ArticlePLoS computational biology2014
Canonical correlation analysis for gene-based pleiotropy discovery.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Multivariate analysis of genome-wide data to identify potential pleiotropic genes for five major psychiatric disorders using MetaCCA.Journal of affective disorders · 2019Pooled it
- A kernel density estimation-based approach for quantifying O-GlcNAcylation dysregulation in cancer from gene expression data.Bioinformatics advances · 2026Article
- Multivariate canonical correlation analysis identifies additional genetic variants for chronic kidney disease.NPJ systems biology and applications · 2024Article
- CAT Bridge: an efficient toolkit for gene-metabolite association mining from multiomics data.GigaScience · 2024Article
- Pleiotropic genetic association analysis with multiple phenotypes using multivariate response best-subset selection.BMC genomics · 2023Article
- Fair Canonical Correlation Analysis.Advances in neural information processing systems · 2023Article
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes.The international journal of biostatistics · 2023Article
- In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model.Biomolecules · 2022Article
- Exploring the Pleiotropic Genes and Therapeutic Targets Associated with Heart Failure and Chronic Kidney Disease by Integrating metaCCA and SGLT2 Inhibitors' Target Prediction.BioMed research international · 2021Article
- Identification of 67 Pleiotropic Genes Associated With Seven Autoimmune/Autoinflammatory Diseases Using Multivariate Statistical Analysis.Frontiers in immunology · 2020Article
- Pleiotropy informed adaptive association test of multiple traits using genome-wide association study summary data.Biometrics · 2019Article
- Integrate multiple traits to detect novel trait-gene association using GWAS summary data with an adaptive test approach.Bioinformatics (Oxford, England) · 2019Article
- Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data.Bioinformatics (Oxford, England) · 2019Article
- Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits.Computational and mathematical methods in medicine · 2018Article
- Statistical methods to detect pleiotropy in human complex traits.Open biology · 2017Review
- A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data.PLoS computational biology · 2017Article
- Genome-wide association test of multiple continuous traits using imputed SNPs.Statistics and its interface · 2017Article
- An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.Human heredity · 2016Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy) and for testing multiple variants for association with a single phenotype (gene-based association tests). Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA) measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women's Heart and Health Study), we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1) with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.
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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.