Abstract
Mapping expression quantitative trait loci (eQTLs) has been shown as a powerful tool to uncover the genetic underpinnings of many complex traits at 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 consistent across populations, accounting for population heterogeneity in allele frequencies and linkage disequilibrium patterns. Furthermore, by integrating genomic annotations, our analysis framework enables high-resolution functional 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) jointly analysis across population groups greatly improves the power of eQTL discovery and the resolution of fine mapping of causal eQTL 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 × 10-22).
Highlights
Expression quantitative trait loci, or eQTLs, are genetic variants that are associated with gene expression levels
Expression quantitative trait loci are genetic variants associated with gene expression phenotypes
Mapping eQTLs enables us to study the genetic basis of gene expression variation across individuals
Summary
Expression quantitative trait loci, or eQTLs, are genetic variants that are associated with gene expression levels. Because a gene is typically regulated by many regulatory elements, it is highly likely that there exist multiple independent eQTLs in its proximity (i.e., cis region). In this scenario, a multi-SNP analysis is required to uncover all relevant cis acting genetic factors involved in the gene regulation process [9]. Linking genomic annotations to eQTLs goes beyond genetic association analysis, and helps gain a better understanding of the underlying biological processes Some of these three issues have been discussed by previous works. [3, 9, 13,14,15,16] discussed single SNP analysis of eQTLs jointly from different studies, populations or tissues. To the best of our knowledge, there is no existing approach that jointly addresses all three issues in a systematic way
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