Longitudinal data analysis in pedigree studies.
Longitudinal family studies provide a valuable resource for investigating genetic and environmental factors that influence long-term averages and changes over time in a complex trait. This paper summarizes 13 contributions to Genetic Analysis Workshop 13, which include a wide range of methods for genetic analysis of longitudinal data in families. The methods can be grouped into two basic approaches: 1) two-step modeling, in which repeated observations are first reduced to one summary statistic per subject (e.g., a mean or slope), after which this statistic is used in a standard genetic analysis, or 2) joint modeling, in which genetic and longitudinal model parameters are estimated simultaneously in a single analysis. In applications to Framingham Heart Study data, contributors collectively reported evidence for genes that affected trait mean on chromosomes 1, 2, 3, 5, 8, 9, 10, 13, and 17, but most did not find genes affecting slope. Applications to simulated data suggested that even for a gene that only affected slope, use of a mean-type statistic could provide greater power than a slope-type statistic for detecting that gene. We report on the results of a small experiment that sheds some light on this apparently paradoxical finding, and indicate how one might form a more powerful test for finding a slope-affecting gene. Several areas for future research are discussed.
Gauderman
W James
WJ
Department of Preventive Medicine, University of Southern California, Los Angeles, 90089, USA. jimg@usc.edu
Macgregor
Stuart
S
Briollais
Laurent
L
Scurrah
Katrina
K
Tobin
Martin
M
Park
Taesung
T
Wang
Dai
D
Rao
Shaoqi
S
John
Sally
S
Bull
Shelley
S
D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000235:genetics*; D003198:Computer Simulation / Q000706:statistics & numerical data*; D008040:Genetic Linkage; D006801:Humans; D008126:Lod Score; D008137:Longitudinal Studies; D008957:Models, Genetic; D015233:Models, Statistical; D010375:Pedigree; D012307:Risk Factors