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Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data

Longitudinal studies that collect repeated measurements on the same subjects over time have long been considered as being more powerful and providing much better information on individual changes than cross-sectional data. We propose a three-level linear mixed-effects model for testing genetic main...

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Detalles Bibliográficos
Autores principales: Shi, Gang, Rice, Treva K, Gu, Chi Charles, Rao, Debeeru C
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2795992/
https://www.ncbi.nlm.nih.gov/pubmed/20018085
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author Shi, Gang
Rice, Treva K
Gu, Chi Charles
Rao, Debeeru C
author_facet Shi, Gang
Rice, Treva K
Gu, Chi Charles
Rao, Debeeru C
author_sort Shi, Gang
collection PubMed
description Longitudinal studies that collect repeated measurements on the same subjects over time have long been considered as being more powerful and providing much better information on individual changes than cross-sectional data. We propose a three-level linear mixed-effects model for testing genetic main effects and gene-age interactions with longitudinal family data. The simulated Genetic Analysis Workshop 16 Problem 3 data sets were used to evaluate the method. Genome-wide association analyses were conducted based on cross-sectional data, i.e., each of the three single-visit data sets separately, and also on the longitudinal data, i.e., using data from all three visits simultaneously. Results from the analysis of coronary artery calcification phenotype showed that the longitudinal association tests were much more powerful than those based on single-visit data only. Gene-age interactions were evaluated under the same framework for detecting genetic effects that are modulated by age.
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spelling pubmed-27959922009-12-18 Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data Shi, Gang Rice, Treva K Gu, Chi Charles Rao, Debeeru C BMC Proc Proceedings Longitudinal studies that collect repeated measurements on the same subjects over time have long been considered as being more powerful and providing much better information on individual changes than cross-sectional data. We propose a three-level linear mixed-effects model for testing genetic main effects and gene-age interactions with longitudinal family data. The simulated Genetic Analysis Workshop 16 Problem 3 data sets were used to evaluate the method. Genome-wide association analyses were conducted based on cross-sectional data, i.e., each of the three single-visit data sets separately, and also on the longitudinal data, i.e., using data from all three visits simultaneously. Results from the analysis of coronary artery calcification phenotype showed that the longitudinal association tests were much more powerful than those based on single-visit data only. Gene-age interactions were evaluated under the same framework for detecting genetic effects that are modulated by age. BioMed Central 2009-12-15 /pmc/articles/PMC2795992/ /pubmed/20018085 Text en Copyright ©2009 Shi 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.
spellingShingle Proceedings
Shi, Gang
Rice, Treva K
Gu, Chi Charles
Rao, Debeeru C
Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data
title Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data
title_full Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data
title_fullStr Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data
title_full_unstemmed Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data
title_short Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data
title_sort application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2795992/
https://www.ncbi.nlm.nih.gov/pubmed/20018085
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