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Longitudinal data analysis in pedigree studies
Author(s) -
Gauderman W. James,
Macgregor Stuart,
Briollais Laurent,
Scurrah Katrina,
Tobin Martin,
Park Taesung,
Wang Dai,
Rao Shaoqi,
John Sally,
Bull Shelley
Publication year - 2003
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.10280
Subject(s) - statistic , trait , statistics , framingham heart study , biology , summary statistics , longitudinal study , longitudinal data , genetic model , type i and type ii errors , genetics , mathematics , computer science , gene , framingham risk score , data mining , medicine , disease , pathology , programming language
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. Genet Epidemiol 25 (Suppl. 1):S18–S28, 2003. © 2003 Wiley‐Liss, Inc.