ArticleBMC proceedings2007
Genome-wide sparse canonical correlation of gene expression with genotypes.
Article in BMC proceedings, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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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.
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Who cites it
24 citing papers in PubMed, 69 citations in OpenAlex.
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- Structure-adaptive canonical correlation analysis for microbiome multi-omics data.Frontiers in genetics · 2024Article
- Uncover spatially informed variations for single-cell spatial transcriptomics with STew.Bioinformatics advances · 2024Article
- Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis.EMBO molecular medicine · 2021Article
- Multi-Omics Data Fusion for Cancer Molecular Subtyping Using Sparse Canonical Correlation Analysis.Frontiers in genetics · 2021Article
- Set-Wise Differential Interaction Between Copy Number Alterations and Gene Expressions of Lower-Grade Glioma Reveals Prognosis-Associated Pathways.Entropy (Basel, Switzerland) · 2020Article
- Permutation inference for canonical correlation analysis.NeuroImage · 2020Article
- Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities.Frontiers in genetics · 2019Review
- Multi-Table Differential Correlation Analysis of Neuroanatomical and Cognitive Interactions in Turner Syndrome.Neuroinformatics · 2018Article
- Article
- Sparse canonical correlation analysis between an alcohol biomarker and self-reported alcohol consumption.Communications in statistics: Simulation and computation · 2017Article
- From big data analysis to personalized medicine for all: challenges and opportunities.BMC medical genomics · 2015Review
- Canonical correlation analysis for gene-based pleiotropy discovery.PLoS computational biology · 2014Article
- Multivariate Gene Selection and Testing in Studying the Exposure Effects on a Gene Set.Statistics in biosciences · 2012Article
- Simulating systems genetics data with SysGenSIM.Bioinformatics (Oxford, England) · 2011Article
- Statistical approaches for the analysis of DNA methylation microarray data.Human genetics · 2011Review
- Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome.BMC bioinformatics · 2010Article
- Multivariate multi-way analysis of multi-source data.Bioinformatics (Oxford, England) · 2010Article
- Phenomics: the systematic study of phenotypes on a genome-wide scale.Neuroscience · 2009Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a growing interest in studying natural variation in human gene expression. Studies mapping genetic determinants of expression profiles are often carried out considering the expression of one gene at a time, an approach that is computationally intensive and may be prone to high false-discovery rate because the number of genes under consideration often exceeds tens of thousands. We present an exploratory method for investigating such data and apply it to the data provided as Problem 1 of Genetic Analysis Workshop 15 (GAW15). In multivariate analysis, canonical correlation analysis is a common way to inspect the relationship between two sets of variables based on their correlation. It determines linear combinations of all variables from each data set such that the correlation between the two linear combinations is maximized. However, due to the large number of genes, linear combinations involving all single-nucleotide polymorphism (SNP) loci and gene expression phenotypes lack biological plausibility and interpretability. We introduce sparse canonical correlation analysis, which examines the relationships of many genetic loci and gene expression phenotypes by providing sparse linear combinations that include only a small subset of loci and gene expression phenotypes. These correlated sets of variables are sufficiently small for biological interpretability and further investigation. Applying this method to the GAW15 Problem 1 data, we identified groups of 41 loci and 150 gene expressions with the highest between-group correlation of 43%.
Identifiers
What OpenQuestion holds
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.