search for: nsnps

...types, each pair of haplotypes forms a genotype, and each column corresponds to a SNP. I'm using resampling to compute the null distribution of the maximum over correlated SNPs of a simple statistic. The code: #------------------------------------------------------------------------------- nSNPs <- 1000 H <- matrix(sample(0:1, 120*nSNPs , replace=T), nrow=120) G <- matrix(0, nrow=3, ncol=nSNPs) # Keep in mind that the real H is 120 x 65000 nResamples <- 3000 pair <- replicate(nResamples, sample(1:120, 2)) gen <- function(x){g <- sum(x); c(g==0, g==1, g==2)} for (i i...

...woutput.txt") # loading data and bringing in GenABEL format rawfile="simulation.raw" convert.snp.ped(pedfile, mapfile, rawfile) simulation.GenABEL = load.gwaa.data(phenofile = phenofil, genofile = rawfile, force=F,makemap=F,sort=F) pedigree=read.table(pedfile) pedsize=nrow(pedigree) nsnps=(ncol(pedigree)-6)/2 # minor allele count and handling missing genotype data allelic = function(k){ geno=pedigree[,(5+2*k):(6+2*k)] allelic=rowSums(geno==2)-(geno[,1]==0 & geno[,2]==0) # -1 for missing, 0,1,2 gives count of variant allele } # preparing MB-MDR SNPS = matrix(0,nrow=p...

I am using dgc.genetics to perform TDT analysis on SNP data from a cohort of trios. I now have a file with about 6008 variables. The first few variables related to the pedigree data such as the pedigree ID the person ID etc. Thereafter each variable is a specific locus or marker. The variables are named by a pattern such as "Genotype.nnnnn" with nnnnn corresponding to a number which

...4 on a Linux x86_64 Redhat cluster system. When I log in, based on the specs I provide [qsub -I -X -l arch=x86_64] I am randomly assigned to a x86_64 node. I am using package GenABEL. My data (~ 650,000 SNPs, 3,000 people) loads in okay and I am able to look at the data using basic commands [nids, nsnps, names(phdata)] The problem occurs when I try to run the extended analysis: xs <- mlreg(GASurv(age,dm2)~sex,dta) ****************** 1) I have looked through the memory limits on R mem.limits() nsize vsize NA NA 2) Code: gc() used (Mb) gc trigger (Mb) max used (Mb) Ncell...

...<- rbind(value, new) assign("srcinfo", value, entry) Apply this "fix" would result in snpMatrix's "R CMD check" churning out: --------------------- .ld.withmany: local variable ?names.components? assigned but may not be used .ld.withmany: local variable ?nsnps.for.each? assigned but may not be used misinherits: local variable ?nc.snps? assigned but may not be used misinherits: local variable ?nr.snps? assigned but may not be used qq.chisq: local variable ?lab? assigned but may not be used read.HapMap.data: local variable ?base? assigned but may not be us...

...dure multiple times (~1000) permuting the cases and controls (affection status). It seems straightforward to implement it like this: ############################################# for (iter in 1:1000) { set.seed(iter) # get the permuted affection status permut <- sample(affSt) for (j in 1:nSNPs) { test <- tapply(all.geno[[j]], permut, HWE.exact) pvalControls[j] <- test$"1"$p.value pvalCases[j] <- test$"2"$p.value } } ############################################## The problem is that it takes ~1 min/iteration (on AMD Opteron 252 processor runnin...

...with possible haplotype configurations for each subject weighted by their posterior probabilities given genotype data. Are your markers SNPs? If so you can use a utility function from the hapassoc package to get started. For example, if your data is in a dataframe dat, with nsnp SNPs in the last nsnps columns, you could create an augmented data frame (augmented by pseudo-individuals for each subject with ambiguous phase) with library(hapassoc) ph<-pre.hapassoc(dat,nsnps) augdat<-cbind(ph$nonHaploDM,ph$haploDM) wts<-ph$wt and then use coxph with augdat as the data frme and wts as the...