ArticleBiometrics2015
An exposure-weighted score test for genetic associations integrating environmental risk factors.
Article in Biometrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- 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
- A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank.American journal of human genetics · 2019Article
- A semiparametric efficient estimator in case-control studies for gene-environment independent models.Journal of multivariate analysis · 2019Article
- Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.American journal of epidemiology · 2018Article
- Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma.Frontiers in oncology · 2018Article
- Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies.Biometrika · 2017Article
- Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.American journal of epidemiology · 2017Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Current methods for detecting genetic associations lack full consideration of the background effects of environmental exposures. Recently proposed methods to account for environmental exposures have focused on logistic regressions with gene-environment interactions. In this report, we developed a test for genetic association, encompassing a broad range of risk models, including linear, logistic and probit, for specifying joint effects of genetic and environmental exposures. We obtained the test statistics by maximizing over a class of score tests, each of which involves modified standard tests of genetic association through a weight function. This weight function reflects the potential heterogeneity of the genetic effects by levels of environmental exposures under a particular model. Simulation studies demonstrate the robust power of these methods for detecting genetic associations under a wide range of scenarios. Applications of these methods are further illustrated using data from genome-wide association studies of type 2 diabetes with body mass index and of lung cancer risk with smoking.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.