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Family- and population-based designs identify different rare causal variants
Both family- and population-based samples are used to identify genetic variants associated with phenotypes. Each strategy has demonstrated advantages, but their ability to identify rare variants and genes containing rare variants is unclear. To compare these two study designs in the identification o...
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/PMC3287872/ https://www.ncbi.nlm.nih.gov/pubmed/22373077 http://dx.doi.org/10.1186/1753-6561-5-S9-S36 |
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author | Zhang, Xue He, Hua Ding, Lili Baye, Tesfaye M Kurowski, Brad G Martin, Lisa J |
author_facet | Zhang, Xue He, Hua Ding, Lili Baye, Tesfaye M Kurowski, Brad G Martin, Lisa J |
author_sort | Zhang, Xue |
collection | PubMed |
description | Both family- and population-based samples are used to identify genetic variants associated with phenotypes. Each strategy has demonstrated advantages, but their ability to identify rare variants and genes containing rare variants is unclear. To compare these two study designs in the identification of rare causal variants, we applied various methods to the population- and family-based data simulated by the Genetic Analysis Workshop 17 with knowledge of the simulated model. Our results suggest that different variants can be identified by different study designs. Family-based and population-based study designs can be complementary in the identification of rare causal variants and should be considered in future studies. |
format | Online Article Text |
id | pubmed-3287872 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32878722012-02-28 Family- and population-based designs identify different rare causal variants Zhang, Xue He, Hua Ding, Lili Baye, Tesfaye M Kurowski, Brad G Martin, Lisa J BMC Proc Proceedings Both family- and population-based samples are used to identify genetic variants associated with phenotypes. Each strategy has demonstrated advantages, but their ability to identify rare variants and genes containing rare variants is unclear. To compare these two study designs in the identification of rare causal variants, we applied various methods to the population- and family-based data simulated by the Genetic Analysis Workshop 17 with knowledge of the simulated model. Our results suggest that different variants can be identified by different study designs. Family-based and population-based study designs can be complementary in the identification of rare causal variants and should be considered in future studies. BioMed Central 2011-11-29 /pmc/articles/PMC3287872/ /pubmed/22373077 http://dx.doi.org/10.1186/1753-6561-5-S9-S36 Text en Copyright ©2011 Zhang 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 Zhang, Xue He, Hua Ding, Lili Baye, Tesfaye M Kurowski, Brad G Martin, Lisa J Family- and population-based designs identify different rare causal variants |
title | Family- and population-based designs identify different rare causal variants |
title_full | Family- and population-based designs identify different rare causal variants |
title_fullStr | Family- and population-based designs identify different rare causal variants |
title_full_unstemmed | Family- and population-based designs identify different rare causal variants |
title_short | Family- and population-based designs identify different rare causal variants |
title_sort | family- and population-based designs identify different rare causal variants |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287872/ https://www.ncbi.nlm.nih.gov/pubmed/22373077 http://dx.doi.org/10.1186/1753-6561-5-S9-S36 |
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