ArticlePLoS computational biology2020
Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 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
- Integrating common and rare variants improves polygenic risk prediction across diverse populations.Nature communications · 2026Article
- Analysis of left ventricular mass for African American individuals using multi-ancestry polygenic risk scores: the Jackson Heart Study.Open heart · 2025Article
- A review of methods and software for polygenic risk score analysis.PeerJ. Computer science · 2025Article
- Advancements and limitations in polygenic risk score methods for genomic prediction: a scoping review.Human genetics · 2024Article
- ON BLOCKWISE AND REFERENCE PANEL-BASED ESTIMATORS FOR GENETIC DATA PREDICTION IN HIGH DIMENSIONS.Annals of statistics · 2024Article
- Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data.Nature communications · 2024Article
- Exploring the role of underrepresented populations in polygenic risk scores for neurodegenerative disease risk prediction.Frontiers in neuroscience · 2024Article
- Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.Journal of Korean medical science · 2023Review
- Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations.Nature communications · 2023Article
- A new method for multiancestry polygenic prediction improves performance across diverse populations.Nature genetics · 2023Article
- Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry.American journal of human genetics · 2023Article
- Evaluating Approaches for Constructing Polygenic Risk Scores for Prostate Cancer in Men of African and European Ancestry.medRxiv : the preprint server for health sciences · 2023Article
- Gene-environment interactions and the effect on obesity risk in low and middle-income countries: a scoping review.Frontiers in endocrinology · 2023Article
- Article
- The Role of Polygenic Susceptibility on Air Pollution-Associated Asthma between German and Japanese Elderly Women.International journal of environmental research and public health · 2022Article
- Polygenic Risk Score in African populations: progress and challenges.F1000Research · 2022Review
- Polygenic power calculator: Statistical power and polygenic prediction accuracy of genome-wide association studies of complex traits.Frontiers in genetics · 2022Article
- Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi-phased study of prostate cancer.Cancer communications (London, England) · 2021Article
- A novel transcriptional risk score for risk prediction of complex human diseases.Genetic epidemiology · 2021Article
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4 authors.
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Abstract
Genetic risk prediction is an important problem in human genetics, and accurate prediction can facilitate disease prevention and treatment. Calculating polygenic risk score (PRS) has become widely used due to its simplicity and effectiveness, where only summary statistics from genome-wide association studies are needed in the standard method. Recently, several methods have been proposed to improve standard PRS by utilizing external information, such as linkage disequilibrium and functional annotations. In this paper, we introduce EB-PRS, a novel method that leverages information for effect sizes across all the markers to improve prediction accuracy. Compared to most existing genetic risk prediction methods, our method does not need to tune parameters nor external information. Real data applications on six diseases, including asthma, breast cancer, celiac disease, Crohn's disease, Parkinson's disease and type 2 diabetes show that EB-PRS achieved 307.1%, 42.8%, 25.5%, 3.1%, 74.3% and 49.6% relative improvements in terms of predictive r2 over standard PRS method with optimally tuned parameters. Besides, compared to LDpred that makes use of LD information, EB-PRS also achieved 37.9%, 33.6%, 8.6%, 36.2%, 40.6% and 10.8% relative improvements. We note that our method is not the first method leveraging effect size distributions. Here we first justify our method by presenting theoretical optimal property over existing methods in this class of methods, and substantiate our theoretical result with extensive simulation results. The R-package EBPRS that implements our method is available on CRAN.
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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.