ArticleAmerican journal of human genetics2020
Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets.
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 93 papers, 2 of them syntheses that pooled it.
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Who cites it
93 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multiancestry and Multitrait GWAS Meta-Analysis on Schizophrenia With a Sample of 322,321 Unveils Genetic Links to Chronic Lung Diseases.Genes, brain, and behavior · 2026Pooled it
- Fast analysis of biobank-size data and meta-analysis using the BGLR R-package.G3 (Bethesda, Md.) · 2025Pooled it
- Cross-ancestry pleiotropic analysis of imaging-derived phenotypes enhances risk stratification of depression.Molecular psychiatry · 2026Article
- Rare extreme polygenic risk scores strongly indicate Alzheimer's disease risk.medRxiv : the preprint server for health sciences · 2026Article
- Cross-ancestry genetic architecture reveals shared biological pathways of major psychiatric disorders.Molecular psychiatry · 2026Article
- MIXPRS enables multi-population and multi-method polygenic risk scores using summary statistics.Nature genetics · 2026Article
- Profiling the Onco-metabolic Nexus and Improving Cancer Risk Prediction Performance: A Large-scale Cohort and Genome-Wide Pleiotropic Analysis.Cancer research communications · 2026Article
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- Clinical use of polygenic risk scores: current status, barriers and future directions.Nature reviews. Genetics · 2026Review
- Unraveling the genetic interplay and therapeutic potentials between major depressive disorder and metabolic syndrome: multi-ancestry and multi-trait genome-wide association analyses.BMC psychiatry · 2026Article
- ProteoNexus: an integrative database to characterize genetic architecture, estimate mediation effects, and construct and evaluate prediction models of the plasma proteome.Nucleic acids research · 2026Article
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- Multi-omics and comprehensive multi-trait analysis identify shared genetic etiology of heart failure and related cancers.Human genomics · 2025Article
- Deciphering the underlying genetics of galling resistance to the blueberry stem gall wasp in northern highbush blueberry.Horticulture research · 2025Article
- Statistical construction of calibrated prediction intervals for polygenic score-based phenotype prediction.Nature genetics · 2025Article
- Leveraging global genetics resources to enhance polygenic prediction across ancestrally diverse populations.HGG advances · 2025Article
- Protective Effects of Genetic Proxies of Cognitive Reserve in Parkinson's Disease: A Longitudinal Multi-Cohort Study.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- LDAK-KVIK performs fast and powerful mixed-model association analysis of quantitative and binary phenotypes.Nature genetics · 2025Article
- Recent Statistical Innovations in Human Genetics.Annals of human genetics · 2025Review
33 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
Accurate construction of polygenic scores (PGS) can enable early diagnosis of diseases and facilitate the development of personalized medicine. Accurate PGS construction requires prediction models that are both adaptive to different genetic architectures and scalable to biobank scale datasets with millions of individuals and tens of millions of genetic variants. Here, we develop such a method called Deterministic Bayesian Sparse Linear Mixed Model (DBSLMM). DBSLMM relies on a flexible modeling assumption on the effect size distribution to achieve robust and accurate prediction performance across a range of genetic architectures. DBSLMM also relies on a simple deterministic search algorithm to yield an approximate analytic estimation solution using summary statistics only. The deterministic search algorithm, when paired with further algebraic innovations, results in substantial computational savings. With simulations, we show that DBSLMM achieves scalable and accurate prediction performance across a range of realistic genetic architectures. We then apply DBSLMM to analyze 25 traits in UK Biobank. For these traits, compared to existing approaches, DBSLMM achieves an average of 2.03%-101.09% accuracy gain in internal cross-validations. In external validations on two separate datasets, including one from BioBank Japan, DBSLMM achieves an average of 14.74%-522.74% accuracy gain. In these real data applications, DBSLMM is 1.03-28.11 times faster and uses only 7.4%-24.8% of physical memory as compared to other multiple regression-based PGS methods. Overall, DBSLMM represents an accurate and scalable method for constructing PGS in biobank scale datasets.
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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.