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Estimation of gene–environment interaction by pooling biospecimens

Authors

  • M. R. Danaher,

    1. Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD, U.S.A.
    2. University of Maryland, Baltimore County, Baltimore, MD, U.S.A.
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  • E. F. Schisterman,

    Corresponding author
    • Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD, U.S.A.
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  • A. Roy,

    1. Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD, U.S.A.
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  • P. S. Albert

    1. Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD, U.S.A.
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Enrique Schisterman, Division of Epidemiology, Statistics and Prevention Research, NICHD - NIH 6100 Executive Blvd, Rm 7B03 Rockville, MD 20852, USA.

E-mail: schistee@mail.nih.gov

Abstract

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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