Eric Gamazon
Last active: 10/10/2020

A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis.

Zhou D, Jiang Y, Zhong X, Cox NJ, Liu C, Gamazon ER
Nat Genet. 2020

PMID: 33020666 · DOI:10.1038/s41588-020-0706-2

Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outperforms other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of transcriptome-wide association study interpretation) and performs causal inference with type I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits and the suitability of Mendelian randomization as a causal inference strategy for transcriptome-wide association studies. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data, and extensive simulations show substantially improved statistical power, replication and causal mapping rate for JTI relative to existing approaches.

MeSH Terms (0)

Connections (1)

This publication is referenced by other Labnodes entities:

Links