metaCCA (version 1.0.2)

estimateSyy: Function to estimate correlations between phenotypic variables from summary statistics

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.

References

Cichonska et al. (2016) metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics, btw052 (in press, to be updated).

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 )