g-LDSC V1.0.0

g-LDSC is a tool for estimating heritability and functional enrichment from GWAS summary statistics. g-LDSC is written under R-4.1.0.

Tutorial

g-LDSC is written under R-4.1.0. In this tutorial, we would give a demonstration of how g-LDSC is run under a Linux-based system.

Installation

Install gldsc (R package) via devtools

#install.packages('devtools')
devtools::install_github("xzw20046/gldsc")

Input file

3 files are required to run g-LDSC:

• GWAS summary statistics
• Pre-calculated (partitioned) LD score matrix
• g-LDSC function file

GWAS summary statistics

For GWAS summmary statistics, the required formate is shown as follows:

SNP A1 A2 N Z
rs1000000 G A 361194 1.5397
rs10000010 T C 361194 -0.850433
rs1000002 C T 361194 0.672368

To convert your GWAS result in such format, you could use munge_sumstats.py from ldsc.

Pre-calculated LD score matrix

This file contains the information of LD matrix and annotation information, to get this file, see tutorial of Calculate LD score matrix in the futher section.

g-LDSC function file

The R file functions.R that contain all g-LDSC functions.

Usage

Calculate LD score matrix

To calculate LD score matrix you could use the command shown as follow:

Rscript gldsc.run.R \
LDpath=ldblk_ukbb_eur \
annopath=/your file path/baseline \
mafpath=/your file path/1000G_frq \
function=mlfun.R \
out=/your out path/ \
cores=4

• LDpath This flag tells g-LDSC which LD matrix files to use in calculating LD score matrix. Under this folder all LD matrixs file should be in .hdf5 format. LD matrix of 1000G and UKBB could be download here.
• annopath & mafpath This two flag give the location of the annotation and MAF of SNPs. The input format here remain the same as .annot and .M_5_50in ldsc. Detail see here.
• out This flag tells g-LDSC where to print the the output.
• cores This flag tells g-LDSC how many cores you would like to use for parallel computing.

Output of LD score matrix calculation

This function will output a file called LDSM.pannel.Rdata. The size of this output file is approximately 10GB.

Estimate heritability and functional enrichment

You could use the command shown as follow:

Rscript gldsc.run.R \
panel=LDSM.pannel.Rdata \
gwas=BMI.sumstats \
function=mlfun.R \
out=/your out path/BMI \
cores=4 

Out put of heritability and functional enrichment estimation

The output of this process will return a data frame with rows represent the result of each functional annotation and columns shown as follow:

Taus  Partition_H2  Partition_H2_SD  Enrichment  Enrichment_SD  intercept  intercept_SD  e.stat  P  tau_SD   

• Taus annotation contributor
• Taus_SD standard error of annotation contributor
• Partition_H2 partitioned SNP-heritability
• Partition_H2_SD standard error of partitioned SNP-heritability
• Enrichment fold of enrichment
• Enrichment_SD jackknife standard error of fold of enrichment
• intercept confounding bias
• intercept_SD standard error of confounding bias
• e.stat t-statistics of function enrichment
• P P-value of function enrichment (t-test)

More information

Author: Zewei Xiong (the University of Hong Kong): xzw20046@163.com