LD score regression
snp_ldsc( ld_score, ld_size, chi2, sample_size, blocks = 200, intercept = NULL, chi2_thr1 = 30, chi2_thr2 = Inf, ncores = 1 ) snp_ldsc2(corr, df_beta, blocks = NULL, intercept = 1, ...)
ld_score  Vector of LD scores. 

ld_size  Number of variants used to compute 
chi2  Vector of chisquared statistics. 
sample_size  Sample size of GWAS corresponding to chisquared statistics. Possibly a vector, or just a single value. 
blocks  Either a single number specifying the number of blocks,
or a vector of integers specifying the block number of each 
intercept  You can constrain the intercept to some value (e.g. 1).
Default is 
chi2_thr1  Threshold on 
chi2_thr2  Threshold on 
ncores  Number of cores used. Default doesn't use parallelism. You may use nb_cores. 
corr  Sparse correlation matrix. 
df_beta  A data frame with 3 columns:

...  Arguments passed on to 
Vector of 4 values (or only the first 2 if blocks = NULL
):
[["int"]]
: LDSC regression intercept,
[["int_se"]]
: SE of this intercept,
[["h2"]]
: LDSC regression estimate of (SNP) heritability (also see
coef_to_liab),
[["h2_se"]]
: SE of this heritability estimate.
bigsnp < snp_attachExtdata() G < bigsnp$genotypes y < bigsnp$fam$affection  1 corr < snp_cor(G, ind.col = 1:1000) gwas < big_univLogReg(G, y, ind.col = 1:1000) df_beta < data.frame(beta = gwas$estim, beta_se = gwas$std.err, n_eff = 4 / (1 / sum(y == 0) + 1 / sum(y == 1))) snp_ldsc2(corr, df_beta)#> int h2 #> 1.0000000 0.2335429snp_ldsc2(corr, df_beta, blocks = 20, intercept = NULL)#> int int_se h2 h2_se #> 0.4986445 0.2526338 0.6195226 0.1818980