similar to: Dominance in qtl model

This will probably seem very simple to experienced R programmers: I am doing a snp association analysis and am at the model-fitting stage. I am using the Stats package's "drop1" with the following code: ##geno is the dataset ## the dependent variable (casectrln) is dichotomous and coded 0,1 ## rs743572_2 is one of the snps (which is coded 0,1,2 for the 3 genotypes)

Bottom Line Up Front: How does 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

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 R helpers I have a table and i need to make new table table1: sire snp1 snp2 snp3 snp4 snp5 snp6 snp7 snp8 snp9 snp10 snp11 snp12 snp13 snp14 snp15 8877 -1 -1 -1 -1 0 0 -1 -1 -1 0 1 1 1 -1 -1 7765 1 1 1 0 0 0 -1 1 1 1 0 0 0 1 0 8766 1 1 -1 0 -1 -1 0 -1 0 -1 -1 -1 0 1 0 6756 0 1 0 -1 1 -1 -1 0 0 0 0 -1 0 1 1 5644 -1 0 1 -1 0 0 0 0 -1 -1 0 0 0 0 1 I have table2 sire

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

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

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 with the column means already substracted followed by a

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

Hi, My query is regarding permutation test and reshuffling of genotype/phenotype data I have been using the haplo.stats package of R. for haplotype analysis and I would like to perform an analysis which I'm requesting your advice. I have a data set of individuals genotyped for 12 SNP and a dichotomous phenotype. At first, I have tested each of those SNP independently in order to bypass

Hi, I have a list p with different size dataframes and length of over 8000. I'm trying to calculate correlations between the rows of dataframes of this list and columns of another dataset (type data.frame also) so that first column is correlated with all the rows in the list dataframe. Some information from another dataset is also included to the final output (all.corrs). This worked a

Dear All, I am trying to calculate the Hardy-Weinberg Equilibrium p-value for 42 SNPs. I am using the function HWE.exact from the package "genetics". In order not to do a lot of coding "by hand", I have a for loop that goes through each column (each column is one SNP) and gives me the p.value for HWE.exact. Unfortunately some SNP have reached fixation and HWE.exact requires a

I'm having trouble getting summaries out of the 250K snp chips in R. I'm using the oligo package and when I attempt to create the necessary SnpQSet object (to get genotype calls and intensities) using snprma, I encounter memory issues. Anyone have an alternative package or workaround for these large snp chips? -- View this message in context:

Hello, I need some help with the simulatedSNPs function from scrime package. I am trying to simulate some genotype of a case/control disease locus. The allele frequence are cases/controls Sample cases controls 2000 .5 .10 1500 .6 .40 In each of the row, i need to simulate 100 snp and calculate the pvalue ##############Download Scrime

Hi, I am new to R. and even though I've made my code to run and do what it needs to . It is taking forever and I can't use it like this. I was wondering if you could help me find ways to fix the code to run faster. Here are my codes.. the data set is a bunch of 0s and 1s in a data.frame. What I am doing is this. I pick a column and make up a new column Y with values associated with that

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

Dear R-experts, My problem is how to handle a 10GB data file containing genotype data. The file is in a particular format (Illumina final report) and needs to be altered and merged with phenotype data for further analysis. PERL seems to be an frequently used solution for this type of work, however I am inclined to think it should be doable with R. How do I open a text-file, line by line,

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

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

Hello R Forum users, I was hoping someone could help me with the following problem. Consider the following "toy" dataset: Accession SNP_CRY2 SNP_FLC Phenotype 1 NA A 0.783143079 2 BQ A 0.881714811 3 BQ A 0.886619488 4 AQ B 0.416893034 5 AQ B 0.621392903 6 AS B 0.031719125 7 AS NA 0.652375037 "Accession"