nullSim {QTLRel}R Documentation

Simulate null distribution

Description

Simulate distribution under the hypothesis of no QTL by permutation (of genotypic data) or gene dropping.

Usage

nullSim(y, x, gdat, prdat, ped, gmap, hap,
	method = c("permutation","gene dropping"), vc = NULL, intcovar = NULL,
	test = c("None","F","Chisq"), minorGenoFreq = 0.05, rmv = TRUE,
	gr = 2, ntimes = 10)

Arguments

y

A numeric vector or a numeric matrix of one column (representing a phenotype for instance).

x

A data frame or matrix, representing covariates if not missing.

gdat

Genotype data without missing values. Should be a matrix or a data frame, with each row representing an observation and each column a marker locus. Ignored in the case of gene dropping.

prdat

An object from genoProb, or in the same form.

ped

A pedigree, which is a data frame (id, sex, sire, dam, ...). In "sex", male should be "M", "Male" or 1, and female should be "F", "Female" or 2 (other than 0 and 1). If given, "generation" can be numeric 0, 1, 2, ... or non-numeric "F0", "F1", "F2", ... Note that 0 is reserved for missing values. Ignored in the case of permutation.

gmap

A genetic map. Should be data frame (snp, chr, dist, ...), where "snp" is the SNP (marker) name, "chr" is the chromosome where the "snp" is, and "dist" is the genetic distance in centi-Morgan (cM) from the left of the chromosome. Ignored in the case of permutation.

hap

Founders' haplotype data if not missing. Rows correspond to all founders, which should be in the first places in the pedigree ped, in the exact order and columns correspond to loci in the genetic map gmap in the exact order. For an individual, the haplotype should be (f1 m1 f2 m2 ...) where fi is the allele from father at the i-th locus and mi is the allele from mother at the i-th locus. Elements should be non-negative integers that are not larger than 16384. If missing, two founders with alleles 1 and 2 are assumed.

method

Permutation or gene dropping.

vc

An object from estVC or aicVC, or an estimated variance-covariance matrix induced by relatedness. The scan will assume no polygenic variation if vc is NULL.

intcovar

Covariates that interact with QTL.

test

"None", "F" or "Chisq".

minorGenoFreq

Specify the minimum tolerable minor genotype frequency at a scanning locus if gdat is used.

rmv

A logical variable. If true, then the scanning locus will be skipped if the minor genotype frequency at the locus is smaller than minorGenoFreq. Otherwise, the scanning process will stop and return with NULL.

gr

The generation under consideration.

ntimes

Number of simulations.

Details

Two methods considered here are permutation test and gene dropping test as described as follows.

Permutation test. Depending on the genome-scan, one can provide either gdat or prdat respectively corresponding to single-marker analysis or interval mapping. Then only arguments in scanOne are needed in addition to method and ntimes.

Gene dropping test. If prdat is provided, then gdat will be ignored. The procedure will first call genoSim to generate new genotype data and then call genoProb to generate data for Haley-Knott interval mapping. If prdat is not provided, then gdat should be provided. The procedure will generate new genotype data and scan the genome using these generated genotype data. Haldane mapping function is used to generate data.

Value

A vector of numbers of length ntimes if minorGenoFreq > 0 and rmv = TRUE, each element of which is maximum of the test statistic over the genome scan (so test should be "None"), or a matrix of ntimes rows, each row of which records a genome scan.

See Also

genoSim, genoProb and scanOne.

Examples

data(miscEx)

## Not run: 
# impute missing genotypes
pheno<- pdatF8[!is.na(pdatF8$bwt) & !is.na(pdatF8$sex),]
ii<- match(rownames(pheno), rownames(gdatF8))
geno<- gdatF8[ii,]
ii<- match(rownames(pheno), rownames(gmF8$AA))
v<- list(A=gmF8$AA[ii,ii], D=gmF8$DD[ii,ii])

gdatTmp<- genoImpute(geno, gmap=gmapF8, step=Inf,
   gr=8, na.str=NA)
# estimate variance components
o<- estVC(y=pheno$bwt, x=pheno$sex, v=v)

# scan marker loci & permutation
ex1<- nullSim(y=pheno$bwt, x=pheno$sex, gdat=gdatTmp,
	method="permutation", vc=o, ntimes=10)
dim(ex1)

# scan marker loci & gene dropping
ex2<- nullSim(y=pheno$bwt, x=pheno$sex, gdat=gdatTmp, ped=ped,
	gmap=gmapF8, method="gene", vc=o, ntimes=10)
dim(ex2)

# Haley-Knott method & permutation
gdtmp<- geno
   gdtmp<- replace(gdtmp,is.na(gdtmp),0)
prDat<- genoProb(gdat=gdtmp, gmap=gmapF8, step=Inf,
   gr=8, method="Haldane", msg=TRUE)
ex3<- nullSim(y=pheno$bwt, x=pheno$sex, prdat=prDat,
	method="permutation", vc=o, ntimes=10)
dim(ex3)

# Haley-Knott method & gene dropping
ex4<- nullSim(y=pheno$bwt, x=pheno$sex, prdat=prDat, ped=ped,
	gmap=gmapF8, method="gene", vc=o, gr=8, ntimes=10)
dim(ex4)

## End(Not run)
[Package QTLRel version 1.0 Index]