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Efficient identification of context dependent subgroups of risk from genome-wide association studies

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  • Dyson Greg

    (Department of Oncology, Wayne State University, Detroit, MI, USA)

  • Sing Charles F.

    (Department of Human Genetics, University of Michigan, 1241 E. Catherine Street, Ann Arbor, MI 48109-0618, USA)

Abstract

We have developed a modified Patient Rule-Induction Method (PRIM) as an alternative strategy for analyzing representative samples of non-experimental human data to estimate and test the role of genomic variations as predictors of disease risk in etiologically heterogeneous sub-samples. A computational limit of the proposed strategy is encountered when the number of genomic variations (predictor variables) under study is large (>500) because permutations are used to generate a null distribution to test the significance of a term (defined by values of particular variables) that characterizes a sub-sample of individuals through the peeling and pasting processes. As an alternative, in this paper we introduce a theoretical strategy that facilitates the quick calculation of Type I and Type II errors in the evaluation of terms in the peeling and pasting processes carried out in the execution of a PRIM analysis that are under-estimated and non-existent, respectively, when a permutation-based hypothesis test is employed. The resultant savings in computational time makes possible the consideration of larger numbers of genomic variations (an example genome-wide association study is given) in the selection of statistically significant terms in the formulation of PRIM prediction models.

Suggested Citation

  • Dyson Greg & Sing Charles F., 2014. "Efficient identification of context dependent subgroups of risk from genome-wide association studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(2), pages 217-226, April.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:2:p:217-226:n:7
    DOI: 10.1515/sagmb-2013-0062
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