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, ...)

Arguments

ld_score	Vector of LD scores.
ld_size	Number of variants used to compute `ld_score`.
chi2	Vector of chi-squared statistics.
sample_size	Sample size of GWAS corresponding to chi-squared 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 `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()`.
intercept	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`.
chi2_thr1	Threshold on `chi2` in step 1. Default is `30`. This is equivalent to parameter `--two-step`.
chi2_thr2	Threshold on `chi2` in step 2. Default is `Inf` (none).
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: `$beta`: effect size estimates `$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