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Updated: Jun 17, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
Published on: September 17, 2019
Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of
Gang Shi1, Treva K Rice, Chi Charles Gu
1Division of Biostatistics, Washington University School of Medicine, 660 South Euclid Avenue, Box 8067, St, Louis, Missouri 63110, USA. gang@wubios.wustl.edu.
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.
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