Bibliographical Connections for Semiparametric Analysis in Case-Control Studies on Gene-Environment Interactions

Analyzing the gene-environment interaction (GEI) is crucial for understanding the etiology of many complex traits. Among various types of study designs, case-control studies are popular for analyzing gene-environment interactions due to their efficiency in collecting covariate information. Extensive literature explores efficient estimation under various assumptions made about the relationship between genetic and environmental variables. In this paper, we comprehensively review the methods based on or related to the retrospective likelihood, including the methods based on the hypothetical population concept, which has been largely overlooked in GEI research in the past decade. Furthermore, we establish the methodological connection between these two groups of methods by deriving a new estimator from both the retrospective likelihood and the hypothetical population perspectives. The validity of the derivation is demonstrated through numerical studies.

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Publication Details

Journal
Journal of Data Science
Published
2024-10-16
DOI
https://doi.org/10.6339/24-jds1155
Citations
1
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
0.23
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article

Bibliographical Connections for Semiparametric Analysis in Case-Control Studies on Gene-Environment Interactions

Tianying Wang, Aijing Wu, Jianxuan Liu
1 citations
Journal of Data Science
Gene expression and cancer classification
0.23
article

Bibliographical Connections for Semiparametric Analysis in Case-Control Studies on Gene-Environment Interactions

Tianying Wang, Aijing Wu, Jianxuan Liu
article en
1 citations

Abstract

Analyzing the gene-environment interaction (GEI) is crucial for understanding the etiology of many complex traits. Among various types of study designs, case-control studies are popular for analyzing gene-environment interactions due to their efficiency in collecting covariate information. Extensive literature explores efficient estimation under various assumptions made about the relationship between genetic and environmental variables. In this paper, we comprehensively review the methods based on or related to the retrospective likelihood, including the methods based on the hypothetical population concept, which has been largely overlooked in GEI research in the past decade. Furthermore, we establish the methodological connection between these two groups of methods by deriving a new estimator from both the retrospective likelihood and the hypothetical population perspectives. The validity of the derivation is demonstrated through numerical studies.

Journal of Data Science
Openalex Percentile: Top 42%
Gene expression and cancer classification
0.23
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Bibliographical Connections for Semiparametric Analysis in Case-Control Studies on Gene-Environment Interactions — Tianying Wang, Aijing Wu, et al. · Journal of Data Science (2024) | TGRS Research Map | TGRS