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Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data

Genome-wide association studies for complex traits are based on the common disease/common variant (CDCV) and common disease/rare variant (CDRV) assumptions. Under the CDCV hypothesis, classical genome-wide association studies using single-marker tests are powerful in detecting common susceptibility...

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Autores principales: Saad, Mohamad, Pierre, Aude Saint, Bohossian, Nora, Macé, Matthias, Martinez, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287869/
https://www.ncbi.nlm.nih.gov/pubmed/22373523
http://dx.doi.org/10.1186/1753-6561-5-S9-S33
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author Saad, Mohamad
Pierre, Aude Saint
Bohossian, Nora
Macé, Matthias
Martinez, Maria
author_facet Saad, Mohamad
Pierre, Aude Saint
Bohossian, Nora
Macé, Matthias
Martinez, Maria
author_sort Saad, Mohamad
collection PubMed
description Genome-wide association studies for complex traits are based on the common disease/common variant (CDCV) and common disease/rare variant (CDRV) assumptions. Under the CDCV hypothesis, classical genome-wide association studies using single-marker tests are powerful in detecting common susceptibility variants, but under the CDRV hypothesis they are not as powerful. Several methods have been recently proposed to detect association with multiple rare variants collectively in a functional unit such as a gene. In this paper, we compare the relative performance of several of these methods on the Genetic Analysis Workshop 17 data. We evaluate these methods using the unrelated individual and family data sets. Association was tested using 200 replicates for the quantitative trait Q1. Although in these data the power to detect association is often low, our results show that collapsing methods are promising tools. However, we faced the challenge of assessing the proper type I error to validate our power comparisons. We observed that the type I error rate was not well controlled; however, we did not find a general trend specific to each method. Each method can be conservative or nonconservative depending on the studied gene. Our results also suggest that collapsing and the single-locus association approaches may not be affected to the same extent by population stratification. This deserves further investigation.
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spelling pubmed-32878692012-02-28 Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data Saad, Mohamad Pierre, Aude Saint Bohossian, Nora Macé, Matthias Martinez, Maria BMC Proc Proceedings Genome-wide association studies for complex traits are based on the common disease/common variant (CDCV) and common disease/rare variant (CDRV) assumptions. Under the CDCV hypothesis, classical genome-wide association studies using single-marker tests are powerful in detecting common susceptibility variants, but under the CDRV hypothesis they are not as powerful. Several methods have been recently proposed to detect association with multiple rare variants collectively in a functional unit such as a gene. In this paper, we compare the relative performance of several of these methods on the Genetic Analysis Workshop 17 data. We evaluate these methods using the unrelated individual and family data sets. Association was tested using 200 replicates for the quantitative trait Q1. Although in these data the power to detect association is often low, our results show that collapsing methods are promising tools. However, we faced the challenge of assessing the proper type I error to validate our power comparisons. We observed that the type I error rate was not well controlled; however, we did not find a general trend specific to each method. Each method can be conservative or nonconservative depending on the studied gene. Our results also suggest that collapsing and the single-locus association approaches may not be affected to the same extent by population stratification. This deserves further investigation. BioMed Central 2011-11-29 /pmc/articles/PMC3287869/ /pubmed/22373523 http://dx.doi.org/10.1186/1753-6561-5-S9-S33 Text en Copyright ©2011 Saad 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
Saad, Mohamad
Pierre, Aude Saint
Bohossian, Nora
Macé, Matthias
Martinez, Maria
Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data
title Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data
title_full Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data
title_fullStr Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data
title_full_unstemmed Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data
title_short Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data
title_sort comparative study of statistical methods for detecting association with rare variants in exome-resequencing data
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287869/
https://www.ncbi.nlm.nih.gov/pubmed/22373523
http://dx.doi.org/10.1186/1753-6561-5-S9-S33
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