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

Zhu, Zhihong, Zhang, Futao, Hu, Han, Bakshi, Andrew, Robinson, Matthew R., Powell, Joseph E., Montgomery,Grant W., Goddard, Michael E., Wray, Naomi R., Visscher, Peter M. and Yang, Jian (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature Genetics, 48 5: 481-487. doi:10.1038/ng.3538

Author Zhu, Zhihong
Zhang, Futao
Hu, Han
Bakshi, Andrew
Robinson, Matthew R.
Powell, Joseph E.
Montgomery,Grant W.
Goddard, Michael E.
Wray, Naomi R.
Visscher, Peter M.
Yang, Jian
Title Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets
Journal name Nature Genetics   Check publisher's open access policy
ISSN 1546-1718
Publication date 2016-05
Year available 2016
Sub-type Article (original research)
DOI 10.1038/ng.3538
Open Access Status Not Open Access
Volume 48
Issue 5
Start page 481
End page 487
Total pages 7
Place of publication New York, NY, United States
Publisher Nature Publishing Group
Collection year 2017
Language eng
Formatted 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.
Keyword Genome-wide association studies (GWAS)
Genetic variants
Human complex traits
Summary data–based Mendelian randomization (SMR)
Linkage disequilibrium (LD)
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

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