METHYLATION ASSAY

TECHNICAL FIELD

[1] The present disclosure relates to methods and assays for detecting a food allergy or a propensity to develop a food allergy in a subject based on gene methylation status.

BACKGROUND

[2] The prevalence of allergic diseases worldwide is rising dramatically in both developed and developing countries. These diseases include asthma; rhinitis; anaphylaxis; drug, food, and insect allergy; eczema; and urticaria (hives) and angioedema. This increase is especially problematic in children (Panwankar et al. (2011) WAO White Book on Allergy, pg 1 - 24) . For example, food allergy affects up to 10% of children in countries most affected (Osborne et al. (2011) J Allergy Clin Immunol., Ill , 668-76), and has become a substantial public health concern. Rising rates of food allergy among young children have been coincidental with an increase in potentially life-threatening anaphylaxis (Prescott & Allen (2011) Pediatr Allergy Immunol. 22, 155-160).

[3] The causal factors underpinning the rise in allergic diseases such as food allergy are poorly understood but are likely driven by complex gene and environment interactions operative during critical periods of immune development.

[4] Lab testing for the detection of allergen- specific IgE (slgE) is widely used in the diagnosis of IgE-mediated food allergy, which offers proof of sensitization, however further evidence of clinical manifestations on exposure is required to make a definitive diagnosis (Ito, K. (2013) Asia Pac Allergy, 3, 59-69). This is because the majority of children with a positive skin prick test or slgE test are not allergic to that food (Santos & Lack (2012) Pediatr Allergy Immunol, 23, 698-706).

[5] At present, oral food challenges (OFC) are the "gold standard" method to diagnose food allergy. However, OFC is highly invasive, requires a dedicated team of specialists, and because of the risk of anaphylaxis can be dangerous for the allergic individual. As a result of this, OFC are often not performed potentially leading to over diagnosis of food allergy or unnecessary avoidance programs (Santos & Lack (2012) Pediatr Allergy Immunol., 23, 698-706).

[6] At present there are no reliable biomarkers for clinical food allergy. Accordingly, there is a need to improve current allergy testing practices to assist in making a more efficient and accurate diagnosis regarding a patients risk of reactivity on exposure to an allergen.

SUMMARY

[7] The present inventors have identified that various sites within the genome are differentially methylated in subjects with food allergy or a propensity to develop food allergy. Surprisingly, the present inventors have found that the methylation status of the identified sites is predictive of food allergy or a propensity to develop food allergy in a subject. The identified sites are often referred to as "CpG sites" or "CG sites" or "CpNpG sites". These sites are regions of DNA where a cytosine nucleotide occurs next to a guanine nucleotide in the linear sequence of bases along its length. These findings of the present inventors suggest that determining the methylation status of the CpG sites of the present disclosure may provide a useful in-vitro method of detecting a food allergy or a propensity to develop a food allergy in a subject. Accordingly, in one example, the present disclosure provides an in-vitro method to detect a food allergy in a subject. In another example, the present disclosure provides an in-vitro method to detect a propensity to develop a food allergy in a subject.

[8] Surprisingly, the present inventors have also identified that the predictive power of the identified CpG sites is additive. Accordingly, the methylation status of multiple sites may be combined to produce a multivariate methylation pattern or methylation signature with improved accuracy or predictive power for diagnosing a food allergy or a propensity to develop a food allergy in a subject.

[9] For example, methylation patterns or signatures comprising a plurality of the methylation sites disclosed herein may be assessed to detect a food allergy or a propensity to develop a food allergy in a subject. Thus, in another example, the present disclosure provides an in-vitro method to detect a food allergy or a propensity to develop a food allergy in a subject, the method comprising, determining the methylation status of at least 2 CpG sites in DNA obtained from the subject, the CpG sites being selected from the group shown in Table 2, and detecting a food allergy or the propensity to develop a food allergy based on a methylation status of the CpG sites.

[10] In another example, the present disclosure relates to an in-vitro method to determine a subjects response to allergen immunotherapy, the method comprising, determining the methylation status of at least 2 CpG sites in DNA obtained from the subject, the CpG sites being selected from the group shown in Table 2, and determining whether the subject has responded to allergen immunotherapy based on the methylation status of the CpG sites. [11] In performing the above exemplified methods, the methylation status of at least 5 CpG sites may be determined. In another example, the methylation status of at least 10 CpG sites are determined. In another example, the methylation status of at least 15 CpG sites are determined. In another example, the methylation status of at least 20 CpG sites are determined. In another example, the methylation status of at least 50 CpG sites are determined.

[12] By further analysing the CpG sites of the present disclosure, the present inventors found that the methylation status of some CpG sites were more predictive than others for detecting a food allergy or a propensity to develop a food allergy in a subject. Accordingly, in one example, the CpG sites are selected from the group shown in Table 3. In another example, the CpG sites are selected from the group shown in Table 4. In another example, the CpG sites are selected from the group shown in Table 5.

[13] In one example, the methylation status of the CpG sites selected from the group shown in Table 2 are determined, wherein the CpG sites reside in or across the genes shown in Table 2. In an example, the methylation status of the CpG sites selected from the group shown in Table 2 are determined, wherein the CpG sites reside in or across genes in the mitogen activated protein (MAP) kinase pathway.

[14] In one example, determining the methylation status involves comparing the methylation status of the CpG sites in DNA obtained from the subject relative to the methylation status of the CpG sites in DNA obtained from a control. In an example, the control is a positive control and no change in the methylation status relative to the control is indicative of the presence of food allergy or a propensity to develop food allergy. In another example, the control is a negative control and a change in the methylation status relative to the negative control is indicative of food allergy or a propensity to develop food allergy.

[15] In an example, determining the methylation status comprises calculating the methylation ratio of each CpG site and/or gene assessed. In another example, determining the methylation status further comprises calculating the sum of the methylation ratios for each CpG site and/or gene assessed to obtain a methylation score. In another example, the indicative change in the methylation status relative to the control is an increase in methylation status and/or methylation score relative to a negative control.

[16] Methylation status may be determined using an assay selected from the group consisting of bisulfite MALDI-TOF methylation, methylation sensitive PCR, methylation specific melting curve analysis (MS-MCA), high resolution melting (MS- HRM), MALDI-TOF MS, methylation specific MLPA; combination of methylated - DNA precipitation and methylation-sensitive restriction enzymes (COMPARE-MS), methylation sensitive oligonucleotide microarray, antibody immunoprecipitation, pyrosequencing, NEXT generation sequencing, DEEP sequencing.

[17] In an example, the food allergy detected using the methods of the present disclosure is selected from the group consisting of milk, buckwheat, egg, tree nuts, peanut, fish, shellfish, fruit, yeast, onion, tomato, legume, lupin, garlic, oats, peppers, gluten, tartazine, sulfites, sesame, wheat, rice, corn, soy, meats, meat substitutes. For example, the food allergy can be a peanut allergy. In another example, the food allergy can be an egg or egg white allergy.

[18] In one example, the DNA used in the methods of the present disclosure is isolated from a biological sample selected from the group consisting of blood (including whole blood), blood mononuclear cells, blood plasma, blood serum, hemolysate, lymph, synovial fluid, spinal fluid, urine, cerebrospinal fluid, stool, sputum, mucus, amniotic fluid, lacrimal fluid, cyst fluid, sweat gland secretion, bile, milk, tears, saliva, earwax, B cells, dendritic cells, granulocytes ,innate lymphoid cells (ILCs), megakaryocytes, monocytes/macrophages, natural killer (NK) cells ,platelets, red blood cells (RBCs), T cells, thymocytes.

[19] In one example, the methods of the present disclosure further comprise, performing an additional test for food allergy selected from the group consisting of skin prick test, slgE test, component diagnostic tests. In an example, the additional test is a skin prick test. In this example, a food allergy or a propensity to develop a food allergy is detected based on the methylation status of the CpG sites and the results of the additional test.

[20] In an example, the methods of the present disclosure are used to resolve an inconclusive diagnostic test for allergy such as a skin prick test. For example, the methods of the present disclosure can be used to detect a food allergy or the propensity to develop a food allergy based on the methylation status of the CpG sites in a subject with a resulting skin prick test that produces a wheal size of between about 2mm and about 8mm in response to an allergen. In another example, the methods of the present disclosure can be used to detect a food allergy or the propensity to develop a food allergy based on the methylation status of the CpG sites in a subject with a resulting skin prick test that produces a wheal size of between about 3mm and about 7mm in response to an allergen.

[21] Wheal size indicative of an inconclusive skin prick test can vary depending on the allergen. Thus, in another example, the methods of the present disclosure can be used to detect peanut allergy or the propensity to develop peanut allergy based on the methylation status of the CpG sites in a subject with a resulting skin prick test that produces a wheal size of between about 2mm and about 8mm in response to peanut allergen. In another example, the methods of the present disclosure can be used to detect egg allergy or the propensity to develop egg allergy based on the methylation status of the CpG sites in a subject with a resulting skin prick test that produces a wheal size of between about 2mm and about 4mm in response to egg allergen.

[22] In an example, the additional test is an sIgE test. In an example, the methods of the present disclosure are used to resolve an inconclusive sIgE test. For example, the methods of the present disclosure can be used to detect peanut allergy or the propensity to develop peanut allergy based on the methylation status of the CpG sites in a subject with an sIgE test that provides peanut sIgE between about 0.30 kUA/L and about 15 kUA/L in response to peanut allergen. For example, the methods of the present disclosure can be used to detect egg allergy or the propensity to develop egg allergy based on the methylation status of the CpG sites in a subject with an sIgE test that produces egg sIgE between about 0.30 kUA/L and about 1.20kUA/L in response to egg allergen.

[23] In another example, the present disclosure relates to a kit comprising:

a) a reactant able to modify DNA sequence to convert unmethylated cytosines to uracils; and;

b) a polypeptide capable of binding methylated DNA; and/or

c) probes and/or primers specific for at least at least 2 CpG sites selected from the group shown in Table 2.

[24] In another example, the present disclosure relates to a microarray having probes and/or primers able to determine the methylation status of at least 2 CpG sites selected from the group shown in Table 2.

[25] In one example, the present disclosure encompasses a system configured to perform the methods of the present disclosure.

[26] In another example, the present disclosure relates to a method of treating allergy comprising, detecting a food allergy or the propensity to develop a food allergy using the methods of the present disclosure and administering allergen immunotherapy.

[27] With knowledge of the methylation sites outlined in the present disclosure genomic DNA can be sent for assessment of methylation and subsequently assessed to detect a food allergy or a propensity to develop a food allergy in a test subject using a computer implemented method. Accordingly, in another example the present disclosure provides a computer implemented method of detecting a food allergy or a propensity to develop a food allergy in a test subject, the method operable in a computing system comprising a processor and a memory, the method comprising:

a) receiving data indicating the methylation status of at least 2 CpG sites in DNA obtained from the subject, the CpG sites being selected from the group shown in Table 2;

b) processing the data to detect allergy or the propensity to develop food allergy based on methylation status of the CpG sites;

[28] c) outputting the presence of the allergy or a propensity to develop the allergy in a subject by means of a risk score. In one example, the receiving comprises receiving data indicating the methylation status of the CpG sites relative to the control from a user interface coupled to the computing system. In another example, the receiving comprises receiving data indicating the methylation status of the CpG sites relative to the control from a remote device across a wireless communications network. For example, the user interface or remote device may be a methylation array platform. In an example, outputting comprises outputting information to a user interface coupled to the computing system. In another example, outputting comprises transmitting information to a remote device across a wireless communications network.

[29] In another example, the present disclosure relates to a computer program product comprising a non-transitory computer readable medium with computer executable instructions, which when executed is effective to carry out the methods of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

[30] Figure 1: Development of a predictive model based on blood-derived DNA methylation levels, (a) MDS analysis of sample relationships based on 4485 probes significantly associated (ANOVA p < 0.01) with phenotype. EA = egg allergic, ES = egg sensitized, PA = peanut allergic, PS = peanut sensitized (b) Error curves from tenfold cross-validation experiments. The model was trained to predict food challenge outcome. Misclassification errors are shown (y-axis) for each threshold at which noisy CpG were removed, (c) Class centroids are represented for each food challenge outcome group. Approximately 50 centroids are shown for ease of visualization. FA = food allergy, FS = food sensitized (d) Scatterplots of Beta DNA methylation values from top 6 ranked CpG sites based on average cross validation scores. X-axis shows samples (red = FA, blue = FS), y-axis is Beta methylation value, (e) MDS analysis of validation study. Top panel shows sample relationships based on all somatic probes, bottom panel is sample relationships based on 96 predictive CpG signature. Samples are numbered by age (0 = birth, 1 = 12-month) and colored by phenotype (FA = green, NA = orange). PC = principal component of variation.

[31] Figure 2: Sensitivity and specificity analysis of DNAm patient scores, (a) Distribution of methylation ratios for the 96 CpG signature stratified by phenotype. (b) Boxplots of total patient DNAm scores showing median and range. Statistical analysis by Man- Whitney test, (c) ROC curve analysis of DNAm scores for predicting clinical allergy between different groups (d) Performance comparisons of DNAm scores against serum IgE measures (e) Performance comparisons of DNAm scores against egg skin prick test wheal size among egg sensitized individuals (f) Performance comparisons of DNAm scores against peanut skin prick test wheal size among peanut sensitized individuals

[32] Figure 3: Starburst plot of patient methylation scores for discovery (left) and replication cohorts (right). Concentric numerals denotes sample number, patient scores are shown on the vertical axis in bold. Patient methylation scores derived from CD4+ T-cells (red) were consistently lower than scores derived from total PBMCS (blue) however; differences between phenotype classes were conserved in each cohort. Solid circles are visual guides for diagnostic cutoffs determined by sensitivity analysis.

DETAILED DESCRIPTION

General Techniques and Definitions

[33] Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., in epigenetics, gene expression, molecular genetics, protein chemistry, biochemistry, diagnostics).

[34] As will be understood by those of skill in the art, various molecular techniques and DNA modification and detection methods utilized in the present disclosure are standard procedures, well known to those skilled in the art. Such techniques are described and explained throughout the literature in sources such as, J. Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons (1984), J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbour Laboratory Press (1989), T.A. Brown (editor), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, JUL Press (1991), D.M. Glover and B.D. Hames (editors), and F.M. Ausubel et al. (editors), Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-Interscience (1988, including all updates until present), Ed Harlow and David Lane (editors) Antibodies: A Laboratory Manual, Cold Spring Harbour Laboratory, (1988), and J.E. Coligan et al. (editors) Current Protocols in Immunology, John Wiley & Sons (including all updates until present).

[35] Those skilled in the art will appreciate that the disclosure described herein is susceptible to variations and modifications other than those specifically described. It is to be understood that the disclosure includes all such variations and modifications. The disclosure also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations or any two or more of said steps or features. For example, one of skill in the art would be aware of "linkage disequilibrium" which relates to the non-random association of alleles at two or more loci that descend from single, ancestral chromosomes. As outlined below the present disclosure describes a methylation status comprising a series of CpG sites associated with a food allergy or the propensity to develop a food allergy. The CpG sites of the present disclosure encompass related sites in linkage disequilibrium. Moreover, determining the methylation status of the CpG sites of the present disclosure includes determining the methylation status of other markers in linkage disequilibrium with the particular CpG sites.

[36] The in-vitro methods of the present disclosure can be performed as an assay. As one of skill in the art would appreciate, an assay is an investigative (analytic) procedure or method for qualitatively assessing or quantitatively measuring the presence or amount or the functional activity of a target. For example, an assay can assess methylation of various CpG sites.

[37] In an example, a method or assay according to the present disclosure may be incorporated into a treatment regimen. For example, a method of treating allergy in a subject in need thereof may comprise performing an assay that embodies the methods of the present disclosure. In an example, a clinician or similar may wish to perform or request performance of an assay according to the present disclosure before administering or modifying treatment to a patient. For example, a clinician may perform or request performance of an assay according to the present disclosure on a subject before electing to administer or modify therapy such as allergen immunotherapy.

[38] The present disclosure is not to be limited in scope by the specific embodiments described herein, which are intended for the purpose of exemplification only. Functionally-equivalent products, compositions and methods are clearly within the scope of the disclosure, as described herein.

[39] Any example disclosed herein shall be taken to apply mutatis mutandis to any other example unless specifically stated otherwise. [40] The term "and/or", e.g., "X and/or Y" shall be understood to mean either "X and Y" or "X or Y" and shall be taken to provide explicit support for both meanings or for either meaning.

[41] As used herein, the term "about", unless stated to the contrary, refers to +/- 10%, more preferably +/- 5%, of the designated value.

[42] Throughout this specification, unless specifically stated otherwise or the context requires otherwise, reference to a single step, composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter.

[43] Throughout this specification the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

Food Allergy

[44] The term "food allergy" is used in the context of the present disclosure to refer to allergies caused by exposure or ingestion of food allergens. For example, the methods of the present disclosure may be used to detect a food allergy or propensity to develop a food allergy to allergens such as milk, buckwheat, egg, tree nuts, peanut, fish, shellfish, fruit, yeast, onion, tomato, legume, lupin, garlic, oats, peppers, gluten, tartazine, sulfites, sesame, wheat, rice, corn, soy, meats, meat substitutes. In an example, the food allergy detected using the methods of the present disclosure is a legume allergy. In another example, the allergy is an peanut allergy. In another example, the allergy is an egg allergy. In another example, the allergy is an egg white allergy.

