Integrating Gene Expression with Genome-wide Association Summary Statistics to Identify Genes Associated with 30 Complex Traits

Nicholas Mancuso
David Geffen School of Medicine
University of California, Los Angeles

Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. Here, we introduce a method for estimating the local genetic correlation between gene expression and a complex trait and use it to estimate the genome-wide genetic correlation caused by predicted expression between pairs of traits using summary data. We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. We then used our approach to find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, eight were not found through genetic correlation at the SNP level. Finally, we used bi-directional regression to find evidence that BMI causally influences triglyceride levels and that triglyceride levels causally influence low-density lipoprotein. Together, our results provide insight into the role of gene expression in the susceptibility of complex traits and diseases.

Host: Greg Gibson

Event Details


  • Thursday, March 15, 2018
    10:55 am
Location: Room 1005, Roger A. and Helen B. Krone Engineered Biosystems Building (EBB), 950 Atlantic Dr NW, Atlanta, GA 30332

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  • Nick Mancuso

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