Variability single nucleotide polymorphisms linking stochastic epigenetic variation and common disease

A method of predicting risk for a condition or disorder in a subject, comprising:

• (a) obtaining genotype data from a plurality of samples;
• (b) obtaining genome-wide gene expression data from the samples;
• (c) performing a first variability test for the genotype data, thereby obtaining genotype variability information;
• (d) performing a second variability test for at least one selected gene expression from the samples, thereby obtaining gene expression variability information, wherein the selected gene expression correlates with the condition or disorder;
• (e) performing a first association study between the genotype variability information of (c) and the gene expression variability information of (d), thereby identifying at least one variability genotype associated with the selected gene expression;
• (f) performing a second association study between each of the at least one variability genotype identified in (e) and the genome-wide gene expression data of (b), thereby identifying at least one expression variable trait loci (eVTL), wherein the at least one eVTL is associated with the condition or disorder;
• (g) identifying a plurality of variably methylated regions (VMRs) correlated with the selected gene expression;
• (h) performing a linkage disequilibrium (LD) study between the at least one variability genotype identified in (e) and the VMRs correlated with the selected gene expression identified in (g), thereby identifying at least one VMR correlated with the variability genotype;
• (i) measuring expression level of the at least one eVTL in (f) in a biological sample from the subject;
• (j) measuring methylation level of the at least one VMR correlated with the variability genotype identified in (g) in a biological sample from the subject;
• and
• (k) predicting the risk for the condition or disorder in the subject based on the expression level of the eVTL in (i) and the methylation level measured in (j).

A method for analyzing epigenetic information, using suitable computer software for use on a computer, comprising:

• (a) performing a first variability test for genotype data obtained from a plurality of samples, thereby obtaining genotype variability information;
• (b) performing a second variability test for at least one selected gene expression from the samples, thereby obtaining gene expression variability information;
• (c) performing a first association study between the genotype variability information of (a) and the gene expression variability information of (b), thereby identifying at least one variability genotype associated with the selected gene expression;
• (d) performing a second association study between each of the at least one variability genotype identified in (c) and genome-wide gene expression data obtained from the samples, thereby identifying at least one expression variable trait loci (eVTL);
• and
• (e) performing a linkage disequilibrium (LD) study between the at least one variability genotype identified in (c) and a plurality of variably methylated regions (VMRs) correlated with the selected gene expression, thereby identifying at least one VMR correlated with the variability genotype.

A system for identifying expression variable trait loci (eVTL) and variably methylated regions (VMRs) for predicting risk for a condition or disorder in a subject, comprising:

• (a) a first variability module performing a first variability test for genotype data obtained from a plurality of samples, thereby obtaining genotype variability information;
• (b) a second variability module performing a second variability test for at least one selected gene expression, thereby obtaining gene expression variability information, wherein the selected gene expression correlates with the condition or disorder;
• (c) a first association module performing a first association study between the genotype variability information of (a) and the gene expression variability information of (b), thereby identifying at least one variability genotype associated with the selected gene expression;
• (d) a second association module performing a second association study between each of the at least one variability genotype identified in (c) and genome-wide gene expression data obtained from the samples, thereby identifying at least one expression variable trait loci (eVTL);
• and
• (e) a linkage disequilibrium module performing a linkage disequilibrium (LD) study between the at least one variability genotype identified in (c) and a plurality of VMRs correlated with the selected gene expression, thereby identifying at least one VMR correlated with the variability genotype.

A method for predicting risk for a condition or disorder in a subject, comprising:

• (a) measuring intra-sample change over time for genome-wide variably methylated regions (VMRs) from a plurality of samples;
• (b) performing gene ontology analysis for the VMRs;
• (c) identifying at least one VMR correlated with the condition or disorder using a linear regression model;
• (d) measuring methylation level of the at least one VMRs correlated with the condition or disorder in a biological sample from the subject;
• and
• (e) predicting the risk for the condition or disorder in the subject based on the methylation level measured in (d).

A method for generating an epigenetic signature for a subject, comprising:

• (a) measuring intra-sample change over time for genome-wide variably methylated regions (VMRs) from a plurality of samples;
• (b) separating selected VMRs into two groups using a two component Gaussian mixture model based on the measured intra-sample change of (a), wherein the VMRs in the higher distribution are designated as dynamic VMRs and the VMRs in the lower distribution are designated as stable VMRs;
• (c) measuring methylation levels of a plurality of stable VMRs in a biological sample from the subject;
• and
• (d) generating the epigenetic signature for the subject based on the methylation levels measured in (c).

A method for simulating epigenetic plasticity across generations, comprising:

• (a) generating a plurality of genotype variants, wherein the genotype variants are genetically inherited;
• (b) applying natural selection favoring a first subset of the genotype variants;
• (c) enabling a plurality of stochastic epigenetic elements, wherein the stochastic epigenetic elements change phenotypes without changing the genotype variants;
• (d) allowing a changing environment across generations favoring a second subset of the genotype variants;
• and
• (e) monitoring fluctuations of mean phenotype across generations.