Abstract
Genome-wide associations have shown a lot of promise in dissecting the genetics of complex traits in humans with single variants, yet a large fraction of the genetic effects is still unaccounted for. Analyzing genetic interactions between variants (epistasis) is one of the potential ways forward. We investigated the abundance and functional impact of a specific type of epistasis, namely the interaction between regulatory and protein-coding variants. Using genotype and gene expression data from the 210 unrelated individuals of the original four HapMap populations, we have explored the combined effects of regulatory and protein-coding single nucleotide polymorphisms (SNPs). We predict that about 18% (1,502 out of 8,233 nsSNPs) of protein-coding variants are differentially expressed among individuals and demonstrate that regulatory variants can modify the functional effect of a coding variant in cis. Furthermore, we show that such interactions in cis can affect the expression of downstream targets of the gene containing the protein-coding SNP. In this way, a cis interaction between regulatory and protein-coding variants has a trans impact on gene expression. Given the abundance of both types of variants in human populations, we propose that joint consideration of regulatory and protein-coding variants may reveal additional genetic effects underlying complex traits and disease and may shed light on causes of differential penetrance of known disease variants.
Highlights
Most disease association studies to date attempt to link single genetic variants to a specific phenotype [1,2,3,4]
The ultimate goal of genome-wide association studies (GWAS) is to explain the proportion of variation in a phenotypic trait that can be attributed to genetic factors
Interaction between genetic variants, is a largely under-explored factor, which may shed some light in this area
Summary
Most disease association studies to date attempt to link single genetic variants to a specific phenotype [1,2,3,4]. The prevalence and biological significance of epistasis has always been an area of interest in the field of human genetics and quantitative genetics, but its contribution to phenotypic variation has remained obscure, largely because genetic interactions have proven difficult to test [7] This difficulty arises primarily because it is unclear which variant combinations should be tested and under which model of epistasis. Most strategies that address the effects of epistasis in humans involve millions of agnostic pairwise tests and fall into two broad categories: exhaustive testing of interactions between all pairs of variants across the genome [8], or testing of interactions between all pairs of those variants that each have an independent main effect on the phenotype of interest [8,9,10] It is not entirely clear whether improvements in statistical methods will be sufficient to address the problem of epistasis. The development of realistic biological models of epistatic interactions may reduce the statistical cost of dealing with many comparisons and facilitate the development of such methodologies
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