A gene-based test of association using canonical correlation analysis

作者：Tang Clara S; Ferreira Manuel A R^*

来源：Bioinformatics, 2012, 28(6): 845-850.

DOI：10.1093/bioinformatics/bts051

Motivation: 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.
Results: 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 (< 100 kb); (iv) provides greater power when the causal variants are uncommon; (v) provides considerably less power for larger genes (>= 100 kb) when the causal variants have a broad minor allele frequency (MAF) spectrum. Application to a GWAS of leukocyte levels identified SAFB and a histone gene cluster as novel putative loci harboring multiple independent variants regulating lymphocyte and neutrophil counts.

• 出版日期2012-3-15
• 单位河北医科大学