Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Abstract Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design....

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Autores principales:	Hongyan Cao, Zhi Li, Haitao Yang, Yuehua Cui, Yanbo Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/df13efaeca734d6c994cf7dd22e9c555
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spelling	oai:doaj.org-article:df13efaeca734d6c994cf7dd22e9c5552021-12-02T16:06:55ZLongitudinal data analysis for rare variants detection with penalized quadratic inference function10.1038/s41598-017-00712-92045-2322https://doaj.org/article/df13efaeca734d6c994cf7dd22e9c5552017-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00712-9https://doaj.org/toc/2045-2322Abstract Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.Hongyan CaoZhi LiHaitao YangYuehua CuiYanbo ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hongyan Cao Zhi Li Haitao Yang Yuehua Cui Yanbo Zhang Longitudinal data analysis for rare variants detection with penalized quadratic inference function
description	Abstract Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.
format	article
author	Hongyan Cao Zhi Li Haitao Yang Yuehua Cui Yanbo Zhang
author_facet	Hongyan Cao Zhi Li Haitao Yang Yuehua Cui Yanbo Zhang
author_sort	Hongyan Cao
title	Longitudinal data analysis for rare variants detection with penalized quadratic inference function
title_short	Longitudinal data analysis for rare variants detection with penalized quadratic inference function
title_full	Longitudinal data analysis for rare variants detection with penalized quadratic inference function
title_fullStr	Longitudinal data analysis for rare variants detection with penalized quadratic inference function
title_full_unstemmed	Longitudinal data analysis for rare variants detection with penalized quadratic inference function
title_sort	longitudinal data analysis for rare variants detection with penalized quadratic inference function
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/df13efaeca734d6c994cf7dd22e9c555
work_keys_str_mv	AT hongyancao longitudinaldataanalysisforrarevariantsdetectionwithpenalizedquadraticinferencefunction AT zhili longitudinaldataanalysisforrarevariantsdetectionwithpenalizedquadraticinferencefunction AT haitaoyang longitudinaldataanalysisforrarevariantsdetectionwithpenalizedquadraticinferencefunction AT yuehuacui longitudinaldataanalysisforrarevariantsdetectionwithpenalizedquadraticinferencefunction AT yanbozhang longitudinaldataanalysisforrarevariantsdetectionwithpenalizedquadraticinferencefunction
_version_	1718384811206246400

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

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