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. Number of variants used to compute ld_score. Vector of chi-squared statistics. Sample size of GWAS corresponding to chi-squared statistics. Possibly a vector, or just a single value. Either a single number specifying the number of blocks, or a vector of integers specifying the block number of each chi2 value. Default is 200 for snp_ldsc(), dividing into 200 blocks of approximately equal size. NULL can also be used to skip estimating standard errors, which is the default for snp_ldsc2(). You can constrain the intercept to some value (e.g. 1). Default is NULL in snp_ldsc() (the intercept is estimated) and is 1 in snp_ldsc2() (the intercept is fixed to 1). This is equivalent to parameter --intercept-h2. Threshold on chi2 in step 1. Default is 30. This is equivalent to parameter --two-step. Threshold on chi2 in step 2. Default is Inf (none). Number of cores used. Default doesn't use parallelism. You may use nb_cores. Sparse correlation matrix. A data frame with 3 columns: $beta: effect size estimates $beta_se: standard errors of effect size estimates $n_eff: sample size when estimating beta (in the case of binary traits, this is 4 / (1 / n_control + 1 / n_case)) Arguments passed on to snp_ldsc ## Value Vector of 4 values (or only the first 2 if blocks = NULL): • [["int"]]: LDSC regression intercept, • [["int_se"]]: SE of this intercept, • [["h2"]]: LDSC regression estimate of (SNP) heritability (also see coef_to_liab), • [["h2_se"]]: SE of this heritability estimate. ## Examples bigsnp <- snp_attachExtdata() G <- bigsnp$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 0.2335429 snp_ldsc2(corr, df_beta, blocks = 20, intercept = NULL)
#>       int    int_se        h2     h2_se
#> 0.4986445 0.2526338 0.6195226 0.1818980