Methylation Status

[45] The methods of the present disclosure are used to detect a food allergy or the propensity to develop a food allergy in a subject based on methylation status. The term "methylation status" is used to indicate whether a particular site or gene is methylated or not. In the context of the present disclosure, the term "methylation status" encompasses methylation status or hydroxymethylation status of "— C— phosphate— G— " (CpG) sites or "— C— phosphate— any base (N)— phosphate— G— " (CpNpG) sites and genes. In an example, the term "methylation status" also encompasses methylation status of non CpG sites or non-CG methylation. [46] Various methods are available to those of skill in the art to determine methylation status. In some instances it may be desirable to assess methylation status using a particular method. For example, a suitable method for assessing methylation status is exemplified below (DNA is bisulphite treated using the Human Genomic Signatures MethylEasy Xceed kit; analysed using Illumina Infinium HumanMethylation450 (HM450) arrays). In another example, methylation status can be determined using assays such as bisulfite MALDI-TOF methylation, methylation sensitive PCR, methylation specific melting curve analysis (MS-MCA), high resolution melting (MS-HRM), MALDI-TOF MS, methylation specific MLPA; combination of methylated-DNA precipitation and methylation- sensitive restriction enzymes (COMPARE-MS), methylation sensitive oligonucleotide microarray, antibody immunoprecipitation, pyrosequencing, NEXT generation sequencing, DEEP sequencing. Such assays are available commercially.

[47] It will be apparent to those of skill in the art that in performing the methods of the present disclosure, DNA obtained from a subject can be modified relative to its naturally occurring counterpart before determining methylation status. For example, genomic DNA isolated from a subject can be treated with bisulfite for a time and under conditions sufficient to convert non-methylated cytosine to uracils. In another example, DNA can be digested with methylation sensitive restriction endonucleases. In another example, DNA can amplified to produce cDNA. Methylation status of treated/digested DNA or cDNA can then be assessed via sequencing or via a suitable amplification based system such as those discussed above.

[48] When performing the methods of the present disclosure, the methylation status of multiple sites will be assessed. In an example, the methylation status of the CpG sites of the present disclosure can be combined to produce a multivariate methylation pattern or methylation signature indicative of a food allergy or a propensity to develop a food allergy in a subject. Such a pattern or signature can be used as a comparative reference for determining whether a subject has an allergy or a propensity to develop an allergy. In an example, the methylation status of at least 2 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 5 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 10 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 20 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 50 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 70 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 90 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 96 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 100 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 110 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 120 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 140 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 160 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 180 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 200 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 220 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 240 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 260 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 280 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 300 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 320 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 340 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 360 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 380 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 400 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 420 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 440 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 460 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 480 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 500 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 520 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 540 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 560 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 580 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 600 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 620 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 640 CpG sites selected from the group shown in Table 2 are determined. In another example, the methylation status of at least 649 CpG sites selected from the group shown in Table 2 are determined.

[49] In another example, the methylation status of at least 2 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 5 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 10 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 15 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 20 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 25 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 30 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 35 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 40 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 45 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 50 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 55 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 60 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 65 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 70 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 75 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 80 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 81 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 82 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 83 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 84 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 85 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 86 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 87 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 88 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 89 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 90 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 91 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 92 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 93 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 94 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 95 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of at least 96 CpG sites selected from the group shown in Table 3 are determined.

[50] In another example, the methylation status of at least 2 CpG sites selected from the group shown in Table 4 are determined. In another example, the methylation status of at least 3 CpG sites selected from the group shown in Table 4 are determined. In another example, the methylation status of at least 4 CpG sites selected from the group shown in Table 4 are determined. In another example, the methylation status of at least 4 CpG sites selected from the group shown in Table 4 are determined. In another example, the methylation status of at least 5 CpG sites selected from the group shown in Table 4 are determined. In another example, the methylation status of at least 6 CpG sites selected from the group shown in Table 4 are determined. In another example, the methylation status of at least 7 CpG sites selected from the group shown in Table 4 are determined. In another example, the methylation status of at least 8 CpG sites selected from the group shown in Table 4 are determined. In another example, the methylation status of at least 9 CpG sites selected from the group shown in Table 3 are determined. In another example, the methylation status of 10 CpG sites selected from the group shown in Table 4 are determined.

[51] In another example, the methylation status of at least 2 CpG sites selected from the group shown in Table 5 are determined. In another, example the methylation status of at least 5 CpG sites selected from the group shown in Table 5 are determined. In another example, the methylation status of at least 10 CpG sites selected from the group shown in Table 5 are determined. In another example, the methylation status of at least 15 CpG sites selected from the group shown in Table 5 are determined. In another example, the methylation status of at least 20 CpG sites selected from the group shown in Table 5 are determined. In another example, the methylation status of at least 25 CpG sites selected from the group shown in Table 5 are determined. In another example, the methylation status of at least 30 CpG sites selected from the group shown in Table 5 are determined. In another example, the methylation status of at least 35 CpG sites selected from the group shown in Table 5 are determined. In another example, the methylation status of at least 40 CpG sites selected from the group shown in Table 5 are determined. In another example, the methylation status of at least 45 CpG sites selected from the group shown in Table 5 are determined. In another example, the methylation status of 50 CpG sites selected from the group shown in Table 5 are determined.

[52] As one of skill in the art would appreciate, CpG sites can reside within or overlapping genes and regulatory regions thereof. For example, CpG sites may reside upstream of genes important for mounting an allergic response to an allergen. Thus, in an example, the methods of the present disclosure encompass assessing methylation sites in coding and non-coding regions such as introns, in or across intron/exon boundaries, in or across splicing regions of the gene transcripts. Thus, by assessing multiple selected CpG sites, the methods of the present disclosure can encompass assessing methylation status of genes. Exemplary genes that may be assessed using the methods of the present disclosure are provided in Tables 2, 3, 5 and 6. In an example, the methods of the present disclosure encompass assessing the methylation status of one or more genes selected from the group shown in Table 2. For example, the methylation status of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72 genes can be assessed.

[53] All selected CpG sites of the present disclosure need not be completely methylated to indicate allergy or a propensity to develop allergy. For example, predictive CpG methylation status can range from about 10 % to about 90 % methylation of CpG sites in a particular gene or regulatory region thereof. In another example, predictive CpG methylation status can range from about 20 % to about 80 % methylation of CpG sites in a particular gene or regulatory region thereof. In another example, predictive CpG methylation status can range from about 25 % to about 75 % methylation of CpG sites in a particular gene or regulatory region thereof. In another example, predictive CpG methylation status can range from about 30 % to about 70 % methylation of CpG sites in a particular gene or regulatory region thereof. In another example, predictive CpG methylation status is at least about 10 % methylation, at least about 15 % methylation, at least about 20 % methylation, at least about 25 % methylation, at least about 30 % methylation, at least about 35 % methylation, at least about 40 % methylation, at least about 45 % methylation, at least about 55 % methylation, at least about 60 % methylation, at least about 65 % methylation, at least about 70 % methylation, at least about 75 % methylation, at least about 80 % methylation, at least about 85 % methylation, at least about 90 % methylation, at least about 95 % methylation of CpG sites in a particular gene or regulatory region thereof. In another example, predictive CpG methylation status is 100% methylation of CpG sites in a particular gene or regulatory region thereof.

[54] The methylation status of the CpG sites of the present disclosure can be represented in various ways. In one example, determining the methylation status comprises calculating the ratio between methylated and un-methylated alleles for each CpG site and/or gene assessed. In an example, the ratio based on the methylated and un-methylated status can be represented as:

(methylated allele status) / ((un-methylated allele status + methylated allele status) x 100) = methylation ratio. [55] In one example, the methylation status for each allele is determined using an Infinium HumanMethylation450 BeadChip as exemplified below. The ratio based on the methylated and un-methylated intensity can be represented as:

(methylated allele intensity) / ((un-methylated allele intensity + methylated allele intensity) x 100) = methylation ratio.

[56] In an example, the process of determining the methylation ratio can be performed for each CpG assessed and the resulting ratios can be added together to provide a methylation score.

[57] Because the predictive power of the identified CpG sites is additive, one of skill will appreciate that a methylation score indicative of a food allergy or the propensity to develop a food allergy will largely depend on the number of CpG sites assessed. For example, when the 96 CpG sites shown in Table 3 are assessed, a methylation score of at least about 30 is indicative of a food allergy or the propensity to develop a food allergy. In other examples, a methylation score of at least about 31, at least about 32, at least about 33, at least about 34, at least about 35, at least about 36, at least about 37, at least about 38, at least about 39, at least about 40, at least about 41, at least about 42, at least about 43, at least about 44, at least about 45, at least about 46, at least about 46.5, at least about 47, at least about 48, at least about 49, at least about 50, at least about 51, at least about 52, at least about 53, at least about 54, at least about 55, at least about 56, at least about 57, at least about 58, at least about 59, at least about 60 is indicative of a food allergy or the propensity to develop a food allergy.

[58] A methylation status indicative of a food allergy or the propensity to develop a food allergy can be identified by assessing the CpG sites of the present disclosure relative to a control. The control sample may be a biological sample obtained from a subject either positive (positive control) or negative (negative control) for allergy. As one of skill in the art would appreciate, the control sample is dictated by the test or experimental sample in that it must provide the necessary comparison for detecting allergy.

[59] For example, the control sample could be negative for the allergy being tested, a negative control. In this example, the control sample may be from a healthy mammal that has no symptoms of the allergy (e.g. does not mount an allergic response to the food allergen). In an example, the negative control is not allergy free but rather has an alternative allergy to the allergy being detected. In an example, the negative control does not have a food allergy. When using a negative control, a change in the methylation status in the test sample relative to the control is indicative of the presence of the allergy or a propensity to develop the allergy. For example, an increase in methylation status or score relative to the negative control is indicative of allergy or propensity to develop a food allergy.

[60] In another example, the control sample is positive for the allergy being tested. In this example, the control sample provides a comparative or baseline level of methylation that indicates the presence of a food allergy or the propensity to develop a food allergy. A test sample having comparative or increased levels of methylation relative to the positive control indicates that the subject has the allergy or a propensity to develop the allergy. In an example, the positive control is a statistically validated standard.

[61] In an example, both positive and negative controls are used in the methods of the present disclosure.

[62] In another example, the control sample may consist of a plurality of samples. In one example, a plurality of control samples are used to establish a robust baseline or threshold level of methylation that can be compared with a test sample to detect a food allergy of the propensity to develop a food allergy. In another example, the control sample may include samples positive for various different allergies. For example, the control may comprise samples obtained from individuals known to have peanut, egg, milk or shellfish allergies. In this example, the control samples can be used to provide a control panel that can be compared against a sample from a subject to detect the presence or absence of a series of allergies or a propensity to develop a series of allergies.

[63] Various methods can be used to determine a change in the methylation status in the test sample relative to the control. For example, a change may be evident from a side by side comparison of methylation status between a test sample and a control(s). In another example, methylation status of test samples and controls can be compared statistically to identify a statistically significant difference in methylation status. There are a number of statistical tests for identifying a statistically significant difference in methylation status that vary significantly, including the conventional t-test. However, it may be generally more convenient appropriate and/or accurate to use other common tests to assess for such statistical significance such as ANOVA, Kruskal-Wallis, Wilcoxon, Mann- Whitney and odds ratio.

Sample Preparation and Analysis

[64] In performing the methods of the present disclosure, a sample from a subject is required. It is considered that terms such as "sample" and "specimen" are terms that can, in context, be used interchangeably in the present disclosure. The sample used in the present disclosure can be a biological sample from a human. Any biological material can be used as the above-mentioned sample so long as it can be collected from the subject and DNA can be isolated and analysed according to the methods of the present disclosure.

[65] For example, the sample may be selected from the group consisting of blood (including whole blood), blood plasma, blood serum, hemolysate, lymph, synovial fluid, spinal fluid, urine, cerebrospinal fluid, stool, sputum, mucus, amniotic fluid, lacrimal fluid, cyst fluid, sweat gland secretion, bile, milk, tears, saliva, earwax.

[66] A sample such as a blood sample may be treated to remove particular cells using various methods such as such centrifugation, affinity chromatography (e.g. immunoabsorbent means), immuno selection and filtration. Thus, in an example, the sample can comprise a specific cell type or mixture of cell types isolated directly from the subject or purified from a sample obtained from the subject (e.g. purifying T-cells from whole blood). In an example, the biological sample is peripheral blood mononuclear cells (pBMC). In other examples, the sample may be selected from the group consisting of B cells, dendritic cells, granulocytes, innate lymphoid cells (ILCs), megakaryocytes, monocytes/macrophages, natural killer (NK) cells ,platelets, red blood cells (RBCs), T cells, thymocytes. Various methods of purifying sub-populations of cells are known in the art. For example, pBMC can be purified from whole blood using various known Ficoll based centrifugation methods (e.g. Ficoll-Hypaque density gradient centrifugation). Other cells such as T-cells can also be purified by selecting for the appropriate phenotype using techniques such as immunomagnetic cell sorting (e.g. Dynabeads, Invitrogen). For example, T-cells can be purified using a two step selection process that firstly removes CD8+ cells and then selects CD4+ cells. Cell population purity can be confirmed by assessing the appropriate markers such as CD19-FITC, CD3-PE, CD8-PerCP, CDl lc-PE Cy7, CD4-APC and CD14-APC Cy7 using commercially available antibodies (e.g. BD Biosciences).

[67] DNA is extracted from the sample for methylation analysis. In an example, the DNA is genomic DNA. Various methods of isolating DNA, in particular genomic DNA are known to those of skill in the art. In general, known methods involve disruption and lysis of the starting material followed by the removal of proteins and other contaminants and finally recovery of the DNA. For example, techniques involving alcohol precipitation; organic phenol/chloroform extraction and salting out have been used for many years to extract and isolate DNA. One example of DNA isolation is exemplified below (e.g. Qiagen All-prep kit). However, there are various other commercially available kits for genomic DNA extraction (Life technologies; Sigma). Purity and concentration of DNA can be assessed by various methods, for example, spectrophotometry.

Additional Testing

[68] The claimed method may be performed as a reflexive test. In the context of the present disclosure, a "reflexive test" or "reflex test" can refer to a subsequent test (e.g., a second test) that is undertaken based upon the results obtained in a previous test (e.g., a first test). For example, when detecting whether a subject has a food allergy or a propensity to develop the allergy, positive skin prick or IgE-based (e.g. total IgE, specific-IgE, component IgE) testing can lead to a desire to perform a further, more conclusive test to determine whether a subject has a food allergy or a propensity to develop the allergy. In this example, the desire to perform an additional test (i.e. determine methylation status) is driven by previous finding that the majority of individuals that test positive for skin prick or IgE tests do not have a food allergy to the tested allergen. Thus, in one example, the methods of the present disclosure can be used as a reflex test from a positive or inconclusive skin prick or slgE test.

[69] The results of the claimed method may also direct the performance of one or more additional tests. For example, determining methylation status according to the present disclosure and detecting a food allergy in a subject can lead to a desire to perform a further "gold standard" food challenge test to confirm that a subject has a food allergy. Thus, in another example, the methods of the present disclosure can be used as a reflex test to an oral food challenge.

[70] In applying the methods of the present disclosure, it is considered that a diagnostic determination regarding a food allergy or the propensity to develop and allergy can be made by determining methylation status according to the present disclosure. However, the diagnostic determination may or may not be conclusive with respect to the definitive diagnosis upon which a treating physician will determine a course of treatment or intervention. Thus, in an example, the methods of the present disclosure can be used in combination with other methods or "additional test(s)" of clinical assessment known in the art (e.g. IgE-targeted tests) in providing an evaluation of the presence of a food allergy or an increased risk of developing an allergy.

[71] Thus, in an example, the methods of the present disclosure may also be performed as an adjunctive test. In the context of the present disclosure, an "adjunctive test" provides information that adds to or assists in the interpretation of the results of other tests, and/or provides information useful for confirming or resolving an inconclusive test. For example, in a clinical setting a skin prick, scratch or scrape test, a skin patch test, slgE test or similar may reveal that a subject has a food allergy or propensity to develop an allergy. Such an assessment is generally inconclusive and requires confirmation. Therefore, in an example, to assist in determining whether the subject has a food allergy or the propensity to develop an allergy, the methylation status can also be determined using the methods of the present disclosure as an adjunct to the skin prick, slgE or similar IgE-based tests.

[72] In the skin prick, scratch and scrape tests, a few drops of purified allergen is gently pricked on to the skin surface. If a subject is sensitized to a particular allergen, the skin at the prick site will swell up to produce a wheal.

[73] In general, a wheal size less than about 2 mm is indicative that the subject is not allergic to the particular allergen and no further testing is required. A wheal size of greater than about 2 mm suggests that the subject may be clinically sensitised to a particular allergen.

[74] While a wheal size greater than about 4mm for egg allergy or 8 mm for peanut allergy is highly indicative of a food allergy and correlates well with a positive oral food challenge, a wheal size of less than about 4mm (egg) or 8mm (peanut) is poorly predictive of allergy and is poorly predictive of a subjects response to oral food challenge.

[75] Accordingly, a skin prick test is generally inconclusive with a wheal size greater than between about 2 mm (all food allergies) and less than about 4mm (egg allergy) or 8 mm (peanut allergy).

