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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...

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Detalles Bibliográficos
Autores principales: Culverhouse, Robert C, Hinrichs, Anthony L, Suarez, Brian K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
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.
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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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