yu-1011 / cov-ldsc

cov-LDSC

covariate-adjusted LD score regression (cov-LDSC) is a method to provide robust hg2 estimates from GWAS summary statistics and in-sample LD estimates in admixed populations. Check out the latest preprint of cov-LDSC on bioRxiv.

Repo Contents

• ldscore: scripts used for running cov-LDSC to obtain LD scores
• example: example files used in demo
• manuscript: data and scripts to reproduce the result in the manuscript

Getting Started

To download cov-ldsc you should clone this repository via the command

git clone https://github.com/immunogenomics/cov-ldsc.git

Estimating LD score

To obtain LD scores of your genotype data, the first step is to calculate global principal components (PCs) from the raw whole-genome genotypes. Different methods can be applied to measure PCs (e.g PLINK, EIGENSOFT). We applied EIGENSOFT (Patterson et al. 2006) on whole genome LD pruned data.

The PC files should be formatted that the first two columns of the covariance file are family IDs and individual IDs and the subsequent columns are the covariates that you want to include in adjusting LD. E.g.

HG00551 HG00551 -0.0478 -0.0194 -0.0485 0.0113 0.0383 -0.0785 0.1377 -0.0498 -0.0785 0.0798
HG00553 HG00553 -0.0465 0.0597 -0.0462 0.0082 -0.0131 0.0067 0.0053 0.0502 -0.1537 0.0290
...

When estimating LD scores in an admixed population, one should include the genome-wide PCs with the flags --cov. We recommend to use 20cM for LD window size (--ld-wind-cm). For computational efficiency, we recommend to split the whole genome to chromosomes and run multiple chromosomes in parallel.

Demo

We used 1000 Genome Project Phase III Admixed American (AMR) chromosome 21 data as an example. We provide a pre-generated 10PC file on .example/AMR_chr21/AMR.evec. To get cov-adjusted LD scores, run the following command:

python ldsc.py --bfile example/AMR_chr21/AMR_chr21_cm --l2 --ld-wind-cm 20 --cov example/AMR_chr21/AMR.evec --out example/AMR_chr21/AMR_chr21_20cm_covldsc

There are four output files: .M files contain number of SNPs in LD score calculation; .M_5_50 is the number of SNPs with MAF >5% in LD score calculation; .ldscore.gz files have per-SNPs LD score; and .log files are the summary files.

Below is the summary file for the example we used and the run time is around 3 minutes on local machine:

*********************************************************************
* covariate-adjusted LD Score Regression (cov-LDSC)
* Version 1.0.0
# (C) 2018 Yang Luo and Xinyi Li
* Based original LDSC: 2014-2015 Brendan Bulik-Sullivan and Hilary Finucane
* https://github.com/bulik/ldsc/wiki
* GNU General Public License v3
*********************************************************************
Call:
./ldsc.py \
--ld-wind-cm 20.0 \
--out example/AMR_chr21/AMR_chr21_20cm_covldsc \
--bfile example/AMR_chr21/AMR_chr21_cm \
--cov example/AMR_chr21/AMR.evec \
--l2  

Beginning analysis at Thu Jan 10 15:16:20 2019
Read list of 15632 SNPs from example/AMR_chr21/AMR_chr21_cm.bim
Read list of 347 individuals from example/AMR_chr21/AMR_chr21_cm.fam
Reading genotypes from example/AMR_chr21/AMR_chr21_cm.bed
After filtering, 15632 SNPs remain
Estimating LD Score.
Writing LD Scores for 15632 SNPs to example/AMR_chr21/AMR_chr21_20cm_covldsc.l2.ldscore.gz

Summary of LD Scores in example/AMR_chr21/AMR_chr21_20cm_covldsc.l2.ldscore.gz
         MAF        L2
mean  0.2584   22.8973
std   0.1302   14.2304
min   0.0504    0.4198
25%   0.1441   13.3488
50%   0.2464   19.3735
75%   0.3703   28.4681
max   0.5000  106.4251

MAF/LD Score Correlation Matrix
       MAF     L2
MAF  1.000  0.264
L2   0.264  1.000
Analysis finished at Thu Jan 10 15:19:30 2019
Total time elapsed: 3.0m:10.28s

Heritability estimation

For estimating heritability in admixed populations, you can follow the same command used in original LD score regression. Refer to instructions of original LD score regression : https://github.com/bulik/ldsc/wiki

Prerequisites

1. Python (3 > version >= 2.7)
2. bitarray: 0.8
3. numpy: 1.12
4. pandas: 0.20
5. scipy: 0.18

License

Citations

For citing code or the paper, please use the bioRxiv