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Nat Genet. 2016 May;48(5):481-7. doi: 10.1038/ng.3538. Epub 2016 Mar 28.

Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.

Author information

1
Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia.
2
State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, China.
3
Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.
4
Molecular Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
5
Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, Victoria, Australia.
6
Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia.
7
University of Queensland Diamantina Institute, Translation Research Institute, Brisbane, Queensland, Australia.

Abstract

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human complex traits. However, the genes or functional DNA elements through which these variants exert their effects on the traits are often unknown. We propose a method (called SMR) that integrates summary-level data from GWAS with data from expression quantitative trait locus (eQTL) studies to identify genes whose expression levels are associated with a complex trait because of pleiotropy. We apply the method to five human complex traits using GWAS data on up to 339,224 individuals and eQTL data on 5,311 individuals, and we prioritize 126 genes (for example, TRAF1 and ANKRD55 for rheumatoid arthritis and SNX19 and NMRAL1 for schizophrenia), of which 25 genes are new candidates; 77 genes are not the nearest annotated gene to the top associated GWAS SNP. These genes provide important leads to design future functional studies to understand the mechanism whereby DNA variation leads to complex trait variation.

PMID:
27019110
DOI:
10.1038/ng.3538
[Indexed for MEDLINE]

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