ArticleNature genetics2015
Efficient Bayesian mixed-model analysis increases association power in large cohorts.
Article in Nature genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 963 papers, 7 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
963 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Genetic landscape of adult executive function reveals a cell-type-specific developmental origin.Nature communications · 2026Pooled it
- Cross-ancestry genome-wide association studies of liver function biomarkers uncover pleiotropic variants, systemic disease links and therapeutic targets.Genome medicine · 2026Pooled it
- Large-scale genome-wide analyses with proteomics integration reveal novel loci and biological insights into frailty.Nature aging · 2025Pooled it
- Genome-wide association studies in a large Korean cohort identify quantitative trait loci for 36 traits and illuminate their genetic architectures.Nature communications · 2025Pooled it
- Cross-ancestry analyses of Chinese and European populations reveal insights into the genetic architecture and disease implication of metabolites.Cell genomics · 2025Pooled it
- Missense variants in FRS3 affect body mass index in populations of diverse ancestries.Nature communications · 2025Pooled it
- metaGE: Investigating genotype x environment interactions through GWAS meta-analysis.PLoS genetics · 2025Pooled it
- Germline Variation and Somatic Alterations in Ewing Sarcoma.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Translating functional molecular knowledge into crop-breeding success.Nature reviews. Genetics · 2026Review
- Decomposing population differences in type 2 diabetes through a high-resolution GWAS of ~600,000 individuals with East Asian ancestry.Communications biology · 2026Article
- Genome-wide mapping of common and rare variant effects on adiposity across childhood.Nature genetics · 2026Article
- Genetic mechanisms of mitotic error in 840,853 blood genomes.medRxiv : the preprint server for health sciences · 2026Article
- Glycemic and bodyweight effects ofScience advances · 2026Article
- Article
- X chromosome-wide association studies for quantitative trait loci based on the mixture of general pedigrees and additional unrelated individuals.Briefings in bioinformatics · 2026Article
- JanusX: an integrated and high-performance platform for scalable genome-wide association studies and genomic selection.The Plant journal : for cell and molecular biology · 2026Article
- TLS-Tractor: A transfer learning framework for incorporating summary-statistics into local ancestry-aware GWAS in admixed populations.medRxiv : the preprint server for health sciences · 2026Article
- Proteomic organ-specific signatures, ageing traits, disease risks and genetic architecture in an East Asian population.EBioMedicine · 2026Article
- Extending genome-wide association studies to admixed cohorts with high degrees of relatedness.Nature genetics · 2026Article
- Atlas of glomerular disease-specific genetic effects on blood transcriptome.medRxiv : the preprint server for health sciences · 2026Article
903 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding. However, existing methods are computationally intractable in large cohorts and may not optimize power. All existing methods require time cost O(MN(2)) (where N is the number of samples and M is the number of SNPs) and implicitly assume an infinitesimal genetic architecture in which effect sizes are normally distributed, which can limit power. Here we present a far more efficient mixed-model association method, BOLT-LMM, which requires only a small number of O(MN) time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes. We applied BOLT-LMM to 9 quantitative traits in 23,294 samples from the Women's Genome Health Study (WGHS) and observed significant increases in power, consistent with simulations. Theory and simulations show that the boost in power increases with cohort size, making BOLT-LMM appealing for genome-wide association studies in large cohorts.
Indexed as
Identifiers
What OpenQuestion holds
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.