PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis
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PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis Yuhua Zhang1† , Corbin Quick1,2† , Ketian Yu1 , Alvaro Barbeira3 , The GTEx Consortium, Francesca Luca4 , Roger Pique-Regi4 , Hae Kyung Im3 and Xiaoquan Wen1* *Correspondence: [email protected] † Yuhua Zhang and Corbin Quick contributed equally to this work. 1 Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA Full list of author information is available at the end of the article
Abstract We propose a new computational framework, probabilistic transcriptome-wide association study (PTWAS), to investigate causal relationships between gene expressions and complex traits. PTWAS applies the established principles from instrumental variables analysis and takes advantage of probabilistic eQTL annotations to delineate and tackle the unique challenges arising in TWAS. PTWAS not only confers higher power than the existing methods but also provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type-specific gene-to-trait effects. We illustrate the power of PTWAS by analyzing the eQTL data across 49 tissues from GTEx (v8) and GWAS summary statistics from 114 complex traits. Keywords: TWAS, eQTLs, GWAS, Causal inference, Genetic association, Instrumental variable
Introduction Over the past two decades, genome-wide association studies (GWAS) have identified an abundance of genetic loci associated with complex traits and diseases [1]. However, most GWAS loci are located in the non-coding regions of the genome, and our understanding of the mechanisms and causal relationships underlying these associations is still lacking. Direct experimental investigation of the causal path from genotype to complex trait (e.g., via randomized controlled experiments on human subjects using CRISPR) is limited by technical difficulties and ethical constraints. Thus, statistical methods for causal inference from observational data become a logical alternative, especially with the increasing availability of large-scale GWAS and molecular QTL data. Instrumental variable (IV) analysis is an established inference framework to investigate causal relationships from observational data in the presence of possible confounding, which has been widely applied in econometrics and epidemiology. In observational epidemiology, the study of
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