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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-05

AUTHORS

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

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. More... »

PAGES

481-487

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ng.3538

    DOI

    http://dx.doi.org/10.1038/ng.3538

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1013588188

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27019110


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