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


ABSTRACT: 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.

SUBMITTER: Shi G 

PROVIDER: S-EPMC2795992 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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

Shi Gang G   Rice Treva K TK   Gu Chi Charles CC   Rao Debeeru C DC  

BMC proceedings 20091215


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  ...[more]

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