search for: snps

Hi, The program below work very well. (snps = c('rs621782_G', 'rs8087639_G', 'rs8094221_T', 'rs7227515_A', 'rs537202_C')) Selec = todos[ , colnames(todos) %in% snps] head(Selec) But, I have a data set with 1.000 columns and I need extract 70 to use (like snps in command above). This 70 snps are in a...

...ten me to a better solution. I have data frame of chromosome/position pairs (along with other data for the location). For each pair I need to determine if it is with in a given data frame of ranges. I need to keep only the pairs that are within any of the ranges for further processing. Example: snps<-NULL snps$CHR<-c("1","2","2","3","X") snps$POS<-as.integer(c(295,640,670,100,1100)) snps$DAT<-seq(1:length(snps$CHR)) snps<-as.data.frame(snps, stringsAsFactors=FALSE) snps CHR POS DAT 1 1 295 1 2 2 640 2 3 2 670...

Hi, Following my earlier posts about having problems performing a PCA, I have worked out what the problem is. The problem lies within the PLINK to gds conversion. It seems as though the SNPs are imported as "samples" and in turn, the samples are recognised as SNPs: >snpsgdsSummary("chr2L") Some values of snp.position are invalid (should be > 0)! Some values of snp.chromosome are invalid (should be finite and >=1)! Some of snp.allele are not standard! E.g,...

...have a solution that works, but it doesn't seem as simple as it should be from a user's point of view, nor does it seem robust to errors. What should I do better? More details: My package is a relatively simple set of plotting functions, that plot user supplied data across a number of SNPs from their experiment, and annotates the plot using some external data. For example, the user supplies SNP value A 2 B 1.2 C 7.8 etc The external datasets will allow me to look up the location of SNPs A, B, C, ... on the human genome so the user's data can be plotted in relation to that...

If I run an analysis which generates statistical tests on many SNPs I would naturally want to get more details on the most significant SNPs. Directly from within R one can get the information by loading RSNPer (from Bioconductor) and simply issuing a command SNPinfo(2073285). Unfortunately, the command cannot handle a vector and therefore only wants to do one at a...

Hello, I'm (eventually) attempting a singular value decomposition of a 3200 x 527829 matrix in R version 2.10.1. The script is as follows: ###---------Begin Script here-------### library(Matrix) snps <- 527829 ## Number of SNPs N <- 3200 ## Sample size y <- rnorm(N, 100,1) ## simulated phenotype system.time( ## read in matrix 3200 x 527829 x <- scan("gedi7.raw", what=rep(0,snps), nmax=N*snps, skip=1)) system.time(x &lt...

...uot; "spl.2mb" "spl.5mb" > > names(missgp2) [1] "spl.1mb" "spl.2mb" "spl.5mb" > ( In case it matters the functions accept and return one argument: block.size <- spl.1mb(ic) ) Then, I have 2 data frames with identical structure: > snps.missgp intvl.mb ic 1 1e+06 0.597 2 2e+06 0.504 3 5e+06 0.327 4 1e+07 0.204 > > strs.missgp intvl.mb ic 1 1e+06 0.67200 2 2e+06 0.62325 3 5e+06 0.51000 4 1e+07 0.38775 > I would like to apply the functions on these data frames piece-wise and create a...

...output.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 a using plink on a large SNP dataset with a .map and .ped file. I want to get some sort of file say a list of all the SNPs that plink is saying that I have. ANyideas on how to do this? -- Thanks, Jim. [[alternative HTML version deleted]]

Hi All, I have a problem with weighted logistic regression. I have a number of SNPs and a case/control scenario, but not all genotypes are as "guaranteed" as others, so I am using weights to downsample the importance of individuals whose genotype has been heavily "inferred". My data is quite big, but with a dummy example: > status <- c(1,1,1,0,0) >...

snpMatrix package is quite nice (read.plink())

...only reason for Microsoft Access is that I have had well over a decade's worth of experience using it at an intermediate level. With the magic of SQL I can mix and match these tables. But creating the table that is 180 rows long and about 12010 variables wide is daunting. Essentially the 6000 SNPs represent each human having 12000 repeated measures (6000SNPs times 2 alleles) I presume I would be able to use the reshape function in R: "Reshape Grouped Data Description This function reshapes a data frame between 'wide' format with repeated measurements in separate columns of the...

Hello, I did a pca with over 200000 snps for 340 observations (ids). If I plot the eigenvectors (called rotation in prcomp) 2,3 and 4 (e.g. plot (rotation[,2]) I see a strange "column" in my data (see attachment). I suggest it is an artefact (but of what?). Suggestion: I used prcomp this way: prcomp (mat), where mat is a matrix...

Dear Mme/Mr. Hope you are doing well. I am doing some genetic analysis using The R software and I have difficulties to find how I can perform an Interaction/epistasis analysis using 3 or more SNPs (=markers) ? (In the instructive manual, there is only an interaction/epistasis analysis with 2 markers). In addition can you please inform me how I can perform Haplotype analysis and if there is an option for the haplotype interaction? Thanks in advance and best regards, Fathalli ferid MD.

Hi, Does anybody know how to obtain the imputed SNP genotype probabilities from the snpStats package? I am interested in using an imputation method implemented in R to be further used in a simulation study context. I have found the snpStats package that seems to contain suitable functions to do so. As far as I could find out from the package vignette examples and its help, it give...

Hi, I am using the sim1000G R package to simulate data for case/control study. I can not figure out how to manipulate this code to be able to generate 10% or 50% causal SNPs in R. This is whole code provided as example on GitHub: library(sim1000G) vcf_file = "region-chr4-357-ANK2.vcf.gz" #nvariants = 442, ss=1000 vcf = readVCF( vcf_file, maxNumberOfVariants = 442 ,min_maf = 0.0005,max_maf = 0.01) #lowest MAF dim( vcf$gt1 ) #rows represent number of varian...

...lt;- 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...

...do what the user wants (the LSB_JOBINDEX stuff is added by me so that this can work on many hundreds of input data files as LSF jobs - it's the nested loops I'm really interested in): gtpfile<-paste("genotype_chunk.", sep="", Sys.getenv("LSB_JOBINDEX")) snps<-read.table(gtpfile,header=TRUE) exp<-read.table("data.TXT",header=TRUE) # the above two files have columns for individuals and snps (or genes) for rows # so the next two lines simply transpose these data matrices tsnps<-t(snps) texp<-t(exp) sink(paste("output.", se...

Hello, I have indicators for the present of absent of a snps in columns and the categorey (case control column). I would like to extract ONLY the tables and the indices (SNPS) that give me 2 x 3 tables. Some gives 2x 2 tables when one of the allelle is missing. The data look like the matrix snpmat below: so the first snp should give me the following table: (...

...run each interaction ~10,000 times. Thanks in advance, Cheers Davy. getInteractions2 = function(data, fSNPcol, ccCol) { #fSNPcol is the number of the column that contains the first SNP #ccCol is the number of the column that contains the outcome variable require(lmtest) a = data.frame() snps = names(data)[-1:-(fSNPcol-1)] names(data)[ccCol] = "PHENOTYPE" terms = as.data.frame(t(combn(snps,2))) attach(data) fit1 = c() fit2 = c() pval = c() for(i in 1:length(terms$V1)) { fit1 = glm(PHENOTYPE~get(as.character(terms$V1[i]))+get(as.character(terms$V2...