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Rare Variant Association Testing for Next-Generation Sequencing Data via Hierarchical Clustering
OBJECTIVES: It is thought that a proportion of the genetic susceptibility to complex diseases is due to low-frequency and rare variants. Next-generation sequencing in large populations facilitates the detection of rare variant associations to disease risk. In order to achieve adequate power to detec...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
S. Karger AG
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3668801/ https://www.ncbi.nlm.nih.gov/pubmed/23594494 http://dx.doi.org/10.1159/000346022 |
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author | Tachmazidou, Ioanna Morris, Andrew Zeggini, Eleftheria |
author_facet | Tachmazidou, Ioanna Morris, Andrew Zeggini, Eleftheria |
author_sort | Tachmazidou, Ioanna |
collection | PubMed |
description | OBJECTIVES: It is thought that a proportion of the genetic susceptibility to complex diseases is due to low-frequency and rare variants. Next-generation sequencing in large populations facilitates the detection of rare variant associations to disease risk. In order to achieve adequate power to detect association at low-frequency and rare variants, locus-specific statistical methods are being developed that combine information across variants within a functional unit and test for association with this enriched signal through so-called burden tests. METHODS: We propose a hierarchical clustering approach and a similarity kernel-based association test for continuous phenotypes. This method clusters individuals into groups, within which samples are assumed to be genetically similar, and subsequently tests the group effects among the different clusters. RESULTS: The power of this approach is comparable to that of collapsing methods when causal variants have the same direction of effect, but its power is significantly higher compared to burden tests when both protective and risk variants are present in the region of interest. Overall, we observe that the Sequence Kernel Association Test (SKAT) is the most powerful approach under the allelic architectures considered. CONCLUSIONS: In our overall comparison, we find the analytical framework within which SKAT operates to yield higher power and to control type I error appropriately. |
format | Online Article Text |
id | pubmed-3668801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | S. Karger AG |
record_format | MEDLINE/PubMed |
spelling | pubmed-36688012013-08-02 Rare Variant Association Testing for Next-Generation Sequencing Data via Hierarchical Clustering Tachmazidou, Ioanna Morris, Andrew Zeggini, Eleftheria Hum Hered Paper OBJECTIVES: It is thought that a proportion of the genetic susceptibility to complex diseases is due to low-frequency and rare variants. Next-generation sequencing in large populations facilitates the detection of rare variant associations to disease risk. In order to achieve adequate power to detect association at low-frequency and rare variants, locus-specific statistical methods are being developed that combine information across variants within a functional unit and test for association with this enriched signal through so-called burden tests. METHODS: We propose a hierarchical clustering approach and a similarity kernel-based association test for continuous phenotypes. This method clusters individuals into groups, within which samples are assumed to be genetically similar, and subsequently tests the group effects among the different clusters. RESULTS: The power of this approach is comparable to that of collapsing methods when causal variants have the same direction of effect, but its power is significantly higher compared to burden tests when both protective and risk variants are present in the region of interest. Overall, we observe that the Sequence Kernel Association Test (SKAT) is the most powerful approach under the allelic architectures considered. CONCLUSIONS: In our overall comparison, we find the analytical framework within which SKAT operates to yield higher power and to control type I error appropriately. S. Karger AG 2013-04 2013-04-11 /pmc/articles/PMC3668801/ /pubmed/23594494 http://dx.doi.org/10.1159/000346022 Text en Copyright © 2013 by S. Karger AG, Basel http://creativecommons.org/licenses/by/3.0/ This is an Open Access article licensed under the terms of the Creative Commons Attribution 3.0 Unported license (CC BY 3.0) (www.karger.com/OA-license-WT), applicable to the online version of the article only. Users may download, print and share this work on the Internet, provided the original work is properly cited, and a link to the original work on http://www.karger.com and the terms of this license are included in any shared versions. |
spellingShingle | Paper Tachmazidou, Ioanna Morris, Andrew Zeggini, Eleftheria Rare Variant Association Testing for Next-Generation Sequencing Data via Hierarchical Clustering |
title | Rare Variant Association Testing for Next-Generation Sequencing Data via Hierarchical Clustering |
title_full | Rare Variant Association Testing for Next-Generation Sequencing Data via Hierarchical Clustering |
title_fullStr | Rare Variant Association Testing for Next-Generation Sequencing Data via Hierarchical Clustering |
title_full_unstemmed | Rare Variant Association Testing for Next-Generation Sequencing Data via Hierarchical Clustering |
title_short | Rare Variant Association Testing for Next-Generation Sequencing Data via Hierarchical Clustering |
title_sort | rare variant association testing for next-generation sequencing data via hierarchical clustering |
topic | Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3668801/ https://www.ncbi.nlm.nih.gov/pubmed/23594494 http://dx.doi.org/10.1159/000346022 |
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