ArticleCommunications biology2023
Improving genetic risk prediction across diverse population by disentangling ancestry representations.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations.Nature methods · 2026Article
- The Potential Physiologic Mechanisms by Which Early-Life Nutrition May Influence Type 2 Diabetes Pathophysiology in Humans.Annual review of nutrition · 2026Review
- Evidence standards for multi-ancestry polygenic prediction.Genome biology · 2026Review
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- Genomic risk prediction of type 2 diabetes in people living with and without HIV.Scientific reports · 2026Article
- Insights Into Stroke Risk and Prediction for Underweight Individuals.Neurosciences (Riyadh, Saudi Arabia) · 2026Review
- Data Leakage in Deep Learning for Alzheimer's Disease Diagnosis: A Scoping Review of Methodological Rigor and Performance Inflation.Diagnostics (Basel, Switzerland) · 2025Review
- The impact of artificial intelligence on biomarker discovery.Emerging topics in life sciences · 2025Article
- A survey on deep learning for polygenic risk scores.Briefings in bioinformatics · 2025Review
- Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.International journal of molecular sciences · 2025Article
- Using pre-training and interaction modeling for ancestry-specific disease prediction using multiomics data from the UK Biobank.PloS one · 2025Article
- Article
- Highly parameterized polygenic scores tend to overfit to population stratification via random effects.bioRxiv : the preprint server for biology · 2024Article
- Validation of a Community-Based Approach Toward Personalized Dementia Risk Reduction: The Kimel Family Centre for Brain Health and Wellness.The journal of prevention of Alzheimer's disease · 2024Article
- Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease.Genome biology · 2023Article
- Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics.Nature communications · 2023Article
- FairPRS: adjusting for admixed populations in polygenic risk scores using invariant risk minimization.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Risk prediction models using genetic data have seen increasing traction in genomics. However, most of the polygenic risk models were developed using data from participants with similar (mostly European) ancestry. This can lead to biases in the risk predictors resulting in poor generalization when applied to minority populations and admixed individuals such as African Americans. To address this issue, largely due to the prediction models being biased by the underlying population structure, we propose a deep-learning framework that leverages data from diverse population and disentangles ancestry from the phenotype-relevant information in its representation. The ancestry disentangled representation can be used to build risk predictors that perform better across minority populations. We applied the proposed method to the analysis of Alzheimer's disease genetics. Comparing with standard linear and nonlinear risk prediction methods, the proposed method substantially improves risk prediction in minority populations, including admixed individuals, without needing self-reported ancestry information.
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Registered trials
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