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Title: Transcriptome‐wide association studies: a view from Mendelian randomization
Background

Genome‐wide association studies (GWASs) have identified thousands of genetic variants that are associated with many complex traits. However, their biological mechanisms remain largely unknown. Transcriptome‐wide association studies (TWAS) have been recently proposed as an invaluable tool for investigating the potential gene regulatory mechanisms underlying variant‐trait associations. Specifically, TWAS integrate GWAS with expression mapping studies based on a common set of variants and aim to identify genes whose GReX is associated with the phenotype. Various methods have been developed for performing TWAS and/or similar integrative analysis. Each such method has a different modeling assumption and many were initially developed to answer different biological questions. Consequently, it is not straightforward to understand their modeling property from a theoretical perspective.

Results

We present a technical review on thirteen TWAS methods. Importantly, we show that these methods can all be viewed as two‐sample Mendelian randomization (MR) analysis, which has been widely applied in GWASs for examining the causal effects of exposure on outcome. Viewing different TWAS methods from an MR perspective provides us a unique angle for understanding their benefits and pitfalls. We systematically introduce the MR analysis framework, explain how features of the GWAS and expression data influence the adaptation of MR for TWAS, and re‐interpret the modeling assumptions made in different TWAS methods from an MR angle. We finally describe future directions for TWAS methodology development.

Conclusions

We hope that this review would serve as a useful reference for both methodologists who develop TWAS methods and practitioners who perform TWAS analysis.

 
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Award ID(s):
1712933
NSF-PAR ID:
10474502
Author(s) / Creator(s):
 ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Quantitative Biology
Volume:
9
Issue:
2
ISSN:
2095-4689
Format(s):
Medium: X Size: p. 107-121
Size(s):
["p. 107-121"]
Sponsoring Org:
National Science Foundation
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  2. Background

    Genome‐wide association studies (GWAS) have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade, however, they are still hampered by limited statistical power and difficulties in biological interpretation. With the recent progress in expression quantitative trait loci (eQTL) studies, transcriptome‐wide association studies (TWAS) provide a framework to test for gene‐trait associations by integrating information from GWAS and eQTL studies.

    Results

    In this review, we will introduce the general framework of TWAS, the relevant resources, and the computational tools. Extensions of the original TWAS methods will also be discussed. Furthermore, we will briefly introduce methods that are closely related to TWAS, including MR‐based methods and colocalization approaches. Connection and difference between these approaches will be discussed.

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    Finally, we will summarize strengths, limitations, and potential directions for TWAS.

     
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    By leveraging large datasets from the PsychENCODE consortium, we conducted a genome-wide survey of trans-eQTLs in the human dorsolateral prefrontal cortex. We also performed colocalization and mediation analyses to identify mediators in trans-regulation and use trans-eQTLs to link GWAS loci to schizophrenia risk genes.

    Results

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    Results

    We introduce a meta-analysis model that addresses these problems in existing methods. We focus on the problem of finding eGenes in gene expression data from many tissues, and show that our model is better than other types of meta-analyses.

    Availability and Implementation

    Source code is at https://github.com/datduong/RECOV.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
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