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

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.

Genetic effects on gene expression across human tissues

“…To assess the extent to which GTEx cis -eQTLs are responsible for common phenotypic variation, we applied co-localization analysis to examine local linkage disequilibrium and sharing of association signals using GWAS summary statistics across 21 traits 42–44 (Supplementary Table 16). Among tested loci, 52% of trait-associated variants co- localized with an eQTL in one or more tissues (Fig.…”

“…Genetic variants associated with complex traits have been suggested to be enriched for trans -eQTLs 6,44–47 . Accordingly, we performed trans -eQTL mapping, restricting it to variants associated with a complex trait in a GWAS (Extended Data Fig.…”

10 Years of GWAS Discovery: Biology, Function, and Translation

“…One fertile area of method development is integrating data from GWASs and expression quantitative trait locus (eQTL) studies to identify associations between transcripts and complex traits. 56,61,62 These methods are useful for prioritizing genes from known GWAS loci for functional follow-up, detecting novel gene-trait associations, and inferring the directions of associations. 21,27,62 The analytical results that only about one-third of the associated genes are the nearest genes 61,62 are informative for the design of fine-mapping experiments.…”

“…The use of additional information, such as prior knowledge of the likely function of specific variants given their location and surrounding DNA motif(s), 139,140 could help to reduce the set of statistical candidates to a smaller number. This is already a fertile area of statistical and bioinformatic research 56,62,131,141,142 bringing together trait or disease GWAS results with those of tissue gene expression. More research on the resolution of fine-mapping is warranted, and this will be fueled by an expected increase in GWAS data on tissue-and cell-specific gene expression.…”

Genetic Regulation of Adipose Gene Expression and Cardio-Metabolic Traits

“…49 We performed a transcriptome-wide association for 61 cardio-metabolic traits and 24,383 probe sets with cis-eQTLs (<1 Mb) and focused on GWAS loci (p < 5 3 10 À8 ). Probe sets with p SMR < 2.1 3 10 À6 (0.05/24,383 probe sets) were then tested for pleiotropy via heterogeneity in dependent instruments (HEIDI).…”