ArticleG3 (Bethesda, Md.)2022
Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice.
Article in G3 (Bethesda, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 29 citations in OpenAlex.
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- Genomic prediction for stem rust resistance in the southern United States elite oat (Frontiers in plant science · 2026Article
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- Data-driven frameworks to robustly predict solubility parameter of diverse polymers.Scientific reports · 2025Article
- Incorporating information of causal variants in genomic prediction using GBLUP or machine learning models in a simulated livestock population.Journal of animal science and biotechnology · 2025Article
- Parametric optimization of the slot waveguide characteristics using a machine-learning approach.Scientific reports · 2025Article
- A manganese metabolism-related gene signature stratifies prognosis and immunotherapy efficacy in kidney cancer.Discover oncology · 2025Article
- Clinical prediction of pathological complete response in breast cancer: a machine learning study.BMC cancer · 2025Article
- Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis.Frontiers in medicine · 2025Article
- Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models.Genetics, selection, evolution : GSE · 2024Article
- Maximizing efficiency in sunflower breeding through historical data optimization.Plant methods · 2024Article
- Factors influencing recurrence and model development for recurrence of minimally invasive percutaneous transhepatic lithotripsy: a single-center retrospective study.American journal of translational research · 2024Article
- Genetic Parameter and Hyper-Parameter Estimation Underlie Nitrogen Use Efficiency in Bread Wheat.International journal of molecular sciences · 2023Article
- Gene-based burden scores identify rare variant associations for 28 blood biomarkers.BMC genomic data · 2023Article
- Accurate prediction of quantitative traits with failed SNP calls in canola and maize.Frontiers in plant science · 2023Article
- Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence.G3 (Bethesda, Md.) · 2022Article
- Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding.The plant genome · 2022Article
- Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP markers and phenotypes for 835 animals from 6 generations. Traits analyzed were bone mineral density, body weight at 10, 15, and 20 weeks, fat percentage, circulating cholesterol, glucose, insulin, triglycerides, and urine creatinine. The youngest generation was used as a validation subset, and predictions were based on all older generations. Model performance was evaluated by comparing predictions for animals in the validation subset against their adjusted phenotypes. Linear models outperformed gradient boosting machine for 7 out of 10 traits. For bone mineral density, cholesterol, and glucose, the gradient boosting machine model showed better prediction accuracy and lower relative root mean squared error than the linear models. Interestingly, for these 3 traits, there is evidence of a relevant portion of phenotypic variance being explained by epistatic effects. Using a subset of top markers selected from a gradient boosting machine model helped for some of the traits to improve the accuracy of prediction when these were fitted into linear and gradient boosting machine models. Our results indicate that gradient boosting machine is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Although the linear models outperformed gradient boosting machine for the polygenic traits, our results suggest that gradient boosting machine is a competitive method to predict complex traits with assumed epistatic effects.
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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.