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

ABSTRACT: 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.

SUBMITTER: Zhou D

PROVIDER: S-EPMC7606598 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Transcriptome-wide association studies: a view from Mendelian randomization.

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A statistical framework for cross-tissue transcriptome-wide association analysis.

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| S-EPMC11341121 | biostudies-literature

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