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J Am Stat Assoc. 2009 Mar 1;104(485):220-233.

Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies.

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1
Yi-Hau Chen is Associate Research Member with the Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan, Republic of China (E-mail: yhchen@stat.sinica.edu.tw ). Nilanjan Chatterjee is Chief and Principle Investigator with the Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Department of Health and Human Services, Rockville Maryland 20852 (E-mail: chattern@mail.nih.gov ). Raymond J. Carroll is Distinguished Professor with the Department of Statistics, Texas A&M University, College Station, Texas 77843-3143 (E-mail: carroll@stat.tamu.edu ).

Abstract

Case-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.

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