ArticlePloS one2022
Human genotype-to-phenotype predictions: Boosting accuracy with nonlinear models.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Modeling nonlinear and interaction effects of spatiotemporal and nongenetic factors improves prediction for complex traits.Nature communications · 2026Article
- Predicting VNN resistance in European sea bass using machine learning on high dimensional low sample size data.Frontiers in bioinformatics · 2026Article
- Partially connected neural networks for complex trait prediction: application to human height.Human genetics · 2025Article
- Review
- A compact encoding of the genome suitable for machine learning prediction of traits and genetic risk scores.BioData mining · 2025Article
- Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models.ArXiv · 2025Article
- Improving genetic variant identification for quantitative traits using ensemble learning-based approaches.BMC genomics · 2025Article
- Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Linking Protein Stability to Pathogenicity: Predicting Clinical Significance of Single-Missense Mutations in Ocular Proteins Using Machine Learning.International journal of molecular sciences · 2024Article
- Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Deep learning captures the effect of epistasis in multifactorial diseases.Frontiers in medicine · 2024Article
- Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Genotype-to-phenotype prediction is a central problem of human genetics. In recent years, it has become possible to construct complex predictive models for phenotypes, thanks to the availability of large genome data sets as well as efficient and scalable machine learning tools. In this paper, we make a threefold contribution to this problem. First, we ask if state-of-the-art nonlinear predictive models, such as boosted decision trees, can be more efficient for phenotype prediction than conventional linear models. We find that this is indeed the case if model features include a sufficiently rich set of covariates, but probably not otherwise. Second, we ask if the conventional selection of single nucleotide polymorphisms (SNPs) by genome wide association studies (GWAS) can be replaced by a more efficient procedure, taking into account information in previously selected SNPs. We propose such a procedure, based on a sequential feature importance estimation with decision trees, and show that this approach indeed produced informative SNP sets that are much more compact than when selected with GWAS. Finally, we show that the highest prediction accuracy can ultimately be achieved by ensembling individual linear and nonlinear models. To the best of our knowledge, for some of the phenotypes that we consider (asthma, hypothyroidism), our results are a new state-of-the-art.
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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.