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Testing and Estimating Gene–Environment Interactions in Family‐Based Association Studies
Author(s) -
Vansteelandt Stijn,
DeMeo Dawn L.,
LaskySu Jessica,
Smoller Jordan W.,
Murphy Amy J.,
McQueen Matt,
Schneiter Kady,
Celedon Juan C.,
Weiss Scott T.,
Silverman Edwin K.,
Lange Christoph
Publication year - 2008
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00925.x
Subject(s) - estimator , confounding , population stratification , causal inference , inference , instrumental variable , single nucleotide polymorphism , statistics , sample size determination , computational biology , population , econometrics , biology , computer science , genetics , gene , mathematics , medicine , artificial intelligence , environmental health , genotype
Summary We propose robust and efficient tests and estimators for gene–environment/gene–drug interactions in family‐based association studies in which haplotypes, dichotomous/quantitative phenotypes, and complex exposure/treatment variables are analyzed. Using causal inference methodology, we show that the tests and estimators are robust against unmeasured confounding due to population admixture and stratification, provided that Mendel's law of segregation holds and that the considered exposure/treatment variable is not affected by the candidate gene under study. We illustrate the practical relevance of our approach by an application to a chronic obstructive pulmonary disease study. The data analysis suggests a gene–environment interaction between a single nucleotide polymorphism in the Serpine2 gene and smoking status/pack‐years of smoking. Simulation studies show that the proposed methodology is sufficiently powered for realistic sample sizes and that it provides valid tests and effect size estimators in the presence of admixture and stratification.