A gene-based test of association using canonical correlation analysis

Canonical correlation analysis (CCA) measures the association between two sets of multidimensional variables. We reasoned that CCA could provide an efficient and powerful approach for both univariate and multivariate gene-based tests of association without the need for permutation testing. Compared...

Full description

Saved in:
Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2012-03, Vol.28 (6), p.845-850
Main Authors: TANG, Clara S, FERREIRA, Manuel A. R
Format: Article
Language:English
Subjects:
Biological and medical sciences
Fundamental and applied biological sciences. Psychology
Gene Frequency
General aspects
Genome-Wide Association Study
Humans
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Multivariate Analysis
Quantitative Trait, Heritable
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Canonical correlation analysis (CCA) measures the association between two sets of multidimensional variables. We reasoned that CCA could provide an efficient and powerful approach for both univariate and multivariate gene-based tests of association without the need for permutation testing. Compared with a commonly used permutation-based approach, CCA (i) is faster; (ii) has appropriate type-I error rate for normally distributed quantitative traits; (iii) provides comparable power for small to medium-sized genes (
ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/bts051