Using Cases and Parents to Study Multiplicative Gene-by-Environment Interaction

American Journal of Epidemiology, Aug 2009

With case-parent triads, one can estimate genotype relative risks by measuring the apparent overtransmission of susceptibility genotypes from parents to affected offspring. Results obtained using such designs, properly analyzed, resist both bias due to population structure and bias due to self-selection. Most diseases are not purely genetic, and environmental cofactors can also be important. In this paper, the authors describe how a polytomous logistic regression method previously developed for studying genetic effects on a quantitative trait can be used with case-parent data to study multiplicative gene-by-environment interaction. The idea is that if the joint effect of exposure and genotype on risk is submultiplicative or supermultiplicative, then, conditional on the parental genotypes, inheritance of a susceptibility genotype by affected offspring will appear to have been influenced by the offspring's exposure level. The authors' approach tolerates exposure-complicated genetic population structure, and simulations suggest power and Type I error rates comparable to those of competitors. With this approach, one can estimate the usual interaction parameters under a much less stringent assumption than gene-environment independence in the source population. Incompletely genotyped triads can contribute through an expectation-maximization algorithm. To illustrate, the authors consider polymorphisms in detoxification pathway genes and maternal smoking in relation to the birth defect oral cleft.

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Using Cases and Parents to Study Multiplicative Gene-by-Environment Interaction

Emily O. Kistner ) Min Shi Clarice R. Weinberg With case-parent triads, one can estimate genotype relative risks by measuring the apparent overtransmission of susceptibility genotypes from parents to affected offspring. Results obtained using such designs, properly analyzed, resist both bias due to population structure and bias due to self-selection. Most diseases are not purely genetic, and environmental cofactors can also be important. In this paper, the authors describe how a polytomous logistic regression method previously developed for studying genetic effects on a quantitative trait can be used with case-parent data to study multiplicative gene-by-environment interaction. The idea is that if the joint effect of exposure and genotype on risk is submultiplicative or supermultiplicative, then, conditional on the parental genotypes, inheritance of a susceptibility genotype by affected offspring will appear to have been influenced by the offspring's exposure level. The authors' approach tolerates exposure-complicated genetic population structure, and simulations suggest power and Type I error rates comparable to those of competitors. With this approach, one can estimate the usual interaction parameters under a much less stringent assumption than gene-environment independence in the source population. Incompletely genotyped triads can contribute through an expectationmaximization algorithm. To illustrate, the authors consider polymorphisms in detoxification pathway genes and maternal smoking in relation to the birth defect oral cleft. case-control studies; epidemiologic methods; genetic epidemiology; genetic markers; genotype-environment interaction; logistic models Abbreviations: CYP2E1, cytochrome P-450 2E1; FBAT-I, family-based association test with interaction; QPL, quantitative polytomous logistic; QTDT, quantitative transmission disequilibrium test. - Family-based designs are of particular interest when studying diseases with onset early in life, such as asthma (1), autism, or birth defects. Investigators collect DNA from cases and their parents (producing a triad) in order to find genetic markers related to risk; one can also study maternally mediated genetic effects and parent-of-origin (imprinting) effects. Genotype relative risks can be estimated using a log-linear approach (25). One can also use case-parent triads to study multiplicative gene-by-environment interactions for a dichotomous exposure or a categorical exposure (5, 6). Several other family-based approaches have been proposed, including the case-sibling design (7), the pseudosibling analysis (8), the family-based association test with interaction (FBAT-I) (9), and a method recently developed by Vansteelandt et al. (10). While a case-only approach could also be used, its reliance on gene/environment independence in the source population should worry a careful investigator. A triad design tolerates a much weaker, within-family, independence assumption and enables assessment of genotype main effects. Thus, the case-parent design is both more robust and more informative than the case-only design. Triad designs also offer advantages over the usual casecontrol design, which can be subject to self-selection, differential recall, and bias due to genetic population stratification, if subpopulations have a higher prevalence of the allele and a higher baseline risk of disease. The latter can bias interaction assessments if exposure prevalence also varies across subpopulations. By conditioning on parental genotypes, triad designs avoid bias due to these kinds of confounding. Even if parents self-select for their genes or for their affected childs exposures, self-selection will not produce bias unless the decision to participate is also influenced by which of their alleles they happened to pass on to their offspring. Case-control designs applied to diseases with early-life onset also typically do not enable assessment of important potential confounders and contributors to risk, such as prenatal effects of the maternal genotype and imprinting effects. In addition, for a study of gene-by-environment interaction, family-based designs generally offer better power than would a case-control design for the same number of cases (6, 11). On the other hand, case-control designs, unlike family-based designs, enable estimation of the exposure effect in addition to the interaction effect. This important advantage can sometimes distort assessment of interaction, however, because misspecification of the main effect of a continuous exposure can cause bias. In this paper, we describe how a method previously developed for studying genetic effects on a quantitative trait (12) can be used to assess gene-by-environment interaction involving continuous or categorical exposures. The method uses an approach we call quantitative polytomous logistic (QPL) (12). Suppose an autosomic diallelic marker, such as a single nucleotide polymorphism, is studied in case-parent (...truncated)


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Emily O. Kistner, Min Shi, Clarice R. Weinberg. Using Cases and Parents to Study Multiplicative Gene-by-Environment Interaction, American Journal of Epidemiology, 2009, pp. 393-400, 170/3, DOI: 10.1093/aje/kwp118