Empowering Personal Genomics by Considering Regulatory Cis-Epistasis and Heterogeneity

Will Bush and I just heard that our paper "Multivariate Analysis of Regulatory SNPs: Empowering Personal Genomics by Considering Cis-Epistasis and Heterogeneity" was accepted for publication and a talk at the Personal Genomics session of the 2011 Pacific Symposium in Biocomputing.

Your humble GGD contributors embarked on our first collaborative paper using genome-wide transcriptome data and genome-wide SNP data from HapMap lymphoblastoid cell lines to examine an alternative mechanism for how epistasis might affect human traits. Many human traits are driven by alterations in gene expression, and it's known that common genetic variation affects the expression of nearby genes. We also know that epistasis is ubiquitous and affects human traits. Combining these three ideas, is it possible that genetic variation can interact epistatically to exert a cis-regulatory effect on the expression of nearby genes? If so, what is the genomic and statistical structure of these epistatically interacting multilocus models? Are genes which are affected by cis-epistasis associated with complex human disease or morphological phenotypes? If so, how might we use this knowledge to guide the reanalysis of existing datasets? We addressed these questions here using experimental data from HapMap cell lines. If you're interested in seeing the paper please email me, or try to catch our talk at PSB (a meeting worth going to!).

Abstract: 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.

Pacific Symposium in Biocomputing 2011