simcrop: 'Simulate QTL phenotype with normal distribution'

In jyanglab/g3tools: For Genomics, quantGen, and popGen studies, especially for crop species.

Description

Simulate crop phenotype using real genotype data for given number of QTLs, heritability. The SNP effect can be drawn from normal and gamma distributions.

Usage

simcrop(snpdf, h2, nqtl, distribution = "norm", usegcta = TRUE,
  gctapar = c("test", "snplist.txt", 1, "outfile.txt"))

Arguments

snpdf	A data.frame contains snpid information, must contain col: snpid. [data.frame].
h2	Broad sense heritability of the trait. [numeric(0-1)].
nqtl	number of QTL. [interger].
distribution	Distribution of the effects. [character=norm/gamma]
usegcta	For large set of SNP data, use external software GCTA to rapid simulate it. [TRUE/FALSE].
gctapar	If usegcta==TRUE, here is the parameters passed to the gcta command. [vector] 1. bfile, PLINK format 2. path for causal.snpfile 3. replication number 4. path for output file.

Value

return A list of many values or a script to gcta command.

Examples

geno <- read.table("data/geno.txt", header=TRUE)
simcrop <- function(snpid, h2, nqtl, distribution="norm", usegcta=TRUE,
                    gctapar=c("test", "snplist.txt", 1, "outfile.txt"))
y <- pheno[['y']]

gcta64  --bfile test  --simu-qt  --simu-causal-loci causal.snplist  --simu-hsq 0.5
--simu-rep 3  --keep test.indi.list --out test

jyanglab/g3tools documentation built on May 20, 2019, 6:27 a.m.