A multiple coefficient of determination-based method for parsing SNPs that correlate with mRNA expression

In this study, we present a novel, multiple coefficient of determination (R(2)(M))-based method for parsing SNPs located within the chromosomal neighborhood of a gene into semi-independent families, each of which corresponds to one or more functional variants that regulate transcription of the gene. Specifically, our method utilizes a matrix equation framework to calculate R(2)(M) values for SNPs within a chromosome region of interest (ROI) based upon the choices of 1-4 “index” SNPs (iSNPs) that serve as proxies for underlying regulatory variants. Exhaustive testing of sets of 1–4 candidate iSNPs identifies iSNP models that best account for estimated R(2) values derived from single-variable linear regression analysis of correlations between mRNA expression and genotypes of individual SNPs. Subsequent genotype-based estimation of pairwise r(2) linkage disequilibrium (LD) coefficients between each iSNP and the other ROI SNPs allows the SNPs to be parsed into semi-independent families. Analysis of mRNA expression and genotypes data downloaded from Gene Expression Omnibus (GEO) and database for Genotypes and Phenotypes (dbGAP) demonstrates the usefulness of this method for parsing SNPs based on experimental data. We believe that this method will be widely applicable for the analysis of the genetic basis of mRNA expression and visualizing the contributions of multiple genetic variants to the regulation of individual genes.