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A multi-level model for analyzing whole genome sequencing family data with longitudinal traits

Compared with microarray-based genotyping, next-generation whole genome sequencing (WGS) studies have the strength to provide greater information for the identification of rare variants, which likely account for a significant portion of missing heritability of common human diseases. In WGS, family-b...

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Detalles Bibliográficos
Autores principales: Chen, Taoye, Santawisook, Patchara, Wu, Zheyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143693/
https://www.ncbi.nlm.nih.gov/pubmed/25519414
http://dx.doi.org/10.1186/1753-6561-8-S1-S86
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author Chen, Taoye
Santawisook, Patchara
Wu, Zheyang
author_facet Chen, Taoye
Santawisook, Patchara
Wu, Zheyang
author_sort Chen, Taoye
collection PubMed
description Compared with microarray-based genotyping, next-generation whole genome sequencing (WGS) studies have the strength to provide greater information for the identification of rare variants, which likely account for a significant portion of missing heritability of common human diseases. In WGS, family-based studies are important because they are likely enriched for rare disease variants that segregate with the disease in relatives. We propose a multilevel model to detect disease variants using family-based WGS data with longitudinal measures. This model incorporates the correlation structure from family pedigrees and that from repeated measures. The iterative generalized least squares algorithm was applied to estimation of parameters and test of associations. The model was applied to the data of Genetic Analysis Workshop 18 and compared with existing linear mixed-effect models. The multilevel model shows higher power at practical p-value levels and a better type I error control than linear mixed-effect model. Both multilevel and linear mixed-effect models, which use the longitudinal repeated information, have higher power than the methods that only use data collected at one time point.
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spelling pubmed-41436932014-09-02 A multi-level model for analyzing whole genome sequencing family data with longitudinal traits Chen, Taoye Santawisook, Patchara Wu, Zheyang BMC Proc Proceedings Compared with microarray-based genotyping, next-generation whole genome sequencing (WGS) studies have the strength to provide greater information for the identification of rare variants, which likely account for a significant portion of missing heritability of common human diseases. In WGS, family-based studies are important because they are likely enriched for rare disease variants that segregate with the disease in relatives. We propose a multilevel model to detect disease variants using family-based WGS data with longitudinal measures. This model incorporates the correlation structure from family pedigrees and that from repeated measures. The iterative generalized least squares algorithm was applied to estimation of parameters and test of associations. The model was applied to the data of Genetic Analysis Workshop 18 and compared with existing linear mixed-effect models. The multilevel model shows higher power at practical p-value levels and a better type I error control than linear mixed-effect model. Both multilevel and linear mixed-effect models, which use the longitudinal repeated information, have higher power than the methods that only use data collected at one time point. BioMed Central 2014-06-17 /pmc/articles/PMC4143693/ /pubmed/25519414 http://dx.doi.org/10.1186/1753-6561-8-S1-S86 Text en Copyright © 2014 Chen 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Proceedings
Chen, Taoye
Santawisook, Patchara
Wu, Zheyang
A multi-level model for analyzing whole genome sequencing family data with longitudinal traits
title A multi-level model for analyzing whole genome sequencing family data with longitudinal traits
title_full A multi-level model for analyzing whole genome sequencing family data with longitudinal traits
title_fullStr A multi-level model for analyzing whole genome sequencing family data with longitudinal traits
title_full_unstemmed A multi-level model for analyzing whole genome sequencing family data with longitudinal traits
title_short A multi-level model for analyzing whole genome sequencing family data with longitudinal traits
title_sort multi-level model for analyzing whole genome sequencing family data with longitudinal traits
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143693/
https://www.ncbi.nlm.nih.gov/pubmed/25519414
http://dx.doi.org/10.1186/1753-6561-8-S1-S86
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