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Run LD Score Regression

Source: R/ldsc.R
ldsc.Rd

Estimates genetic covariance matrix (S), sampling covariance matrix (V), and intercept matrix (I) from GWAS summary statistics using LD Score Regression with block jackknife.

Usage

ldsc(
  traits,
  sample.prev,
  population.prev,
  ld,
  wld,
  trait.names = NULL,
  sep_weights = FALSE,
  chr = 22,
  n.blocks = 200,
  ldsc.log = NULL,
  stand = FALSE,
  select = FALSE,
  chisq.max = NA,
  parallel = TRUE,
  cores = NULL
)

Arguments

traits

Character vector of paths to .sumstats.gz files

sample.prev

Numeric vector of sample prevalences (NA for continuous traits)

population.prev

Numeric vector of population prevalences (NA for continuous)

ld

Path to LD score directory (containing <chr>.l2.ldscore.gz files)

wld

Path to weight LD score directory (defaults to ld)

trait.names

Character vector of trait names (defaults to V1, V2, ...)

sep_weights

Use separate weight LD scores (ignored in gsemr)

chr

Number of chromosomes (default 22)

n.blocks

Number of jackknife blocks (default 200)

ldsc.log

Log file path (ignored in gsemr)

stand

Standardize output (default FALSE)

select

Variable selection method (default FALSE)

chisq.max

Maximum chi-square filter (default NA = auto)

parallel

Use a parallel rayon worker pool for the per-pair regression loop (default TRUE). Set to FALSE to force single-threaded execution.

cores

Integer cap on the rayon pool size. When NULL (the default) rayon honours RAYON_NUM_THREADS if set, else it uses the number of logical cores reported by the OS. On many-core machines (32+) or when the underlying BLAS is multithreaded, set this explicitly to avoid oversubscribing CPUs with nested BLAS threads.

Value

A list with components:

S

Genetic covariance matrix (k x k)

V

Sampling covariance matrix of S (k* x k*, where k* = k(k+1)/2)

I

LDSC intercept matrix (k x k)

N

Sample size vector

m

Number of SNPs used

Examples

if (FALSE) { # \dontrun{
covstruc <- ldsc(
  traits = c("T1.sumstats.gz", "T2.sumstats.gz", "T3.sumstats.gz"),
  sample.prev = c(NA, NA, NA),
  population.prev = c(NA, NA, NA),
  ld = "eur_w_ld_chr/",
  wld = "eur_w_ld_chr/",
  trait.names = c("V1", "V2", "V3")
)
covstruc$S   # genetic covariance matrix
covstruc$I   # LDSC intercept matrix
} # }