[76] Nonetheless, a large number of subjects with a wheal size greater than about 2 to 3mm and less than about 7 to 8mm may be diagnosed as clinically sensitised to an allergen and subsequently prescribed an unnecessary avoidance program or additional testing such as an oral food challenge. The methods of the present disclosure may be used to reduce implementation of unnecessary avoidance programs or additional testing such as oral food challenge. For example, the methods of the present disclosure may be used to resolve an inconclusive skin prick test or slgE test, or to resolve inconclusive diagnoses based on a combination of the two. In one example, the present disclosure encompasses a method of detecting a food allergy or a propensity to develop a food allergy in a subject, wherein a skin prick test performed on the subject produces a wheal size of between about 2mm and about 8mm. In other examples, a skin prick test performed on the subject produces a wheal size of between about 3mm and about 7mm, between about 4mm and about 6mm. In other examples, the subjects skin prick test produces a wheal size of about 2mm, about 3mm, about 4mm, about 5mm, about 6mm, about 7mm, about 8mm, about 9mm, about 10 mm. [77] In a further example, the present disclosure encompasses a method of detecting a food allergy or a propensity to develop a food allergy in a subject, wherein a slgE test performed on the subject provides peanut slgE between about 0.30 kUA/L and about 15 kUA/L in response to peanut allergen or between about 0.30 kUA/L and about 1.20 kUA/L in response to egg allergen. In another example, the present disclosure encompasses a method of detecting a food allergy or a propensity to develop a food allergy in a subject, wherein a slgE test performed on the subject provides peanut slgE between about 0.35 kUA/L and about 14.9 kUA/L in response to peanut allergen or between about 0.35 kUA/L and about 1.17 kUA/L in response to egg allergen.

[78] In performing adjunctive testing methylation status can be determined at or about the same time as the additional test (e.g. skin prick or slgE tests). However, these steps may be performed separately.

[79] When performing the methods of the present disclosure in combination with an additional test, each test may be performed on separate samples obtained from the same subject. For example, a subject may provide two samples with one being sent for assessment using the methods of the present disclosure and the other for assessment using a slgE test. Of course, in a clinical setting, obtaining additional samples for further testing can be difficult and can delay further testing that is desirable. Further, when additional samples need to be processed prior to testing, it is desirable to minimize the costs associated with processing (e.g., reagents, and the like). Accordingly, when performing the methods of the present disclosure in combination with an additional test each test may be performed on the same sample obtained from the patient.

Subjects

[80] As used herein, the "subject" can be any organism which can have an allergy. In an example, the subject is a mammal. The mammal may be a companion animal such as a dog or cat, or a livestock animal such as a horse or cow. In one example, the subject is a human. For example, the human subject can be a child. Terms such as "subject", "patient" or "individual" are terms that can, in context, be used interchangeably in the present disclosure. In an example, the methods of the present disclosure can be used for routine screening of subjects. Alternatively, in another example, the methods of the present disclosure can be used to detect a food allergy or the propensity to develop a food allergy in a subject with symptoms that may be indicative of an allergy. In the context of a food allergy, the present disclosure would be applicable to a subject presenting to the clinic with symptoms such as tingling or itching in the mouth, hives, itching or eczema, swelling of the lips, face, tongue and throat or other parts of the body, wheezing, nasal congestion or trouble breathing, abdominal pain, diarrhoea, nausea or vomiting, dizziness, light headedness or fainting.

[81] In all cases, a subject must first be exposed to an allergen to develop antibodies, which then react to further exposures. Such subjects are referred to as being "sensitised" due to the presence of Immunoglobulin E (IgE) antibodies. In the context of food allergy, IgE antibodies specific to food allergens are detectable and such subjects are referred to as being food "sensitised". In an example, a food "sensitised" subject has a positive skin prick test for a food allergen and a negative oral food challenge outcome for that same allergen. The methods of the present disclosure may be used to detect whether a "sensitised" subject, such as a food sensitised subject has a food allergy or the propensity to develop an allergy.

[82] The immune system of a "sensitised" subject can react to an allergen by way of an immune response characterised by the excessive activation of specific white blood cells by allergen binding to cognate IgE present on the surface of these cells. Subjects with an immune system that reacts to an allergen are known as "allergic" to the allergen. In the context of food allergy, such subjects are referred to as being food "allergic". In an example, an "allergic" subject has a positive skin prick test for an allergen and a positive oral food challenge outcome for the allergen. The methods of the present disclosure may also be used to detect whether a subject is allergic to a particular food. Accordingly, the methods of the present disclosure may be used to detect whether a subject, "allergic" or "sensitized", has a food allergy or the propensity to develop an allergy.

[83] For the avoidance of doubt, the methods of the present disclosure may be used to determine whether a subject does not have a food allergy based on the methylation status of the CpG sites of the present disclosure. For example, the disclosed methods can be used to identify subjects that are not "allergic" for an allergen. For example, the disclosed methods can be used to identify subjects that are not food "reactive" or food "sensitised". In these examples, the methylation status of the CpG sites of the present disclosure would indicate the lack of an allergy.

Method of Treatment

[84] The methods of the present disclosure can be incorporated into methods of treating food allergy. If a food allergy or a propensity to develop a food allergy is detected in a subject using the methods of the present disclosure the subject can be directed or prescribed an appropriate treatment for the allergy. For example, allergy detected using the methods of the present disclosure may be treated with a pharmacological agent. Suitable exemplary therapies include, administration of an antihistamine or allergen immunotherapy or IgE neutralization using anti-IgE monoclonal antibody or vaccination with an allergen peptide.

[85] Another exemplary therapy is described in WO 2009/094717 and relates to the administration of a probiotic and an appropriate food allergen to induce tolerance to the food allergen. For example, the probiotic may be from a species such as Lactobacillus, Bifidobacterium, Escherichia, Saccharomyces, Streptococcus or Bacillus or a combination thereof. The appropriate allergen will depend on the allergy being treated. For example, treatment of a peanut allergy will require administration of a peanut allergen.

[86] In another example, allergy detected using the methods of the present disclosure may be treated by behavioural intervention such as establishing an avoidance program for the subject.

Screening, Prognostic and Theranostic Applications

[87] In an example, the methods of the present disclosure can be used in a pre- screening or prognostic manner to assess whether a subject has an allergy, and if warranted, a further definitive diagnosis can be conducted. For example, a "food challenge" test could be used to obtain a definitive diagnosis of an allergy.

[88] In another example, the methods of the present disclosure are used to determine the likelihood that a subject will develop a food allergy (prognostic). For example, the methods of the present disclosure can provide measures of relative risk that a subject has a food allergy or a propensity to develop an allergy.

[89] In another example, the methods of the present disclosure can indicate or determine the therapeutic effectiveness of a drug or therapy (theranostic). For example, the methods of the present disclosure can be used to determine a subjects response to allergen immunotherapy. Allergen immunotherapy, also known as desensitisation\ aims to desensitise patients to a particular allergen by administering increasing doses of allergen(s) to accustom a patients body to particular substances. In this example, a reduction in methylation of the CpG sites of the present disclosure is indicative of a positive response to allergen immunotherapy. For example, a patient may provide a sample before allergen immunotherapy is initiated and provide additional samples over time as treatment progresses. The initial sample can be used as a baseline and a decrease in methylation indicates that the patient is responding to immunotherapy. In another example, a sample can be obtained from patients subject to allergen immunotherapy and compared with a control sample. Such assessments can be repeated at various time points as treatment progresses and/or escalates to detect whether the subject is responding to therapy.

Computer Implemented Method

[90] The methods of the present disclosure may be implemented by a system. In an example, the system is a computer system comprising one or a plurality of processors which may operate together (referred to for convenience as "processor") connected to a memory. The memory may be a non-transitory computer readable medium, such as a hard drive, a solid state disk or CD-ROM. Software, that is executable instructions or program code, such as program code grouped into code modules, may be stored on the memory, and may, when executed by the processor, cause the computer system to perform functions such as determining that a task is to be performed to assist a user to determine the methylation status of CpG sites in DNA obtained from the subject, the CpG sites being selected from the present disclosure (e.g Tables 2 to 5); receiving data indicating the methylation status of CpG sites in DNA obtained from the subject; processing the data to detect a food allergy or the propensity to develop a food allergy based on a methylation status of the CpG sites; outputting the presence of the allergy or a propensity to develop the allergy in a subject.

[91] In an example, the memory comprises program code which when executed by the processor causes the system to determine the methylation status of CpG sites in DNA obtained from the subject, the CpG sites being selected from the present disclosure or receive data indicating the methylation status of the CpG sites in the subject; process the data to detect a food allergy or the propensity to develop a food allergy based on the methylation status of the CpG sites; report the presence of the allergy or a propensity to develop the allergy in a subject.

[92] In another example, the system may be coupled to a user interface to enable the system to receive information from a user and/or to output or display information. For example, the user interface may comprise a graphical user interface, a voice user interface or a touchscreen.

[93] In an example, the system may be configured to communicate with at least one remote device or server across a communications network such as a wireless communications network. For example, the system may be configured to receive information from the device or server across the communications network and to transmit information to the same or a different device or server across the communications network. In other embodiments, the system may be isolated from direct user interaction.

[94] In another example, performing the methods of the present disclosure to detect a food allergy or a propensity to develop a food allergy in a subject, by determining the methylation status of CpG sites in DNA obtained from the subject, enables establishment of a diagnostic or prognostic rule based on the methylation status. For example, the diagnostic or prognostic rule can be based on the methylation status relative to a control.

[95] In another example, the diagnostic or prognostic rule is based on the application of a statistical and machine learning algorithm. Such an algorithm uses relationships between methylation profiles and disease status observed in training data (with known disease status) to infer relationships which are then used to predict the status of patients with unknown status. An algorithm is employed which provides an index of probability that a patient has a food allergy or propensity to develop an allergy. The algorithm performs a multivariate or univariate analysis function.

[96] In one example, the present disclosure relates to a knowledge base of training data comprising methylation status at a particular CpG site(s) or in or across a gene(s) in DNA obtained from a subject with a food allergy to generate an algorithm which, upon input of a second knowledge base of data comprising corresponding levels of methylation from a patient with an unknown allergy status, provides a probability that predicts the nature of unknown allergy status or response to treatment.

[97] The term "training data" can include knowledge of the methylation status in individuals with confirmed allergy status, i.e. allergic status and non-allergic status.

[98] In another example, the present disclosure relates to a method of allowing a user to determine the status, prognosis and/or treatment response of a subject with an allergy, the method including (a) receiving data indicating the methylation status of the CpG sites in the subject; b) processing the data to determine the methylation status of the CpG sites; and c) outputting the status, prognosis and/or treatment response of a subject.

[99] In an example, the methylation status provides a correlation to the presence, state, classification, remission or progression of the allergy.

Sensitivity and Specificity

[100] In various embodiments, the sensitivity achieved by the methods of the present disclosure is at least about 50%, at least about 60%, at least about 70%, at least about 71%, at least about 72%, at least about 73%, at least about 74%, at least about 75%, at least about 76%, at least about 77%, at least about 78%, at least about 79%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%.

[101] In various embodiments, the specificity achieved by the methods of the present disclosure is at least about 50%, at least about 60%, at least about 70%, at least about 71%, at least about 72%, at least about 73%, at least about 74%, at least about 75%, at least about 76%, at least about 77%, at least about 78%, at least about 79%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%.

Compositions/Kits

[102] In one example, the present disclosure relates to a kit comprising PCR primer pairs specifically configured to amplify the CpG sites outlined in the present disclosure (see for example Tables 2, 3, 6 and 7) for use in the methods of the present disclosure.

[103] For example, the kit can comprise:

a) a reactant able to modify DNA sequence to convert unmethylated cytosines to uracils; and/or;

b) a polypeptide capable of binding methylated DNA;

c) probes and/or primers specific for at least at least 2 CpG sites selected from the group shown in Table 2.

[104] In an example, the kit components may be packaged in or with a suitable solvent or in lyophilised form.

[105] The kit components may optionally be packaged in a suitable container with written instructions for performing the method of the present disclosure, such as treating genomic DNA isolated from a subject for a time and under conditions sufficient to convert non-methylated cytosine to uracils; digesting and amplifying treated DNA using PCR using primer pairs specifically configured to amplify the CpG sites outlined in the present disclosure (see for example Tables 2, 3, 6 and 7).

[106] All publications discussed and/or referenced herein are incorporated herein in their entirety.

[107] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.

[108] The present application claims priority from AU 2014905153 filed 19 December 2014, the disclosures of which are incorporated herein by reference.

EXAMPLES

Example 1 - Results

[109] Individuals sensitized to either egg or peanut were selected, to represent both common and severe forms of food allergy respectively. Patients were divided into major subgroups according to their food challenge outcome as shown in Table 1.

Table 1. Sample groupings for HealthNuts blood samples

Major group Sub Group Clinical Outcome

FA Egg SPT > 2mm to egg, unequivocal allergic reaction

(n=15) during OFC

Peanut SPT > 2mm to peanut, unequivocal allergic

(n=14) reaction during OFC

FS Egg SPT > 2mm to egg, no allergic reaction during

(n=14) OFC

Peanut SPT > 2mm to peanut, no allergic reaction during

(n=15) OFC

NA - SPT negative to peanut, egg, sesame, cows milk,

(n=13) no allergic reaction during OFC to these

FA = Food Allergic, FS = Food Sensitized, NA = Nonallergic control, SPT = skin prick test, OFC = oral food challenge.

[110] Non-allergic (NA) controls had a negative SPT with a negative oral food challenge outcome. Food sensitized (FS) individuals had a positive skin prick test (either egg or peanut), and a negative OFC outcome (either egg or peanut). Food allergic (FA) individuals had a positive skin prick test (either egg or peanut) and an unequivocal allergic reaction during OFC (either egg or peanut). Using these definitions the study cohort consisted of n=71 samples in total comprising sub groups of egg sensitized (ES, n=14), egg allergic (EA, n=15), peanut sensitized (PS, n=15), peanut allergic (PA, n=14) or non-atopic (NA, n=13) individuals. This sample size, provided approximately 80% power to detect between-group differences in methylation of 4% or greater assuming 99% of the genome is not differentially methylated.

[I l l] Parallel flow cytometry cell counts were obtained from ex vivo blood samples to assess for confounding effects of blood cell composition on methylation profiles. DNA methylation assay was carried out using the Infinium HumanMethylation450 BeadChip as previously described (Martino, D. et al. (2014) Epigenetics 9; Martino, D. et al. (2013) Genome Biol 14, R42 (2013).

[112] Following data pre-processing and quality control, probes most variably methylated between subgroups of interest (EA, ES, PA, PS) were identified using ANOVA. A classifier model trained to predict food challenge outcome was then built from these most variably methylated probes.

[113] Non-allergics were excluded from building the classifier model as these individuals would not be seen in clinic, and were therefore not of clinical relevance, but were reintroduced for the subsequent sensitivity analysis. The approach is outlined in Figure 1.

[114] In total 4485 probes varied significantly between clinical subgroups of interest (EA, ES, PA, PS, ANOVA p < 0.01). Multidimensional scaling analysis (MDS) was employed to cluster the 58 samples according to the 4485 variable probes. All samples clustered according to their food allergy phenotype, suggesting these probes correlate with the clinical phenotypes of interest (Figure 1A).

[115] A nearest shrunken centroid classifier was trained to predict the food challenge outcome group (FA or FS) to which each patient belonged. The 58 samples were partitioned into a training set consisting of 80% of the sample population for model building (n=22 FA, n=24 FS), and a validation set consisting of n=7 FA and n=5 FS. Further independent validation was assessed subsequently in a separate population (discussed below).

[116] Using a 10-fold cross validation on the training data set, a classifier model was developed based on 649 CpG (Table 2), a threshold at which cross-validation errors were minimized at 2.2% in the training data set (Figure IB). The median false discovery rate (FDR; q-value) was calculated using 100 permutations and was controlled at 0.04.

