ArticleCommunications biology2022
Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.
- Polygenic Risk Scores for Myopia: A Systematic Review of Predictive Performance and Clinical Potential.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Pooled it
- Multimodal deep learning enhances genomic risk prediction for cardiometabolic diseases.Briefings in bioinformatics · 2026Article
- Development and application of type 1 diabetes polygenic scores across diverse populations.Diabetologia · 2026Review
- ALPAR: automated learning pipeline for antimicrobial resistance.Bioinformatics (Oxford, England) · 2026Article
- Explicitly modeling genetic ancestry to improve polygenic prediction accuracy for height in a large, admixed cohort of US Latinos: Findings from HCHS/SOL.HGG advances · 2026Article
- Data Representation Bias and Conditional Distribution Shift Drive Predictive Performance Disparities in Multi-Population Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen,Ecology and evolution · 2026Article
- Leveraging Machine Learning to Predict Warfarin Sensitivity in the Puerto Rican Population: A Pharmacogenomic Approach.International journal of environmental research and public health · 2026Article
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- A deep ensemble encoder network method for improved polygenic risk score prediction.BioData mining · 2026Article
- Diagnosing migraine from genome-wide genotype data: a machine learning analysis.Brain : a journal of neurology · 2026Article
- Personalized polygenic profiling based on the genetic architecture of lipid metabolism in the Russian population.Frontiers in cardiovascular medicine · 2026Article
- An integrated lifestyle-, genetic- and metabolomics-based prediction model for mild cognitive aging in the HCHS/SOL.NPJ dementia · 2026Article
- Predicting maize hybrid performance with machine learning and a locus-specific weighted degree of dominance transformation.Frontiers in plant science · 2026Article
- Partially connected neural networks for complex trait prediction: application to human height.Human genetics · 2025Article
- Complex genetic effects linked to plasma protein abundance in the UK Biobank.Nature communications · 2025Article
- Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method.Annals of medicine · 2025Article
- Adiposity-lipid-glycemic clusters as potential warning signals of bone mass reduction in Asia's largest urban communities - based bone health assessment via ultrasound.Lipids in health and disease · 2025Article
- Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.Scientific reports · 2025Article
- Open chromatin-guided interpretable machine learning reveals cancer-specific chromatin features in cell-free DNA.Communications biology · 2025Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors at 13 institutions in 1 country.
Funding
Abstract
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22% for height, 27% for HDL cholesterol, 43% for body mass index, 50% for sleep duration, 58% for systolic blood pressure, 64% for total cholesterol, 66% for triglycerides, 77% for LDL cholesterol, and 100% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.
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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.