Efficient identification of context dependent subgroups of risk from genome-wide association studies
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Greg Dyson
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.
Acknowledgments
The authors wish to thank Richard Merkle and Alex Sadovsky for their assistance in programming. This work was supported by National Institutes of Health grant number HL072905, National Institute of General Medical Science grant number GM065509 and National Cancer Institute grant number 5P30CA022453.
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©2014 by Walter de Gruyter Berlin/Boston
Articles in the same Issue
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- Research Articles
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- Penalized differential pathway analysis of integrative oncogenomics studies
- A data-smoothing approach to explore and test gene-environment interaction in case-parent trios
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- Efficient identification of context dependent subgroups of risk from genome-wide association studies
Articles in the same Issue
- frontmatter
- Research Articles
- Combining dependent F-tests for robust association of quantitative traits under genetic model uncertainty
- Penalized differential pathway analysis of integrative oncogenomics studies
- A data-smoothing approach to explore and test gene-environment interaction in case-parent trios
- Scan statistics analysis for detection of introns in time-course tiling array data
- Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure
- Improved variational Bayes inference for transcript expression estimation
- Efficient identification of context dependent subgroups of risk from genome-wide association studies