[117] The 649 predictive CpG were then annotated according to their genomic location to identify overlapping protein coding genes and performed an enrichment test for the gene list. The list was substantially enriched for immunological signatures derived from numerous cell types, including genes differentially expressed between plasma cells and memory B-lymphocytes (VEGFA, TG, TIMP2, PLA2G16, VKORC1) (FDR q-value 7.09 E-04), effector memory and central memory cells (DTX1, MAP3K1, SLC24A6, PKMYT1) (FDR q-value 8.70E-04), Tregs and conventional T-cells (CAMKKl, TRPM1, SCL45A2, ARF4) (FDR q-value 8.70E-04) and genes induced by dendritic cells upon stimulation. The enrichment test was consistent with a mixed cell- type epigenetic signature. Table 2. Differentially methylated CpG sites predictive of food allergy (n = 649) allergic- sensiti zed- av-rank-in-

C S score score CV chr os strand UCSC RefGene Name cg02866639 0.2646 -0.2425 3.3 chrl7 1808743 +

cf.00044796 0.263 -0.2411 4.9 chrl 146552390

cg24584002 0.2264 -0.2076 6.7 chr2 3606104 RNASEH1 cf.25261377 0.2143 -0.1964 9.4 chr6 30313517 RPP21

cg07060505 -0.2128 0.1951 11.7 chrl2 116756136

VEGFA;VEGFA;VEGFA;VE GFA;VEGFA;VEGFA;VEGF A;VEGFA;VEGFA;VEGFA;V EGFA;VEGFA;VEGFA;VEG cg21099624 -0.2101 0.1926 14.1 chr6 43737749 FA;VEGFA;VEGFA cg01112801 0.2049 -0.1878 13.4 chr21 30443603 CCT8

cg04480106 -0.2041 0.1871 21 chr5 72934606 + RGNEF

cgl l229343 0.2009 -0.1842 12.4 chr2 242808427

cgl5377988 -0.1888 0.1731 20.9 chr22 37594933 +

cgl7167159 0.1863 -0.1708 21.3 chrl5 81620559

cgl9675731 -0.1853 0.1698 17.4 chrl 202129192 + PTPN7;PTPN7 cgl l941920 -0.184 0.1687 17.2 chr6 79339195 +

cg24708354 0.1743 -0.1598 28.5 chr2 242888658 +

cgl3560030 0.1734 -0.159 22.6 chrl2 96099778 + NTN4

cg24851651 0.1686 -0.1545 44.3 chrl l 66362959 CCS

cg26124569 0.1667 -0.1528 26.9 chr3 187975150 LPP;LPP;LPP

RASGRP2;RASGRP2;RASG cg08378782 -0.163 0.1494 36.5 chrl l 64512197 + RP2

cgl5388570 0.163 -0.1494 36.5 chrl4 78328609 ADCK1 ;ADCK1 cg20502977 0.1608 -0.1474 42.6 chr2 238235136 + COL6A3 ;COL6A3 ;COL6A3 cg02788266 0.1589 -0.1456 35.2 chr5 50694104 +

cg23502162 0.1575 -0.1444 47.7 chr2 189839015 + COL3A1

cg09276486 0.1544 -0.1416 47.2 chrl 6999547 + CAMTA1 cg24187345 0.1526 -0.1399 48 chr6 40361096 LRFN2

cg24076083 -0.1518 0.1391 47.6 chrl9 12946093 RTBDN;RTBDN cg05074645 -0.1515 0.1389 42.4 chrl3 101327922 + TMTC4;TMTC4 cg02681173 0.1515 -0.1388 43.3 chrl2 130527106 + LOC100190940 cgl4120583 0.1499 -0.1374 43.2 chrl9 453860 SHC2

cg08214808 0.1483 -0.1359 51.2 chrl l 45922166 MAPK8IP1 cg02857312 0.1475 -0.1352 48.3 chr2 28449036 + BRE;BRE;BRE;BRE;BRE cg097SSS79 0.1465 -0.1343 45.3 chr2 61645972 SNORA70B;USP34 cg03716942 0.1448 -0.1327 50.2 chrl 1821981 + GNB1

cgl3417268 0.1445 -0.1325 59.1 chrl5 26108642 + ATP10A

cg02988826 -0.1439 0.132 57.1 chr3 155587661 GMPS

cg04852972 -0.1393 0.1277 59.8 chrl 1897728 KIAA1751 cg07184807 0.1365 -0.1252 57.4 chr3 157537400 + TMEM176B;TMEM 176B;TM

EM176B;TMEM176B;TMEM cg03964111 0.136 -0.1247 65.4 chr7 150498493 + 176A

cf.20558320 0.136 -0.1246 64.4 chr2 235210313

cg04353106 -0.1358 0.1245 88.3 chrl 163136667 + RGS5

cgl 8449739 0.1345 -0.1233 57.3 chrl2 113495701 + DTX1 ;DTX1 cgl3887261 -0.133 0.1219 59.3 chr9 96082521 C9orfl29;WNK2 cg04995300 0.1324 -0.1213 71.5 chr3 66848608 +

cg03249890 -0.132 0.121 62.7 chr2 175199803 + SP9

cgl 2596505 -0.1317 0.1208 69.8 chrl 213090240

cgl 8660064 0.1297 -0.1 189 78.8 chrl 23504632 +

RASGRP2;RASGRP2;RASG cg06409432 -0.1286 0.1 179 95.9 chrl l 64512199 + RP2

cgl9738182 -0.1282 0.1175 88 chr2 223726189 ACSL3;ACSL3 cg00395420 0.1273 -0.1167 83.8 chr5 140716485 PCDHGA1 cgl3968099 -0.1272 0.1 166 89.9 chr5 162864768 CCNG1;CCNG1 ;CCNG1 cg26792755 0.1266 -0.1161 76.2 chr7 140714919 MRPS33;MRPS33 21 19820 -0.1252 0.1 147 80 chr7 28081543 + IAZF1

cgl 0292139 0.125 -0.1146 83.5 chr8 97507561 SDC2

TMEM176B;TMEM 176B;TM EM176B;TMEM 176B;TMEM cg05726600 0.1246 -0.1 142 88.8 chr7 150498594 176A

cgOl 855013 -0.124 0.1 137 82.2 chrl6 31489209 TGFB 111 ;TGFB 111 ;TGFB 111 cg26963090 0.1239 -0.1136 92.3 chrl7 76887989 TIMP2

cgl0461264 -0.1238 0.1 134 80.9 chrl2 125131065 +

cg24004368 0.1231 -0.1128 75.8 chr6 30706647 + FLOT1

cg24417499 0.1225 -0.1123 94.2 chrl 33351653 HPCA

cg04091209 -0.1218 0.1 117 84.9 chrl5 75744113 + SIN3A;SIN3A;SIN3A cgl l 721361 0.1215 -0.11 14 79.4 chrl 145139908 +

cg04746699 0.1212 -0.11 11 85.6 chrl9 17336907 OCEL1

cg06067394 -0.1212 0.1 11 1 90.2 chrl l 133789110 + IGSF9B

cgl6484943 -0.1194 0.1094 95.8 chrl l 63382065 + PLA2G16;PLA2G16 cg03068039 0.1184 -0.1085 99.1 chr8 146219928 ZNF252;TMED10P cgl0541674 -0.1183 0.1084 90.6 chrl2 576104 1 NXPH4

cgl 1390957 0.1165 -0.1068 97.9 chrlO 118033312 + GFRA1 ;GFRA1 ;GFRA 1 eg 15909600 0.1155 -0.1059 102.2 chr22 51038723 + MAPK8IP2

PSRC 1 ;PSRC 1 ;PSRC 1 ;PSRC 1 cg l4216290 0.1152 -0.1056 108. 1 chrl 109825773 + ;PSRC 1 ;PSRC 1 ;PSRC 1 cgl 9029127 -0.1 152 0.1056 91.9 chr4 37604497 + RELLl

cg07740525 -0.1148 0.1052 100.6 chr9 1 16861532 KIF12

cg04386614 0.1129 -0.1034 103.1 chr7 126364460 + GRM8;GRM8;GRM8 cg00875272 -0.1128 0.1034 106.4 chrl 47695321 + TALI ;TAL1 cg00217859 -0.1126 0.1032 114.5 chrl6 69789140 + NOB1

cgl3299325 -0.1123 0.103 103.1 chr6 447777

cg03217880 0.1118 -0.1025 102.8 chrl 230468464 + PGBD5

INTS12;INTS 12;GSTCD;INT cg02514062 0.1093 -0.1002 109.4 chr4 106629846 + S12;INTS 12 cgOl 822573 0.109 -0.1 137.1 chrl7 45772473 + TBKBP1 cgl9124424 0.1087 -0.0997 112.7 chr7 511498 +

cg04226232 -0.1085 0.0994 110.9 chr5 123775157

cg08889347 -0.1073 0.0983 113.3 chr3 193227429 + ATP13A4 cgOl 872024 0.1068 -0.0979 118.7 chrl l 94502804 AMOTL1 cs>22718891 -0.1066 0.0977 1 1 1.5 chrl 52035148 +

cg04902302 0.1049 -0.0961 117.9 chr2 175594739

cg22213821 -0.1042 0.0955 129.9 chrl7 31255268 TMEM98;TMEM98 cgl5553408 0.1038 -0.0951 133.4 chr6 31549010 LTB;LTB cg00557840 0.1035 -0.0949 118.1 chr20 4982389 + SLC23A2;SLC23A2 cg04493169 -0.1032 0.0946 138.3 chr7 27912112 JAZF1

cg03772020 0.1032 -0.0946 126.7 chr6 148879122 +

cg02108645 -0.1029 0.0943 136.8 chr8 144303773

cg01079652 -0.1023 0.0938 132.8 chrl 791 18191 IFI44

cg l7611045 0.1021 -0.0936 135.4 chr3 192289245 FGF12

cg07620573 0.1019 -0.0934 138.1 chr3 192289293 + FGF12

cgl4334310 -0.1013 0.0928 125.1 chrl4 103558502

cg01327552 0.1004 -0.092 135.6 chr6 167275809 RPS6KA2 cg05861255 -0.1 0.0917 130.5 chrl2 123921688 + RILPL2

cgl3772055 0.0995 -0.0912 138.5 chrl4 31024522 +

cg06931327 0.0987 -0.0904 142.6 chr5 95297900 ELL2

cgl 8107409 0.0986 -0.0904 139.2 chrl4 102228802 PPP2R5QPPP2R5C

STH;MAPT;MAPT;MAPT;M cgl4431592 -0.098 0.0898 140.1 chrl7 44076415 + APT;MAPT;MAPT cg07572563 -0.0975 0.0894 138.7 chrl4 70826338 + COX16;COX16 cg017431 10 0.0975 -0.0894 151.6 chrl 5 65294293 + MTFMT

cgl 0592245 0.0974 -0.0893 139.7 chrl 215256451 + KCNK2;KCNK2;KCNK2 cg08235883 -0.0971 0.089 137.9 chrl2 122238836 + LOC338799 cg04543115 -0.0969 0.0888 148.3 chrl9 12958913 MAST1

cgl5185986 -0.0962 0.0882 152.3 chrl3 98919378 + FARP1

cg09021898 -0.0961 0.0881 137.9 chr2 174075476 + ZAK;ZAK cg04464446 -0.0958 0.0879 154.3 chrl l 68452845 + GAL

cg24633242 -0.0949 0.087 146.6 chrl6 53537307 + AKTIP;AKTIP cg00143998 0.0943 -0.0865 151 chrl 228645859 + HIST3H2A;HIST3H2BB cg20622410 -0.0941 0.0862 156.6 chrl7 39930302 + JUPJUP

cg22346119 -0.0936 0.0858 163.6 chrl 6453452 ACOT7

cgl2206093 -0.0935 0.0857 162.6 chrl l 65154388 + ERMD8

cg26080111 -0.0934 0.0856 167.2 chr7 559651 + PDGFA;PDGFA cgl4975160 -0.0932 0.0854 161.4 chrl3 37269125 + C13orf36 cg23187159 -0.0932 0.0854 149.1 chr8 33456955 + DUSP26

cg00442802 -0.0932 0.0854 143.7 chrl l 18138394 SAA3P

cgl4463292 0.0929 -0.0852 162.5 chr3 192289252 FGF12 cgl0324096 0.0929 -0.0851 171.3 chrl4 92587714 + CPSF2;NDUFB1 cgl7694494 0.0929 -0.0851 169.1 chr2 159260771 + CCDC148;CCDC148 cg05039488 0.0928 -0.0851 168.8 chr6 79577232 + IRAK1BP1;IRAK1BP1 cg01246444 0.0925 -0.0848 156.5 chr2 161775589 +

cg00718452 0.0917 -0.084 170 chrl2 46299877 + ARID2

cgl4981189 -0.0916 0.084 156.6 chrlO 5113871 +

cg01899810 0.0913 -0.0837 180.5 chr3 132378241 + UB A5 ;UB A5 ; AC AD 11 cgl3497017 0.0912 -0.0836 161.8 chrl 237214259 + RYR2

cgl6553182 -0.0907 0.0831 173.2 chrl 160068681 + IGSF8

cgl2982259 -0.0907 0.0831 189.6 chr7 141463881 + TAS2R3

cg23039325 -0.0894 0.082 155.8 chr3 175797430

cgl 8763100 0.0894 -0.0819 165.4 chr6 32917411 + HLA-DMA cg04015759 0.0893 -0.0819 176.7 chr2 27718181 + FNDC4

cg05240614 -0.0891 0.0817 168.9 chrl9 49122848 + RPL18;SPHK2 cg22710065 0.0891 -0.0817 181.6 chrl9 54668256 + TMC4;TMC4 cgl7462356 0.0891 -0.0817 164.4 chrl7 80056334 FASN

cg04532834 -0.0889 0.0814 158.7 chrl9 4535188 + PLIN5;PLIN5 cg05057720 0.0887 -0.0813 161.6 chrl4 38724675 CLEC14A

cg22468123 0.088 -0.0807 171.9 chrl9 55996652 NAT 14

cg01632865 -0.0876 0.0803 167.5 chrl2 132681397 + GALNT9;GALNT9 cgl 8798744 -0.087 0.0797 173.1 chr5 138775375 DNAJC18 cgl2050358 0.0867 -0.0795 171.8 chrl7 36612909 ARHGAP23 cg08996805 -0.0864 0.0792 173.6 chr3 11010256 +

cg01418261 0.0862 -0.0791 209.8 chrl 47697663 +

cg00939931 0.0859 -0.0787 185.5 chr7 1570663 MAFK

cgl l418508 0.0858 -0.0787 177.2 chrl9 2888943

cg00076774 0.0858 -0.0786 180.2 chr2 45016971

cg20932251 0.0854 -0.0783 196 chrl l 72852302 + FCHSD2

cg26958486 0.0849 -0.0778 182 chr2 174828331 SP3;SP3

cgl2176709 -0.0846 0.0776 175.5 chrl l 113346485 + DRD2;DRD2 cg06578342 0.0844 -0.0773 183.1 chrl6 4349215 +

cg24740320 0.0843 -0.0773 187.9 chrl4 44418808 +

cgl3632752 0.0843 -0.0773 173.8 chr8 36842289

cg21992044 0.0831 -0.0762 185.5 chr6 32918123 HLA-DMA cg24616138 0.0826 -0.0757 192.8 chrlO 126850126 + CTBP2;CTBP2 cgl7464271 -0.0825 0.0756 208.4 chrl 1447047 + ATAD3A;ATAD3A;ATAD3A cg20595215 0.082 -0.0751 200.9 chr3 8543014 LMCD1

cg26816581 0.0819 -0.0751 186.2 chrl2 104458109 HCFC2

cg24746738 -0.0817 0.0749 189.2 chrl4 107284280

cgl5889061 0.0812 -0.0744 196 chrl4 35184805 + CFL2;CFL2;CFL2;CFL2 cg02311630 0.0807 -0.074 211.1 chr2 151070505 + cg03659354 -0.0805 0.0738 203.8 chrl3 111642443

cg03616030 0.0803 -0.0736 211 chr8 94766944 TMEM67 ;TMEM67 ;TMEM67 cgl4458575 -0.0801 0.0734 201.3 chr2 238380390 +

cg01432552 0.0799 -0.0733 218.8 chrl2 71040268 PTPRR;PTPRR cg02840025 -0.0797 0.0731 197.3 chr9 1045050 +

cg07225277 0.0797 -0.073 212.5 chrl l 67188871 ATPGD1;ATPGD1 cg04520704 0.0795 -0.0729 222.3 chr22 18325160 + MICAL3;MICAL3 cgl4014506 -0.0791 0.0725 198 chr9 116861404 KIF12

cg09618933 0.0789 -0.0723 213.8 chr8 23571724

cg20463995 -0.0782 0.0717 201.9 chrl5 96817359

C20orf 199;C20orf 199;SNORD cg01188753 -0.077 0.0706 201.2 chr20 47897068 12;C20orfl99 cg20529070 0.0768 -0.0704 210.8 chr20 42142751 L3MBTL;L3MBTL cg09168692 -0.0767 0.0703 218 chrl 7887560 PER3

cg23461906 0.0761 -0.0697 211.2 chrl2 107349191 C12orf23

cgl4599091 0.0754 -0.0691 215 chrl3 79171679 +

cg24265969 0.075 -0.0687 226.6 chr3 55654481 + ERC2

cgl4831653 0.0745 -0.0683 265.2 chr4 155513189 FGA;FGA

cg01665212 -0.0745 0.0683 224.7 chr6 30712373 IER3

cg03205584 0.0741 -0.0679 229 chrl2 79258407 SYT1 ;SYT1 cg24160660 0.0736 -0.0675 231 chr22 18121281 + BCL2L13

cgl0894318 0.0736 -0.0674 270.2 chr6 160389536 IGF2R

cgl3907859 -0.0729 0.0668 237.2 chrl l 120009124 + TRIM29

KLC2;RAB1B;KLC2;KLC2;K cgl9553869 -0.0726 0.0665 241.6 chrl l 66034926 + LC2

cg06628000 0.0724 -0.0663 240.2 chrl 109756191 + SARS

cgl9856002 -0.0723 0.0663 219.3 chr4 159593054 + C4orf46 ;C4orf46 ;ETFDH cgl4581165 0.0719 -0.0659 240.6 chr7 129072830 FAM40B;FAM40B cg05423144 0.0715 -0.0655 233.2 chr4 139792229 +

cgl3598010 -0.0713 0.0654 231.6 chr7 72838776 +

cg07294734 -0.0709 0.065 234.8 chrl9 1242323 + ATP5D;ATP5D cgl6692538 -0.0705 0.0646 253.6 chr5 1848046

cgl5318697 -0.0703 0.0644 242.5 chr3 32187901 + GPD1L

ATXN3 ; ATXN3 ; ATXN3 ; AT XN3;ATXN3;ATXN3;ATXN3 ;ATXN3 ; ATXN3 ;ATXN3; AT XN3;ATXN3;ATXN3;ATXN3 ;ATXN3 ; ATXN3 ;ATXN3; AT XN3;ATXN3;ATXN3;ATXN3 ;ATXN3 ; ATXN3 ;ATXN3; AT XN3;ATXN3;ATXN3;ATXN3 ;ATXN3 ; ATXN3 ;ATXN3; AT cg09996633 0.07 -0.0642 249 chrl4 92566961 + XN3

cg21176755 0.0699 -0.0641 254.4 chr9 99418634 C9orf21

cgl0079327 -0.0698 0.064 249.7 chrl4 101508789 + MIR1185-1 cg06478987 0.0695 -0.0637 253.1 chr5 56128454 MAP3K1

cg06367354 -0.0695 0.0637 251.5 chr3 8363442 + cgl6547579 0.0691 -0.0633 242.4 chr20 4954333 + SLC23A2;SLC23A2 cg26055210 0.0691 -0.0633 259 chrl l 62310752 + AHNAK;AHNAK

