ArticleNature communications2017
Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 1 of them a synthesis that pooled it.
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Who cites it
86 citing papers in PubMed, 1 synthesis or guideline pooled it, 158 citations in OpenAlex.
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- Cross-cohort insights into the association of handgrip strength transitions and burdens with cardiovascular disease risk.medRxiv : the preprint server for health sciences · 2026Article
- An integrative association analysis for complex diseases in underrepresented groups by leveraging the trans-ethnic genetic similarity.Briefings in bioinformatics · 2026Article
- GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Briefings in bioinformatics · 2026Article
- Multi-tissue transcriptome-wide association study identifies 29 risk genes associated with attention-deficit/hyperactivity disorder.medRxiv : the preprint server for health sciences · 2026Article
- FSBLUP: a novel strategy of fusion similarity matrix construction via optimally integrating intermediate omics data to enhance genomic prediction.Genome biology · 2026Article
- Exploring Contraindicated Medications and Corresponding Targeted Genes for Migraine Through Integrated Genetic Approaches.Brain and behavior · 2025Article
- Statistical construction of calibrated prediction intervals for polygenic score-based phenotype prediction.Nature genetics · 2025Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.bioRxiv : the preprint server for biology · 2025Article
- PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts.Genome medicine · 2025Article
- Proteome-wide association studies using summary pQTL data of brain, CSF, and plasma identify 30 risk genes of Alzheimer's disease dementia.Alzheimer's research & therapy · 2025Article
- Genetic insights into idiopathic pulmonary fibrosis: a multi-omics approach to identify potential therapeutic targets.Journal of translational medicine · 2025Article
- Influence and role of polygenic risk score in the development of 32 complex diseases.Journal of global health · 2025Article
- MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies.Genome biology · 2025Article
- Proteome-Wide Association Study for Finding Druggable Targets in Progression and Onset of Parkinson's Disease.CNS neuroscience & therapeutics · 2025Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly used prediction methods with simulations. We further apply Dirichlet process regression to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.Genetic prediction of complex traits with polygenic architecture has wide application from animal breeding to disease prevention. Here, Zeng and Zhou develop a non-parametric genetic prediction method based on latent Dirichlet Process regression models.
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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.