Publication | Open Access
Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets
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2016
Year
The authors note that while GWAS have uncovered thousands of trait‑associated variants, the genes or functional elements mediating these effects remain largely unknown. They propose SMR, a method that integrates GWAS and eQTL summary data to identify genes whose expression is linked to complex traits through pleiotropy. SMR was applied to GWAS data from up to 339,224 individuals and eQTL data from 5,311 individuals across five complex traits, prioritizing 126 genes—including 25 novel candidates and 77 non‑nearest genes—to guide functional follow‑up. The prioritized genes provide key leads for future functional studies to elucidate how DNA variation drives complex trait variation.
Jian Yang and colleagues propose a method that integrates summary data from GWAS and eQTL studies to identify genes whose expression levels are associated with complex traits because of pleiotropy. They apply the method to five human complex traits and prioritize 126 genes for future functional studies. 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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