Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1782272481213546496 |
---|---|
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. |
format | Online Article Text |
id | pubmed-3675126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT longnanye leveragingpriorinformationtodetectcausalvariantsviamultivariantregression AT dicksonsamuelp leveragingpriorinformationtodetectcausalvariantsviamultivariantregression AT maiajessicam leveragingpriorinformationtodetectcausalvariantsviamultivariantregression AT kimheeshin leveragingpriorinformationtodetectcausalvariantsviamultivariantregression AT zhuqianqian leveragingpriorinformationtodetectcausalvariantsviamultivariantregression AT allenandrews leveragingpriorinformationtodetectcausalvariantsviamultivariantregression |