ArticlePLoS genetics2017
Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.
Article in PLoS genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-trait analysis for genome-wide association study of five psychiatric disorders.Translational psychiatry · 2020Pooled it
- Estimating genetic correlation jointly using individual-level and summary-level GWAS data.Nature communications · 2026Article
- MIXPRS enables multi-population and multi-method polygenic risk scores using summary statistics.Nature genetics · 2026Article
- Integrating network annotation from multiple correlated traits to improve polygenic risk scores based on GWAS summary statistics.Research square · 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
- Statistical construction of calibrated prediction intervals for polygenic score-based phenotype prediction.Nature genetics · 2025Article
- The Role of Polygenic Risk Score in the General Population: Current Status and Future Prospects.Journal of atherosclerosis and thrombosis · 2025Review
- Pathway-Specific Polygenic Scores for Predicting Clinical Lithium Treatment Response in Patients With Bipolar Disorder.Biological psychiatry global open science · 2025Article
- Almost Free Enhancement of Multi-Population PRS: From Data-Fission to Pseudo-GWAS Subsampling.bioRxiv : the preprint server for biology · 2025Article
- Incorporating multiple functional annotations to improve polygenic risk prediction accuracy.Cell genomics · 2025Article
- A cross-tissue transcriptome-wide association study identifies novel susceptibility genes for atrial fibrillation.Journal of arrhythmia · 2025Article
- Efficient blockLASSO for polygenic scores with applications to all of us and UK Biobank.BMC genomics · 2025Article
- Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms.Schizophrenia (Heidelberg, Germany) · 2025Article
- One score to rule them all: regularized ensemble polygenic risk prediction with GWAS summary statistics.bioRxiv : the preprint server for biology · 2024Article
- Advancements and limitations in polygenic risk score methods for genomic prediction: a scoping review.Human genetics · 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
- mtPGS: Leverage multiple correlated traits for accurate polygenic score construction.American journal of human genetics · 2023Article
- Benchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies.Briefings in bioinformatics · 2023Article
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6 authors.
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Abstract
Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS) in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.
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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.