Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects

Abstract Complex traits with multiple phenotypic values changing over time are called longitudinal traits. In traditional genome-wide association studies (GWAS) for longitudinal traits, a combined/averaged estimated breeding value (EBV) or deregressed proof (DRP) instead of multiple phenotypic measu...

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Autores principales:	Chao Ning, Huimin Kang, Lei Zhou, Dan Wang, Haifei Wang, Aiguo Wang, Jinluan Fu, Shengli Zhang, Jianfeng Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/380f47d13aba433e8521364a0d1e83f1
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spelling	oai:doaj.org-article:380f47d13aba433e8521364a0d1e83f12021-12-02T11:52:26ZPerformance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects10.1038/s41598-017-00638-22045-2322https://doaj.org/article/380f47d13aba433e8521364a0d1e83f12017-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00638-2https://doaj.org/toc/2045-2322Abstract Complex traits with multiple phenotypic values changing over time are called longitudinal traits. In traditional genome-wide association studies (GWAS) for longitudinal traits, a combined/averaged estimated breeding value (EBV) or deregressed proof (DRP) instead of multiple phenotypic measurements per se for each individual was frequently treated as response variable in statistical model. This can result in power losses or even inflate false positive rates (FPRs) in the detection due to failure of exploring time-dependent relationship among measurements. Aiming at overcoming such limitation, we developed two random regression-based models for functional GWAS on longitudinal traits, which could directly use original time-dependent records as response variable and fit the time-varied Quantitative Trait Nucleotide (QTN) effect. Simulation studies showed that our methods could control the FPRs and increase statistical powers in detecting QTN in comparison with traditional methods where EBVs, DRPs or estimated residuals were considered as response variables. Besides, our proposed models also achieved reliable powers in gene detection when implementing into two real datasets, a Chinese Holstein Cattle data and the Genetic Analysis Workshop 18 data. Our study herein offers an optimal way to enhance the power of gene detection and further understand genetic control of developmental processes for complex longitudinal traits.Chao NingHuimin KangLei ZhouDan WangHaifei WangAiguo WangJinluan FuShengli ZhangJianfeng LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chao Ning Huimin Kang Lei Zhou Dan Wang Haifei Wang Aiguo Wang Jinluan Fu Shengli Zhang Jianfeng Liu Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects
description	Abstract Complex traits with multiple phenotypic values changing over time are called longitudinal traits. In traditional genome-wide association studies (GWAS) for longitudinal traits, a combined/averaged estimated breeding value (EBV) or deregressed proof (DRP) instead of multiple phenotypic measurements per se for each individual was frequently treated as response variable in statistical model. This can result in power losses or even inflate false positive rates (FPRs) in the detection due to failure of exploring time-dependent relationship among measurements. Aiming at overcoming such limitation, we developed two random regression-based models for functional GWAS on longitudinal traits, which could directly use original time-dependent records as response variable and fit the time-varied Quantitative Trait Nucleotide (QTN) effect. Simulation studies showed that our methods could control the FPRs and increase statistical powers in detecting QTN in comparison with traditional methods where EBVs, DRPs or estimated residuals were considered as response variables. Besides, our proposed models also achieved reliable powers in gene detection when implementing into two real datasets, a Chinese Holstein Cattle data and the Genetic Analysis Workshop 18 data. Our study herein offers an optimal way to enhance the power of gene detection and further understand genetic control of developmental processes for complex longitudinal traits.
format	article
author	Chao Ning Huimin Kang Lei Zhou Dan Wang Haifei Wang Aiguo Wang Jinluan Fu Shengli Zhang Jianfeng Liu
author_facet	Chao Ning Huimin Kang Lei Zhou Dan Wang Haifei Wang Aiguo Wang Jinluan Fu Shengli Zhang Jianfeng Liu
author_sort	Chao Ning
title	Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects
title_short	Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects
title_full	Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects
title_fullStr	Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects
title_full_unstemmed	Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects
title_sort	performance gains in genome-wide association studies for longitudinal traits via modeling time-varied effects
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/380f47d13aba433e8521364a0d1e83f1
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Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects

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