Comparison of collapsing methods for the statistical analysis of rare variants

Comparison of collapsing methods for the statistical analysis of rare variants

Novel technologies allow sequencing of whole genomes and are considered as an emerging approach for the identification of rare disease-associated variants. Recent studies have shown that multiple rare variants can explain a particular proportion of the genetic basis for disease. Following this assum...

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Main Authors: Dering, Carmen, Ziegler, Andreas, König, Inke R, Hemmelmann, Claudia
Format: Online
Language:English
Published: BioMed Central 2011
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287839/
id pubmed-3287839
recordtype oai_dc
spelling pubmed-32878392012-02-28 Comparison of collapsing methods for the statistical analysis of rare variants Dering, Carmen Ziegler, Andreas König, Inke R Hemmelmann, Claudia Proceedings Novel technologies allow sequencing of whole genomes and are considered as an emerging approach for the identification of rare disease-associated variants. Recent studies have shown that multiple rare variants can explain a particular proportion of the genetic basis for disease. Following this assumption, we compare five collapsing approaches to test for groupwise association with disease status, using simulated data provided by Genetic Analysis Workshop 17 (GAW17). Variants are collapsed in different scenarios per gene according to different minor allele frequency (MAF) thresholds and their functionality. For comparing the different approaches, we consider the family-wise error rate and the power. Most of the methods could maintain the nominal type I error levels well for small MAF thresholds, but the power was generally low. Although the methods considered in this report are common approaches for analyzing rare variants, they performed poorly with respect to the simulated disease phenotype in the GAW17 data set. BioMed Central 2011-11-29 /pmc/articles/PMC3287839/ /pubmed/22373249 http://dx.doi.org/10.1186/1753-6561-5-S9-S115 Text en Copyright ©2011 Dering 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.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Dering, Carmen
Ziegler, Andreas
König, Inke R
Hemmelmann, Claudia
spellingShingle Dering, Carmen
Ziegler, Andreas
König, Inke R
Hemmelmann, Claudia
Comparison of collapsing methods for the statistical analysis of rare variants
author_facet Dering, Carmen
Ziegler, Andreas
König, Inke R
Hemmelmann, Claudia
author_sort Dering, Carmen
title Comparison of collapsing methods for the statistical analysis of rare variants
title_short Comparison of collapsing methods for the statistical analysis of rare variants
title_full Comparison of collapsing methods for the statistical analysis of rare variants
title_fullStr Comparison of collapsing methods for the statistical analysis of rare variants
title_full_unstemmed Comparison of collapsing methods for the statistical analysis of rare variants
title_sort comparison of collapsing methods for the statistical analysis of rare variants
description Novel technologies allow sequencing of whole genomes and are considered as an emerging approach for the identification of rare disease-associated variants. Recent studies have shown that multiple rare variants can explain a particular proportion of the genetic basis for disease. Following this assumption, we compare five collapsing approaches to test for groupwise association with disease status, using simulated data provided by Genetic Analysis Workshop 17 (GAW17). Variants are collapsed in different scenarios per gene according to different minor allele frequency (MAF) thresholds and their functionality. For comparing the different approaches, we consider the family-wise error rate and the power. Most of the methods could maintain the nominal type I error levels well for small MAF thresholds, but the power was generally low. Although the methods considered in this report are common approaches for analyzing rare variants, they performed poorly with respect to the simulated disease phenotype in the GAW17 data set.
publisher BioMed Central
publishDate 2011
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287839/
_version_ 1611508736542638080

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