Longitudinal data methods for evaluating genome-by-epigenome interactions in families

Justin C. Strickland, I. Chen Chen, Chanung Wang, David W. Fardo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Article number82
JournalBMC Genetics
StatePublished - Sep 17 2018

Bibliographical note

Publisher Copyright:
© 2018 The Author(s).


  • Change
  • Epigenetics
  • Family
  • GEE
  • Longitudinal
  • Mixed model
  • Power
  • QIF

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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