PCDHGA4;PCDHGA9;PCDH GA1 ;PCDHGB 1 ;PCDHGB7;P CDHGB6;PCDHGB3;PCDHG B7;PCDHGA6;PCDHGA8;PC DHGA10;PCDHGA5;PCDHG B4;PCDHGA3 ;PCDHGA2;PC DHGB2;PCDHGA7;PCDHGB cgl3933262 -0.0688 0.063 239.9 chr5 140797172 5

cg25741118 -0.0684 0.0627 241.3 chrl 54482237 + LDLRAD1 cg24249791 0.0679 -0.0622 265.7 chr7 142012611 +

cgl2281517 0.0677 -0.0621 269.5 chr4 153576804 + TMEM154

ACACA;TADA2A;ACACA;T cgl5839435 -0.0676 0.062 244.5 chrl7 35767057 ADA2A;TADA2A cg27604200 -0.0676 0.062 252.4 chrlO 101094135 CNNM1

PRKD2;PRKD2;PRKD2;PRK cg03340501 0.0674 -0.0618 251.1 chrl9 47214109 + D2

cgl768SS79 0.0674 -0.0618 253.5 chrl3 114914537

cgl0008757 0.0665 -0.061 250 chrlO 8097183 + GATA3;GATA3 cg24349668 0.0665 -0.061 261.2 chrl 8 61441546 + SERPINB7;SERPINB7 cgl 1940495 0.0664 -0.0609 269.2 chrl3 21141764 IFT88;IFT88 cg06210070 0.0664 -0.0609 271.6 chr6 33085063 HLA-DPB2 cgl 8906353 -0.0661 0.0606 281.7 chr6 30640154 + DHX16;DHX16 cg26677995 -0.0661 0.0606 254.4 chrl l 1422907 BRSK2

cg06677890 0.0661 -0.0606 297.2 chr5 149887486 NDST1

cgl3486641 -0.066 0.0605 266.7 chr20 25479848 + NINL

cg26209546 -0.0657 0.0603 256.7 chrl l 67798292 + NDUFS8

cg01600921 0.0654 -0.06 272.2 chr2 97649526 + FAM178B

LOC339524;LOC339524;LOC 339524;LOC339524;LOC3395 cg04579203 -0.0653 0.0598 272.1 chrl 87597566 + 24

cg09451903 -0.0651 0.0597 253.7 chrl7 3501338 + TRPV1;TRPV1 cg08981224 -0.0651 0.0597 250.7 chrl l 118530412 + TREH

cg04943225 -0.0648 0.0594 262.5 chr2 47799165 +

cg20688987 -0.0642 0.0588 259.3 chrl2 56110162 BLOC1S1

cgl4348191 0.0641 -0.0588 262.4 chrl5 81180686 + KIAA1199 cg25124366 0.064 -0.0587 278.8 chr2 54557904 + C2orf73

cg26882419 0.0639 -0.0585 268.8 chrl 227751228 ZNF678;ZNF678;ZNF678 cg22319885 0.0638 -0.0584 269.5 chr7 135195151 CNOT4;CNOT4 cgl 8827097 -0.0634 0.0581 263.7 chr2 3522506 + ADIl

cg02495310 0.063 -0.0577 269.8 chr5 134366831 PITX1

cg07068191 0.0628 -0.0576 271.5 chr3 192959176 HRASLS;HRASLS;MGC2889 cg09993699 -0.0628 0.0575 275.9 chrl9 17517008 + BST2

cg27494470 -0.0627 0.0575 282.8 chr6 84172561 +

cg09806318 0.0627 -0.0575 273.1 chr6 1383064

cgl0035583 0.0624 -0.0572 266.5 chr6 39869840 DAAM2

cg07202012 -0.0621 0.0569 278.2 chr4 1684825 + FAM53A cgl6126671 0.0619 -0.0568 284.8 chrl l 73879436 + C2CD3

cgl7460909 0.0617 -0.0566 280.1 chrl5 65821580 + PTPLAD1

ILF3 ;ILF3 ;LOC 147727;ILF3 ;I cgl2956079 -0.0615 0.0564 282.7 chrl9 10764738 + LF3;ILF3

cf.22508930 0.0606 -0.0556 281.8 chrl2 69016159 RAP1B;RAP1B cg08154612 -0.0605 0.0555 291.7 chrl7 7037098

eg 15253672 -0.0605 0.0555 291.4 chr2 731 14960 + SPR

cg04388244 -0.0601 0.0551 308.6 chrlO 105421005 + SH3PXD2A cg06410630 0.06 -0.055 267.2 chil7 78341424 RNF213;LOC100294362 cg06222397 -0.0599 0.0549 289.2 chrl6 86953019

cg21012788 0.0594 -0.0545 272.8 chr4 8642306 +

SNORD115-10;SNORD115- cgl l582484 0.0594 -0.0545 286.3 chrl5 25491315 42

cgl5340874 0.0594 -0.0544 304 chrl 174969621 CACYBP;CACYBP cgl5188491 -0.0594 0.0544 279.6 chrl 146644106 PRKAB 2;PRKAB 2 cgl9714913 -0.0593 0.0544 284.8 chrl 226411033 MIXL1

cg l3942103 -0.0593 0.0544 293.6 chrl 11 1177829

cgOl 962758 0.0592 -0.0543 306.3 chr4 78783825 + MRPL1 ;MRPL1 cg25890092 0.0585 -0.0536 300.8 chrl7 80273322 CD7

cgl3063704 -0.0583 0.0534 289.9 chr3 48885665 + PRKAR2A

cg05980719 -0.0582 0.0534 297.4 chrl6 75788403

cgl7943999 0.0582 -0.0533 289.5 chr8 95274687 GEM;GEM

TMEM176B;TMEM176B;TM EM176B;TMEM176B;TMEM cgl0409299 0.0577 -0.0529 309.7 chr7 150498843 + 176A

cg04848823 0.0576 -0.0528 298.8 chrl6 9536122 +

cg22851485 0.0575 -0.0527 294.1 chrl2 105380772 + C12orf45

cgl 6360182 0.0574 -0.0526 305.4 chrl4 104314518 + PPP1R13B cgl 8391442 -0.0564 0.0517 322 chr2 56318614 +

cg24748191 0.0558 -0.0511 295.1 chrl4 76604613

cg20560186 0.0553 -0.0507 323.6 chr6 109804103 ZBTB24;ZBTB24 cg01708458 -0.0547 0.0501 326.4 chr2 49097906

cgl3264811 -0.0546 0.0501 322.4 chr2 239999466 HDAC4

cg05008688 0.0546 -0.05 312.4 chr5 121464372 + ZNF474

cg03520976 -0.0545 0.05 304.9 chrlO 112432321 + RBM20

eg 12533335 0.0544 -0.0499 295.8 chrl6 84220778 + TAF1C;TAF1C cgl 0022966 -0.054 0.0495 324.6 chrl2 34518957

cg06848185 -0.0533 0.0489 316.6 chrl7 75368902 SEPT9;SEPT9;SEPT9;SEPT9 cg04836792 -0.0532 0.0488 355.6 chr2 204192791 ABI2

cg07661340 0.053 -0.0486 332.4 chrl9 47354324 AP2S1 ;AP2S1

TYW3;CRYZ;CRYZ;TYW3;T cg21906852 -0.0528 0.0484 316.8 chrl 75198582 + YW3;CRYZ;CRYZ cgl 1937920 0.0525 -0.0481 331.1 chrl5 70995381 + UACA;UACA cgl9287711 0.0525 -0.0481 316 chr4 5900244 +

CD209;CD209;CD209;CD209 cg02794892 0.0524 -0.048 315.9 chrl9 7813901 ;CD209;CD209;CD209;CD20 9 cg21885897 0.0524 -0.048 336.5 chrl9 44008145 + PHLDB3

cg00580230 -0.0523 0.0479 318.2 chr5 57756537 PLK2

cgl 8674643 0.0522 -0.0479 339.1 chr4 154713748

cf.07047589 0.0521 -0.0478 336.3 chrl5 40642998 PHGR1

cg08538509 0.0521 -0.0478 309.2 chrl3 30777550 + KATNAL1 ;KATNAL 1 cg l0898127 -0.0519 0.0475 333 3 chrf 1986783 + WHSC2

cg05878630 -0.051 0.0475 331.1 chrlS 91474889 HDDC3;UNC45A cg20295248 0.0518 -0.0475 362.7 chr20 5485270 + LOC149837

LOC404266;LOC404266;HO cgl 8684142 0.0515 -0.0472 337.5 chrl7 46682394 + XB6

cgl7926678 0.0511 -0.0469 315.8 chrl4 69865058 + SLC39A9;ERH cg00199399 -0.051 0.0467 332.6 chr8 49304043

cg04951476 -0.0507 0.0465 326.4 chr6 3848243 FAM50B

cg08313638 -0.0504 0.0462 336.2 chrlO 121 10577 DHTKD1

cg07475000 0.0502 -0.046 353.3 chr7 22162928 RAPGEF5 cgl6538496 -0.0501 0.046 349.2 chr3 31574062 STT3B

cg03191045 0.0498 -0.0456 328.6 chr20 36040903

cg09737685 -0.0492 0.0451 315.7 chrl7 22052189 +

cg07249860 0.0492 -0.0451 383.1 chrl3 36421844 MIR548F5;DCLK1 cg24906992 -0.0492 0.0451 335.8 chr20 33999761 + UQCC;UQCC cg03946731 0.0486 -0.0446 333.7 chrl6 3028109 + PKMYT1 ;PKMYT1 cg25488082 -0.0486 0.0446 344.7 chr2 68384862 + PN01;WDR92

FLJ90757;BAIAP2;BAIAP2;B cg27141898 -0.0485 0.0445 363.4 chrl7 79008816 AIAP2;BAIAP2 cg08908855 -0.0484 0.0444 355.5 chr6 24140899 + NRSN1

SORBS2;SORBS2;SORBS2;S ORBS2;SORBS2;SORBS2;SO cg26785346 0.0483 -0.0443 370.5 chr4 186732936 RBS2

cgl 1802635 -0.0482 0.0442 344 chrl7 4487606 SMTNL2;SMTNL2 cgl4786024 -0.0482 0.0442 365.3 chr3 133748678 SLC02A1;SLC02A1 cg26590449 0.0481 -0.0441 356.7 chr7 33941205 +

SERBP1 ;SERBP1 ;SERBP1 ;S cg01056310 -0.0481 0.0441 331.9 chrl 67895337 + ERBP1

FAM123C;FAM123C;FAM12 cg25816545 0.0477 -0.0437 375.8 chr2 131513066 + 3C;FAM123C cg07653289 0.0475 -0.0435 358.3 chr6 30325734 +

cg02333467 0.0475 -0.0435 381.5 chr7 100290845

PPP1R9A;PPP1R9A;PPP1R9 cg04 16107 0.0475 -0.0435 360.2 chr7 94537805 A;PPP1R9A;PPP1R9A

MFSD2A;MFSD2A;MFSD2A cg03317400 -0.0472 0.0433 383.9 chrl 40420823 ;MFSD2A

cg06132727 -0.0471 0.0432 353.7 chr4 174422191

cgl 1516377 0.0471 -0.0431 359.2 chrl7 1933153 DPH1

cg08640609 0.0469 -0.043 332 chrl 7764642 + CAMTA1 cg09289567 0.0459 -0.0421 364.3 chr2 197226973 HECW2

cg09282085 -0.0454 0.0416 373.1 chr22 39713086 RPL3;RNU86;RPL3 cgl6894974 0.0452 -0.0414 358.4 chrl5 55700780 + CCPG1;CCPG1 cg02072322 0.0448 -0.041 1 357.2 chrl 24470205 IL22RA1 cg04832325 0.0446 -0.0409 369.3 chrl l 126607483 KIRREL3 ; KIRREL3 cg02143778 -0.0439 0.0402 363.2 chrl6 575420

cgl7068462 -0.0439 0.0402 374.5 chr6 32961369 +

cf.23556923 -0.0439 0.0402 375.1 chrl7 62774590 + LOC146880;LOC146880 cg01336233 0.0438 -0.0401 383.2 chr3 15156079 +

cg21601059 -0.0432 0.0396 362.8 chrl2 56122983 + CD63;CD63 cg09856590 0.0428 -0.0392 363.5 chrl6 58551916 SETD6;SETD6 cg21189727 -0.0427 0.0391 381.9 chr8 67940950 + LRRC67

cg23976388 -0.0423 0.0387 374.2 chrl l 61510081 DAGLA

cgl9673155 0.0422 -0.0387 403.9 chrlO 72995181 UNC5B

cg05791946 -0.0421 0.0386 373.4 chrl 3160851 PRDM16;PRDM16 cgl4354472 0.0418 -0.0383 379.6 chr9 135037189 NTNG2

cg24533720 -0.0416 0.0382 388.6 chr7 6099233 EIF2AK1 ;EIF2AK1 cgl5250507 0.0415 -0.038 376.6 chrl 200860365 Clorfl06

eg 14552508 -0.0413 0.0379 408.5 chr2 8628173

cg l6434331 -0.0413 0.0378 387 chrl7 70712540 SLC39A1 1 ;SLC39A1 1 cgl4820653 0.0407 -0.0374 369.6 chi2 173046018 +

cg04394691 0.0407 -0.0373 415.5 chr6 31466174 MICB

cg26401541 -0.0406 0.0372 418.7 chr6 91078974 +

egl 8232597 0.0405 -0.0371 422.8 chrl7 29624396 NFl;OMG;NFl cg26738987 0.0405 -0.0371 401.8 chrl l 94135029 + GPR83

cg05508355 0.0404 -0.037 422.9 chrl2 64415282 SRGAPl

cgl5130459 -0.0404 0.037 419 chr5 40834228 SNORD72;RPL37 cg21809007 0.0404 -0.037 434.9 chrl9 6739524 TRIP10

cg04569381 0.0403 -0.037 410.4 chr6 69344576 + BAD

SNORD27;SLC3A2;SLC3A2; SNORD28;SNORD25;SLC3A 2;SLC3A2;SNORD26;SLC3A cg23457925 -0.0402 0.0369 398.7 chrl l 62623434 2;SNHG1

cg25503323 0.04 -0.0367 417.9 chr7 36567098 AOAH

cg05321656 -0.0399 0.0366 419.3 chrlO 69991522 + ATOH7;ATOH7 cg06398524 0.0398 -0.0365 394.3 chr7 138710963 + ZC3HAV1L cg02491003 0.0395 -0.0362 420.1 chr3 57581393 ARF4

cg21199355 -0.0394 0.0361 389.9 chr4 41984148 + DCAF4L1

cg22095604 -0.0391 0.0358 422.1 chrl 8 31803241 + NOL4;NOL4 cgO 1046905 -0.0391 0.0358 398.7 chr6 159515345

cg07113199 0.0388 -0.0356 426.1 chrl2 52258163 +

CSNK1G3;CSNK1 G3;CSN 1 cg00068305 0.0388 -0.0355 415.1 chr5 122847680 + G3;CSNK1G3 cg22643080 -0.0384 0.0352 409.1 chr6 4136196 PECI;PECI;PECI;PECI cg07205860 -0.038 0.0348 395.9 chr6 31833864 SLC44A4

cgOl 895740 0.038 -0.0348 445.2 chrl9 7852739 CLEC4GP1 egl 8884295 0.038 -0.0348 422.5 chr6 131234267 + EPB41L2;EPB41L2;EPB41L2

