%0 Journal Article
%T Gene-environment interaction tests for family studies with quantitative phenotypes: A review and extension to longitudinal measures
%A Hortensia Moreno-Macias
%A Isabelle Romieu
%A Stephanie J London
%A Nan M Laird
%J Human Genomics
%D 2010
%I BioMed Central
%R 10.1186/1479-7364-4-5-302
%X In spite of the multiple efforts to find genetic factors conferring susceptibility to complex diseases, the success of genetic association studies is still hampered by the difficulty in replicating findings in different populations. Among the plausible explanations for this lack of replication is the fact that the effects of environmental factors, which can interact with genetic factors, are not always taken into consideration [1]. There is an increasing interest in studying different susceptibilities to environmental factors in subjects with different genotypes; however, power and bias issues with regard to the statistical estimation of gene-environment interaction effects persist.High-quality information about individual environmental exposure is crucial for the assessment of gene-environment interactions [2]. Failure to measure changes in exposure levels over time could lead to an underestimation of the role of the environment in the interaction. Repeated measurements of the temporal relationship between an outcome and the exposure may overcome such a problem when both the endpoint and the exposure are time-dependent variables. In addition, potential misclassification due to ambiguity in the definition of complex diseases may be avoided through the measurement of quantitative disease-related phenotypes as the outcomes of interest. For example, quantifying the decrements in lung function over time through repeated spirometric tests may provide insights into the pathogenesis of chronic obstructive pulmonary disease (COPD) or asthma. Many disease 'predictor' phenotypes are thought to change within-subject because of both environmental and genetic factors, and of their potential interactions over time.On the genetic side, population substructure is an important practical issue for genetic association studies. When the study population is not a collection of randomly mating individuals, several discrete subgroups that are genetically different may be identified; the c
%K gene-environment interaction
%K longitudinal phenotypes
%K power
%K bias
%K population substructure
%U http://www.humgenomics.com/content/4/5/302