ArticleJournal of the American Statistical Association2009
Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies.
Article in Journal of the American Statistical Association, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Methods for modeling gene-environment interplay using polygenic risk scores.Statistical applications in genetics and molecular biology · 2026Review
- Efficient inference of parent-of-origin effect using case-control mother-child genotype data.Journal of statistical planning and inference · 2024Article
- Joint modeling of gene-environment correlations and interactions using polygenic risk scores in case-control studies.American journal of epidemiology · 2024Article
- A Robust Test for Additive Gene-Environment Interaction Under the Trend Effect of Genotype Using an Empirical Bayes-Type Shrinkage Estimator.American journal of epidemiology · 2021Article
- Sub-multiplicative interaction between polygenic risk score and household coal use in relation to lung adenocarcinoma among never-smoking women in Asia.Environment international · 2021Article
- A Likelihood Ratio Test for Gene-Environment Interaction Based on the Trend Effect of Genotype Under an Additive Risk Model Using the Gene-Environment Independence Assumption.American journal of epidemiology · 2021Article
- Comparison of haplotype-based tests for detecting gene-environment interactions with rare variants.Briefings in bioinformatics · 2020Review
- A semiparametric efficient estimator in case-control studies for gene-environment independent models.Journal of multivariate analysis · 2019Article
- A fast imputation algorithm in quantile regression.Computational statistics · 2018Article
- Robust distributed lag models using data adaptive shrinkage.Biostatistics (Oxford, England) · 2018Article
- Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies.Genetic epidemiology · 2018Article
- Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.American journal of epidemiology · 2018Article
- Dimension reduction and estimation in the secondary analysis of case-control studies.Electronic journal of statistics · 2018Article
- Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies.Biometrika · 2017Article
- Meta-analysis of gene-environment interaction exploiting gene-environment independence across multiple case-control studies.Statistics in medicine · 2017Article
- Update on the State of the Science for Analytical Methods for Gene-Environment Interactions.American journal of epidemiology · 2017Article
- Semiparametric Estimation in the Secondary Analysis of Case-Control Studies.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2016Article
- A penalized likelihood approach for investigating gene-drug interactions in pharmacogenetic studies.Biometrics · 2015Article
- Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures.Statistics in medicine · 2015Article
- Testing departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting.Statistics in medicine · 2014Article
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3 authors.
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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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