title: Three Approaches to Modeling Gene-Environment Interactions in Longitudinal Family Data: Gene-Smoking Interactions in Blood Pressure.
abstract: Blood pressure (BP) has been shown to be substantially heritable, yet identified genetic variants explain only a small fraction of the heritability. Gene-smoking interactions have detected novel BP loci in cross-sectional family data. Longitudinal family data are available and have additional promise to identify BP loci. However, this type of data presents unique analysis challenges. Although several methods for analyzing longitudinal family data are available, which method is the most appropriate and under what conditions has not been fully studied. Using data from three clinic visits from the Framingham Heart Study, we performed association analysis accounting for gene-smoking interactions in BP at 31,203 markers on chromosome 22. We evaluated three different modeling frameworks: generalized estimating equations (GEE), hierarchical linear modeling, and pedigree-based mixed modeling. The three models performed somewhat comparably, with multiple overlaps in the most strongly associated loci from each model. Loci with the greatest significance were more strongly supported in the longitudinal analyses than in any of the component single-visit analyses. The pedigree-based mixed model was more conservative, with less inflation in the variant main effect and greater deflation in the gene-smoking interactions. The GEE, but not the other two models, resulted in substantial inflation in the tail of the distribution when variants with minor allele frequency <1% were included in the analysis. The choice of analysis method should depend on the model and the structure and complexity of the familial and longitudinal data.
authors.affiliation: Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors.forename: Jacob
authors.initials: J
authors.lastname: Basson
authors.affiliation: Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors.forename: Yun Ju
authors.initials: YJ
authors.lastname: Sung
authors.affiliation: Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors.forename: Lisa
authors.initials: L
authors.lastname: de las Fuentes
authors.affiliation: Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors.forename: Karen L
authors.initials: KL
authors.lastname: Schwander
authors.affiliation: Department of Epidemiology Biostatistics, Michigan State University, East Lansing, Michigan, United States of America.
authors.forename: Ana
authors.initials: A
authors.lastname: Vazquez
authors.affiliation: Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors.forename: Dabeeru C
authors.initials: DC
authors.lastname: Rao
mesh_terms: D000328:Adult; D001794:Blood Pressure; D003430:Cross-Sectional Studies; D005260:Female; D005787:Gene Frequency; D059647:Gene-Environment Interaction; D014644:Genetic Variation; D006801:Humans; D006973:Hypertension / Q000453:epidemiology* / Q000235:genetics*; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D012907:Smoking / Q000453:epidemiology*
keywords: blood pressure; family; gene-environment; interactions; longitudinal