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

Handle URI:
http://hdl.handle.net/10754/599365
Title:
Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies
Authors:
Chen, Yi-Hau; Chatterjee, Nilanjan; Carroll, Raymond J.
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.
Citation:
Chen 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.
Publisher:
Informa UK Limited
Journal:
Journal of the American Statistical Association
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
Mar-2009
DOI:
10.1198/jasa.2009.0104
PubMed ID:
19430598
PubMed Central ID:
PMC2679507
Type:
Article
ISSN:
0162-1459; 1537-274X
Sponsors:
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.
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Full metadata record

DC FieldValue Language
dc.contributor.authorChen, Yi-Hauen
dc.contributor.authorChatterjee, Nilanjanen
dc.contributor.authorCarroll, Raymond J.en
dc.date.accessioned2016-02-28T06:05:39Zen
dc.date.available2016-02-28T06:05:39Zen
dc.date.issued2009-03en
dc.identifier.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.en
dc.identifier.issn0162-1459en
dc.identifier.issn1537-274Xen
dc.identifier.pmid19430598en
dc.identifier.doi10.1198/jasa.2009.0104en
dc.identifier.urihttp://hdl.handle.net/10754/599365en
dc.description.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.en
dc.description.sponsorshipChen'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.en
dc.publisherInforma UK Limiteden
dc.subjectEmpirical bayesen
dc.subjectGenetic epidemiologyen
dc.subjectLASSO (Least Absolute Shrinkage and Selection Operator)en
dc.subjectModel averagingen
dc.subjectModel robustnessen
dc.subjectModel selectionen
dc.titleShrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studiesen
dc.typeArticleen
dc.identifier.journalJournal of the American Statistical Associationen
dc.identifier.pmcidPMC2679507en
dc.contributor.institutionYi-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 ).en
kaust.grant.numberKUS-CI-016-04en
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