Abstract
Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical framework for powerfully detecting eQTLs in multiple tissues or cell types (or, more generally, multiple subgroups). The framework explicitly models the potential for each eQTL to be active in some tissues and inactive in others. By modeling the sharing of active eQTLs among tissues, this framework increases power to detect eQTLs that are present in more than one tissue compared with “tissue-by-tissue” analyses that examine each tissue separately. Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our framework to re-analyze data from transformed B cells, T cells, and fibroblasts, we find that it substantially increases power compared with tissue-by-tissue analysis, identifying 63% more genes with eQTLs (at FDR = 0.05). Further, the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues.
Highlights
Regulatory variation plays an essential role in the genetics of disease and other phenotypes as well as in evolutionary change [1,2,3]
Separately in each tissue, we identified the threshold of the test statistic value that yields a FDR of 0.05 in that tissue, based on the true active/inactive status of each SNP in that tissue. (A SNP that is an eQTL in some tissues but not others counts as a ‘‘false discovery’’ if it is called as an eQTL in a tissue where it is inactive.) For the Bayesian methods we obtained results both using ‘‘default’’ weights on configurations (BFBMA), and using weights estimated from the data by the hierarchical model (BFHBMMA)
We have presented a statistical framework for analyzing and identifying eQTLs, combining data from multiple tissues
Summary
Regulatory variation plays an essential role in the genetics of disease and other phenotypes as well as in evolutionary change [1,2,3]. Understanding the biology of organismal phenotypes, such as diseases, is likely to require understanding regulatory variation in many different tissues ([9,10]). If regulatory variants differ across tissues, in understanding GWAS hits, and using them to understand the biology of disease, we would like to know which variants are affecting which tissues. EQTL studies have been performed in a relatively narrow range of tissue types. This is changing quickly: for example, the NIH ‘‘Genotype-Tissue Expression’’ (GTEx) project aims to collect expression and genotype data in 30 tissues across 900 individuals. Here we describe and illustrate a statistical framework for mapping eQTLs in expression data on multiple tissues
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