Abstract
Integrating results from genome-wide association studies (GWASs) and gene expression studies through transcriptome-wide association study (TWAS) has the potential to shed light on the causal molecular mechanisms underlying disease etiology. Here, we present a probabilistic Mendelian randomization (MR) method, PMR-Egger, for TWAS applications. PMR-Egger relies on a MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and is scalable to hundreds of thousands of individuals. In simulations, PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust under various types of model misspecifications, is more powerful than existing TWAS/MR approaches, and can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank.
Highlights
Integrating results from genome-wide association studies (GWASs) and gene expression studies through transcriptome-wide association study (TWAS) has the potential to shed light on the causal molecular mechanisms underlying disease etiology
probabilistic twosample Mendelian randomization (PMR)-Egger is described in the Methods, with technical details provided in the Supplementary Notes
We have presented PMR-Egger, a new method that conducts Mendelian randomization (MR) analysis using multiple correlated instruments while controlling for horizontal pleiotropic effects
Summary
Integrating results from genome-wide association studies (GWASs) and gene expression studies through transcriptome-wide association study (TWAS) has the potential to shed light on the causal molecular mechanisms underlying disease etiology. SMR3 or GSMR4 directly tests the causal association between gene expression and disease trait under a Mendelian randomization (MR) framework, using either a single instrument or multiple independent instruments While each of these integrative methods was originally proposed to solve a different problem, as we will show here, all of them can be viewed as a two-sample MR method with different modeling assumptions. Because of their relationship to MR, these methods effectively attempt to identify genes causally associated with diseases or complex traits in the context of transcriptome-wide association studies (TWAS). As we will show below, LDA MR-Egger cannot handle realistic LD pattern among cis-SNPs for TWAS applications
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