ArticleJournal of the American Statistical Association2021
A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information.
Article in Journal of the American Statistical Association, 2021. 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.
- Constructing Genetic Risk Scores: Robust Bayesian Approach through Projected Summary Statistics and Flexible Shrinkage.Journal of the American Statistical Association · 2026Article
- Transfer learning enhances clinical utility of polygenic scores with small, phenotypically refined cohorts.Genome research · 2026Article
- Integrating network annotation from multiple correlated traits to improve polygenic risk scores based on GWAS summary statistics.Research square · 2026Article
- Incorporating multiple functional annotations to improve polygenic risk prediction accuracy.Cell genomics · 2025Article
- Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations.Genetic epidemiology · 2025Article
- One score to rule them all: regularized ensemble polygenic risk prediction with GWAS summary statistics.bioRxiv : the preprint server for biology · 2024Article
- Optimizing and benchmarking polygenic risk scores with GWAS summary statistics.Genome biology · 2024Article
- Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error.Biostatistics (Oxford, England) · 2024Article
- Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction.BMC bioinformatics · 2024Article
- mtPGS: Leverage multiple correlated traits for accurate polygenic score construction.American journal of human genetics · 2023Article
- Multivariate extension of penalized regression on summary statistics to construct polygenic risk scores for correlated traits.HGG advances · 2023Article
- Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics.Nature communications · 2023Article
- Multi-Task Learning with Summary Statistics.Advances in neural information processing systems · 2023Article
- Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores.Annual review of biomedical data science · 2022Review
- Genetic prediction of complex traits with polygenic scores: a statistical review.Trends in genetics : TIG · 2021Review
- PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics.Genome biology · 2021Article
- Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer.Prostate cancer and prostatic diseases · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Large-scale genome-wide association (GWAS) studies provide opportunities for developing genetic risk prediction models that have the potential to improve disease prevention, intervention or treatment. The key step is to develop polygenic risk score (PRS) models with high predictive performance for a given disease, which typically requires a large training data set for selecting truly associated single nucleotide polymorphisms (SNPs) and estimating effect sizes accurately. Here, we develop a comprehensive penalized regression for fitting
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