similar to: Is there any package can be used to solve individual haplotyping problem?

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

Dear All, I'd like to perform adonis (from the vegan package) rather than amova (in ade4) on some haplotype data, as I have crossed factors. Is there a simple way to tweak the source to allow weights (haplotype frequencies) in a similar way to amova? Best Simon

Dear R mailing list, I have a dataset with genotypes from trios and I would like to infer haplotypes for each mother, father and child. The package that I could find that can do this is tdthap. But when the mother is homozygous (e.g., 2/2) the haplotype is called as not possible to infer (0); I would prefer for it to call the genotype (2). From what I understand it is doing what I would like

Dear friends - a couple of papers in PNAS (lastly:framework for making better predictions by directly estimating variables' predictivity, Lo et al PNAS 2016; 113:14277-14282) have focused interest on mapping complex traits to multiple loci spread all over the genome. I have been around on the relevant taskview(s) I hope but fail to see that the backward haplotype transmission association

Hi, I'm doing haplotype networks with the package pegas and the script from Jimmy O'Donell's blog. The networks which I obtain are a little ugly and I'd like to change some aspects of their appearance, but I'm just starting with R and I don't know how to do it. I have the following problems: -Some nodes overlap. I increase the scale.ratio but then I get a tiny legend. So

Hi, Anybody knows a function that can fit haplotype data to a Cox model. I've been searching it in the web without succeed. I use "haplo.stats" package, but unfortunatelly it's not possible to analyse survival data, amb I right?. Thanks in advance. Isaac Subirana (isubirana@imim.es) [[alternative HTML version deleted]]

Version 1.0 of hapassoc now available from CRAN hapassoc is an R package for likelihood inference of trait associations with SNP haplotypes and other attributes using the EM Algorithm. Recent changes include the addition of anova and logLik methods for the class hapassoc, to allow users to perform likelihood ratio tests of haplotype effects. Other changes include bug-fixes and improvements to

Version 1.0 of hapassoc now available from CRAN hapassoc is an R package for likelihood inference of trait associations with SNP haplotypes and other attributes using the EM Algorithm. Recent changes include the addition of anova and logLik methods for the class hapassoc, to allow users to perform likelihood ratio tests of haplotype effects. Other changes include bug-fixes and improvements to

I'm trying to: Resample with replacement pairs of distinct rows from a 120 x 65,000 matrix H of 0's and 1's. For each resampled pair sum the resulting 2 x 65,000 matrix by column: 0 1 0 1 ... + 0 0 1 1 ... _______ = 0 1 1 2 ... For each column accumulate the number of 0's, 1's and 2's over the resamples to obtain a 3 x 65,000 matrix G. For those

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 file. So I create a file to extract them with

Dear R developers: I am a PHD candidate student in the school of public health of Peking University and my major is genetic epidemiology. I am learning the FAM-MDR algorithm, which is used to detect the gene-gene and gene-environment interactions in the data of pedigree. The codes were written by Tom Cattaert of the University of Liege. The algorithms and the sample datasets are available at

I have a solution (actually a few) to this problem, but none are computationally efficient enough to be useful. I'm hoping someone can enlighten 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

Dear R-devel, I am writing for help on how I should include parallel sets of data in my package. Brief summary: I am new to using data within packages. I want a user to be able to specify one of two alternative versions of within-package datasets to use, and I want to load just that one. I have a solution that works, but it doesn't seem as simple as it should be from a user's

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

Dear list, I am trying to write a package for simulating meioses in R. We defined a class 'haplotype' which contains the basic units of our simulation: setClass("haplotype",representation(snp = "numeric",qtl = "list", hID = "numeric",phID0 = "numeric",phID1 = "numeric"),

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 gives 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):

Dear all, After struggling for some time with *apply() and eva() without success, I decided to ask for help. I have 3 lists labeled with, each contains 3 different interpolation functions with identical names: > names(missgp0) [1] "spl.1mb" "spl.2mb" "spl.5mb" > > names(missgp1) [1] "spl.1mb" "spl.2mb" "spl.5mb" > >

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) > SNPs <-

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