Use of longitudinal data in genetic studies in the genome-wide association studies era: summary of Group 14
Article first published online: 18 NOV 2009
© 2009 Wiley-Liss, Inc.
Supplement: Genetic Analysis Workshop 16: Approaches to Analysis of Genome-Wide Data
Volume 33, Issue Supplement 1, pages S93–S98, 2009
How to Cite
Kerner, B., North, K. E. and Fallin, M. D. (2009), Use of longitudinal data in genetic studies in the genome-wide association studies era: summary of Group 14. Genet. Epidemiol., 33: S93–S98. doi: 10.1002/gepi.20479
- Issue published online: 18 NOV 2009
- Article first published online: 18 NOV 2009
- NIH. Grant Number: R01 GM031575
- NIMH. Grant Number: K08 MH074057
- phenotype definition;
- risk estimation;
- growth modeling;
- sampling of controls
Participants analyzed actual and simulated longitudinal data from the Framingham Heart Study for various metabolic and cardiovascular traits. The genetic information incorporated into these investigations ranged from selected single-nucleotide polymorphisms to genome-wide association arrays. Genotypes were incorporated using a broad range of methodological approaches including conditional logistic regression, linear mixed models, generalized estimating equations, linear growth curve estimation, growth modeling, growth mixture modeling, population attributable risk fraction based on survival functions under the proportional hazards models, and multivariate adaptive splines for the analysis of longitudinal data. The specific scientific questions addressed by these different approaches also varied, ranging from a more precise definition of the phenotype, bias reduction in control selection, estimation of effect sizes and genotype associated risk, to direct incorporation of genetic data into longitudinal modeling approaches and the exploration of population heterogeneity with regard to longitudinal trajectories. The group reached several overall conclusions: (1) The additional information provided by longitudinal data may be useful in genetic analyses. (2) The precision of the phenotype definition as well as control selection in nested designs may be improved, especially if traits demonstrate a trend over time or have strong age-of-onset effects. (3) Analyzing genetic data stratified for high-risk subgroups defined by a unique development over time could be useful for the detection of rare mutations in common multifactorial diseases. (4) Estimation of the population impact of genomic risk variants could be more precise. The challenges and computational complexity demanded by genome-wide single-nucleotide polymorphism data were also discussed. Genet. Epidemiol. 33 (Suppl. 1):S93–S98, 2009. © 2009 Wiley-Liss, Inc.