search for: allele1

...ix(genfile) > snp.name <- scan("c:/tina/phd/bs871/hw/genfile.txt",nline=1,what="character") Read 100 items > n.snp <- length(snp.name) > n.id <- 1 #number of fields for ids, sex and affection status > > ###form gntp using the two alleles of each SNP > allele1 <- snp.dat[,seq(1,2*n.snp,2)+n.id ] > allele2 <- snp.dat[,seq(2,2*n.snp,2)+n.id ] > temp <- matrix(paste(allele1,allele2,sep="|"),dim(allele1)) > temp <- data.frame(temp) > convt <- function(x) x <- factor(as.character(x),exclude="0|0") > > g...

...data', it's easy to add these rows, but for 'gwaa at gtdata', I think I need to create SNP data as '0 0 0 0 0.....' for all the dummy parents first. I am using the function 'convert.snp.ped', so I need a 'pedfile' of this format: #ped id fa mo sex trait snp1.allele1 snp1.allele2 snp2.allele1 snp2.allele2 ...# 1 1 0 0 1 2 0 0 0 0 ... 1 2 0 0 1 0 0 0 0 0 ... 1 3 0 0 2 1 0 0 0 0 ... . . 100 101 0 0 2 1 0 0 0 0 ... If we use the 1M microarray, usually, after QC, there will be ~800 thousands SNPs, so this file is really huge. I created this matrix in R, and then...

...ar results in Statistica clicking on "Estimate" button instead of "1LR" (what does my supervisor which gives only log-likelihood and p of variables but doesn't estimate parameter). But there is still one problem I can't explain. When we fit interaction terms (site x allele1, site x allele2 and so on) the results are completely different. I tried several different contrasts in R, such as contr.SAS, contr.treatment etc but I couldn't get nothing similar to Statistica output. Have anybody any idea how to deal with that? Or how to explain why R results are differ...

...ect. Data: Genomics SNP information Goal: I need to add Chromosome and SNP position to the data frame I'm using through apply. I'd like to add new column from text processed through apply function. For example: 10:60523:T:G (Column 2) CHR: 10 Position: 60523 Dataset: chr rs ps n_miss allele1 allele0 af beta se l_remle p_wald -9 10:60523:T:G -9 0 T G 0.977 -1.769354e-02 3.597196e-02 1.566731e-01 6.228309e-01 -9 10:60684:A:C -9 0 A C 0.973 1.698925e-02 2.942366e-02 1.561001e-01 5.636926e-01 -9 10:61331:A:G -9 0 A G 0.973 1.708586e-02 2.942424e-02 1.560944e-01 5.614851e-01 -9 10:62010:C:T...

...t; For example: 10:60523:T:G (Column 2) > CHR: 10 > Position: 60523 Assuming Position is "P", what are your "SNP" and "BP" in the names you assigned below as c("SNP","P","CHR","BP")? > Dataset: > chr rs ps n_miss allele1 allele0 af beta se l_remle p_wald > -9 10:60523:T:G -9 0 T G 0.977 -1.769354e-02 3.597196e-02 1.566731e-01 6.228309e-01 > -9 10:60684:A:C -9 0 A C 0.973 1.698925e-02 2.942366e-02 1.561001e-01 5.636926e-01 > -9 10:61331:A:G -9 0 A G 0.973 1.708586e-02 2.942424e-02 1.560944e-01 5.614851e-01...

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...s one reshape genetic data from long to wide? I currently have a lot of data. About 180 individuals (some probands/patients, some parents, rare siblings) and SNP data from 6000 loci on each. The standard formats seem to be something along the lines of Famid, pid, fatid, motid, affected, sex, locus1Allele1, locus1Allele2, locus2Allele1, locus2Allele2, etc In other words one human, one row. If there were multiple loci then the variables would continue to be heaped up on the right. This kind of orientation, shall be referred to as "wide". Given how big my dataset is, it is easier to manage...