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

By Zhihong Zhu, Futao Zhang, Han-qiao Hu, A. Bakshi, M. Robinson, Joseph E. Powell, G. Montgomery, Michael E. Goddard, Naomi R. Wray, Peter M. Visscher and 1 more. Nature Genetics, 2016

2,949 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/57531f575d6b17df2d13e76d4437ffdd7c0aaee8

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Citations in the corpus

Orienting the causal relationship between imprecisely measured traits using GWAS summary data (2017, background): “However, if there is only a single instrument it is difficult to separate between the two competing models of g instrumenting a trait which causes another trait, and g having pleiotropic effects on both traits independently [44].”
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The MRC IEU OpenGWAS data infrastructure (2020, background): “Methodological advancements have been made in particular in Mendelian randomization (MR) 6 , genetic colocalisation 7–9 and genetic correlation 10,11 that require only GWAS summary data to explore the shared aetiology of two or more traits.”
Fine-mapping from summary data with the “Sum of Single Effects” model (2021, positive): “We also emphasize the importance of computing zscores at different SNPs from the exact same samples, using genotype imputation if necessary [50].”
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Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, methodology): “Such outlier removal strategies are present in the MR-PRESSO (61), and generalized summary MR (GSMR) approaches (62).”
Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018, positive): “For orthogonal validation, we conducted summary-data–based Mendelian randomization (SMR), a complementary method that tests for pleiotropic associations in the cis window with an accompanying HEIDI test to distinguish linkage from pleiotropy (48).”