Background: Longitudinal measurement is commonly employed in health research and provides numerous benefits for understanding disease and trait progression over time. More broadly, it allows for proper treatment of correlated responses within clusters. We evaluated 3 methods for analyzing genome-by-epigenome interactions with longitudinal outcomes from family data.
Results: Linear mixed-effect models, generalized estimating equations, and quadratic inference functions were used to test a pharmacoepigenetic effect in 200 simulated posttreatment replicates. Adjustment for baseline outcome provided greater power and more accurate control of Type I error rates than computation of a pre-to-post change score.
Conclusions: Comparison of all modeling approaches indicated a need for bias correction in marginal models and similar power for each method, with quadratic inference functions providing a minor decrement in power compared to generalized estimating equations and linear mixed-effects models.
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Publication of this article was supported by NIH R01 GM031575. This work was partially supported by the National Institute on Aging (DWF: K25AG043546) and National Science Foundation (JCS: 1247392). The GAW is supported by the National Institute of General Medical Sciences grant R01GM031575. The GAW20 phenotype and sequence data were provided by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, which is supported by the National Heart, Lung, and Blood Institute grants R01HL104135 and U01HL72524.
The data that support the findings of this study are available from the Genetic Analysis Workshop (GAW), but restrictions apply to the availability of these data, which were used under license for the current study. Qualified researchers may request these data directly from GAW.
Strickland, Justin C.; Chen, I-Chen; Wang, Chanung; and Fardo, David W., "Longitudinal Data Methods for Evaluating Genome-by-Epigenome Interactions in Families" (2018). Psychology Faculty Publications. 155.