Integration of Summary Data from GWAS and eQTL Studies Predicts Complex Trait Gene Targets  

Oleh:

Zhihong Zhu ; Futao Zhang ; Han Hu ; Bakshi, Andrew ; Robinson, Matthew R.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Nature Genetics vol. 48 no. 05 (May 2016), page 481-487.
Topik: Gene Expression; Genome-Wide Association Studies

Ketersediaan

Perpustakaan FK Nomor Panggil: N12.K Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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