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Stratify or adjust? Dealing with multiple populations when evaluating rare variants
The unrelated individuals sample from Genetic Analysis Workshop 17 consists of a small number of subjects from eight population samples and genetic data composed mostly of rare variants. We compare two simple approaches to collapsing rare variants within genes for their utility in identifying genes...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287824/ https://www.ncbi.nlm.nih.gov/pubmed/22373399 http://dx.doi.org/10.1186/1753-6561-5-S9-S101 |
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author | Culverhouse, Robert C Hinrichs, Anthony L Suarez, Brian K |
author_facet | Culverhouse, Robert C Hinrichs, Anthony L Suarez, Brian K |
author_sort | Culverhouse, Robert C |
collection | PubMed |
description | The unrelated individuals sample from Genetic Analysis Workshop 17 consists of a small number of subjects from eight population samples and genetic data composed mostly of rare variants. We compare two simple approaches to collapsing rare variants within genes for their utility in identifying genes that affect phenotype. We also compare results from stratified analyses to those from a pooled analysis that uses ethnicity as a covariate. We found that the two collapsing approaches were similarly effective in identifying genes that contain causative variants in these data. However, including population as a covariate was not an effective substitute for analyzing the subpopulations separately when only one subpopulation contained a rare variant linked to the phenotype. |
format | Online Article Text |
id | pubmed-3287824 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32878242012-02-28 Stratify or adjust? Dealing with multiple populations when evaluating rare variants Culverhouse, Robert C Hinrichs, Anthony L Suarez, Brian K BMC Proc Proceedings The unrelated individuals sample from Genetic Analysis Workshop 17 consists of a small number of subjects from eight population samples and genetic data composed mostly of rare variants. We compare two simple approaches to collapsing rare variants within genes for their utility in identifying genes that affect phenotype. We also compare results from stratified analyses to those from a pooled analysis that uses ethnicity as a covariate. We found that the two collapsing approaches were similarly effective in identifying genes that contain causative variants in these data. However, including population as a covariate was not an effective substitute for analyzing the subpopulations separately when only one subpopulation contained a rare variant linked to the phenotype. BioMed Central 2011-11-29 /pmc/articles/PMC3287824/ /pubmed/22373399 http://dx.doi.org/10.1186/1753-6561-5-S9-S101 Text en Copyright ©2011 Culverhouse et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Culverhouse, Robert C Hinrichs, Anthony L Suarez, Brian K Stratify or adjust? Dealing with multiple populations when evaluating rare variants |
title | Stratify or adjust? Dealing with multiple populations when evaluating rare variants |
title_full | Stratify or adjust? Dealing with multiple populations when evaluating rare variants |
title_fullStr | Stratify or adjust? Dealing with multiple populations when evaluating rare variants |
title_full_unstemmed | Stratify or adjust? Dealing with multiple populations when evaluating rare variants |
title_short | Stratify or adjust? Dealing with multiple populations when evaluating rare variants |
title_sort | stratify or adjust? dealing with multiple populations when evaluating rare variants |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287824/ https://www.ncbi.nlm.nih.gov/pubmed/22373399 http://dx.doi.org/10.1186/1753-6561-5-S9-S101 |
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