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1. A test for gene-environment interaction in the presence of measurement error in the environmental variable

Author

Hugues Aschard, Donna Spiegelman, Molin Wang, Vincent Laville, Pete Kraft, Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Harvard T.H. Chan School of Public Health, The authors thank the reviewers for their helpful comments that have improved the paper. This research is supported by grant R01CA050597 from the National Cancer Institute (NCI) and R21HG007687 from the National Human Genome Research Institute (NHGRI). The authors have no conflict of interest to declare., and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, Epidemiology, Genome-wide association study, Breast Neoplasms, MESH: Logistic Models, Interaction, Logistic regression, 01 natural sciences, Article, Correlation, 010104 statistics & probability, 03 medical and health sciences, Bias, Statistics, Linear regression, MESH: Bias, Humans, normal discriminant analysis, MESH: Genetic Variation, MESH: Models, Genetic, 0101 mathematics, Gene–environment interaction, gene-environment interaction test, Genetics (clinical), Mathematics, [STAT.AP]Statistics [stat]/Applications [stat.AP], Observational error, MESH: Humans, Models, Genetic, Genetic Variation, Reproducibility of Results, MESH: Gene-Environment Interaction, Linear discriminant analysis, 3. Good health, MESH: Reproducibility of Results, 030104 developmental biology, Logistic Models, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, genome-wide association studies, MESH: Genome-Wide Association Study, Gene-Environment Interaction, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [STAT.ME]Statistics [stat]/Methodology [stat.ME], measurement error, MESH: Breast Neoplasms, Genome-Wide Association Study
Abstract

International audience; The identification of gene-environment interactions in relation to risk of human diseases has been challenging. One difficulty has been that measurement error in the exposure can lead to massive reductions in the power of the test, as well as in bias toward the null in the interaction effect estimates. Leveraging previous work on linear discriminant analysis, we develop a new test of interaction between genetic variants and a continuous exposure that mitigates these detrimental impacts of exposure measurement error in ExG testing by reversing the role of exposure and the diseases status in the fitted model, thus transforming the analysis to standard linear regression. Through simulation studies, we show that the proposed approach is valid in the presence of classical exposure measurement error as well as when there is correlation between the exposure and the genetic variant. Simulations also demonstrated that the reverse test has greater power compared to logistic regression. Finally, we confirmed that our approach eliminates bias from exposure measurement error in estimation. Computing times are reduced by as much as fivefold in this new approach. For illustrative purposes, we applied the new approach to an ExGWAS study of interactions with alcohol and body mass index among 1,145 cases with invasive breast cancer and 1,142 controls from the Cancer Genetic Markers of Susceptibility study.

Published
2018

2. Attribute-Level Heterogeneity

Author

Peter Ebbes, John Liechty, Rajdeep Grewal, Matthew Tibbits, Ecole des Hautes Etudes Commerciales (HEC Paris), and HEC Paris Research paper Series

Subjects
Bayes estimator, Computer science, Mixture Models, Regression analysis, Markov chain Monte Carlo, Reversible-jump Markov chain Monte Carlo, computer.software_genre, Mixture model, Conjoint analysis, Reversible Jump MCMC, symbols.namesake, Segmentation, Linear regression, symbols, Hierarchical Bayes, [SHS.GESTION]Humanities and Social Sciences/Business administration, Data mining, Conjoint Analysis, Random variable, computer
Abstract

Modeling consumer heterogeneity helps practitioners understand market structures and devise effective marketing strategies. In this research we study finite mixture specifications for modeling consumer heterogeneity where each regression coefficient has its own finite mixture, that is, an attribute finite mixture model. An important challenge of such an approach to modeling heterogeneity lies in its estimation. A proposed Bayesian estimation approach, based on recent advances in reversible jump Markov Chain Monte Carlo (MCMC) methods, can estimate parameters for the attribute-based finite mixture model, assuming that the number of components for each finite mixture is a discrete random variable. An attribute specification has several advantages over traditional, vector-based, finite mixture specifications; specifically, the attribute mixture model offers a more appropriate aggregation of information than the vector specification facilitating estimation. In an extensive simulation study and an empirical application, we show that the attribute model can recover complex heterogeneity structures, making it dominant over traditional (vector) finite mixture regression models and a strong contender compared with mixture-of-normals models for modeling heterogeneity.The appendices for this paper are available at the following URL: http://ssrn.com/abstract=2370423

Published
2010