ArticleThe annals of applied statistics2017
BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES.
Article in The annals of applied statistics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 2 of them syntheses that pooled it.
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Who cites it
113 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure.Briefings in bioinformatics · 2022Pooled it
- Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets.Nature communications · 2021Pooled it
- Genetic factor analysis for characterizing phenome-wide patterns of genetic pleiotropy.Nature genetics · 2026Article
- Causal inference for multiple risk factors and diseases from genomics data.Nature communications · 2026Article
- Constructing Genetic Risk Scores: Robust Bayesian Approach through Projected Summary Statistics and Flexible Shrinkage.Journal of the American Statistical Association · 2026Article
- Buffering of gene dosage response curves for human complex traits.Cell genomics · 2026Article
- Recalibrating Mendelian randomization under winner's curse, sample structure and polygenicity.medRxiv : the preprint server for health sciences · 2026Article
- Multi-tissue integrated Mendelian randomization method identifies disease risk genes.Briefings in bioinformatics · 2026Article
- Improved heritability partitioning and enrichment analyses using summary statistics with graphREML.Nature genetics · 2026Article
- Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping.Nature communications · 2026Article
- UK BioCoin: swift trait-specific summary statistics regression for UK Biobank.Nature communications · 2026Article
- Mapping disease loci to biological processes via joint pleiotropic and epigenomic partitioning.Cell genomics · 2026Article
- Inferring on Joint Associations From Marginal Associations and a Reference Sample.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Genome-wide fine-mapping improves identification of causal variants.Nature genetics · 2026Article
- Genetic architectures of brain-related traits are shaped by strong selective constraints.bioRxiv : the preprint server for biology · 2026Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.Cell reports methods · 2026Article
- scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.Nature communications · 2026Article
- Mind the gap: Characterizing bias due to population mismatch in two-sample Mendelian randomization.American journal of human genetics · 2026Article
- Rapid Evolution in a Coral Population Following a Mass Mortality Event.Evolutionary applications · 2026Article
- High-definition likelihood inference of genetic colocalization reveals protein biomarkers for human complex diseases.GigaScience · 2026Article
53 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
Abstract
Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a "Regression with Summary Statistics" (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss.
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