Estimation of gene-environment interaction by pooling biospecimens
University of Maryland, Baltimore County, Baltimore, MD, U.S.A.Statistics in Medicine (Impact Factor: 1.83). 11/2012; 31(26):3241-52. DOI: 10.1002/sim.5357
Case-control studies are prone to low power for testing gene-environment interactions (GXE) given the need for a sufficient number of individuals on each strata of disease, gene, and environment. We propose a new study design to increase power by strategically pooling biospecimens. Pooling biospecimens allows us to increase the number of subjects significantly, thereby providing substantial increase in power. We focus on a special, although realistic case, where disease and environmental statuses are binary, and gene status is ordinal with each individual having 0, 1, or 2 minor alleles. Through pooling, we obtain an allele frequency for each level of disease and environmental status. Using the allele frequencies, we develop a new methodology for estimating and testing GXE that is comparable to the situation when we have complete data on gene status for each individual. We also explore the measurement process and its effect on the GXE estimator. Using an illustration, we show the effectiveness of pooling with an epidemiologic study, which tests an interaction for fiber and paraoxonase on anovulation. Through simulation, we show that taking 12 pooled measurements from 1000 individuals achieves more power than individually genotyping 500 individuals. Our findings suggest that strategic pooling should be considered when an investigator designs a pilot study to test for a GXE. Published 2012. This article is a US Government work and is in the public domain in the USA.
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ABSTRACT: We conducted a case-referent study of the effect of exposure to bisphenol-A on fetal growth in utero in full-term, live-born singletons in Alberta, Canada. Newborns <10 percentile of expected weight for gestational age and sex were individually matched on sex, maternal smoking and maternal age to referents with weight appropriate to gestational age. Exposure of the fetus to bisphenol-A was estimated from maternal serum collected at 15-16 weeks of gestation. We pooled sera across subjects for exposure assessment, stratified on case-referent status and sex. Individual 1:1 matching was maintained in assembling 69 case and 69 referent pools created from 550 case-referent pairs. Matched pools had an equal number of aliquots from individual women. We used an analytical strategy conditioning on matched set and total pool-level values of covariates to estimate individual-level effects. Pools of cases and referents had identical geometric mean bisphenol-A concentrations (0.5 ng/mL) and similar geometric standard deviations (2.3-2.5). Mean difference in concentration between matched pools was 0 ng/mL, standard deviation: 1 ng/mL. Stratification by sex and control for confounding did not suggest bisphenol-A increased fetal growth restriction. Our analysis does not provide evidence to support the hypothesis that bisphenol-A contributes to fetal growth restriction in full-term singletons.
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ABSTRACT: We sought to determine the potential effects of pooling on power, false positive rate (FPR), and bias of the estimated associations between hypothetical environmental exposures and dichotomous autism spectrum disorders (ASD) status. Simulated birth cohorts in which ASD outcome was assumed to have been ascertained with uncertainty were created. We investigated the impact on the power of the analysis (using logistic regression) to detect true associations with exposure (X1) and the FPR for a non-causal correlate of exposure (X2, r = 0.7) for a dichotomized ASD measure when the pool size, sample size, degree of measurement error variance in exposure, strength of the true association, and shape of the exposure-response curve varied. We found that there was minimal change (bias) in the measures of association for the main effect (X1). There is some loss of power but there is less chance of detecting a false positive result for pooled compared to individual level models. The number of pools had more effect on the power and FPR than the overall sample size. This study supports the use of pooling to reduce laboratory costs while maintaining statistical efficiency in scenarios similar to the simulated prospective risk-enriched ASD cohort.
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