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Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression

Although many methods are available to test sequence variants for association with complex diseases and traits, methods that specifically seek to identify causal variants are less developed. Here we develop and evaluate a Bayesian hierarchical regression method that incorporates prior information on...

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Detalles Bibliográficos
Autores principales: Long, Nanye, Dickson, Samuel P., Maia, Jessica M., Kim, Hee Shin, Zhu, Qianqian, Allen, Andrew S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3675126/
https://www.ncbi.nlm.nih.gov/pubmed/23762022
http://dx.doi.org/10.1371/journal.pcbi.1003093
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author Long, Nanye
Dickson, Samuel P.
Maia, Jessica M.
Kim, Hee Shin
Zhu, Qianqian
Allen, Andrew S.
author_facet Long, Nanye
Dickson, Samuel P.
Maia, Jessica M.
Kim, Hee Shin
Zhu, Qianqian
Allen, Andrew S.
author_sort Long, Nanye
collection PubMed
description Although many methods are available to test sequence variants for association with complex diseases and traits, methods that specifically seek to identify causal variants are less developed. Here we develop and evaluate a Bayesian hierarchical regression method that incorporates prior information on the likelihood of variant causality through weighting of variant effects. By simulation studies using both simulated and real sequence variants, we compared a standard single variant test for analyzing variant-disease association with the proposed method using different weighting schemes. We found that by leveraging linkage disequilibrium of variants with known GWAS signals and sequence conservation (phastCons), the proposed method provides a powerful approach for detecting causal variants while controlling false positives.
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spelling pubmed-36751262013-06-12 Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression Long, Nanye Dickson, Samuel P. Maia, Jessica M. Kim, Hee Shin Zhu, Qianqian Allen, Andrew S. PLoS Comput Biol Research Article Although many methods are available to test sequence variants for association with complex diseases and traits, methods that specifically seek to identify causal variants are less developed. Here we develop and evaluate a Bayesian hierarchical regression method that incorporates prior information on the likelihood of variant causality through weighting of variant effects. By simulation studies using both simulated and real sequence variants, we compared a standard single variant test for analyzing variant-disease association with the proposed method using different weighting schemes. We found that by leveraging linkage disequilibrium of variants with known GWAS signals and sequence conservation (phastCons), the proposed method provides a powerful approach for detecting causal variants while controlling false positives. Public Library of Science 2013-06-06 /pmc/articles/PMC3675126/ /pubmed/23762022 http://dx.doi.org/10.1371/journal.pcbi.1003093 Text en © 2013 Long et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Long, Nanye
Dickson, Samuel P.
Maia, Jessica M.
Kim, Hee Shin
Zhu, Qianqian
Allen, Andrew S.
Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression
title Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression
title_full Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression
title_fullStr Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression
title_full_unstemmed Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression
title_short Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression
title_sort leveraging prior information to detect causal variants via multi-variant regression
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3675126/
https://www.ncbi.nlm.nih.gov/pubmed/23762022
http://dx.doi.org/10.1371/journal.pcbi.1003093
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