Multivariate analysis of regulatory SNPs: empowering personal genomics by considering cis-epistasis and heterogeneity.
Understanding how genetic variants impact the regulation and expression of genes is important for forging mechanistic links between variants and phenotypes in personal genomics studies. In this work, we investigate statistical interactions among variants that alter gene expression and identify 79 genes showing highly significant interaction effects consistent with genetic heterogeneity. Of the 79 genes, 28 have been linked to phenotypes through previous genomic studies. We characterize the structural and statistical nature of these 79 cis-epistasis models, and show that interacting regulatory SNPs often lie far apart from each other and can be quite distant from the gene they regulate. By using cis-epistasis models that account for more variance in gene expression, investigators may improve the power and replicability of their genomics studies, and more accurately estimate an individual's gene expression level, improving phenotype prediction.
Center for Human Genetics Research, Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN 37232, United States. stephen.turner@vanderbilt.edu
Stephen D
SD
Turner
William S
WS
Bush
D019295:Computational Biology; D004843:Epistasis, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D018740:Genetic Heterogeneity; D023281:Genomics / Q000706:statistics & numerical data; D006801:Humans; D008957:Models, Genetic; D015999:Multivariate Analysis; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci