Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies
KAUST Grant NumberKUS-CI-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/599365
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AbstractCase-control association studies often aim to investigate the role of genes and gene-environment interactions in terms of the underlying haplotypes (i.e., the combinations of alleles at multiple genetic loci along chromosomal regions). The goal of this article is to develop robust but efficient approaches to the estimation of disease odds-ratio parameters associated with haplotypes and haplotype-environment interactions. We consider "shrinkage" estimation techniques that can adaptively relax the model assumptions of Hardy-Weinberg-Equilibrium and gene-environment independence required by recently proposed efficient "retrospective" methods. Our proposal involves first development of a novel retrospective approach to the analysis of case-control data, one that is robust to the nature of the gene-environment distribution in the underlying population. Next, it involves shrinkage of the robust retrospective estimator toward a more precise, but model-dependent, retrospective estimator using novel empirical Bayes and penalized regression techniques. Methods for variance estimation are proposed based on asymptotic theories. Simulations and two data examples illustrate both the robustness and efficiency of the proposed methods.
CitationChen Y-H, Chatterjee N, Carroll RJ (2009) Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies. Journal of the American Statistical Association 104: 220–233. Available: http://dx.doi.org/10.1198/jasa.2009.0104.
SponsorsChen's research was supported by the National Science Council of ROC (NSC 95-2118-M-001-022-MY3). Chatterjee's research was supported by a gene-environment initiative grant from the National Heart Lung and Blood Institute (RO1HL091172-01) and by the Intramural Research Program of the National Cancer Institute. Carroll's research was supported by grants from the National Cancer Institute (CA57030, CA104620) and by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors thank the editor, associate editor, and referees for their helpful comments.
PublisherInforma UK Limited
PubMed Central IDPMC2679507
CollectionsPublications Acknowledging KAUST Support
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