ArticlePLoS genetics2021
A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.
Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus.Nature communications · 2023Pooled it
- Identifying shared polygenic risk across cancers.PLoS genetics · 2026Article
- Cell-type-specific polygenic risk scores reveal adipocyte-related interactions with lipids in coronary artery disease.medRxiv : the preprint server for health sciences · 2026Article
- Widespread and Biologically Driven Sex Disparities in Polygenic Risk Prediction Across Complex Traits.Research square · 2026Article
- Integrating common and rare variants improves polygenic risk prediction across diverse populations.Nature communications · 2026Article
- Scalable and accurate rare-variant association tests for whole genome sequencing time-to-event analysis in large biobanks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Multi-tissue transcriptome-wide association study identifies 29 risk genes associated with attention-deficit/hyperactivity disorder.medRxiv : the preprint server for health sciences · 2026Article
- Genetic risk of chronic pain conditions associated with risk of suicide death through an integrative analysis of EHR and genomics data.Translational psychiatry · 2026Article
- PAD-associated Genetic Variants are More Strongly Associated with Surgical Intervention than Premature Onset.Journal of cardiovascular translational research · 2026Article
- Comprehensive benchmarking single and multi ancestry polygenic score methods with the PGS-hub platform.Nature communications · 2026Article
- Joint modeling of effect sizes for two correlated traits: Characterizing trait properties to enhance polygenic risk prediction.PLoS genetics · 2026Article
- Improving polygenic risk prediction performance by integrating electronic health records through phenotype embedding.American journal of human genetics · 2025Article
- Exploring Contraindicated Medications and Corresponding Targeted Genes for Migraine Through Integrated Genetic Approaches.Brain and behavior · 2025Article
- Beyond the Genotype: A Multi-Omic Analysis of APOEe4's Role in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2025Article
- Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations.American journal of human genetics · 2025Article
- PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts.Genome medicine · 2025Article
- Proteome-wide association studies using summary pQTL data of brain, CSF, and plasma identify 30 risk genes of Alzheimer's disease dementia.Alzheimer's research & therapy · 2025Article
- Integration of transcriptome-wide association study and gene-based association analysis identifies candidate genes for Hodgkin lymphoma.Journal of cancer research and clinical oncology · 2025Article
- Deep learning-based polygenic scores enhance generalizability of psychiatric disorders prediction.medRxiv : the preprint server for health sciences · 2025Article
- A Bayesian approach to correcting the attenuation bias of regression using polygenic risk score.Genetics · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR.
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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.