318 15

16 621 16

18 793 18

19 1200 19

FGA 21 2414 21 21

22 1461 22

23 687 23

D8S1179 12 1431 12

13 603 132

14 560 14

16 986 16

D21S11 28 1410 28

30 1199 30

32.2 1506 32.2

D18S51 12 471 12 13 386 13

17 1181 17

18 1029 18

D5s818 12 2561 12 12

13 463 13

D13S317 11 1607 11 11

12 834 12

D7S820 8 723 8

10 1203 10 10

11 289 11

D16S539 11 1262 11

12 515 12

13 1253 13

14 514 14

THOl 5 944 5

6 935 6

8 633 8

TPOX 8 1257 8 8

10 984. 10

11 447 11

CSF1PO 10 482 10

11 697 11

12 617 12 At this locus, the observed alleles were 15, 16 and 18. This gives 6 possible (unordered) pairs of allele values: 15/15, 15/16, 15/18, 16/16, 16/18 and 18/18. Subsequently, this produces 12 possible ordered combinations of these pairs for 2 people, (since the total combination of allele values must be identical to those observed in the mixture which would exclude, for example, 15/15 for contributor 1 and 15/16 for contributor 2, since allele 18 is neglected here). The ordered pairs are shown in Table 2.

Table 2. Possible allele pair combinations derived from the data for locus D3S1359 as shown in Table 1.

We then calculate all possible (non-symmetric) mixture proportions in increments of 0:05. In this example, for a 2 person mixture this was 10 possible mixture proportions from 0.05:0.95 to 0.5 and 0.5. It should be noted that, possibly counter-intuitively, greater resolution achieved by using smaller increments than 0.05, did not increase the sensitivity of the model. This is due to the inherent variation displayed in mixtures which in part is a result of the PCR process.

We can then calculate the expected peak area for each allele value, mixture proportion and combination of allele pairs across all loci. As done by Curran et al (2008, Science and Justice, Volume 48, Issue 4, pages 168-177), we can create a Chi-square test statistic for each allele pair combination and mixture proportion.

The list of possible combinations of allele pairs is used at this stage as a parameter to expose a consistent mixture proportion. The developed methodology searches for a consistent mixture proportion across all loci with a low residual for some combination of allele pairs. The mixture proportion with highest likelihood can be inferred when the residuals of all loci simultaneously minimise. The advantage of using this approach to calculate the minimum residuals is that the analysis can support the original inference of the expert by considering all of the possible mixture combinations without any prior conditioning on a genotype combination or mixture proportion.

Having regard to Figure 1, the data can be represented as a visual representation of the matrix for each locus, where the Chi-square statistic has been inverted into a Chi-square distribution to produce peaks rather than troughs for display purposes.

From these surface plots we can see that the 6th mixture proportion, which in this case corresponds to a ratio of 3:7, produces a consistently low residual across all loci.

The developed methodology can identify a consistent mixture proportion by using a normalised threshold method at each locus where any residuals within a of the minimum residua) at that locus are used to determine a possible mixture proportion. The value for a in this example is 0.1 although this parameter can be adjusted. In fact from the results of using this method, certainly for simple (2 person) mixtures, other low residuals at a locus appear to cluster around the 'true' mixture proportion, indicating that a threshold method is desirable in determining the mixture proportion.

The mode and median are then calculated and some sensitivity testing is employed to check for a consistent mixture proportion.

Having regard to Figure 2, the results can be represented as a histogram of mixture proportions for residuals within a (0.1) of the minimum residual at each locus. Clearly the minimum number of mixture proportions that can be identified would be, in this case, 13 since there are 13 loci. We have noted that in some cases, the minimum residual at a locus will not correspond to the ^' correct' mixture proportion, however we have also observed that low residuals tend to cluster around the 'true' mixture proportion and a Gaussian shaped distribution is observed over the true' mixture proportion. It is thus recommended to set a to between 0 and 0:1. It should be noted that a value of 0.1 for a has identified the correct (known) mixture proportion in all analyses performed.

Having regard to Figure 2, a Gaussian shaped distribution, although symmetric since mixture proportions must sum to 1, is produced with peaks over 0.3 and 0.7 which is indicative of a mixture proportion of 30% for the minor contributor and 70% for the major contributor. This part of the analysis can also clearly provide insight into the number of contributors to the mixture - i.e. for a predefined number of contributors of two, is there a clear Gaussian distribution about two values within the plot, and do the values sum to 1 (i.e. 100%).

