ArticleBMC genetics2017
Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression.
Article in BMC genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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16 citing papers in PubMed.
- Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.Cell genomics · 2026Article
- Epstein-Barr Virus, Lower Vitamin D, Low Sun Exposure, and HLA-DRB1*1501 Risk Variant Share Common Epigenetic Pathways Leading to Multiple Sclerosis Onset.Annals of neurology · 2026Article
- Interaction between genetic predisposition to successful ageing and chronic air pollution on lung disease in elderly women: results of the German SALIA cohort.BMJ open respiratory research · 2025Article
- Development of a metabolomic risk score for exposure to traffic-related air pollution: A multi-cohort study.Environmental research · 2024Article
- Influence of blood pressure polygenic risk scores and environmental factors on coronary artery disease in the Korean Genome and Epidemiology Study.Journal of human hypertension · 2024Article
- Efficient gene-environment interaction testing through bootstrap aggregating.Scientific reports · 2023Article
- Gene-environment interactions and the effect on obesity risk in low and middle-income countries: a scoping review.Frontiers in endocrinology · 2023Article
- Development and validation of an RNA-seq-based transcriptomic risk score for asthma.Scientific reports · 2022Article
- Evaluation of tree-based statistical learning methods for constructing genetic risk scores.BMC bioinformatics · 2022Article
- 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
- Polygenic approaches to detect gene-environment interactions when external information is unavailable.Briefings in bioinformatics · 2019Review
- Performing different kinds of physical exercise differentially attenuates the genetic effects on obesity measures: Evidence from 18,424 Taiwan Biobank participants.PLoS genetics · 2019Article
- Dopaminergic and Opioid Pathways Associated with Impulse Control Disorders in Parkinson's Disease.Frontiers in neurology · 2018Article
- Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies.BMC genetics · 2017Article
- Article
- Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFor the analysis of gene-environment (GxE) interactions commonly single nucleotide polymorphisms (SNPs) are used to characterize genetic susceptibility, an approach that mostly lacks power and has poor reproducibility. One promising approach to overcome this problem might be the use of weighted genetic risk scores (GRS), which are defined as weighted sums of risk alleles of gene variants. The gold-standard is to use external weights from published meta-analyses.
methodsIn this study, we used internal weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression and thereby provided a method that can be used if there are no external weights available. We conducted a simulation study for the detection of GxE interactions and compared power and type I error of single SNPs analyses with Bonferroni correction and corresponding analysis with unweighted and our weighted GRS approach in scenarios with six risk SNPs and an increasing number of highly correlated (up to 210) and noise SNPs (up to 840).
resultsApplying weighted GRS increased the power enormously in comparison to the common single SNPs approach (e.g. 94.2% vs. 35.4%, respectively, to detect a weak interaction with an OR ≈ 1.04 for six uncorrelated risk SNPs and n = 700 with a well-controlled type I error). Furthermore, weighted GRS outperformed the unweighted GRS, in particular in the presence of SNPs without any effect on the phenotype (e.g. 90.1% vs. 43.9%, respectively, when 20 noise SNPs were added to the six risk SNPs). This outperforming of the weighted GRS was confirmed in a real data application on lung inflammation in the SALIA cohort (n = 402). However, in scenarios with a high number of noise SNPs (>200 vs. 6 risk SNPs), larger sample sizes are needed to avoid an increased type I error, whereas a high number of correlated SNPs can be handled even in small samples (e.g. n = 400).
conclusionIn conclusion, weighted GRS with weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression were shown to be a powerful tool to detect gene-environment interactions in scenarios of high Linkage disequilibrium and noise.
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