ArticleAmerican journal of human genetics2021
A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits.
Article in American journal of human genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 1 of them a synthesis that pooled it.
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Who cites it
79 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure.Briefings in bioinformatics · 2022Pooled it
- Estimating genetic correlation jointly using individual-level and summary-level GWAS data.Nature communications · 2026Article
- A multi-ancestry polygenic risk score for body mass index predicts longitudinal weight change.Genome medicine · 2026Article
- Evidence standards for multi-ancestry polygenic prediction.Genome biology · 2026Review
- MIXPRS enables multi-population and multi-method polygenic risk scores using summary statistics.Nature genetics · 2026Article
- Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE.bioRxiv : the preprint server for biology · 2026Article
- Contextualizing the Utility of Polygenic Risk Scores using Absolute Risk Models in Diverse Ancestry Populations.medRxiv : the preprint server for health sciences · 2026Article
- Data Representation Bias and Conditional Distribution Shift Drive Predictive Performance Disparities in Multi-Population Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- Cross-Ancestry Polygenic Prediction: Comparing Methods and Assessing Transferability Across Traits.Genetic epidemiology · 2026Article
- S4-multi: Enhancing polygenic score prediction in ancestrally diverse populations.HGG advances · 2026Article
- Partially connected neural networks for complex trait prediction: application to human height.Human genetics · 2025Article
- Logica: A likelihood framework for cross-ancestry local genetic correlation estimation using summary statistics.American journal of human genetics · 2025Article
- PTL-PRS: an R package for transfer learning of polygenic risk scores with pseudovalidation.Bioinformatics (Oxford, England) · 2025Article
- HIGH-DIMENSIONAL STATISTICAL INFERENCE FOR LINKAGE DISEQUILIBRIUM SCORE REGRESSION AND ITS CROSS-ANCESTRY EXTENSIONS.Annals of statistics · 2025Article
- Methodological opportunities in genomic data analysis to advance health equity.Nature reviews. Genetics · 2025Review
- Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations.American journal of human genetics · 2025Article
- Revisiting the Trans-Ancestry Genetic Correlation of Refractive Error.Investigative ophthalmology & visual science · 2025Article
- PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts.Genome medicine · 2025Article
- Almost Free Enhancement of Multi-Population PRS: From Data-Fission to Pseudo-GWAS Subsampling.bioRxiv : the preprint server for biology · 2025Article
- JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation.Nature communications · 2025Article
19 more citing papers are in PubMed but not listed here.
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7 authors.
Funding
Abstract
The development of polygenic risk scores (PRSs) has proved useful to stratify the general European population into different risk groups. However, PRSs are less accurate in non-European populations due to genetic differences across different populations. To improve the prediction accuracy in non-European populations, we propose a cross-population analysis framework for PRS construction with both individual-level (XPA) and summary-level (XPASS) GWAS data. By leveraging trans-ancestry genetic correlation, our methods can borrow information from the Biobank-scale European population data to improve risk prediction in the non-European populations. Our framework can also incorporate population-specific effects to further improve construction of PRS. With innovations in data structure and algorithm design, our methods provide a substantial saving in computational time and memory usage. Through comprehensive simulation studies, we show that our framework provides accurate, efficient, and robust PRS construction across a range of genetic architectures. In a Chinese cohort, our methods achieved 7.3%-198.0% accuracy gain for height and 19.5%-313.3% accuracy gain for body mass index (BMI) in terms of predictive R
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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.