Once the mixture proportion had been estimated, the next step was to analyse the most likely genotypes that produced the mixture for the specific estimated mixture proportion (i.e. 30:70). Our method utilises sampling of mixture proportions from a Gaussian distribution with a mean provided by the estimated mixture proportion and standard deviation of 0:05, to account for the variability observed in mixture proportions across loci.

After each analysis, the combination of genotypes producing the minimum residual were selected. This was performed simultaneously across all loci providing a probability that a genotype combination contributed to the mixture (if enough analyses are used) for each locus. We set simulations to 10,000 for two person mixtures and 1,000 for three person mixtures for time considerations.

The algorithm produced to undertake the calculations takes several seconds for a two person mixture and under a minute for a three person mixture.

Genotype combinations are then ranked from most likely to least likely and a joint probability likelihood can also be constructed if necessary to provide a likelihood across all loci.

Having regard to Figure 3, the Gaussian sampling distributions generated for this specific data is shown, with the standard deviation of 0.05 used. The number of times a genotype combination is identified as having the minimum residual can be interpreted as a probability if divided by the total number of simulations used.

For this data the analysis correctly identified all genotypes as being the highest ranked genotypes with a mixture proportion of 3:7.

Having regard to Figure 4, the probabilities that the identified genotypes are the true genotypes of the two profiles that produced the mixture are shown, and are also detailed in Table 3.

Table 3. The probabilities attributed to the most likely genotypes that created the mixture from the data for the mixture proportion. The genotypes identified correspond to the known victim and offender genotypes at every locus. locus Genotype for minor Genotype for major Probability

contributor contributor

'D3' 15 16 15, 18 0.918

'vwa' 16, 18 15, 19 1

'fga' 21, 23 21, 22 0.99

'd8' 13, 14 12, 16 1

'd2' 30, 30 28, 32.2 0.769

'dl8' 12, 13 17, 18 1

'd5' 12, 13 12, 12 0.956

'dl3' 11, 11 11, 12 0.849

'd7' 10, 11 8, 10 0.996

'dl6' 12, 14 11, 13 1

'th' 8, 8 5, 6 0.769

'tp' 8, 11 8, 10 0.94

'csf 10, 10 11, 12 0.769

Example 2

The method was performed on data obtained from Perlin et al, 2011, Journal of Forensic Sciences, Volume 56, Issue 6, pages 1430-1447., which article was concerned with a validation of TrueAllele.

Table 4. Data from Perlin et al obtained by STR amplification of particular markers (loci), as derived from peak area of electrppherograms.

Locus Allele Value Peak Area

d2 16 1339

d2 18 2992 d2 20 1947 d2 -.I 3722 d3 14 5010 d3 15 4990 d8 9 2832 d8 12 1426 d8 13 3829 d8 14 1913 dl6 11 6801 dl6 13 1607 dl6 14 1593 dl8 12 1504 dl8 13 3290 dl8 14 3443 dl8 17 1764 dl9 12.2 3109 dl9 14 3092 dl9 15 . 3799 d21 27 1289 d21 29 3913 d21 30 4798 fga 19 4621 fga 24 1561 fga 25.2 3817 th 6 1268 th 7 4691

th 9 4041

vwa 17 7265

vwa 18 2735

Having regard to Figure 5 and Figure 6, the estimated mixture proportion, and the Gaussian distribution for the data as evaluated by the method is displayed. Having regard to Figure 7, the probabilities of the most likely genotypes across all loci are displayed with the correct genotypes being identified at all loci. The genotypic information is displayed in Table 5 along with the probability, and joint probability. This can be compared to the results produced by Cowell et.al, 2007, Forensic Science International, Volume 166, Issue 1, pages 28-34, where all the correct genotypes were identified for one (of 4 provided) parameter and model configurations. The joint probability for our model is also higher than that produced by Cowell et.al (0.256704).

Table 5. The result of applying the method on the Perlin et al data.

Locus Allele Pair Allele Pair Probability

Contributor 1 Contributor 2

d2 16, 20 18, 21 1

d3 14, 15 14, 15 1

d8 12, 14 9, 13 1

dl6 13, 14 11, 11 1

dl8 12, 17 13, 14 1

dl9 14, 14 12.2, 15 0.697

d21 27, 30 29, 30 0.996

fga 19, 24 19, 25.2 0.977 th 6, 7 7, 9 0.996

v a 18, 18 17, 17 0.694

Joint 0.4688

Three Person Mixtures

Example 3 - Simulated Three Person Mixture

We simulated data across 10 markers for a three person mixture. We used a mixture proportion of

[0.2, 0.3, 0.5] as a random choice. We present the data set in Table 6.

