Joehanes, RobyZhang, XiaolingHuan, TianxiaoYao, ChenYing, Sai-xiaNguyen, Quang TriDemirkale, Cumhur YusufFeolo, Michael L.Sharopova, Nataliya R.Sturcke, AnneSchäffer, Alejandro A.Heard-Costa, NancyChen, HanLiu, Po-chingWang, RichardWoodhouse, Kimberly A.Tanriverdi, KahramanFreedman, Jane E.Raghavachari, NaliniDupuis, JoséeJohnson, Andrew D.O’Donnell, Christopher J.Levy, DanielMunson, Peter J.2017-02-182017Joehanes, R., X. Zhang, T. Huan, C. Yao, S. Ying, Q. T. Nguyen, C. Y. Demirkale, et al. 2017. “Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies.” Genome Biology 18 (1): 16. doi:10.1186/s13059-016-1142-6. http://dx.doi.org/10.1186/s13059-016-1142-6.1474-7596http://nrs.harvard.edu/urn-3:HUL.InstRepos:30370942Background: Identification of single nucleotide polymorphisms (SNPs) associated with gene expression levels, known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role of phenotype-associated SNPs in genome-wide association studies (GWAS). The small sample sizes of some previous eQTL studies have limited their statistical power. We conducted an eQTL investigation of microarray-based gene and exon expression levels in whole blood in a cohort of 5257 individuals, exceeding the single cohort size of previous studies by more than a factor of 2. Results: We detected over 19,000 independent lead cis-eQTLs and over 6000 independent lead trans-eQTLs, targeting over 10,000 gene targets (eGenes), with a false discovery rate (FDR) < 5%. Of previously published significant GWAS SNPs, 48% are identified to be significant eQTLs in our study. Some trans-eQTLs point toward novel mechanistic explanations for the association of the SNP with the GWAS-related phenotype. We also identify 59 distinct blocks or clusters of trans-eQTLs, each targeting the expression of sets of six to 229 distinct trans-eGenes. Ten of these sets of target genes are significantly enriched for microRNA targets (FDR < 5%). Many of these clusters are associated in GWAS with multiple phenotypes. Conclusions: These findings provide insights into the molecular regulatory patterns involved in human physiology and pathophysiology. We illustrate the value of our eQTL database in the context of a recent GWAS meta-analysis of coronary artery disease and provide a list of targeted eGenes for 21 of 58 GWAS loci. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1142-6) contains supplementary material, which is available to authorized users.en-USJournal Article2017-02-1810.1186/s13059-016-1142-6