A statistical framework for joint eQTL analysis in multiple tissues

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.

Figure 1. The joint analysis has more power across a range of alternatives.

A. Five tissues are simulated, each with the error variance equal to 1. B. Five tissues are simulated, with error variances being 1, 1.5 or 2. C. Twenty tissues are simulated, each with the error variance equal to 1.

Figure 2. The joint analysis efficiently borrows information across genes.

Five tissues are simulated. Some eQTLs were shared by all tissues, some were specific to each tissue, and, as depicted by the cladogram, some were shared by Tissues 1 and 2 only, while others were shared by Tissues 3, 4 and 5. Each tissue has 100 samples, except tissue 1 which has only 60.

Figure 3. The joint analysis is more powerful on the data set from Dimas et al.

A and B. Histograms of gene obtained by the tissue-by-tissue analysis and the joint analysis. C. Scatter plot of the from the joint analysis versus the of the tissue-by-tissue analysis. D. Numbers of eQTLs called by both methods or either one of them.

Figure 4. Example of an eQTL with weak, yet consistent effects.

A. Boxplots of the PC-corrected expression levels from gene ASCC1 (Ensembl id ENSG00000138303) in all three cell types, color-coded by genotype class at SNP rs1678614. B. Forest plot of estimated standardized effect sizes of this eQTL. Note that none of the from the tissue-by-tissue analysis are significant at FDR = 0.05.

Figure 5. Example of an eQTL wrongly called as tissue-specific by the tissue-by-tissue analysis.

A. Boxplots of the PC-corrected expression levels from gene CHPT1 (Ensembl id ENSG00000111666) in all three cell types, color-coded by genotype class at SNP rs10860794. B. Forest plot of estimated standardized effect sizes of this eQTL. Note that, from the of the tissue-by-tissue analysis, the eQTL is significant at FDR = 0.05 only in fibroblasts.