ZNF438;ZNF438;ZNF438;ZN cgl4137286 -0.0379 0.0347 431 chrlO 31288997 F438;ZNF438;ZNF438;ZNF43 8;ZNF438

cgl4525407 -0.0376 0.0345 426.8 chrl5 63570327 + APH1B;APH1B cf.16966340 0.0375 -0.0344 436.1 chrl 48170888

cf.09443479 -0.0375 0.0344 428.9 chr8 108511174 + ANGPTl

cg03970350 -0.0374 0.0343 394.4 chr22 31003010 TCN2

cg25612297 0.0372 -0.0341 440.8 chr2 64046701

cg01749651 0.0372 -0.0341 407.9 chr8 143279569 NCRNA00051 cg25003697 0.0372 -0.0341 425.6 chrl7 79453611

cg02313172 0.0371 -0.034 401.9 chrl4 105260146 + AKT1 ;AKT1 ;AKT1 cg05897163 0.037 -0.0339 424.8 chrl7 37782302 + PPP1R1B

cg09555323 0.0368 -0.0337 435 chr6 32629786 + HLA-DQB 1 cgl3598881 0.0365 -0.0334 407.7 chr6 2783665 WRNIP1 ;WRNIP1 cgl760498S 0.0358 -0.0328 411.4 chr5 124256096 +

cg22620858 0.0357 -0.0328 425.8 chrl3 80917437 +

cf>23267944 -0.0355 0.0326 415.7 chr2 232329239 NCL

cg01074921 -0.0355 0.0325 407.5 chrl 8 34328381 + FHOD3

cgl0301401 -0.0353 0.0323 403 chrl6 938327 + LMF1

cf>13912224 0.0353 -0.0323 413.5 chr6 52172083 +

cf>16218241 -0.0351 0.0322 416.8 chr6 90597591

cgl 1755548 0.035 -0.0321 454.9 chrl l 75144040 +

cg22780475 0.0346 -0.0317 418.8 chrl9 45281526 + CBLQCBLC cf>06862410 0.0346 -0.0317 478.1 chrlO 86001246 + LRIT1

cgl5396204 -0.0341 0.0313 425.3 chrl 107348393 +

cgl2030941 0.0341 -0.0312 436.3 chr4 1558628 +

cf>00684283 -0.0339 0.0311 445.7 chrl4 105943425 CRIP2

cg04704193 0.0333 -0.0305 436.2 chr6 26272200 + HIST1H3G;HIST1H2BI cf>12497319 0.0331 -0.0303 453.4 chrl9 6737669 + GPR108;GPR108 cgO366650O -0.0331 0.0303 474 chr6 92009684 +

cgl0621610 0.033 -0.0303 434.8 chrl 91175627 +

PAICS;PAICS;PPAT;PAICS;P cg04418752 0.0329 -0.0302 423.2 chr4 57301960 AICS

cg09320595 0.0326 -0.0299 468.6 chr6 31478822 + MICB

KCNMAl ;KCNMA1 ;KCNM cgl5200789 0.0323 -0.0296 448.9 chrlO 79285311 A1 ;KCNMA1 cg20592691 0.0323 -0.0296 433.7 chrl3 60447453 DIAPH3;DIAPH3 cf>24376339 -0.0323 0.0296 436.9 chr2 192155833 + MY01B;MY01B;MY01B cgl7261303 -0.0321 0.0294 442.1 chrlO 133948207 JAKMIP3

cgl2930251 -0.0321 0.0294 443 chr20 3767625 + CENPB

cg20795023 0.0319 -0.0293 441.1 chr7 150413564 + GIMAPl

cg04062622 0.0318 -0.0292 444.7 chrl l 28857582

cg07193234 -0.0318 0.0291 440.5 chrl5 28341783 + OCA2

cg08519008 0.0317 -0.0291 480.1 chrl l 123815202 OR6T1 cg07927482 0.0317 -0.029 431.5 chr2 131822093 + FAM168B

cg03200786 -0.0316 0.029 461.4 chr4 2483916 RNF4

cgl 1740068 -0.0315 0.0289 513.3 chrl9 57306983 ZIM2;ZIM2;ZM2 cgl4934280 -0.0313 0.0287 465.3 chrl9 47494587 + GRLF1

cg02030212 0.0312 -0.0286 438.5 chrl4 80018860 + NRXN3 ;NRXN3 ;NRXN3 cgl9038230 -0.0311 0.0285 471.8 chrl3 47126445 LRCH1 ;LRCH1;LRCH1 cg09246347 -0.0311 0.0285 451.7 chr6 32164061 + GPSM3;NOTCH4 cg23539688 -0.0309 0.0284 475.8 chrl9 9545862 + ZNF266;ZNF266 cgl5000794 -0.0308 0.0282 468.5 chr7 86849844 + C7orf23

cg22154385 -0.0307 0.0281 443.1 chr6 57339858 PRIM2

cg22107210 -0.0306 0.0281 463 chr7 21985735 CDCA7L;CDCA7L;CDCA7L cg25199372 0.0306 -0.0281 455.9 chrl3 24650209

BCKDHA;BCKDHA;EXOSC cg05360949 -0.0306 0.028 451 chrl9 41903737 5

cg01973394 -0.0306 0.028 441.1 chrl5 82335947 MEX3B

cg04752591 -0.0305 0.028 455.8 chr6 33422729 ZBTB9

cg03192163 0.0304 -0.0279 450.8 chr6 137365300 IL20RA

cg22127912 0.0303 -0.0278 485.8 chr4 160656980

cg02965870 0.03 -0.0275 474.1 chrl5 56285761 + NEDD4;NEDD4 cg02989940 -0.0298 0.0273 475.3 chrl6 31539234 + AHSP;AHSP cgl6582792 -0.0297 0.0273 459.3 chr2 168104087 + XIRP2;XIRP2

C 1 orf203 ;C 1 orf203 ;C 1 orf203 ; cgl5572086 0.0297 -0.0272 469.9 chrl 116961459 + Clorf203;Clorf203 cg25677571 -0.0295 0.0271 459.5 chrlO 60145269 TFAM;TFAM cg08663890 0.0295 -0.027 474.5 chrl 111746277

cg23565166 -0.0295 0.027 456 chr6 170042028 + WDR27

cg22310240 0.0291 -0.0267 483.6 chr6 27775346 HIST1 H2AI;HIST1 H2BL cg04234597 -0.0289 0.0265 484.5 chr5 3851581

cgl4323910 0.0289 -0.0265 500.6 chr6 32628305 + HLA-DQB 1 cg04409081 0.0288 -0.0264 445.9 chrlO 117707384 ATRNL1

cgl0920297 0.0288 -0.0264 474 chrl l 94803188 SFRS2B;SFRS2B cg06586763 -0.0287 0.0263 481.3 chrl6 29302087 + RUNDC2C cg09861992 0.0286 -0.0262 460.5 chr6 29342815 OR12D3

cgl9750882 -0.0285 0.0261 465.4 chrl3 41885152 + NAA16;NAA16;NAA16 cgl l633770 -0.0285 0.0261 481.2 chr8 36807801 +

cg26889659 0.0281 -0.0258 495.1 chr6 684090 + EXOC2

cg23367351 0.0278 -0.0255 479.5 chr7 2106029 + MAD1L1;MAD1L1 ;MAD1L1 cg06771363 0.0276 -0.0253 482.7 chr5 137798029

cg26910001 -0.0273 0.025 501.2 chrl7 16838321

cgl 8107425 -0.0273 0.025 480.5 chrl5 90319432 MESP2

cg25604067 0.0272 -0.0249 467.1 chrl l 126226068 + ST3GAL4

cg09175928 0.0271 -0.0248 487.3 chrl6 68344995 PRMT7;PRMT7;SLC7A60S cg05251389 0.0269 -0.0247 472.2 chr22 43525330 + BIK cgl9800591 -0.0268 0.0246 493.7 chrl5 44581430 + CASC4;CASC4 cg00886909 0.0268 -0.0246 468.2 chr4 110724358 CFI

cg25101500 0.0266 -0.0244 527.8 chr2 42588340 + COX7A2L;COX7A2L cgOl 175142 0.0265 -0.0243 516.4 chrlO 118387327 PNLIPRP2

cg27311294 0.026 -0.0239 487.7 chr2 92027943

CHAT;CHAT;CHAT;CHAT; cg25 155846 -0.0257 0.0236 505 chrlO 50821078 + CHAT;CHAT;CHAT cgl 9909127 -0.0254 0.0233 505.5 chrl3 1 14075034 +

cf.23535170 0.0254 -0.0233 502.8 chr3 179489892 USP13

MARCH8;MARCH8;MARCH cgl4026106 -0.0254 0.0233 480.8 chrlO 46010523 8

cg02167149 -0.0253 0.0232 499.6 chr8 10683167 MIR1322;PINX1 cf.13323553 -0.0252 0.0231 489.6 chr7 63018494 +

cg23277693 -0.0252 0.02 1 509.3 chr2 1609062 +

cgl0107529 0.025 -0.0229 506.8 chr4 15344779 C1QTNF7

cgl4997226 -0.0249 0.0229 523.4 chr2 100938813 + LONRF2;LONRF2 cg21908259 0.0249 -0.0228 498.6 chrl2 49318998 + FKBP11;FKBP11 ;FKBP11 cg03431727 -0.0249 0.0228 497.1 chr2 217234404 + Feb- 00 cgl3627726 0.0248 -0.0228 510.8 chr4 1 19456450 CEP170L

cg04356381 -0.0248 0.0227 517.5 chr6 25050788 +

cgl7045804 0.0244 -0.0224 492.3 chr5 32710009

cg09949949 -0.0243 0.0223 499.3 chr7 103631129 RELN;RELN cg20639805 0.0237 -0.0218 522.9 chrlO 112404997 RBM20

cg02919936 0.0237 -0.0217 507.7 chr8 70982285 + PRDM14

KIAA1967;KIAA1967;KIAA 1 cg25631518 0.0234 -0.0214 497.1 chr8 22462351 967

eg 1 1821796 0.0229 -0.021 522.1 chrl l 67084644 + LOC100130987 egl 6678099 0.0229 -0.021 535.2 chrl7 40250199 +

cg23047415 0.0229 -0.021 523.1 chr5 33982208 SLC45A2;SLC45A2 cg21826258 -0.0227 0.0208 517 chrl5 31293713 TRPM1

GABBR1;GABBR1 ;GABBR1 cgl9456996 0.0223 -0.0205 504.5 chr6 29600642 + ;GABBR1

cg04777551 0.0222 -0.0204 579.5 chr6 32628953 HLA-DQB 1 cg24885417 -0.0219 0.0201 507.7 chr7 24323764 + NPY

cg05851887 -0.0219 0.0201 517.6 chr22 28199970 +

cg07881405 -0.0217 0.0199 525.9 chr5 3597487 + IRX1

cg l0051935 -0.0216 0.0198 531.5 chrl9 58740549 ZNF544

egl 181 1705 -0.0213 0.0195 534.2 chrl 55462689 +

cgl4994396 -0.0212 0.0195 517.1 chr5 165501887

cg09940677 0.0211 -0.0193 518.8 chrl4 103415458 CDC42BPB cgl2792952 -0.0209 0.0192 515.9 chrl 64857387

cgl7233517 0.0205 -0.0188 530.4 chrl7 45285328 MYL4;MYL4 cg09797446 0.0203 -0.0186 533.4 chr5 177419382 PROP1

cg21464591 0.0202 -0.0186 541.3 chr6 31597034 + BAT2 cg25673668 0.0202 -0.0185 552.1 chr8 895601 +

cgl9909895 -0.0202 0.0185 515.2 chr3 128368930 RPN1

cg08461586 -0.0199 0.0182 532.6 chrlO 97802977 + CCNJ;CCNJ;CCNJ cg06461942 0.0199 -0.0182 513.8 chr2 128462379 WDR33

cg03713585 -0.0197 0.018 533.6 chr2 175693153 + CHN1 ;CHN1 cgl9393006 0.0196 -0.0179 533.1 chr9 135820767 TSC1;TSC1 ;TSC1 cg02124835 -0.0195 0.0179 558.1 chrl9 1242098 + ATP5D;ATP5D cg06100461 0.0194 -0.0178 550.6 chrl3 52419380 + FLJ37307;FLJ37307 cg06209298 0.0192 -0.0176 526.5 chr3 44154913 MIR138-1

cg22853986 -0.0192 0.0176 536 chr8 125463310 TRMT12

cgl4248584 0.0189 -0.0173 573.5 chr9 130115860 + GARNL3

cg03632245 -0.0189 0.0173 548.4 chr5 72590921

cg08164900 -0.0189 0.0173 558.4 chrlO 119134712 + PDZD8

cg20234365 0.0188 -0.0173 548.7 chr3 107439436 + BBX;BBX

cg27546391 0.0188 -0.0172 554.4 chrl2 31742648 DENND5B

cg02958515 0.0187 -0.0171 585.3 chrl5 39650295 +

cg00657460 -0.0187 0.0171 544.1 chrl8 52901961 + TCF4;TCF4

NFYC;NFYC;NFYC;NFYC;N cg24700993 0.0187 -0.0171 523.4 chrl 41222901 FYQMIR30C1 cg21382923 0.0187 -0.0171 547.2 chrl 21055163 + SH2D5;SH2D5 cg06657560 0.0183 -0.0168 540.4 chrl2 121841372 + RNF34;RNF34 cg06634552 0.0182 -0.0167 553.4 chr6 13873667 +

MEIS2;MEIS2;MEIS2;MEIS2 cgl4980255 0.0181 -0.0166 553.5 chrl5 37190694 ;MEIS2;MEIS2;MEIS2 cg26250086 0.018 -0.0165 583.9 chrl 6830972

cg02415651 0.0176 -0.0161 546.7 chr6 164255065

cg01590866 -0.0176 0.0161 552.3 chr2 33217152 + LTBP1

cgl4414100 -0.0175 0.0161 555.6 chr9 19547530 SLC24A2

cg27049517 0.0174 -0.016 565.4 chrl9 38924330 + RYR1;RYR1 cgl7717333 -0.0173 0.0159 556.1 chr2 26101647 + ASXL2

cg07156742 0.0173 -0.0158 581 chr3 142666759

cg02303747 0.0167 -0.0153 577.6 chrl4 60794963 +

cg08036798 -0.0167 0.0153 565 chrl l 76839191 + MY07A;MY07A;MY07A cg07448856 -0.0166 0.0152 551.4 chrl 247242043 + ZNF670;ZNF670 cgl 8175518 0.0166 -0.0152 583.4 chr7 40795696 + C7orfl0

cg00920327 0.0165 -0.0152 569.9 chr6 72130755 + C6orfl55

cg02004723 0.0165 -0.0151 568.1 chr20 42574341 + TOX2;TOX2;TOX2;TOX2

WDR12;ALS2CR8;ALS2CR8; cg06721775 -0.0165 0.0151 587.7 chr2 203776979 ALS2CR8

cg21853021 0.0164 -0.0151 580.1 chrl l 2920146 + SLC22A18AS ;SLC22A18 cgl0685961 0.0163 -0.0149 564.9 chrlO 32557982 + EPC1

cg08154698 0.0162 -0.0149 585.7 chrl9 8274065 LASS4

cgl4307471 0.0156 -0.0143 551.7 chrl 8 31432117 + NOL4 cgl0025645 0.0156 -0.0143 577.1 chrl 17171 1780 + VAMP4

cgl3882835 0.0153 -0.014 587 chr2 172017928 TLK1 ;TLK1 cgl0442913 -0.0152 0.0139 565.3 chrl 116915309 ATP1A1 ;ATP1A1 cg22187125 0.0149 -0.0137 578 chrl9 58565865 + ZSCAN1

ECD;FAM149B1 ;ECD;ECD;E cg07070840 0.0149 -0.0137 587 chrlO 74928184 CD

cg03607946 -0.0148 0.0136 573.5 chr20 34203965 + SPAG4

cg l5420720 0.0148 -0.0136 568 chr5 172751061 + STC2

cg00544413 0.0148 -0.0136 611.5 chi3 195621788 + TNK2;TNK2 cg26994283 -0.0147 0.0134 585.8 chr5 177964757 COL23A1

cgl9162333 -0.0146 0.0134 573.9 chr4 16084676 PROMl ;PROMl cg05088512 -0.0145 0.0133 584.4 chrl9 49866817 DKKLl ;TEAD2 cg04065767 0.0143 -0.0132 584.9 chrlO 133921110 + JAKMIP3

cg22258538 -0.0143 0.013 1 579.3 chrl l 14403163 +

cgl6242770 -0.0142 0.013 583.9 chrl7 39471738 + KRTAP17-1

SNORD115-9;SNORD1 15-

12;SNORD1 15- eg 12 12278 -0.014 0.0128 567.7 chrl5 25436534 10;SNORD1 15-5 cg21973667 0.0137 -0.0125 581.8 chil3 73634424 KLF5

