Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Joehanes, Roby; Zhang, Xiaoling; Huan, Tianxiao; Yao, Chen; Ying, Sai-xia; Nguyen, Quang; Demirkale, Cumhur; Feolo, Michael; Sharopova, Nataliya; Sturcke, Anne; Schäffer, Alejandro; Heard-Costa, Nancy; Chen, Han; Liu, Po-ching; Wang, Richard; Woodhouse, Kimberly; Tanriverdi, Kahraman; Freedman, Jane; Raghavachari, Nalini; Dupuis, Josée; Johnson, Andrew; O'Donnell, Christopher; Levy, Daniel; Munson, Peter
• Is Part Of: Genome biology. Volume 18:Issue 1(2017)
• Page Start: 1
• Page End: 24
• Publication Date: 2017-12
• Subjects: Genomes -- Periodicals; Biology -- Periodicals; Molecular biology -- Periodicals; 572.8633
• DOI: 10.1186/s13059-016-1142-6
• Languages: English
• ISSNs: 1474-760X

Abstract

Abstract Background 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 leadcis -eQTLs and over 6000 independent leadtrans -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. Sometrans -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 oftrans -eQTLs, each targeting the expression of sets of six to 229 distincttrans -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.