synthetic.gwas: Simulate synthetic genotypes

In lgl15/cnmtf: Prioritisation of single nucleotide variants using Corrected non-negative matrix tri-factorisation

Description

Function to create synthetic GWAS data sets

Usage

synthetic.gwas(n.snps = 10, n.pats = 100, n.pops = 3, p.pops = c(0.25,
  0.25, 0.5), c.pops = c(0.2, 0.2, 0.2), r.pops = c(1.5, 1, 1.5),
  fst = 0.01, allele.freq.model = c("Balding-Nichols", "fitted"),
  pl.pop = c(0.1, 0.1, 0.9), prefix.snp = "snp")

Arguments

n.snps	number of SNPs in the synthetic dataset
n.pats	number of patients in the synthetic dataset
n.pops	number of patient populations in the synthetic dataset
p.pops	proportion of patients in each population
c.pops	proportion of cases in each patient population
fst	Factor of differentiation between populations if allele.freq.model == "Balding-Nichols" .
allele.freq.model	define the model to generate the allele frequency c("Balding-Nichols","fitted") .
pl.pop	vector of fitted allele frequencies in each population if allele.freq.model == "fitted" .
prefix.snp	prefix to name the SNP columns
r	risk factor vector ( i.e., genotype relative risks) to generate the genotypes for cases/controls in each population
pcases	proportion of cases

Value

• db.g synthetic GWAS data set

• status disease status of the patient

• pop population ancestry of the patient

• snp.risk genotype relative risk for each SNP

Author(s)

Luis G. Leal, [e-mail]

References

• Mingyao Li, et al. (2010) "Correcting population stratification in GWAS using a phylogenetic approach" Bioinformatics

• Alkes Price, et al. (2006) "Principal components analysis corrects for stratification in genome-wide association studies" Nature genetics

See Also

Other Factorisation functions: clus.membership, cnmtf, consensus.clust, hierarchical.clust, initialise.UV, neg.constrain, parameters.cnmtf, plot.parameter, pos.constrain, psvd.init, regression.snps, score.cnmtf

lgl15/cnmtf documentation built on May 28, 2019, 6:33 p.m.