ArticleFrontiers in cell and developmental biology2021
Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Genomic Selection for Milk Yield and Milk Composition Traits in Dairy Goats Using Machine Learning and Prior-Information Models.Animals : an open access journal from MDPI · 2026Article
- Estimating genotype-tissue specific gene expression using hybrid deep learning.Communications biology · 2026Article
- Partially connected neural networks for complex trait prediction: application to human height.Human genetics · 2025Article
- Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities.Human genomics · 2025Review
- Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic cardiomyopathy.Scientific reports · 2025Article
- Applying traditional and machine learning-based GWAS approaches for marker-trait identification in wheat.Frontiers in plant science · 2025Article
- VirusPredictor: XGBoost-based software to predict virus-related sequences in human data.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
Among the myriad of statistical methods that identify gene-gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene-gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical
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Registered trials
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