Integrating GWA studies and eQTL data to predict complex trait gene targets

Virtually every ‘environmental disease’ is a multifactorial trait, i.e. contribution from hundreds if not thousands of genes plus epigenetic factors plus environmental effects (exposure to toxicants, prescribed and non-prescription drugs).  Genome-wide association (GWA) studies have identified thousands of genetic variants associated with human complex traits. However, the genes, or the functional DNA elements through which these variants exert their effects on each trait, are often unknown.

Authors [see attached] propose a method, called “summary data–based Mendelian randomization” (SMR) that integrates summary-level data from GWA studies with data from expression quantitative trait locus (eQTL) studies to identify genes whose expression levels are associated with a complex trait because of pleiotropy (i.e. when one gene influences two or more seemingly unrelated traits).

Authors applied the method to five human complex traits using GWA studies data on (as many as) 339,224 individuals, plus eQTL data on 5,311 individuals. They prioritized 126 genes (e.g. TRAF1 and ANKRD55 for rheumatoid arthritis; SNX19 and NMRAL1 for schizophrenia). Of these 126 genes, 25 genes are new candidates; and 77 genes are not the nearest annotated gene to the highest-ranked associated GWA study single-nucleotide variant (SNV). These newly identified and prioritized genes should provide important leads into designing future functional studies for understanding the mechanism(s) whereby DNA variation leads to complex trait variation.

 Nat Genet May 2o16; 48: 481-487

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