Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization —GALLOP algorithm

Abstract Genome-wide association studies (GWAS) with longitudinal phenotypes provide opportunities to identify genetic variations associated with changes in human traits over time. Mixed models are used to correct for the correlated nature of longitudinal data. GWA studies are notorious for their co...

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Autores principales:	Karolina Sikorska, Emmanuel Lesaffre, Patrick J. F. Groenen, Fernando Rivadeneira, Paul H. C. Eilers
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dfa5b2fb1569404aba5d6649c5e52604
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spelling	oai:doaj.org-article:dfa5b2fb1569404aba5d6649c5e526042021-12-02T11:41:15ZGenome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization —GALLOP algorithm10.1038/s41598-018-24578-72045-2322https://doaj.org/article/dfa5b2fb1569404aba5d6649c5e526042018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-24578-7https://doaj.org/toc/2045-2322Abstract Genome-wide association studies (GWAS) with longitudinal phenotypes provide opportunities to identify genetic variations associated with changes in human traits over time. Mixed models are used to correct for the correlated nature of longitudinal data. GWA studies are notorious for their computational challenges, which are considerable when mixed models for thousands of individuals are fitted to millions of SNPs. We present a new algorithm that speeds up a genome-wide analysis of longitudinal data by several orders of magnitude. It solves the equivalent penalized least squares problem efficiently, computing variances in an initial step. Factorizations and transformations are used to avoid inversion of large matrices. Because the system of equations is bordered, we can re-use components, which can be precomputed for the mixed model without a SNP. Two SNP effects (main and its interaction with time) are obtained. Our method completes the analysis a thousand times faster than the R package lme4, providing an almost identical solution for the coefficients and p-values. We provide an R implementation of our algorithm.Karolina SikorskaEmmanuel LesaffrePatrick J. F. GroenenFernando RivadeneiraPaul H. C. EilersNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-8 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Karolina Sikorska Emmanuel Lesaffre Patrick J. F. Groenen Fernando Rivadeneira Paul H. C. Eilers Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization —GALLOP algorithm
description	Abstract Genome-wide association studies (GWAS) with longitudinal phenotypes provide opportunities to identify genetic variations associated with changes in human traits over time. Mixed models are used to correct for the correlated nature of longitudinal data. GWA studies are notorious for their computational challenges, which are considerable when mixed models for thousands of individuals are fitted to millions of SNPs. We present a new algorithm that speeds up a genome-wide analysis of longitudinal data by several orders of magnitude. It solves the equivalent penalized least squares problem efficiently, computing variances in an initial step. Factorizations and transformations are used to avoid inversion of large matrices. Because the system of equations is bordered, we can re-use components, which can be precomputed for the mixed model without a SNP. Two SNP effects (main and its interaction with time) are obtained. Our method completes the analysis a thousand times faster than the R package lme4, providing an almost identical solution for the coefficients and p-values. We provide an R implementation of our algorithm.
format	article
author	Karolina Sikorska Emmanuel Lesaffre Patrick J. F. Groenen Fernando Rivadeneira Paul H. C. Eilers
author_facet	Karolina Sikorska Emmanuel Lesaffre Patrick J. F. Groenen Fernando Rivadeneira Paul H. C. Eilers
author_sort	Karolina Sikorska
title	Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization —GALLOP algorithm
title_short	Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization —GALLOP algorithm
title_full	Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization —GALLOP algorithm
title_fullStr	Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization —GALLOP algorithm
title_full_unstemmed	Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization —GALLOP algorithm
title_sort	genome-wide analysis of large-scale longitudinal outcomes using penalization —gallop algorithm
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/dfa5b2fb1569404aba5d6649c5e52604
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Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization —GALLOP algorithm

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