title: A Comparison of Statistical Methods for the Discovery of Genetic Risk Factors Using Longitudinal Family Study Designs.
abstract: The etiology of immune-related diseases or traits is often complex, involving many genetic and environmental factors and their interactions. While methodological approaches focusing on an outcome measured at one time point have succeeded in identifying genetic factors involved in immune-related traits, they fail to capture complex disease mechanisms that fluctuate over time. It is increasingly recognized that longitudinal studies, where an outcome is measured at multiple time points, have great potential to shed light on complex disease mechanisms involving genetic factors. However, longitudinal data require specialized statistical methods, especially in family studies where multiple sources of correlation in the data must be modeled. Using simulated data with known genetic effects, we examined the performance of different analytical methods for investigating associations between genetic factors and longitudinal phenotypes in twin data. The simulations were modeled on data from the Québec Newborn Twin Study, an ongoing population-based longitudinal study of twin births with multiple phenotypes, such as cortisol levels and body mass index, collected multiple times in infancy and early childhood and with sequencing data on immune-related genes and pathways. We compared approaches that we classify as (1) family-based methods applied to summaries of the observations over time, (2) longitudinal-based methods with simplifications of the familial correlation, and (3) Bayesian family-based method with simplifications of the temporal correlation. We found that for estimation of the genetic main and interaction effects, all methods gave estimates close to the true values and had similar power. If heritability estimation is desired, approaches of type (1) also provide heritability estimates close to the true value. Our work shows that the simpler approaches are likely adequate to detect genetic effects; however, interpretation of these effects is more challenging.
authors.affiliation: Department of Mathematics and Statistics, University of Ottawa , Ottawa, ON , Canada.
authors.forename: Kelly M
authors.initials: KM
authors.lastname: Burkett
authors.affiliation: School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa , Ottawa, ON , Canada.
authors.forename: Marie-Hélène
authors.initials: MH
authors.lastname: Roy-Gagnon
authors.affiliation: School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa , Ottawa, ON , Canada.
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Lefebvre
authors.affiliation: School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa , Ottawa, ON , Canada.
authors.forename: Cheng
authors.initials: C
authors.lastname: Wang
authors.affiliation: Nutrition Sciences Program, University of Ottawa , Ottawa, ON , Canada.
authors.forename: Bénédicte
authors.initials: B
authors.lastname: Fontaine-Bisson
authors.affiliation: School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa , Ottawa, ON , Canada.
authors.forename: Lise
authors.initials: L
authors.lastname: Dubois
keywords: family design; generalized estimating equations; genetic association; linear mixed models; longitudinal studies