Genome-Wide Identification of Expression Quantitative Trait Loci (eQTLs) in Human Heart

et al. (2014) Genome-Wide Identification of Expression Quantitative Trait Loci (eQTLs) in Human Heart. PLoS ONE 9(5): e97380. doi:10.1371/journal.pone.0097380 Genome-Wide Identification of Expression Quantitative Trait Loci (eQTLs) in Human Heart Tamara T. Koopmann Michiel E. Adriaens Perry D. Moerland Roos F. Marsman Margriet L. Westerveld Sean Lal Taifang Zhang Christine Q. Simmons Istvan Baczko Cristobal dos Remedios Nanette H. Bishopric Andras Varro Alfred L. George Jr. Elisabeth M. Lodder Connie R. Bezzina John R.B. Perry, Institute of Metabolic Science, United Kingdom In recent years genome-wide association studies (GWAS) have uncovered numerous chromosomal loci associated with various electrocardiographic traits and cardiac arrhythmia predisposition. A considerable fraction of these loci lie within inter-genic regions. The underlying trait-associated variants likely reside in regulatory regions and exert their effect by modulating gene expression. Hence, the key to unraveling the molecular mechanisms underlying these cardiac traits is to interrogate variants for association with differential transcript abundance by expression quantitative trait locus (eQTL) analysis. In this study we conducted an eQTL analysis of human heart. For a total of 129 left ventricular samples that were collected from non-diseased human donor hearts, genome-wide transcript abundance and genotyping was determined using microarrays. Each of the 18,402 transcripts and 897,683 SNP genotypes that remained after pre-processing and stringent quality control were tested for eQTL effects. We identified 771 eQTLs, regulating 429 unique transcripts. Overlaying these eQTLs with cardiac GWAS loci identified novel candidates for studies aimed at elucidating the functional and transcriptional impact of these loci. Thus, this work provides for the first time a comprehensive eQTL map of human heart: a powerful and unique resource that enables systems genetics approaches for the study of cardiac traits. - Funding: The authors acknowledge the support from the Netherlands CardioVascular Research Initiative (CVON PREDICT): the Dutch Heart Foundation, Dutch Federation of University Medical Centres, the Netherlands Organisation for Health Research and Development and the Royal Netherlands Academy of Sciences. Tissue collections performed at Vanderbilt University were supported by NIH grant HL068880 (A.L.G.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. . These authors contributed equally to this work. It is well established that many cardiac traits and susceptibility to heart disease are heritable [1,2,3,4,5,6,7]. Several genome-wide association studies (GWAS) have uncovered common genetic variation, in the form of single nucleotide polymorphisms (SNPs), impacting on cardiac traits such as susceptibility to atrial fibrillation [8], ventricular fibrillation [9], heart rate [10] and electrocardiographic (ECG) indices of cardiac conduction [11,12,13,14] and repolarization [15,16]. There is widespread consensus that functional studies of GWAS-defined loci will advance our understanding of the molecular underpinnings of the associated traits. SNPs identified by GWAS are considered to impact the respective clinical phenotype, either directly or indirectly by virtue of linkage disequilibrium (LD) with the causal variant(s) in the context of a haplotype. Many trait-associated haplotypes occur in non-coding regions of the genome [17] and are hypothesized to modulate the respective trait through effects on gene expression [18]. Such SNPs are particularly challenging to understand because they may exert effects on the trait either by affecting the expression of a neighbouring gene (cis-effect) or the expression of a gene located elsewhere in the genome (trans-effects). One way of understanding GWAS signals thus entails interrogating traitassociated variants for association with differential transcript abundance by expression quantitative trait locus (eQTL) analysis. Studying gene expression level effects of disease-associated haplotypes has successfully uncovered the molecular mechanisms underlying loci associated with increased risk of myocardial infarction [19], coronary artery disease [20] and colorectal cancer [21]. In recent years, multiple genome-wide eQTL resources have become available for various tissues including brain, liver and adipose tissue [22,23,24,25,26,27,28,29]. Because eQTLs may be tissue-specific, a similar resource for human heart is anticipated to have great value [23,29,30,31]. To this end, we have generated a human heart eQTL resource by genome-wide genotyping and determination of transcript abundance in 129 human donor heart samples. We subsequently overlaid previously identified cardiac trait GWAS signals with the identified eQTLs to identify candidate causal genes for the effects at these GWAS loci. This work provides an eQTL map of human heart, a resource that is likely to play an important role in furthering our understanding of the mechanisms associated with loci identified in GWAS on cardiac traits. General design of study We collected left ventricular samples from 180 non-diseased human hearts of unrelated organ donors whose hearts were explanted to obtain pulmonary and aortic valves for transplant surgery or explanted for heart transplantation but not used due to logistical reasons (e.g. no tissue-matched recipient was available). The subjects were assumed to be mainly of Western European descent. mRNA and DNA were isolated according to standard procedures. Transcript abundance was measured using the HumanHT-12 v4.0 whole genome array (Illumina) and genotyping was carried out using the HumanOmniExpress genome-wide SNP arrays (Illumina). Data preprocessing and normalization Gene transcript abundance: Of the 47,231 transcripts whose expression levels were measured on the array, only those that were expressed above background level and for which the probe sequence mapped unambiguously to the genome and did not contain common SNPs, were used in further analyses. This procedure left 18,402 transcripts for eQTL analysis. Model-based background correction and normalization across arrays and transcripts was performed to correct for technical variance present in gene expression levels. A total of 162 arrays passed the standardized microarray gene expression quality control. Genotyping: Manhattan distance clustering and principal component analysis of the genotype data of 154 samples that were successfully genotyped, revealed 13 genetic outliers (Figure S1). To ensure a genetically homogenous group for further analysis, samples pertaining to these clusters were removed. An additional 12 samples were removed due to low call rate (,95%), high proportion of alleles identical-by-state (.95%), or extreme heterozygosity (FDR 1 (...truncated)