title: Parent-child pair design for detecting gene-environment interactions in complex diseases.
abstract: It is becoming clear that the etiology of complex diseases involves not only genetic and environmental factors but also gene-environment (GE) interactions. Therefore, it is important to take account of all these factors to improve the power of an epidemiological study design. We propose here a novel parent-child pair (PCP) design for this purpose. In comparison with conventional designs, this approach has the following advantages: (a) PCP is a 4 x 16 design consisting of pairs of parent-child (PC) genotype statuses, PC exposure statuses and PC disease statuses. Therefore, it utilizes more information than the traditional approaches in association studies; (b) It can determine whether findings in studies of association between disease and genetic or environmental factors and their interaction are spurious, arising from Hardy-Weinberg disequilibrium or the other factors; (c) Since the information from both parents and children of the PC pairs are used in this design, it has high power for detecting association of candidate gene, exposure with a complex disease and GE interaction. We also present a set of estimates of relative risks of candidate genes, exposures and GE interactions under the multiplicative model and a method for computing the sample size requirements to test for these relative risks in the context of the PCP design.
authors.affiliation: Institute of Molecular Medicine, University of Texas-Houston, Houston, TX, USA.
authors.forename: Yuan-De
authors.initials: YD
authors.lastname: Tan
authors.forename: Myriam
authors.initials: M
authors.lastname: Fornage
authors.forename: Varghese
authors.initials: V
authors.lastname: George
authors.forename: Hongyan
authors.initials: H
authors.lastname: Xu
mesh_terms: D000328:Adult; D000483:Alleles; D001699:Biometry; D002648:Child; D004194:Disease / Q000209:etiology*; D004777:Environment; D004812:Epidemiologic Methods; D005260:Female; D020022:Genetic Predisposition to Disease; D005826:Genetics, Medical / Q000379:methods; D006801:Humans; D008297:Male; D011336:Probability; D012307:Risk Factors; D018401:Sample Size