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Adjusting for Spurious Gene-by-Environment Interaction Using Case-Parent Triads

Author

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  • Shin Ji-Hyung

    (Simon Fraser University)

  • Infante-Rivard Claire

    (McGill University)

  • Graham Jinko

    (Simon Fraser University)

  • McNeney Brad

    (Simon Fraser University)

Abstract

In the case-parent trio design, unrelated children affected with a disease are genotyped along with their parents. Information may also be collected on environmental factors in the children. The design permits estimation and testing of genetic effects and gene-by-environment interaction. Recently, it has been demonstrated that when genotypes are measured at a non-causal test locus, population stratification can create spurious interaction. That is, the environmental factor can appear to modify the disease risk associated with genotypes at the test locus without modifying the disease risk of genotypes at the causal locus. One design-based approach that is robust to spurious interaction requires the environmental factor to also be available on an unaffected sibling of the affected child. We explore the source of spurious interaction and suggest an alternate approach that mitigates its effects using case-parent triads. Our approach is based on adjusting the risk model using ancestry informative markers or random markers measured on the affected child and does not require data on unaffected siblings. We apply an approach to generating case-parent data, implemented in a freely-available R package soon to be released on the Comprehensive R Archive Network (CRAN).

Suggested Citation

  • Shin Ji-Hyung & Infante-Rivard Claire & Graham Jinko & McNeney Brad, 2012. "Adjusting for Spurious Gene-by-Environment Interaction Using Case-Parent Triads," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-23, January.
    • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:2:n:7
    DOI: 10.2202/1544-6115.1714
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    References listed on IDEAS

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    1. Zhu, Mu & Ghodsi, Ali, 2006. "Automatic dimensionality selection from the scree plot via the use of profile likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 918-930, November.
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    1. Shin Ji-Hyung & McNeney Brad & Graham Jinko & Infante-Rivard Claire, 2014. "A data-smoothing approach to explore and test gene-environment interaction in case-parent trios," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(2), pages 159-171, April.

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