estimateSyy: Function to estimate correlations between phenotypic...

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

Description

This function computes phenotypic correlation matrix S_YY based on univariate summary statistics S_XY.

Usage

estimateSyy( S_XY )

Arguments

S_XY

Univariate summary statistics. Data frame with row names corresponding to SNP IDs (e.g., position or rs_id) and the following columns: - allele_0 - string composed of "A", "C", "G" or "T", - allele_1 - string composed of "A", "C", "G" or "T", - then, two columns for each trait (phenotypic variable) to be included in the analysis; in turn: 1) traitID_b with linear regression coefficients, 2) traitID_se with corresponding standard errors ("traitID" in the column name must be an ID of a trait specified by a user; do not use underscores "_" in trait IDs outside "_b"/"_se" in order for the IDs to be processed correctly).

Value

S_YY	Matrix containing correlations between traits given as input. Row and column names correspond to trait IDs.

Note

In practice, summary statistics of at least one chromosome should be used in order to ensure good quality of the estimate of phenotypic correlation structure.

Author(s)

Anna Cichonska

References

Cichonska et al. (2016) metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics, 32(13):1981-1989.

Examples

# Estimating correlations between 10 traits given their
# univariate summary statistics across 1000 SNPs
S_YY = estimateSyy( S_XY = S_XY_full_study1 )

# Viewing the resulting phenotypic correlation matrix
print( S_YY, digit = 3 )

metaCCA documentation built on Nov. 8, 2020, 10:58 p.m.