Journal Article

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

Cox, Nancy J., Jiang, Yi, Liu, Chunyu, Zhou, Dan, Zhong, Xue, Gamazon, Eric R.

2020

Overview

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. MR-JTI, a unified framework for joint-tissue imputation and Mendelian randomization, improves prediction performance in a tissue-dependent manner when applied to large-scale biobanks and meta-analysis data.

• Publisher: Nature Publishing Group US,Nature Publishing Group
• Subject: 38/39 / 38/43 / 45 / 631/114 / 631/208 / 692/699 / Agriculture / Animal Genetics and Genomics / Animals / Bayesian analysis / Biomedical and Life Sciences / Biomedicine / Cancer Research / Causal inference / Dirichlet problem / Gene expression / Gene Expression Profiling / Gene Function / Gene mapping / Genetic Association Studies - methods / Genetic research / Genome-wide association studies / Heterogeneity / Human Genetics / Humans / Inference / Lipoproteins, LDL - genetics / Mathematical models / Mendelian Randomization Analysis / Methods / Mice / Models, Genetic / Multifactorial Inheritance - genetics / Pleiotropy / Predictive Value of Tests / Randomization / Regression analysis / Regression models / Statistical analysis / technical-report / Tissues