SynthesisBioinformatics (Oxford, England)2016
metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.
Synthesis in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 3 of them syntheses that pooled it.
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Who cites it
81 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles.Nature genetics · 2022Pooled it
- Multivariate analysis of genome-wide data to identify potential pleiotropic genes for five major psychiatric disorders using MetaCCA.Journal of affective disorders · 2019Pooled it
- Linking Alzheimer's disease and type 2 diabetes: Novel shared susceptibility genes detected by cFDR approach.Journal of the neurological sciences · 2017Pooled it
- Protein mediators of chronic kidney disease in Type 2 diabetes: A mendelian randomization study.PLoS medicine · 2026Article
- Mendelianization: Concentrating Polygenic Signal Into a Single Causal Locus.Genetic epidemiology · 2026Article
- Loss of Neuropeptide Y Signaling Accompanies the Neural-to-Mesenchymal Transcriptional Transition in Glioblastoma: A Multi-Scale Transcriptomic Analysis.International journal of molecular sciences · 2026Article
- Divided-and-combined association test for pleiotropic effects with GWAS summary statistics.BMC genomics · 2026Article
- Disentangling the Effect of BMI on Hepatocellular Carcinoma From Cirrhosis With Multivariable Mendelian Randomization.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Mendelian randomisation analysis to discover plasma metabolites mediating the effect of obesity on cancer risk.British journal of cancer · 2025Article
- Large-scale composite hypothesis testing procedure for omics data analyses.NAR genomics and bioinformatics · 2025Article
- Integrative proteogenomic analysis identifies COL6A3-derived endotrophin as a mediator of the effect of obesity on coronary artery disease.Nature genetics · 2025Article
- Exploring autism spectrum disorder and co-occurring trait associations to elucidate multivariate genetic mechanisms and insights.BMC psychiatry · 2024Article
- Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics.Computational and structural biotechnology journal · 2024Article
- Genome-wide association insights into the genomic regions controlling vegetative and oil production traits in Acrocomia aculeata.BMC plant biology · 2024Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants.Nature communications · 2024Article
- Multivariate canonical correlation analysis identifies additional genetic variants for chronic kidney disease.NPJ systems biology and applications · 2024Article
- Causal associations between modifiable risk factors and isolated REM sleep behavior disorder: a mendelian randomization study.Frontiers in neurology · 2024Article
- Joint regression analysis of multiple traits based on genetic relationships.Bioinformatics advances · 2024Article
- Rare variants with large effects provide functional insights into the pathology of migraine subtypes, with and without aura.Nature genetics · 2023Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
motivationA dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests.
resultsWe introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. AVAILABILITY AND IMPLEMENTATION: Code is available at https://github.com/aalto-ics-kepaco CONTACTS: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.