SLC39A10;SLC39A 10;SLC39 cg00216659 -0.0133 0.0122 579.9 chr2 196521989 + A10

cg21863546 0.0129 -0.01 18 590.4 chrl3 53568518

cg04851505 -0.0128 0.0117 570.7 chrl 37783871 +

cg20067688 -0.0128 0.0117 594.9 chr20 30449097 DUSP15;DUSP15;DUSP15 cg21113746 -0.0128 0.0117 617.6 chrl 59039556 +

cg l3908846 0.0127 -0.01 16 585.8 chr6 42896759 CNPY3

cg02602007 0.0125 -0.01 15 606.1 chr6 168226064 C6orfl24

cg06116095 -0.0123 0.0113 600.5 chrl l 93861851 PANX 1

cg05979118 0.0123 -0.01 12 586.1 chr4 33106338

cg09187054 -0.0122 0.0112 595.4 chrl7 1202156 + TUSC5

cgl5375469 0.0122 -0.01 11 591.2 chr7 29186231 + CPVL;CPVL cg06377473 -0.012 0.011 597.6 chrlO 116528363 +

cgOl 886855 -0.0119 0.0109 616.2 chr21 43786997 TFF1

cgl7182240 0.0116 -0.0107 588.5 chrl l 126768919 + KIRREL3 ; KIRREL3 cg09299086 -0.0116 0.0106 593.8 chrl 17746438 RCC2;RCC2 cg22265458 -0.0115 0.0106 600.7 chrl9 56738933 ZSCAN5A cg2081 1317 0.01 13 -0.0104 633.5 chr7 107969837 NRCAM;NRCAM cg26955132 0.0113 -0.0103 630.5 chrl2 13181499

cgl0601026 -0.0112 0.0103 669.9 chrlO 6457 140 + EGR2

cg08915922 0.011 -0.0101 604.8 chrl 42127381 HIVEP3;HIVEP3 cg25132226 -0.0107 0.0098 604.9 chrl5 50716929 USP8;USP8;USP8 cg02164335 0.0106 -0.0097 600 chrl l 58731634 +

cgl0399899 -0.0105 0.0096 655.4 chr20 62705081 RGS19;RGS19

TRM39;HCG18;HCG18;TRI cg00124432 -0.0104 0.0095 621.2 chr6 30294015 M39 cgl 1676599 -0.0102 0.0093 643.8 chrlO 124133934 + PLEKHA1 cg04356968 0.0101 -0.0093 617.3 chrl 156212005 BGLAP;BGLAP cg05883128 0.01 -0.0092 654.1 chr4 169239131 DDX60

cg01066465 0.01 -0.0092 623 chrl 31191582 + MATNl

cg24715105 -0.0099 0.00 1 658 chrl 228191579 +

cg l5331693 0.0097 -0.0089 616.5 chr6 151373267 MTHFD1L cg25530735 -0.0095 0.0088 651.7 chr5 6572733 +

cg21615831 -0.0095 0.0087 620.8 chrl7 25798202 + KSR1

cgl5800776 -0.0095 0.0087 609.7 chrl9 1 1312691 + DOCK6

cg21610436 0.0094 -0.0087 611.9 chr8 22658864 PEBP4

cgl9376842 0.0094 -0.0087 627.4 chrl l 618909 MUPCDH;MUPCDH cg01517940 0.0094 -0.0086 683.4 chrl4 69865233 + SLC39A9;ERH cgl9767896 0.0093 -0.0085 668.4 chrl9 14529108 DDX39

cgl4326607 0.0092 -0.0085 659.5 chr9 94877344 + SPTLC1;SPTLC1 cgl0210510 -0.0091 0.0083 638.3 chrl 40771 135 + COL9A2

cg l3541588 -0.0089 0.0082 623. 1 chrl7 21903945 + FLJ36000

cg24881334 -0.0087 0.0079 616 chil7 69409104 +

cgl3714299 0.0086 -0.0079 632.8 chrl6 31 105660 + VKORCl ;VKORCl cgl9427701 -0.0084 0.0077 620.6 chr9 140196790 NRARP

cgl4037945 -0.0084 0.0077 666.6 chrl9 58430978 +

cg00135416 0.0083 -0.0076 623.7 chrl7 78172177 CARD14;CARD14 cg09930524 -0.0081 0.0074 646.6 chr20 30467627 TTLL9

cgl 1235291 -0.008 0.0073 624.6 chrl7 38804673 + SMARCEl cg24434320 -0.008 0.0073 628.3 chrl l 59327876 +

cg l5602512 0.0078 -0.0071 679.2 chr3 46734393 + ALS2CL

cg22617406 0.0077 -0.0071 654.6 chrl2 58008183 GEFT;GEFT cgl 8222582 -0.0075 0.0069 629 chrl 232765331

cg23978161 0.0074 -0.0068 680.5 chr4 77870897 SEPT11 ;SEPT11 cgl9981475 -0.0071 0.0065 639.5 chrl2 81544296 + ACSS3

cg24512138 -0.007 0.0064 661.7 chr3 133465360 TF

cg04967982 -0.0069 0.0063 675.2 chrl5 31781955 OTUD7A

cg03208016 -0.0067 0.0062 686.8 chrl 180124071 QSOXl;QSOXl

HNRNPC ;HNRNPC ; HNRNP cg07460838 0.0065 -0.006 643.6 chrl4 21738077 + C;HNRNPC cg06007966 0.0064 -0.0059 683 chr20 261889 1 + MIR663

cg23117699 0.0063 -0.0058 649.8 chi20 50416976 + SALL4

cg21150901 0.0062 -0.0057 653.8 chrl7 34957586 MRM1

cg03366884 -0.0058 0.0053 639.6 chrl7 8844506 PIK3R5

cgl4695840 0.0057 -0.0052 705.8 chr9 71737258 TJP2

cg05675570 0.0057 -0.0052 699.1 chr7 2040555 + MAD1L1;MAD1L1 ;MAD1L1 cg27143570 -0.0057 0.0052 672.3 chrlO 102975075

CAMKKl ;CAMKK1 ;CAMK cg22496986 0.0055 -0.0051 662 chrl7 3794573 Kl cgl9006429 -0.0054 0.0049 659 chr6 30043325 + RNF39; NF39 cg02124291 0.0052 -0.0048 649.6 chrl9 14939260 + OR7A5;OR7A5

RPL28;RPL28;RPL28;RPL28; cgl l 895418 0.0052 -0.0048 645 chrl9 55897195 RPL28

cgl0315375 0.0051 -0.0047 666.4 chr5 176981788 + FAM193B;FAM193B cgl0753073 0.005 -0.0046 657.8 chrl 64669599 UBE2U;UBE2U

OPAl;OPAl;OPAl ;OPAl ;OP eg 14500375 0.005 -0.0046 658.7 chr3 19331085 1 Al ;OPAl ;OPAl ;OPAl eg 1 1348345 0.005 -0.0046 675.3 chrl2 104850219 + CHST11

cgOl 696984 0.0049 -0.0045 712.9 chi3 185080356 MAP3K13

cg09626274 0.0048 -0.0044 690.9 chrl5 77224230 RCN2

cgl6175941 0.0045 -0.0042 683.5 chr6 154779103 + CNKSR3

cg26980477 0.0045 -0.0041 658.2 chrl2 126641914

cg08812802 0.0045 -0.0041 677.8 chrl2 49686619

cgl2498181 0.0045 -0.0041 660.2 chrl3 113436309 ATP11A;ATP11A cg24261808 0.0042 -0.0038 743.6 chrl l 93474459 + Cl lorf54;TAFlD cg24943066 -0.0041 0.0038 698 chr21 27009142

cg07078452 -0.004 0.0037 659.4 chr7 151093041 + WDR86

egl 1397854 0.004 -0.0036 711.5 chr3 13008878 + IQSEC1;IQSEC 1 cg24829483 0.0039 -0.0036 664.1 chr4 15341641 C1QTNF7;C1QTNF7 cg02845063 0.0038 -0.0035 664.7 chr3 121312241 FBXO40;FBXO40 cgl9213045 0.0038 -0.0035 713.3 chrl3 40180622 +

SAP30L;SAP30L;SAP30L;SA cgl2162197 -0.0037 0.0034 679.7 chr5 153826525 + P30L

cg05414235 -0.0037 0.0034 721.8 chrl5 100273828 + LYSMD4

FNDC8 ;RAD51 L3 ;R AD51 L3 ; cg09155905 0.0036 -0.0033 696.8 chrl7 33447236 + RAD51L3

cg27560781 0.0036 -0.0033 686.4 chr4 42363683

cg09983885 0.0034 -0.0031 750.9 chrl l 4415245 + TRM21

cg22819824 0.0032 -0.003 709 chrlO 1 15618670 + NHLRC2

cg09138207 -0.0032 0.003 718.5 chrl l 95962058 MAML2

cg09538031 -0.0032 0.0029 689.7 chr5 41072796 HEATR7B2 cg24034923 0.0025 -0.0023 697.6 chrl 213184996 + ANGEL2

cgl 8412326 0.0025 -0.0023 679.4 chrl9 12807671 FBXW9

cgl5050577 0.0025 -0.0023 686.7 chrl 27024100 + ARID1A;ARID1A cg27456220 -0.0024 0.0022 702.1 chrl2 113773005 + SLC24A6

cg03677952 0.0023 -0.0021 694.7 chr22 50524541 MLC1;MLC1 cg24449885 0.0023 -0.0021 690.2 chrl2 51718355 BIN2

cg23126129 0.0021 -0.002 694.5 chr5 138324552 SIL1 ;SIL1

cg27449975 0.002 -0.0019 672.8 chr4 3208589 HTT

cgl3770114 0.002 -0.0018 690.4 chr9 3874932 + GLIS3;GLIS3 cgl2551739 0.002 -0.0018 725.1 chr5 124489922 +

GIGYF2;GIGYF2;GIGYF2;GI cg04171803 0.002 -0.0018 685.5 chr2 233561907 GYF2

cgl4975227 0.0019 -0.0017 690.7 chr3 40719085 cg04670553 0.0018 -0.0016 690.9 chrl5 80216491 + C15orf37;ST20 cg26242708 0.0017 -0.0016 695.6 chr8 134047325 TG

ZNF502;ZNF502;ZNF502;ZN cg21672276 0.0016 -0.0014 686.9 chr3 44754072 F502

cg08110610 0.0016 -0.0014 698.5 chr2 234216147 SAG

cgl5816511 0.0015 -0.0014 711.1 chr8 95731770 DPY19L4

UBAPl ;UB API ;UBAP1 ;UB A cg21226665 0.0014 -0.0013 687.9 chr9 34178775 + P1;UBAP1 ;UBAP1 cgl9052384 0.0013 -0.0012 691.2 chr7 44622341 TMED4

cg03403072 0.0012 -0.0011 684.6 chrl3 114023770

cg02660350 -0.001 0.001 692.1 chrlO 99186001 + PGAM1

cg26400230 -8.00E-04 7.00E-04 704.9 chr3 43807220 +

cg03123608 -6.00E-04 6.00E-04 698 chrl 42039865 + HIVEP3;HIVEP3 cg24001597 -5.00E-04 4.00E-04 720.6 chrl8 77655878 KCNG2

cgl0399865 -3.00E-04 3.00E-04 690 chr4 721944 + PCGF3

cg09356750 -3.00E-04 2.00E-04 701.5 chrl9 4691020 + DPP9

cg04246113 2.00E-04 -2.00E-04 703.6 chrl 120316152 +

cg06293611 -2.00E-04 2.00E-04 719.6 chr2 27308434 EMILIN 1 ; KHK; KHK cg08415508 -2.00E-04 2.00E-04 702.8 chr8 144303645 +

Table 3. Refined differentially methylated CpG sites predictive of food allergy (n

[118] The 649 probes were then ranked according to their absolute between group difference for the main outcomes of interest (FA v FS). A cutoff for a minimum 5% difference in methylation between groups was applied, a cutoff for which the sample size had reliable detection power, and also within the measurable range of EpiTYPER Mass Spectrometry. The cutoff reduced the list to 96 probes, which overlapped with 72 genes (Table 6). This gene list was significantly enriched for the MAP kinase canonical pathway (q-value = 1.34E-05) including family members MAPK8IP1, MAPK8IP2, MAP3K1, RPS6KA2, RASGRP2, NF1, ZAK and FGF12.

Table 4. Top 10 ranked CpG model

training set

allergic sensitized Class Error rate

allergic 10 5 0.3333333

sensitized 2 13 0.1333333

Overall error rate= 0.23

test set

class

prediction allergic sensitized

allergic 13 3

sensitized 1 11

Overall error rate=0.14 Table 5. Top 50 ranked CpG model name allergic-score sensitized-score av-rank-in-CV chr pos cgl l229343; 0.2444 -0.1901 1.7 chr2 242808427 cg23502162;COL3Al 0.2181 -0.1696 2.7 chr2 189839015 cgl4525407;APHlB;APHlB -0.1988 0.1546 4.1 chrl5 63570327 cg04777551 ;HLA-DQBl 0.1824 -0.1419 7.1 chr6 32628953 cg00044796; 0.1607 -0.125 8.5 chrl 146552390 cgl 8660064; 0.1447 -0.1126 8.9 chrl 23504632 cg01665212;IER3 -0.1349 0.1049 11 chr6 30712373 cg21099624;VEGFA -0.1345 0.1046 10.8 chr6 43737749 cgl3417268;ATP10A 0.1303 -0.1013 12.5 chrl 5 26108642 cg03317400;MFSD2A -0.1283 0.0998 11.9 chrl 40420823 cg01822573;TBKBPl 0.1271 -0.0989 12.6 chrl 7 45772473 cg24851651 ;CCS 0.1208 -0.094 14 chrl l 66362959 cgl9029127;RELLl -0.1146 0.0891 14.1 chr4 37604497 cgl4323910;HLA-DQBl 0.1146 -0.0891 18 chr6 32628305 cg09168692;PER3 -0.1098 0.0854 14.4 chrl 7887560 cgl3968099;CCNGl -0.1075 0.0836 15.6 chr5 162864768 cg02681173 ;LOCl 00190940 0.0923 -0.0718 19.7 chrl 2 130527106 cg01112801 ;CCT8 0.0865 -0.0673 21.2 chr21 30443603 cgl5340874;CACYBP 0.086 -0.0669 20.1 chrl 174969621 cg08996805; -0.0723 0.0563 26.2 chr3 11010256 cg03964111 ;TMEM176B;TMEM176A 0.0711 -0.0553 26.6 chr7 150498493 cg20932251 ;FCHSD2 0.0706 -0.0549 26.3 chrl l 72852302 cg07156742; 0.0704 -0.0548 25.1 chr3 142666759 cgl 8391442; -0.0703 0.0547 24.6 chr2 56318614 cgl5388570;ADCKl;ADCKl 0.0675 -0.0525 26.8 chrl4 78328609 cg09996633;ATXN3 0.0603 -0.0469 28.3 chrl4 92566961 cgl 1941920; -0.0544 0.0423 29.6 chr6 79339195 cg02857312;BRE 0.0492 -0.0383 33.8 chr2 28449036 cg05726600;TMEM176B;TMEM176A 0.0466 -0.0362 32.4 chr7 150498594 cg06578342; 0.0392 -0.0305 35.5 chrl 6 4349215 cgl3560030;NTN4 0.0391 -0.0304 35.3 chrl 2 96099778 cg04353106;RGS5 -0.0391 0.0304 35.1 chrl 163136667 cgl2206093;FRMD8 -0.0389 0.0303 35.7 chrl l 65154388 cg04480106;RGNEF -0.0319 0.0248 39.6 chr5 72934606 cgl6553182;IGSF8 -0.0314 0.0245 37.4 chrl 160068681 cg08640609;CAMTAl 0.0277 -0.0216 40.1 chrl 7764642 cg07068191 ;HRASLS;MGC2889 0.0246 -0.0191 41 chr3 192959176 cg09555323;HLA-DQBl 0.0242 -0.0188 41.5 chr6 32629786 cg04091209;SIN3A -0.0238 0.0185 41.6 chrl 5 75744113 cg24417499;HPCA 0.0219 -0.017 42.3 chrl 33351653 cg07620573;FGF12 0.0201 -0.0156 42.9 chr3 192289293 cgl2030941 ; 0.0195 -0.0152 44.6 chr4 1558628 cgl0894318;IGF2R 0.0178 -0.0139 44.5 chr6 160389536 cg24616138;CTBP2 0.0157 -0.0122 43.6 chrlO 126850126 cg24584002;RNASEHl 0.0116 -0.009 46.6 chr2 3606104 cg21908259;FKBPl l 0.0109 -0.0085 46.4 chrl 2 49318998 cg22107210;CDCA7L -0.0079 0.0062 50.6 chr7 21985735 cgl9738182;ACSL3 -0.0054 0.0042 49.7 chr2 223726189 cg25261377;RPP21 0.0033 -0.0026 50.9 chr6 30313517 cg07060505; -0.0017 0.0013 50.7 chrl 2 116756136 cgl 8107409;PPP2R5C 6.00E-04 -5.00E-04 50.2 chrl4 102228802 train

allergic sensitized Class Error rate

allergic 17 0 0

sensitized 0 16 0

Overall error rate= 0

test

class

prediction allergic sensitized

allergic 10 0

sensitized 2 13

Overall error rate= 0.08

Table 6. Refined list of differentially methylated genes predictive of food allergy (n=72)

Symbol Name

COL3A1 collagen, type III, alpha 1

FASN fatty acid synthase

CCNG1 cyclin Gl

LOC149837 hypothetical protein LOC643406; hypothetical LOC149837

MFSD2A major facilitator superfamily domain containing 2

HPCA hippocalcin

MAP3K1 mitogen-activated protein kinase kinase kinase 1

PER3 period homolog 3 (Drosophila)

SLC23A2 solute carrier family 23 (nucleobase transporters), member 2

BRE brain and reproductive organ-expressed (TNFRSF1 A modulator)

HRASLS HRAS-like suppressor

NTN4 netrin 4

MAML2 mastermind-like 2 (Drosophila)

ACSL3 acyl-CoA synthetase long-chain family member 3

PSRC1 proline/serine-rich coiled-coil 1

HLA-DMA major histocompatibility complex, class II, DM alpha

LPP LIM domain containing preferred translocation partner in lipoma

TMC4 transmembrane channel-like 4

ADCK1 aarF domain containing kinase 1

ZIM2 paternally expressed 3; PEG3 antisense RNA (non-protein coding); zinc finger, imprinted 2

ATXN3 ataxin 3

IGSF8 immunoglobulin superfamily, member 8

TBKBP1 TBK1 binding protein 1

RGS5 regulator of G-protein signaling 5

ARF4 ADP-ribosylation factor 4

MRPS33 mitochondrial ribosomal protein S33

SIN3A SIN3 homolog A, transcription regulator (yeast)

VEGFA vascular endothelial growth factor A

TMEM176B transmembrane protein 176B

ZAK sterile alpha motif and leucine zipper containing kinase AZK

RPP21 ribonuclease P/MRP 21kDa subunit

FKBP11 FK506 binding protein 11, 19 kDa

HDAC4 histone deacetylase 4

GNB 1 guanine nucleotide binding protein (G protein), beta polypeptide 1

CCS copper chaperone for superoxide dismutase

LOCI 00190940 hypothetical LOCI 00190940

LRFN2 leucine rich repeat and fibronectin type III domain containing 2

RNASEH1 ribonuclease HI

FARP1 FERM, RhoGEF (ARHGEF) and pleckstrin domain protein 1 (chondrocyte-derived) ATP 1 OA ATPase, class V, type 10A

MAPK8IP1 mitogen-activated protein kinase 8 interacting protein 1

LTB lymphotoxin beta (TNF superfamily, member 3)

SHC2 SHC (Src homology 2 domain containing) transforming protein 2

ZNF678 zinc finger protein 678

TMEM176A transmembrane protein 176 A

MGC2889 hypothetical protein MGC2889

RASGRP2 RAS guanyl releasing protein 2 (calcium and DAG-regulated)

APH1B anterior pharynx defective 1 homolog B (C. elegans)

RYR2 ryanodine receptor 2 (cardiac)

FRMD8 FERM domain containing 8

CTBP2 C-terminal binding protein 2

PCDHGA1 protocadherin gamma subfamily A, 1

CACYBP similar to calcyclin binding protein; calcyclin binding protein

IER3 immediate early response 3

RELL1 RELT-like 1

PECI peroxisomal D3,D2-enoyl-CoA isomerase

PPP2R5C protein phosphatase 2, regulatory subunit B', gamma isoform

CAMTA1 calmodulin binding transcription activator 1

RBM20 RNA binding motif protein 20

CCT8 similar to chaperonin containing TCPl, subunit 8 (theta); chaperonin containing

TCPl, subunit 8 (theta)

RPS6KA2 ribosomal protein S6 kinase, 90kDa, polypeptide 2; hypothetical LOCI 00127984

CDCA7L cell division cycle associated 7-like

SH3PXD2A SH3 and PX domains 2A

IGF2R insulin-like growth factor 2 receptor

HLA-DQB1 major histocompatibility complex, class II, DQ beta 1

RGNEF Rho-guanine nucleotide exchange factor

OMG oligodendrocyte myelin glycoprotein

NF1 neurofibromin 1

FGF12 fibroblast growth factor 12

MAPK8IP2 mitogen-activated protein kinase 8 interacting protein 2

FCHSD2 FCH and double SH3 domains 2

RAPGEF5 Rap guanine nucleotide exchange factor (GEF) 5

Predicting food challenge outcome

[119] The refined 96-CpG model was used to predict food challenge outcome in validation samples. Predictions were made for each patient with high posterior probabilities in each case (>0.9), and food challenge outcome was correctly predicted for each patient with no misclassification errors (Table 7).

