Journal article

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

Zhihong Zhu, Futao Zhang, Han Hu, Andrew Bakshi, Matthew R Robinson, Joseph E Powell, Grant W Montgomery, Michael E Goddard, Naomi R Wray, Peter M Visscher, Jian Yang

NATURE GENETICS | NATURE PUBLISHING GROUP | Published : 2016

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..

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University of Melbourne Researchers

Grants

Awarded by Australian Research Council


Awarded by Australian National Health and Medical Research Council


Funding Acknowledgements

We thank J. McGrath for helpful comments. This research was supported by the Australian Research Council (DP130102666), the Australian National Health and Medical Research Council (grants 1078037, 1048853 and 1046880) and the Sylvia and Charles Viertel Charitable Foundation. This study makes use of data from the database of Genotypes and Phenotypes (dbGaP) available under accession phs000090.v3.p1 (see the Supplementary Note for the full set of acknowledgments for these data).