Table 6. Data for the simulated three person mixture.

Locus Allele Value Peak Area

d3 7 450

d3 8 1300

d3 9 1750

d3 10 1250

d5 10 1450

d5 11 355

d5 5 2222

d7 3 290

d7 4 300

d7 5 455

d7 6 1222

d7 7 754

d8 4 2200 d8 5 2600 d8 6 1100 d8 7 2000 dl3 4 100 dl3 5 500 dl3 6 300 dl8 1 500 dl8 2 3000 dl8 5 1800 d21 3 1900 d21 4 500 d21 7 500 fga 1 510 fga 2 720 fga 3 450 fga 4 320 vwa 5 1000 vwa 6 600 vwa 7 3000 vwa 9 1700 vwa 8 550 tho 1 1250 t o 2 700 tho 3 1200 tho 4 600 We applied both the normal and light version of the tool to this data set. The light version of the tool does not allow for adjustment of the parameter a to determine the pre-amplification ratio and performs only one simulation to estimate the most likely genotypes that created the mixture. The light version of the tool does not fit a distribution to the estimated pre-amplification mixture proportion but merely ranks the residuals for the estimated pre-amplification mixture proportion; Therefore we cannot attribute probabilities to the final output but produce a list say of the 5 most likely genotypes that produced the mixture at each locus. We also applied the normal version of the tool.

Having regard to Figures 8 and 9, the distributions found for the pre-amplification mixture proportion when two (Figure 8) and three contributors (Figure 9) are considered is shown. For two contributors scenario it can be seen that there are no symmetric distributions, and no strong distribution. We can however see from Figure 9 that Gaussian distributions occur over 0.2, 0.3, and 0.5, and thus that a three person mixture is most likely, with mixture proportion 0.2, 0.3 and 0.5. We present the most likely genotypes expected to produce this mixture in Table 7. We have listed them from the most likely to the 4th most likely. We use bold italics to indicate classification errors. We can see that by the 4th combination we have no classification errors. In fact, the most likely genotype combination correctly identifies 7 of the 10 markers first time.

Table 7. Genotypic combination results for the three person mixture, descending from most likely to fourth most likely. Bold italics are used to indicate incorrect identifications.

Most likely First Person Second Person Third Person d3 8,10 9, 10 8, 9

d5 10, 11 5, 5 10, 5

d7 3, 4 6, 6 6, 7 d8 6,6 4,4 5,5 dl3 4,5 5, 6 5,6 dl8 5,1 2,2 5,2 d21 4,4 3,7 3,3 fga 3,2, 3,4 2,1 vwa 6,6 7,7 7,9 tho 3,1 3,1

Second most likely

d3 8,8 8,9 9, 10 d5 10, 11 5,5 10,5 d7 3,4 6,6 6,7 d8 6,6 4,4 5,5 dl3 4,5 5,6 5,6 dl8 5,1 2,2 5,2 d21 4,4 3,7 3,3 fga 3,2 3,4 2,1 vwa 6,6 7,7 7,9 tho 3,1 3,1 2,1

Third Most likely

d3 8,8 8,9 9, 10 d5 10, 11 5,5 10,5 d7 3,4 6,6 6,7 d8 6,6 4,4 5,5 dl3 4,5 5,6 5,6 dl8 5,1 2,2 5,2 d21 4,4 3,7 3,3

fga 3,2 3,4 2,1

vwa 6,6 7,7 7,9

tho 3,2 3,2 1,1

Fourth Most Likely

d3 8,8 8,9 9, 10

d5 10, 11 5,5 10,5

d7 3,4 6,6 6,7

d8 5,4 4,6 5,5

dl3 4,5 5,6 5,6

dl8 5,1 2,2 5,2

d21 4,4 3,7 3,3

fga 3,2 3,4 2,1

vwa 6,6 7,7 7,9

tho 3,2 3,2 1,1

Having regard to Figure 10, the probabilities of the most likely genotype combinations are shown, as a result of running the normal version of the tool. The lowest probabilities here correspond to the mis-identified genotypes for the highest ranked contributor genotypes in Table 7. This is encouraging as the probabilities output from the normal version of the tool clearly provide a strong indication to the user that genotypes may have been mis-identified.