ArticleAmerican journal of human genetics2020
Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.
Article in American journal of human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure.Briefings in bioinformatics · 2022Pooled it
- Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets.Nature communications · 2021Pooled it
- Evaluation of Sex-Stratified Polygenic Risk Scores for Type 2 Diabetes Mellitus and Glycemic Traits in the Framingham Heart Study.Diabetes & metabolism journal · 2026Article
- Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach.Scientific reports · 2025Article
- Optimization of multi-ancestry polygenic risk score disease prediction models.Scientific reports · 2025Article
- Polygenic scores and their applications in kidney disease.Nature reviews. Nephrology · 2025Review
- 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
- Polygenic risk scores for autoimmune related diseases are significantly different in cancer exceptional responders.NPJ precision oncology · 2024Article
- A Polygenic Risk Analysis for Identifying Ulcerative Colitis Patients with European Ancestry.Genes · 2024Article
- A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease.Scientific reports · 2024Article
- Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction.BMC bioinformatics · 2024Article
- Summary statistics-based association test for identifying the pleiotropic effects with set of genetic variants.Bioinformatics (Oxford, England) · 2023Article
- ExPRSweb: An online repository with polygenic risk scores for common health-related exposures.American journal of human genetics · 2022Article
- Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores.Annual review of biomedical data science · 2022Review
- Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores.Nature genetics · 2022Article
- Genetic prediction of complex traits with polygenic scores: a statistical review.Trends in genetics : TIG · 2021Review
- The distribution of common-variant effect sizes.Nature genetics · 2021Article
- Polygenic risk modeling with latent trait-related genetic components.European journal of human genetics : EJHG · 2021Article
- A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.PLoS genetics · 2021Article
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 1 country.
Funding
Abstract
In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait's heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments.
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Registered trials
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.