Hon-Cheong SO

The R package SumVg provides estimates of the sum of heritability explained by all true susceptibility variants in GWAS. We have also recently derived methods to estimate the standard error (SE) based on re-sampling approaches. These methods have been implemented in the R package SumVg. Please also refer to SumVg.pdf for a detailed documentation of the program.

To install the SumVg R package from GitHub using devtools, run

devtools::install_github("lab-hcso/SumVg")

To install the SumVg R package by file,

downloading the tar file SumVg_1.0.tar.gz from GitHub, run

install.packages("C:/SumVg_1.0.tar.gz", repos=NULL)

library(SumVg)

## Simulate z-statistics under the complete null for testing
zall = rnorm(n=10000, mean=0, sd = 1)

## Examples using delete-d-jackknife 
## Assume the outcome is continuous
SumVg(zall=zall, totalN=10000, method="jack", d=2000, repl=5,out="unconditional") 

## Assume the outcome is binary
## SE and MAF are made up for reference only
SumVg.binary(zall=zall, method="paraboot", d=1, repl=5, out="unconditional", SE= rep(0.1,10000), K=0.01, MAF=rep(0.2, 10000) )

To cite this project in publications use:

• H.-C. So, et al., “Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study,” Genetic Epidemiology, vol. 35, no. 6, 2011, pp. 447-456; DOI https://doi.org/10.1002/gepi.20593.

• So, H. C., Xue, X. & Sham, P. C. (2023). SumVg: Total heritability explained by all variants in genome-wide association studies based on summary statistics with standard error estimates