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BackgroundGenome‐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. ResultsWe 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. ConclusionsWe 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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BEATRICE: Bayesian fine-mapping from summary data using deep variational inference
Abstract MotivationWe introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to sample from the space of causal configurations, which we use to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and noise paradigms, as defined by the relative genetic contributions of causal and noncausal variants. ResultsWe demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimer’s disease. In comparison to the baselines, only BEATRICE can successfully find the APOE ϵ2 allele, a commonly associated variant of Alzheimer’s. Availability and implementationBEATRICE is available for download at https://github.com/sayangsep/Beatrice-Finemapping.
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- PAR ID:
- 10580686
- Editor(s):
- Schwartz, Russell
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 40
- Issue:
- 10
- ISSN:
- 1367-4811
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1093/bioinformatics/btae590
