ReviewBriefings in bioinformatics2019
Polygenic approaches to detect gene-environment interactions when external information is unavailable.
Review in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 19 citations in OpenAlex.
- Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.Cell genomics · 2026Article
- Polygenic risk scores: Navigating the future of precision medicine through economic, ethical, and scientific advancements.iScience · 2026Review
- Associations of polygenic risk score, environmental factors, and their interactions with the risk of schizophrenia spectrum disorders.Psychological medicine · 2025Article
- Gene-environment interactions in human health.Nature reviews. Genetics · 2024Review
- Efficient gene-environment interaction testing through bootstrap aggregating.Scientific reports · 2023Article
- Estimating the Relative Contribution of Environmental and Genetic Risk Factors to Different Aging Traits by Combining Correlated Variables into Weighted Risk Scores.International journal of environmental research and public health · 2022Article
- Chronic air pollution-induced subclinical airway inflammation and polygenic susceptibility.Respiratory research · 2022Article
- The Role of Polygenic Susceptibility on Air Pollution-Associated Asthma between German and Japanese Elderly Women.International journal of environmental research and public health · 2022Article
- CUX2, BRAP and ALDH2 are associated with metabolic traits in people with excessive alcohol consumption.Scientific reports · 2020Article
- Using Genetic Risk Score Approaches to Infer Whether an Environmental Factor Attenuates or Exacerbates the Adverse Influence of a Candidate Gene.Frontiers in genetics · 2020Article
- Performing different kinds of physical exercise differentially attenuates the genetic effects on obesity measures: Evidence from 18,424 Taiwan Biobank participants.PLoS genetics · 2019Article
- Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests.Frontiers in genetics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The exploration of 'gene-environment interactions' (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our 'adaptive combination of Bayes factors method' (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.
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What Socratic 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 Socratic graph.