Estimation of Pleiotropic Heritability from Genome-Wide Association Studies (GWAS) Summary Statistics
Compute single pleioh2g for target disease after correction with refer...
Compute single pleioh2g for target disease before correction with refe...
Compute a vector of pleioh2g for all diseases before correction This f...
Compute inversed elements for the target disease in bias correction pr...
Compute rg + h2g
genomic-block jackknife and compute rg + h2g
Generate samples based on sampling covariance matrix and rg matrix for...
Convert Heritability to Liability Scale
Estimate heritability - refer to ldscr R package (https://github.com/m...
Estimate cross-trait genetic correlations (Robust Version) - refer to ...
Internal Function to make weights - refer to ldscr R package (https://...
Merging summary statistics with LD-score files - refer to ldscr R pack...
Internal function to perform LDSC heritability/covariance analysis - r...
Compute pleioh2g after bias correction for target disease
Compute pleioh2g after bias correction for target disease
Compute pleioh2g before bias correction for target disease
Prune disease selection
Perform pruning in computing pleioh2g and correct bias
Read ld from either internal or external file - refer to ldscr R packa...
Read M from either internal or external file - refer to ldscr R packag...
Read summary statistics from either internal or external file - refer ...
Read wld from either internal or external file - refer to ldscr R pack...
Example munged dataframe - refer to ldscr R package (https://github.co...
Provides tools to compute unbiased pleiotropic heritability estimates of complex diseases from genome-wide association studies (GWAS) summary statistics. We estimate pleiotropic heritability from GWAS summary statistics by estimating the proportion of variance explained from an estimated genetic correlation matrix (Bulik-Sullivan et al. 2015 <doi:10.1038/ng.3406>) and employing a Monte-Carlo bias correction procedure to account for sampling noise in genetic correlation estimates.