ArticlePLoS genetics2019
Bayesian multivariate reanalysis of large genetic studies identifies many new associations.
Article in PLoS genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genetic basis of lacunar stroke: a pooled analysis of individual patient data and genome-wide association studies.The Lancet. Neurology · 2021Pooled it
- Article
- Genome-wide association of tau neuroimaging and plasma biomarkers in adults with Down syndrome.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Article
- A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes.PLoS genetics · 2023Article
- Multivariate genome-wide association analysis by iterative hard thresholding.Bioinformatics (Oxford, England) · 2023Article
- Review
- Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries.American journal of human genetics · 2022Article
- Fine-mapping genetic associations.Human molecular genetics · 2020Review
- Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases.American journal of human genetics · 2020Article
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Authors and funding
2 authors.
Funding
Abstract
Genome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is despite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.
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