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Genome Biol. 2017 Jan 25;18(1):16. doi: 10.1186/s13059-016-1142-6.

Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies.

Author information

1
The Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.
2
Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA.
3
Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School, Boston, MA, USA.
4
Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA.
5
Section of Biomedical Genetics, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
6
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
7
Department of Neurology, Boston University School of Medicine, Boston, MA, USA.
8
School of Public Health, Harvard University, Boston, MA, USA.
9
DNA Sequencing and Genomics Core, National Institutes of Health, Bethesda, MD, USA.
10
Department of Medicine, University of Massachusetts Medical School, Worchester, MA, USA.
11
National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
12
Cardiology Section, Department of Medicine, Boston VA Healthcare, Boston, MA, USA.
13
The Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA. levyd@nhlbi.nih.gov.
14
Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA. munson@mail.nih.gov.
15
National Institutes of Health, Bldg 12A, Room 2003, Bethesda, MD, 20892-5626, USA. munson@mail.nih.gov.

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 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.

PMID:
28122634
PMCID:
PMC5264466
DOI:
10.1186/s13059-016-1142-6
[Indexed for MEDLINE]
Free PMC Article

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