Constraints on eQTL Fine Mapping in the Presence of Multi-site Local Regulation of Gene Expression

Expression QTL (eQTL) detection has emerged as an important tool for unraveling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and magnitude of effects. We describe a series of simulation studies designed to evaluate the impact of linkage disequilibrium on the fine-mapping of causal variants with typical eQTL effect sizes. In the presence of multi-site regulation, even though between 80% and 90% of modeled eSNPs associate with normally distributed traits, up to 10% of all secondary signals could be statistical artefacts, and at least 5% but up to one quarter of credible intervals of SNPs within r2>0.8 of the peak may not even include a causal site. The Bayesian methods eCAVIAR and DAP provide only modest improvement in resolution. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine-mapping of causal variants needs to be adjusted for multi-site influences, as conditional estimates can be highly biased by interference among linked sites, but ultimately experimental verification of individual effects is needed. Presumably similar conclusions apply not just to eQTL mapping, but to multi-site influences on fine mapping of most types of quantitative trait.

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