similar to: simple matching with R

Dear all, I am running qtl mapping. I have 75 RI lines with some residual heterogeneous loci. The loci are code A, B or H(heterogeneous). Questions: 1) R/qtl determine the data is F2 intercross. 2) Warning message about strange genotype pattern > library(qtl) > dat=read.cross("csv", file="rqtl_trt.csv") --Read the following data: 75 individuals

The "genetics" package for handling single-locus genetic data is now available on CRAN in both source and Windows binary formats. The purpose of this package is to make it easy to create and manipulate genetic information, and to facility use of this information in statistical models. The library includes classes and methods for creating, representing, and manipulating genotypes

The "genetics" package for handling single-locus genetic data is now available on CRAN in both source and Windows binary formats. The purpose of this package is to make it easy to create and manipulate genetic information, and to facility use of this information in statistical models. The library includes classes and methods for creating, representing, and manipulating genotypes

Dear all, I'd like to introduce the paramlink package, offering parametric linkage analysis in R, as well as other likelihood-based pedigree analyses. While a rudimentary version of the package has existed for a while, it was never properly introduced to this forum. The present version is a major update with new and improved functions. Features: * Singlepoint LOD scores for

Dear all, I'd like to introduce the paramlink package, offering parametric linkage analysis in R, as well as other likelihood-based pedigree analyses. While a rudimentary version of the package has existed for a while, it was never properly introduced to this forum. The present version is a major update with new and improved functions. Features: * Singlepoint LOD scores for

Hi All, I have some data where the dependent variable is a score, low (1:3) or high (8:9), and the independent variables are 21 genotypic markers. I'm fitting a logistic regression on the whole dataset after transforming the score to 0/1 and normal linear regression on the high and low subsets. I all cases I have a numer of cases of data 'duplications', i.e. different

Hi everyone, I'm using the function 'HWE.exact' of 'genetics' package to compute p-values of the HWE test. My data set consists of ~600 subjects (cases and controls) typed at ~ 10K SNP markers; the test is applied separately to cases and controls. The genotypes are stored in a list of 'genotype' objects, all.geno, and p-values are calculated inside the loop over all

Dear mailing list, I'm still quite a newbie in the statistical analysis of genotype/allele data, resp. more generally in the analysis of categorical variables. Moreover, I'm currently totally confused by the many R packages available to do such analysis. Here is my case: I've got a list of genes, and a number of case-control population pairs, and for each population and gene, the

Dear R users, I have a problem with something called "NaNs" in a nested mixed model. The background is that I have studied the number of insect nymphs emerging from replicated Willow genotypes in the field. I have 15 replicates each of 4 Willow genotypes belonging two 2 Willow species. Now I want to elucidate the effect of Willow genotype on the number of emerging nymphs. Previously I

#Hello, #I have a question about obtaining results from a loop I have written. #Below is a sample of individual genotypes from a genetic question I am working on called "P.genotype.sample ". P.genotype.sample<-matrix(10,10,10) P.genotype.sample[,1]<-c(2,2,1,5,1,1,5,6,1,3) P.genotype.sample[,2]<-c(6,3,3,6,8,1,6,7,2,3) P.genotype.sample[,3]<-c(2,2,2,3,3,2,2,2,3,3)

Hi, I am trying to test for pairwise associations between genotypes ( Rows=individuals, Columns =genes, data are up to 4 genotypes per gene, some with 2,3 or 4) where each chisquare comparison is different depending on the genes tested. The test is the observed multilocus (across columns for each individual) genotypes vs the expectation, which is the product of the individual frequency for each

I want to specify a two-factor model in lme, which should be easy? Here's what I have: factor 1 - treatment FIXED (two levels) factor 2 - genotype RANDOM (160 genotypes in total) I need a model that tells me whether the treatment, genotype and interaction terms are significant. I have been reading 'Mixed effects models in S' but in all examples the random factor is not in the main

I have been merrily using the genetics package and more specifically have been using the makeGenotypes and genotypes function. I check my accomplishments by going > class(g2) [1] "genotype" "factor" and likewise > class(g1) [1] "genotype" "factor" Yet when I execute a command such as allele count I get this > allele.count(g1) D I [1,]

Hi, I'm using R for a QTL analysis of SNP data. I was wondering if anyone had any advice on fitting a dominance effect into the following function; > myfun4 function (x) { x <- scan(con, nmax=169) y <- unique(x[which(!is.na(x))]) if(length(y)>1) { summary(lme(Ad ~ x, random= ~1|sire, na.action="na.omit")) } else {print("no.infomation")} } Con is the

I'm pretty new to R, and I've been given a script by a user who wants some help with it. I know enough about the way R works to know that this is a very inefficient way to 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):

Hi All, I have a problem trying to get a set of columns recognised as a list and can't work out how to do it despite trawling through the mailing list archives, and docs. A short example... to.convert <- NULL n <- 6 for(x in 1:n){ to.convert <- paste(to.convert, paste((2 * x) -1, (2 * x), sep=":"), sep=",") } to.convert <- gsub("^,", "",

Hello, My R skills are somewhere between novice and intermediary, and I am hoping that some of you very helpful forum members, whom I've seen work your magic on other peoples' problems/questions, can help me here. I have a matrix with the following format: (i) individual plants comprising many different genotype groups (i.e., a plant is genotype 1 or genotype 2 or genotype 3, etc). The

I'm working in a Gen/Marker-Phenotype association study in wheat and I'm using a Mixed Model Approach to estimate the effect of the markers. My model has the atribute measured as y (response variable), the markers and the blocks (of a complete random block design) as fixed and the genotypes and the residuals as random. In one hand I'm assuming that there is no correlation between

Sequenom has an odd format of calling a SNP genotype gg [1] "C" "GA" "A" "C" "C" "AG" "C" "C" "T" "G" homozygous A is called A and heterozygous is called AT The genetics package cannot handle the fact that some genotypes are declared with 2 letter while other are declared with only 1.

Dear all,   I have data from the following experimental design and trying to fit a mixed model with lme function according to following steps but struggling. Any help is deeply appreciated.   1) Experimental design: I have 40 plants each of which has 4 clones. Each clone planted to one of 4 blocks. Phenotypes were collected from each clone for 3 consecutive years. I have genotypes of plants. I