Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Pavlides, Jennifer; Zhu, Zhihong; Gratten, Jacob; McRae, Allan; Wray, Naomi; Yang, Jian
• Is Part Of: Genome medicine. Volume 8:Issue 1(2016)
• Page Start: 1
• Page End: 6
• Publication Date: 2016-12
• Subjects: Genome-wide association studies (GWAS) -- Expression quantitative trait loci (eQTL) -- Summary data-based Mendelian randomization (SMR) -- Complex traits; Genomics -- Periodicals; Medical genetics -- Periodicals; 616.042
• DOI: 10.1186/s13073-016-0338-4
• Languages: English
• ISSNs: 1756-994X

Abstract

Abstract Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with complex traits and diseases. However, elucidating the causal genes underlying GWAS hits remains challenging. We applied the summary data-based Mendelian randomization (SMR) method to 28 GWAS summary datasets to identify genes whose expression levels were associated with traits and diseases due to pleiotropy or causality (the expression level of a gene and the trait are affected by the same causal variant at a locus). We identified 71 genes, of which 17 are novel associations (no GWAS hit within 1 Mb distance of the genes). We integrated all the results in an online database (http://www.cnsgenomics/shiny/SMRdb/ ), providing important resources to prioritize genes for further follow-up, for example in functional studies.