SynthesisPLoS genetics2018
An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations.
Synthesis in PLoS genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 5 of them syntheses that pooled it.
What it found
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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
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Who cites it
26 citing papers in PubMed, 5 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Shared genetic architecture of gray matter deficits in schizophrenia and bipolar disorder: evidence from structural neuroimaging-genetic analyses.Psychological medicine · 2026Pooled it
- Bayesian meta-analysis of penetrance for cancer risk.Biometrics · 2024Pooled it
- Immune-related conditions and cancer-specific mortality among older adults with cancer in the United States.International journal of cancer · 2022Pooled it
- Subset testing and analysis of multiple phenotypes.Genetic epidemiology · 2019Pooled it
- Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort.Journal of medical genetics · 2018Pooled it
- Integrative genetic analysis reveals shared genetic architecture underlying coronary artery disease, CT-Defined coronary atherosclerosis, and cardiometabolic risk factors.Functional & integrative genomics · 2026Article
- Bayesian clustering with uncertain data.PLoS computational biology · 2024Article
- Shared Genetic Architecture Among Gastrointestinal Diseases, Schizophrenia, and Brain Subcortical Volumes.Schizophrenia bulletin · 2024Article
- GCPBayes pipeline: a tool for exploring pleiotropy at the gene level.NAR genomics and bioinformatics · 2023Article
- Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank.Cell genomics · 2023Article
- Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits.PLoS genetics · 2022Article
- mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights.Metabolites · 2022Article
- A Method for Improving the Prediction of Outpatient Visits for Hospital Management: Bayesian Autoregressive Analysis.Computational and mathematical methods in medicine · 2022Article
- Review
- Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies.Human molecular genetics · 2021Article
- Detecting local genetic correlations with scan statistics.Nature communications · 2021Article
- Patterns of Human Leukocyte Antigen Class I and Class II Associations and Cancer.Cancer research · 2021Article
- Clustering suicidal phenotypes and genetic associations with brain-derived neurotrophic factor in patients with substance use disorders.Translational psychiatry · 2021Article
- The effects of modified Buzhong Yiqi decoction combined with Gangtai ointment on the wound healing and anal function in circumferential mixed hemorrhoid patients.American journal of translational research · 2021Article
- Genetic feature engineering enables characterisation of shared risk factors in immune-mediated diseases.Genome medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.
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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.