ArticleJournal of computational biology : a journal of computational molecular cell biology2020
Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed, 24 citations in OpenAlex.
- HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts.Genome biology · 2026Article
- REECAP: Contrastive learning of retinal aging reveals genetic loci linking morphology to eye disease.medRxiv : the preprint server for health sciences · 2025Article
- Association of Hypertension With Telomere Length, Considering Non-Genetic and Genetic Factors, in Middle-Aged Koreans.Journal of clinical hypertension (Greenwich, Conn.) · 2025Article
- Leveraging haplotype information in heritability estimation and polygenic prediction.Nature communications · 2025Article
- Accurate haplotype construction and detection of selection signatures enabled by high quality pig genome sequences.Nature communications · 2023Article
- Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits.Nature genetics · 2023Article
- Article
- Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data.PLoS computational biology · 2022Article
- Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods.Environmental health perspectives · 2022Article
- Placental genomics mediates genetic associations with complex health traits and disease.Nature communications · 2022Article
- Protein-coding repeat polymorphisms strongly shape diverse human phenotypes.Science (New York, N.Y.) · 2021Article
- A model and test for coordinated polygenic epistasis in complex traits.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- GBAT: a gene-based association test for robust detection of trans-gene regulation.Genome biology · 2020Article
- Post-GWAS knowledge gap: the how, where, and when.NPJ Parkinson's disease · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 5 countries.
Funding
Abstract
Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRSs). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications, including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed-model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work, we present a novel reference-free method to produce a PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial overfitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues and can produce informative in-sample PRSs over a single cohort without overfitting. We then demonstrate several novel applications of reference-free PRSs, including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.
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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.