Example 2
This example illustrates the impact of different LD estimates on the RSS results. Three types of estimated LD matrices are considered: cohort sample LD, shrinkage panel sample LD (Wen and Stephens 2010) and panel sample LD.
The summary-level data are computed from a simulated GWAS dataset. The simulation scheme is described in Section 4.1 of the RSS paper. Specifically, 10 "causal" SNPs are randomly drawn from 982 SNPs on chromosome 16 (WTCCC UK Blood Service Control Group), with effect sizes coming from N(0,1). The true PVE (SNP heritability) is 0.2.
Three types of LD estimates are considered:
- cohort sample LD:
the sample correlation matrix using genotypes in the cohort (WTCCC UK Blood Service Control Group) - shrinkage panel sample LD:
the shrinkage correlation matrix (Wen and Stephens 2010) using genotypes in the panel (WTCCC 1958 British Birth Cohort) - panel sample LD:
the sample correlation matrix using genotypes in the panel (WTCCC 1958 British Birth Cohort)
To reproduce results of Example 2, please use example2.m.
Before running example2.m, please make sure the MCMC subroutines of RSS are installed. See instructions here.
Step 1. Download the simulated summary-level data example2.mat and LD estimates genotype2.mat. Please contact us if you have trouble accessing these files.
The example2.mat contains the following data.
-
betahat: 982 by 1 vector, the single-SNP effect size estimate for each SNP -
se: 982 by 1 vector, the standard errors of the single-SNP effect size estimates -
Nsnp: 982 by 1 vector, the sample size of each SNP
The genotype2.mat contains three types of LD estimates.
-
cohort_R: cohort sample LD -
shrink_R: shrinkage panel sample LD -
panel_R: panel sample LD
Step 2. Fit three RSS-BVSR models with different LD matrices.
# cohort sample LD
[betasam, gammasam, hsam, logpisam, Naccept] = rss_bvsr(betahat, se, cohort_R, Nsnp, Ndraw, Nburn, Nthin);
# shrinkage panel sample LD
[betasam, gammasam, hsam, logpisam, Naccept] = rss_bvsr(betahat, se, shrink_R, Nsnp, Ndraw, Nburn, Nthin);
# panel sample LD
[betasam, gammasam, hsam, logpisam, Naccept] = rss_bvsr(betahat, se, panel_R, Nsnp, Ndraw, Nburn, Nthin);The simulations in Section 4.1 of the RSS paper are essentially "replications" of the example above. To facilitate reproducible research, we make the simulated datasets in Section 4.1 publicly available (rss_example2_data_*.tar.gz).
Each simulated dataset contains three files: genotype.txt, phenotype.txt and simulated_data.mat. The files genotype.txt and phenotype.txt are the genotype and phenotype files for GEMMA. The file simulated_data.mat contains three cells.
true_para = {pve, beta, gamma, sigma};
individual_data = {y, X};
summary_data = {betahat, se, Nsnp};Only the summary_data cell is used as the input for RSS methods.
The RSS methods also require an estimated LD matrix. The three types of LD matrices are provided in the file genotype2.mat.
After applying RSS methods to these simulated data, we obtain the following results.
True PVE = 0.2
True PVE = 0.02
True PVE = 0.002
