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PLoS One. 2014 May 20;9(5):e97380. doi: 10.1371/journal.pone.0097380. eCollection 2014.

Genome-wide identification of expression quantitative trait loci (eQTLs) in human heart.

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Department of Experimental Cardiology, Heart Failure Research Centre, Academic Medical Center, Amsterdam, The Netherlands.
Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam, The Netherlands.
Muscle Research Unit, Department of Anatomy, Bosch Institute, The University of Sydney, Sydney, Australia.
Department of Medicine, University of Miami School of Medicine, Miami, Florida, United States of America.
Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, Tennessee, United States of America.
Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary.
Department of Medicine, University of Miami School of Medicine, Miami, Florida, United States of America; Department of Molecular and Cellular Pharmacology, University of Miami School of Medicine, Miami, Florida, United States of America.


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.

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