ArticlePloS one2013
A unified framework for association analysis with multiple related phenotypes.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 156 papers, 6 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
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
156 citing papers in PubMed, 6 syntheses or guidelines pooled it, 276 citations in OpenAlex.
- A Bayesian hierarchically structured prior for gene-based association testing with multiple traits in genome-wide association studies.Genetic epidemiology · 2022Pooled it
- Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.Current protocols in human genetics · 2019Pooled it
- Multivariate genome-wide analyses of the well-being spectrum.Nature genetics · 2019Pooled it
- metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.Bioinformatics (Oxford, England) · 2016Pooled it
- Pooled it
- Pooled it
- Common alleles associated with connectivity within and across the brain's main functional networks.Nature communications · 2026Article
- ReverseGWAS identifies combined phenotypes associated with a genotype in GWA studies.Bioinformatics (Oxford, England) · 2026Article
- Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model.Nature genetics · 2026Article
- Mediated pleiotropy drives the negative correlation of total carotenoid and dry matter contents in cassava (Manihot esculenta).Genetics · 2025Article
- Beyond the Standard GWAS-A Guide for Plant Biologists.Plant & cell physiology · 2025Review
- A latent outcome variable approach for Mendelian randomization using the stochastic expectation maximization algorithm.Human genetics · 2025Article
- An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment.Cell genomics · 2025Article
- Multivariate Bayesian Analyses in Nursing Research: An Introductory Guide.Biological research for nursing · 2025Article
- Article
- Genome-Wide Association Study of Body Size Traits in Luning Chickens Using Whole-Genome Sequencing.Animals : an open access journal from MDPI · 2025Article
- Multivariate Bayesian variable selection for multi-trait genetic fine mapping.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Article
- GRAMMAR-Lambda Delivers Efficient Understanding of the Genetic Basis for Head Size in Catfish.Biology · 2025Article
- JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.American journal of human genetics · 2024Article
96 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
We consider the problem of assessing associations between multiple related outcome variables, and a single explanatory variable of interest. This problem arises in many settings, including genetic association studies, where the explanatory variable is genotype at a genetic variant. We outline a framework for conducting this type of analysis, based on Bayesian model comparison and model averaging for multivariate regressions. This framework unifies several common approaches to this problem, and includes both standard univariate and standard multivariate association tests as special cases. The framework also unifies the problems of testing for associations and explaining associations - that is, identifying which outcome variables are associated with genotype. This provides an alternative to the usual, but conceptually unsatisfying, approach of resorting to univariate tests when explaining and interpreting significant multivariate findings. The method is computationally tractable genome-wide for modest numbers of phenotypes (e.g. 5-10), and can be applied to summary data, without access to raw genotype and phenotype data. We illustrate the methods on both simulated examples, and to a genome-wide association study of blood lipid traits where we identify 18 potential novel genetic associations that were not identified by univariate analyses of the same data.
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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 Socratic graph.