qgg (version 1.1.6)

gscore: Genomic scoring based on single marker summary statistics

Description

Computes genomic predictions using single marker summary statistics and observed genotypes.

Usage

gscore(
  Glist = NULL,
  chr = NULL,
  bedfiles = NULL,
  bimfiles = NULL,
  famfiles = NULL,
  stat = NULL,
  fit = NULL,
  ids = NULL,
  scaleMarker = TRUE,
  scaleGRS = TRUE,
  impute = TRUE,
  msize = 100,
  ncores = 1,
  verbose = FALSE
)

Value

Returns the genomic scores based on the provided parameters.

Arguments

Glist

List of information about genotype matrix. Default is NULL.

chr

Chromosome for which genomic scores is computed. Default is NULL.

bedfiles

Names of the PLINK bed-files. Default is NULL.

bimfiles

Names of the PLINK bim-files. Default is NULL.

famfiles

Names of the PLINK fam-files. Default is NULL.

stat

Matrix of single marker effects. Default is NULL.

fit

Fit object output from gbayes. Default is NULL.

ids

Vector of individuals used in the analysis. Default is NULL.

scaleMarker

Logical; if TRUE the genotype markers are scaled to mean zero and variance one. Default is TRUE.

scaleGRS

Logical; if TRUE the GRS are scaled to mean zero and variance one. Default is TRUE.

impute

Logical; if TRUE, missing genotypes are set to its expected value (2*af where af is allele frequency). Default is TRUE.

msize

Number of genotype markers used for batch processing. Default is 100.

ncores

Number of cores used in the analysis. Default is 1.

verbose

Logical; if TRUE, more details are printed during optimization. Default is FALSE.

Author

Peter Soerensen

Examples

 ## Plink bed/bim/fam files
 bedfiles <- system.file("extdata", paste0("sample_chr",1:2,".bed"), package = "qgg")
 bimfiles <- system.file("extdata", paste0("sample_chr",1:2,".bim"), package = "qgg")
 famfiles <- system.file("extdata", paste0("sample_chr",1:2,".fam"), package = "qgg")
 
 # Summarize bed/bim/fam files
 Glist <- gprep(study="Example", bedfiles=bedfiles, bimfiles=bimfiles, famfiles=famfiles)
 
 # Simulate phenotype
 sim <- gsim(Glist=Glist, chr=1, nt=1)
 
 # Compute single marker summary statistics
 stat <- glma(y=sim$y, Glist=Glist, scale=FALSE)
 
 # Compute genomic scores
 gsc <- gscore(Glist = Glist, stat = stat)