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metaCCA (version 1.0.2)

Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis

Description

metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA 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.

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Version

Version

1.0.2

License

MIT + file LICENSE

Maintainer

Anna Cichonska

Last Published

February 15th, 2017

Functions in metaCCA (1.0.2)

S_XY_full_study1

Univariate summary statistics of 10 traits across 1000 SNPs (study 1).
S_XY_full_study2

Univariate summary statistics of 10 traits across 1000 SNPs (study 2).
N2

Number of individuals in study 2.
metaCcaPlusGp

Function to perform genotype-phenotype association analysis according to metaCCA+ algorithm.
S_XX_study1

Correlations between 10 SNPs corresponding to the population underlying study 1.
S_XY_study1

Univariate summary statistics of 10 traits across 10 SNPs (study 1).
S_XX_study2

Correlations between 10 SNPs corresponding to the population underlying study 2.
estimateSyy

Function to estimate correlations between phenotypic variables from summary statistics
metaCcaGp

Function to perform genotype-phenotype association analysis according to metaCCA algorithm.
N1

Number of individuals in study 1.
S_XY_study2

Univariate summary statistics of 10 traits across 10 SNPs (study 2).