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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.

Original publication

DOI

10.1038/ng.3538

Type

Journal article

Journal

Nature genetics

Publication Date

05/2016

Volume

48

Pages

481 - 487

Addresses

Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia.

Keywords

Humans, Data Interpretation, Statistical, Genetic Techniques, Gene Expression Regulation, Quantitative Trait Loci, Genetic Variation, Genome-Wide Association Study, Genetic Linkage, Genetic Pleiotropy, Transcriptome