Table 7. Sensitivity and specificity analysis of patient methylation scores from

[120] To explore the diagnostic utility of the 96 CpG DNAm signature the distribution of measurements from the 96 CpGs were examined. Compared to the genome-wide beta distribution, the distribution of the predictive CpG were restricted to the intermediate methylation range (20 - 80% methylated) (Figure 2a). In addition, the distribution of FA relative to FS individuals was skewed toward higher methylation in this Australian cohort (p = 7.322e-06, Wilcoxon rank sum test). Patient "methylation scores" were computed by summing the methylation ratios (methylated allele intensity)/ ((unmethylated allele intensity + methylated allele intensity) x 100) for all 96 CpG for each individual in the study. The transition to a clinically reactive phenotype was associated with a significantly higher (p < 0.001, Man- Whitney test) patient methylation score (Figure 2b). An area under the curve analysis to determine diagnostic cutoffs for these scores. In this analysis the NA group was included to examine the specific utility of the methylation score in a clinical setting (to predict FA v FS) versus a population screening setting (NA v FS/FA). ROC curve analysis of patients (FA) versus controls (FS) produced an area under the curve of 0.9774 (P<0.0001, CI 0.9482 - 1.007) providing excellent test accuracy. A methylation score of > 46.71 produced 100% sensitivity (CI 88.06% to 100%) and a specificity of 72.41% (CI 52.76% to 87.24%, likelihood ratio 3.625; see Table 7).

[121] Performance was marginally reduced when using the DNAm signature to diagnose FA v NA (area=0.8408, P=0.005, CI 0.6854% to 0.9963%), and the scores were least predictive (albeit still significant) when comparing FS v NA (area=0.6976, P=0.043, CI 0.5126% to 0.8826%) (Figure 2c).

[122] To determine whether the methylation signature would constitute an improvement over existing diagnostic tests a comparative ROC analysis was performed against concurrently measured allergen- specific IgE levels. Patient methylation scores were superior predictors of clinical allergy status than IgE measures for both egg and peanut specific food allergy phenotypes. For individuals with egg sensitization, egg- white IgE levels predicted allergy with an area under the curve of 0.6333 (P=0.2310, CI 0.4204 to 0.8462) whilst patient methylation scores among egg sensitized individuals predicted allergy with an area under the curve of 0.9857 (P<0.0001, CI 0.9546 to 1.017). Patient methylation scores from peanut sensitized individuals predicted allergy with ROC curve of 0.9571 (P<0.0001 CI 0.8897 to 1.025) compared with a peanut IgE test ROC curve of 0.8722 (P=0.00108 CI 0.7422 to 1.022) (Figure 2d). For individuals with egg sensitization, egg SPT wheal size predicted allergy with an area under the curve of 0.56 (P=0.6123, CI 0.3308 to 0.7951), whilst patient methylation scores among egg sensitized individuals predicted allergy with an area under the curve of 0.9405 (P<0.0018, CI 0.8386 to 1.042) (Figure 2e). Patient methylation scores from peanut sensitized individuals predicted allergy with ROC curve of 0.9571 (P<0.0001 CI 0.8897 to 1.025) compared with a peanut IgE test ROC curve of 0.9780 (P=0.0005 CI 0.9228 to 1.033) (Figure 2f).

Validation sample set

[123] Independent reproducibility in a publicly available DNA methylation was assessed using a data set obtained from a West Australian cohort of FA and NA individuals previously generated by our lab (Martino, D. et al. (2014) Epigenetics, 9). This dataset comprised 48 samples in total, 12 FA and 12 NA samples assayed at two time points (birth and 12-months). Food allergy status was determined when infants were 12-months old by prior evidence of clinical reactivity in patient case histories and objective skin prick testing. DNA methylation data was collected at birth and 12- months from total CD4+ T-cells.

[124] Multidimensional scaling analysis was applied to assess sample relationships based on the predictive methylation signature. MDS analysis of all probes or the top 1000 most variable probes did not discriminate FA from NA groups in this data set, suggesting the principal components of variation are driven by unknown effects.

[125] However, when the analysis was restricted to the 96 predictive CpG methylation signature the first principal component of variation was between groups was due to phenotype (Figure le). Patient methylation scores were generated for each sample in the validation cohort using the 96 predictive CpG. The distribution of scores between the validation and the HealthNuts cohorts were then Examined. It was clear that patient methylation scores derived from total CD4+ T-cells were consistently lower than scores derived from PBMCs, however differences in methylation scores between phenotypes were still conserved (Figure 3).

[126] This difference in methylation profiles due to cell type meant that the diagnostics cutoffs determined for total PBMC required recalibration based on total CD4+ T-cells in order to perform predictions. In any case, performing the ROC curve analysis on T-cell-derived patient methylation scores produced an area under the curve of 0.8368 (P <0.001, CI 0.7139 to 0.9598). A sensitivity analysis suggested a patient score of > 45.52 resulted in a test sensitivity of 87.5% (CI 67.64% to 97.34%) and a specificity of 70.83% (CI 48.91% to 87.38%) with a likelihood ratio of 3.0. The difference in diagnostic cutoffs between the PBMC-derived and T-cell derived patient scores are illustrated in Figure 3.

[127] An unsupervised classification was performed on samples in the validation dataset by assigning patients with a methylation score > 45.52 to the non-allergic group. These assignments were then compared to the true phenotype. In total 38/48 samples were correctly classified resulting in a true error rate of 20.8%.

Example 2 - Ranking CpG

To determine the utility of the classifier rule for detecting allergy using different models, we ran cross-validation experiments using 2, 10, 15, 20 and 50 CpGs selected from the 96 CpG signature rule. The data were partitioned into a training set (50% of samples) and test set (50% of samples), cross-validations were run and overall error rates were estimated. These error rates for different combinations of the 96 CpG model are shown below. This analysis shows that even with 2 CpG, the error rate was 33%, better than would be expected by random chance.

Analysis of the CpGs predictive power revealed selections of CpGs with increased predictive power for detecting allergy in subjects. The overall error rates obtained by using the top 2, 10, 15, 20 and 50 ranked CpGs to detect allergy in subjects are shown below. The overall error rate obtained by using the CpGs shown in Table 3 is also shown below.

###########################2 ranked CpGModel################################## train

allergic sensitized Class Error rate

allergic 12 1 0.07692308

sensitized 5 7 0.41666667

Overall error rate= 0.235

test

class

prediction allergic sensitized

allergic 9 4

sensitized 7 13

Overall error rate= 0.33

########################10 top CpG Model################################### pamr.confusion(model.cv, threshold=Delta)

training set

allergic sensitized Class Error rate

allergic 10 5 0.3333333

sensitized 2 13 0.1333333

Overall error rate= 0.23

test set

class

prediction allergic sensitized

allergic 13 3

sensitized 1 11

Overall error rate=0.14

###############15 ranked CpG Model######################################### train

allergic sensitized Class Error rate

allergic 16 1 0.05882353

sensitized 2 9 0.18181818

Overall error rate= 0.106

test class

prediction allergic sensitized

allergic 11 3

sensitized 1 15

Overall error rate= 0.13

###########################20 ranked CpG################################### train

allergic sensitized Class Error rate

allergic 11 1 0.08333333

sensitized 1 13 0.07142857

Overall error rate= 0.076

test

class

prediction allergic sensitized

allergic 16 0

sensitized 1 15

Overall error rate=0.03

######################50 ranked CpG####################################### train

allergic sensitized Class Error rate

allergic 17 0 0

sensitized 0 16 0

Overall error rate= 0

test

class

prediction allergic sensitized

allergic 10 0

sensitized 2 13

Overall error rate= 0.08

#######################96 CpG Model################################## train

allergic sensitized Class Error rate

allergic 14 0 0

sensitized 0 16 0

Overall error rate= 0

test

class

prediction allergic sensitized

allergic 15 0

sensitized 0 13

Overall error rate= 0 Example 3 - Methods

Sample population

[128] All infants in HealthNuts underwent skin prick testing (SPT) to egg white, peanut, sesame and 1 of 2 other foods (cows milk or shrimp). Those with detectable SPT wheal reactions (wheal size >1 mm above negative control) were invited to the HealthNuts research clinic at Melbourne's Royal Childrens Hospital within the next 4 to 8 weeks for repeat SPT and a formal oral food challenge (OFC) which was undertaken irrespective of SPT wheal size and using predetermined challenge criteria (Koplin, J. J. et al. (2012) J Allergy Clin Immunol, 129, pg 1145-1147). Blood was collected into a sodium heparin tube (Sarstedt, Inc, Newton, NC) 1 to 2 hours after the last dose of the OFC (see below). Ethical approval was obtained from the Office for Children HREC (ref. no. CDF/07/492), Department of Human Services HREC (ref. no. 10/07) and Royal Childrens Hospital HREC (ref. no.27047).

Predefined criteria for a positive OFC

[129] The criterion for a positive food challenge result was at least one of the following signs present during OFC: three or more concurrent noncontact hives (urticarial lesion) lasting for more than 5 min, perioral or periorbital angioedema, vomiting (excluding immediate post-ingestion gag/vomits) or evidence of anaphylaxis as defined by the Australian Society of Clinical Allergy and Immunology (evidence of circulatory or respiratory compromise) within 2 h of the last dose of the OFC (Koplin, J. J. et al. (2012) J Allergy Clin Immunol, 129, pg 1145-1147).

Purification of mononuclear cells and nucleic extraction

[130] Peripheral blood mononuclear cells (PBMC) were isolated within 2 hours of collection by density gradient centrifugation and cryopreserved in RPMI with 15% DMSO in fetal calf serum. DNA was collected from PBMC using Qiagen All-prep kits according to manufacturer's instructions (Qiagen, Doncaster, VIC, Australia). Purity and concentration of DNA was assessed by spectrophotometry. Flow cytometry was performed in parallel to estimate cell counts.

Flow cytometry

[131] Quantitation of total CD4+ T-cells numbers and total CD4+ Tregs was performed on all blood samples as a gold standard to assess potential heterogeneity in patient blood cell components (Supplementary data). Cells were stained with fluorochrome-conjugated monoclonal or isotype control antibodies in ΙΟΟμΙ staining volumes for 30 minutes at room temperature. For intracellular staining, cells were subsequently permeabilized, fixed and stained with Foxp3-PE antibody or isotype control according to the manufacturer's instructions (BD, San Jose, CA). All flow cytometric data were acquired on a 10-colour LSR II (BD, San Jose, CA) and analysed with FACS Diva v8.2 software using well-defined gating strategies. Compensation experiments were performed using positive and negative control beads (BD, San Jose, CA). The same compensation settings were used for each flow cytometric analysis. Data were captured from >2xl0 ⁵ cells to obtain >75,000 viable CD4 ⁺ lymphocytes. The Treg population was characterised by CD4 ⁺ CD25 ⁺ Foxp3 ⁺ .

DNA methylation analysis

[132] To quantitate DNA methylation levels, 500 ng of genomic DNA derived from patient PBMC was bisulphite treated using the Human Genomic Signatures MethylEasy Xceed kit (Genetic Signatures, North Ryde, NSW) according to manufacturer's instructions. Successful conversion was verified in all samples using an in-house bisulphite specific PCR assay as previously described (Martino, D. et al. (2013) Genome Biol 14, R42). Bisulphite treated DNA was submitted to the Australian Genome Research Facility (Parkville, Melbourne, Australia) as a single randomized batch for hybridization to Illumina Infinium HumanMethylation450 (HM450) arrays.

Preprocessing of microarray data

[133] Raw .iDAT files were preprocessed using the Minfi package (Aryee, M. J. et al. (2014) Bioinformatics doi: 10.1093/bioinformatics/btu049) from the bioconductor project (http://www.bioconductor.org) in the R statistical environment (http://cran.r- project.org/, version 3.0.2). Quality assessment of control probes on the array indicated high quality data with excellent performance of control probes in all samples. The Minfi package was used for array preprocessing using the stratified quantile normalization method. Technical bias attributable to different probe chemistries between Type I and Type II probes were adjusted in this procedure. Cell counts were estimated empirically from DNAm data using the EstimateCellComposition function in the Minfi package and compared with flow cytometry data collected in parallel (Figure 3). Gender calls were obtained from probe intensities on the sex chromosomes and checked against the recorded genders to rule out any sample mix-ups during processing. Probes on the X and Y-chromosomes were then removed to eliminate gender bias, as were poor performing probes with a signal detection P-value call >0.01 in 1 or more samples. Probes previously demonstrated to potentially cross -hybridize non-specific ally in the genome were also removed (Chen, Y.-A. et al. (2013) Epigenetics, 8). Probes containing a single nucleotide polymorphism (SNP) at the single-base extension site with a minor allele frequency of <0.05 were removed. Methylation percentages were estimated as Beta values given by B=Meth/(Unmeth/Meth* 100) and were used to develop the classifier model. This data set has been submitted to the Gene Expression Omnibus and is available under the accession number GSE59999.

Feature selection

[134] We used the shrunken centroids classifier method of Tibshirani et al to develop a classifier model based on patient methylation profiles (Tibshirani, R. et al. (2002) PNAS, 99, pg 6567-6572). The method computes a standardized centroid for each class, equivalent to a reference profile for each phenotype. This is the average DNA methylation for each CpG in each class divided by the within-class standard deviation for that CpG. Classification takes place by comparing the methylation profile of a new sample, to each of these reference class centroids. The reference centroid closest in squared distance to an individual patient sample is the predicted class for the new sample. Posterior probabilities were estimated for each prediction call to estimate confidence in the prediction. The classifier model was built on a training set consisting of 80% of the sample population and performance was validated in the reserved 20% that was previously unseen by the model. To determine the optimal number of CpGs in the prediction model, 10-fold cross-validations were performed on the training samples and classification errors were estimated for a range of CpGs included in the model. The minimum number of CpGs that produce no misclassification errors was determined empirically from the training data. The false discovery rate was estimated using the q- value method of Storey et al (Storey & Tibshirani (2003) PNAS, 100, pg 9440-9445). The model was validated on the unseen 20% of samples and performance was recorded.

Replication study

[135] Reproducibility was assessed in a publicly available data set previously generated for the Infinium HumanMethylation450 BeadChip derived from total CD4+ T-cells (GSE34639). This study includes genome-wide DNA methylation from 12 food allergic individuals (FA), and 12 age-matched non- sensitized controls (NA) (Martino, D. et al. (2014) Epigenetics, 9). In this study a pediatric allergist had determined allergic status based on clinical outcomes at the 12-month physical assessment, case history and allergy testing. The food allergic group consisted of individuals with IgE food allergy mostly to hens egg (n=l l/12), cows milk (n=l/12) or peanut (n=2/12). Food allergy was defined by clear immediate symptoms (1-2 hours) on exposure to egg, milk or peanut (including anaphylaxis, angioedema or urticaria) and confirmed IgE-mediated sensitivity by virtue of a positive skin prick test > 3mm above negative control at 12-months of age. Matched DNA methylation data derived from total CD4+ T-cells was available at two time-points, birth and 12-months and more detailed experimental procedures can be found at (Martino, D. et al. (2014) Epigenetics, 9).

Serum IgE measures

[136] Whole peanut and egg white IgE were measured in the primary cohort using the ImmunoCAP System FEIA (Phadia AB, Uppsala, Sweden).