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

Yi-Hau Chen; Nilanjan Chatterjee; Raymond J. Carroll

doi:10.1198/jasa.2009.0104

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

Yi-Hau Chen, Nilanjan Chatterjee, Raymond J. Carroll

Research output: Contribution to journal › Article › peer-review

53 Scopus citations

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.

Original language	English (US)
Pages (from-to)	220-233
Number of pages	14
Journal	Journal of the American Statistical Association
Volume	104
Issue number	485
DOIs	https://doi.org/10.1198/jasa.2009.0104
State	Published - Mar 2009
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: Chen'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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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title = "Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies",

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.",

author = "Yi-Hau Chen and Nilanjan Chatterjee and Carroll, {Raymond J.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-CI-016-04 Acknowledgements: Chen'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. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

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N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-CI-016-04 Acknowledgements: Chen'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. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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N2 - 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.

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

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