Cross-population Joint Analysis of eQTLs: Fine Mapping and Functional Annotation

Abstract

Mapping expression quantitative trait loci (eQTLs) has been shown as a powerful tool to uncover the genetic underpinnings of many complex traits at the molecular level. In this paper, we present an integrative analysis approach that leverages eQTL data collected from multiple population groups. In particular, our approach effectively identifies multiple independent cis-eQTL signals that are consistently presented across populations, accounting for heterogeneity in allele frequencies and patterns of linkage disequilibrium. Furthermore, our analysis framework enables integrating high-resolution functional annotations into analysis of eQTLs. We applied our statistical approach to analyze the GEUVADIS data consisting of samples from five population groups. From this analysis, we concluded that i) joint analysis across population groups greatly improves the power of eQTL discovery and the resolution of fine mapping of causal eQTLs; ii) many genes harbor multiple independent eQTLs in their cis regions; iii) genetic variants that disrupt transcription factor binding are significantly enriched in eQTLs (p-value = 4.93 x 10-22).\n\nAuthor SummaryExpression quantitative trait loci (eQTLs) are genetic variants associated with gene expression phenotypes. Mapping eQTLs enables us to study the genetic basis of gene expression variation across individuals. In this study, we introduce a statistical framework for analyzing genotype-expression data collected from multiple population groups. We show that our approach is particularly effective in identifying multiple independent eQTL signals that are consistently presented across populations in the proximity of a gene. In addition, our analysis framework allows effective integration of genomic annotations into eQTL analysis, which is helpful in dissecting the functional basis of eQTLs.