ldscR

The ldscR package is designed for performing LDSC in R. LDSC remains the most popular and convenient method for estimating additive heritability. This package provides efficient functions that allow for the simultaneous estimation of the genetic covariance matrix across multiple traits, without applying the LDSC Python package (https://github.com/bulik/ldsc) using an entry-by-entry scheme. Additionally, we use block-wise subsampling to estimate the standard errors.

Installation

You can install the ldscR package directly from GitHub using the following command:

# Install the devtools package if you haven't already
if (!requireNamespace("devtools", quietly = TRUE))
  install.packages("devtools")

# Install ldscR from GitHub
devtools::install_github("harryyiheyang/ldscR")

Usage

Here's an example workflow using ldscR:

Data Preparation and Harmonization

First, download and prepare your GWAS summary data. Then, harmonize alleles using the filter_align() function:

library(ldscR)
data_url <- "http://tinyurl.com/nhdfwd8v"
temp_file <- tempfile()
download.file(data_url, temp_file, mode="wb")
gwaslist=readRDS(temp_file)
unlink(temp_file)
data("hapmap3")
gwaslist=filter_align(gwas_data_list=gwaslist,ref_panel=hapmap3[,c("SNP","A1","A2")])

Estimating the genetic covariance matrix and estimation error covariance matrix

data("EURLDSC")
fitldsc=ldscR(GWAS_List=gwaslist,LDSC=EURLDSC,estimate_SE=T)
Gcov=fitldsc$GCovEst
Ecov=fitldsc$ECovEst

Alternatively, we suggest setting estimate_SE=F for a quick result when exploring the genetic relationships between multiple traits.

License

This package is under the MIT License.

Contact

For any questions or issues, please contact Yihe Yang at [email protected]