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PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

Authors
  • Zhang, Yuhua1
  • Quick, Corbin1, 2
  • Yu, Ketian1
  • Barbeira, Alvaro3
  • Luca, Francesca4
  • Pique-Regi, Roger4
  • Kyung Im, Hae3
  • Wen, Xiaoquan1
  • 1 University of Michigan, Ann Arbor, MI, USA , Ann Arbor (United States)
  • 2 Harvard University, Cambridge, MA, USA , Cambridge (United States)
  • 3 University of Chicago, Chicago, IL, USA , Chicago (United States)
  • 4 Wayne State University, Detroit, MI, USA , Detroit (United States)
Type
Published Article
Publication Date
Sep 11, 2020
Volume
21
Issue
1
Identifiers
DOI: 10.1186/s13059-020-02026-y
Source
Springer Nature
Keywords
License
Green

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.

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