ArticlePLoS genetics2015
Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model.
Article in PLoS genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 226 papers, 2 of them syntheses that pooled it.
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Who cites it
226 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets.Nature communications · 2021Pooled it
- A systematic review of gene-by-intervention studies of alcohol and other substance use.Development and psychopathology · 2021Pooled it
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- A Multi-Context Regulome-Wide Association Atlas for Genetic Studies of Aging Brain Disorders.medRxiv : the preprint server for health sciences · 2026Article
- Principled measures and estimates of trait polygenicity.American journal of human genetics · 2026Article
- On the genetic origins of phenotypes in genome-wide association studies: the SAFE-hBMC bioinformatics · 2026Article
- Improving genomic prediction accuracy of complex traits by integrating massive types of functional annotation information.Nature communications · 2026Article
- Genome-wide fine-mapping improves identification of causal variants.Nature genetics · 2026Article
- A Flexible Method for Genomics-Based Quantitative Genetics in Wild Study Systems-A Case Study on a House Sparrow Meta-Population.Evolutionary applications · 2026Article
- GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Briefings in bioinformatics · 2026Article
- Opportunities and computational challenges in large-scale whole-genome sequencing data analysis.Journal of animal science · 2026Review
- Improving multibreed genomic prediction for breeds with small populations by modeling heterogeneous genetic (co)variance blockwise accounting for linkage disequilibrium.Journal of animal science and biotechnology · 2025Article
- Allelic variation in CYP3A4 and PLB1 drives feed efficiency and immunometabolic resilience in beef cattle.BMC genomics · 2025Article
- Genomic predictions for growth and feed efficiency traits in a duck breeding population.BMC genomics · 2025Article
- Effect of using preselected markers from imputed whole-genome sequence for genomic prediction in Angus cattle.Genetics, selection, evolution : GSE · 2025Article
- Ensemble of Bayesian alphabets via constraint weight optimization strategy improves genomic prediction accuracy.G3 (Bethesda, Md.) · 2025Article
- MINE: maximally informative next experiment-toward a new GWAS experimental design and methodology.G3 (Bethesda, Md.) · 2025Article
- Enhanced genetic fine mapping accuracy with Bayesian Linear Regression models in diverse genetic architectures.PLoS genetics · 2025Article
- Article
166 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.
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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.