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Article: A Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis

TitleA Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis
Authors
Issue Date2-Jan-2024
PublisherCell Press
Citation
American Journal of Human Genetics, 2024, v. 111, p. 1-14 How to Cite?
Abstract

The aim of fine mapping is to identify genetic variants causally contributing to complex traits or diseases. Existing fine-mapping methods employ Bayesian discrete mixture priors and depend on a pre-specified maximum number of causal variants, which may lead to sub-optimal solutions. In this work, we propose a Bayesian fine-mapping method called h2-D2, utilizing a continuous global-local shrinkage prior. We also present an approach to define credible sets of causal variants in continuous prior settings. Simulation studies demonstrate that h2-D2 outperforms current state-of-the-art fine-mapping methods such as SuSiE and FINEMAP in accurately identifying causal variants and estimating their effect sizes. We further applied h2-D2 to prostate cancer analysis and discovered some previously unknown causal variants. In addition, we inferred 369 target genes associated with the detected causal variants and several pathways that were significantly over-represented by these genes, shedding light on their potential roles in prostate cancer development and progression.


Persistent Identifierhttp://hdl.handle.net/10722/339540
ISSN
2023 Impact Factor: 8.1
2023 SCImago Journal Rankings: 4.516

 

DC FieldValueLanguage
dc.contributor.authorLi, Xiang-
dc.contributor.authorSham, Pak Chung-
dc.contributor.authorZhang, Yan Dora-
dc.date.accessioned2024-03-11T10:37:28Z-
dc.date.available2024-03-11T10:37:28Z-
dc.date.issued2024-01-02-
dc.identifier.citationAmerican Journal of Human Genetics, 2024, v. 111, p. 1-14-
dc.identifier.issn0002-9297-
dc.identifier.urihttp://hdl.handle.net/10722/339540-
dc.description.abstract<p>The aim of fine mapping is to identify genetic variants causally contributing to complex traits or diseases. Existing fine-mapping methods employ Bayesian discrete mixture priors and depend on a pre-specified maximum number of causal variants, which may lead to sub-optimal solutions. In this work, we propose a Bayesian fine-mapping method called h2-D2, utilizing a continuous global-local shrinkage prior. We also present an approach to define credible sets of causal variants in continuous prior settings. Simulation studies demonstrate that h2-D2 outperforms current state-of-the-art fine-mapping methods such as SuSiE and FINEMAP in accurately identifying causal variants and estimating their effect sizes. We further applied h2-D2 to prostate cancer analysis and discovered some previously unknown causal variants. In addition, we inferred 369 target genes associated with the detected causal variants and several pathways that were significantly over-represented by these genes, shedding light on their potential roles in prostate cancer development and progression.</p>-
dc.languageeng-
dc.publisherCell Press-
dc.relation.ispartofAmerican Journal of Human Genetics-
dc.titleA Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis-
dc.typeArticle-
dc.description.naturepreprint-
dc.identifier.doi10.1016/j.ajhg.2023.12.007-
dc.identifier.volume111-
dc.identifier.spage1-
dc.identifier.epage14-
dc.identifier.eissn1537-6605-
dc.identifier.issnl0002-9297-

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