search for: allel

Hi people, I have made some progress trying to work out how to solve this problem but I have got a bit stuck - sorry if this turns out to be a simple exercise . . Allelic Differentiation (AD) in genetics measures the number of different alleles between (say) two populations eg: Organisms in Pop 1 have alleles: a, b, c, d, e Organisms in Pop 2 have alleles: b, b, c, d, e Different (unique) alleles (n) are: a [unique() does not do what I want here for comparin...

#Hello, #I have three data frames, X,Y and Z with two columns each and different numbers of rows. # creation of data frame X X.alleles <- c(1,5,6,7,8) X.Freq <- c(0.35, 0.15, 0.05 , 0.10, 0.35) Loc1 <- cbind( X.alleles,X.Freq) X <- data.frame(Loc1) #creation of data frame Y Y.alleles <- c(1,4,6,8) Y.Freq <- c(0.35, 0.35, 0.10, 0.20 ) Loc2 <- cbind(Y.alleles, Y...

...s is a bit hard to describe, so if my initial description is confusing, please try running my code below] #WHAT I'M TRYING TO DO I'd appreciate any help in trying to speed up some code. I've written a script that converts a matrix of integers (usually between 1-10,000 - these represent allele names) into two new matrices of normally distributed values (representing genetic effects), such that a given number in the integer (allele name) matrix always corresponds to the *same* randomly distributed (genetic) effects. For example, every time my function sees the allele name "3782&quo...

I have a vector: alleles.present<-c("D3", "D16", ... ) The alleles present changes given the case I'm dealing with - i.e. either all of the alleles I use for my calculations are present, or some of them. Depending on what alleles are present, I need to make matrices and do calculations on tho...

...e whether the etiquette is to break up multiple questions or not but I'll keep them together here for now as it may help put the questions in context despite the fact that the post may get a little long. Question 1: My first goal is to calculate the proportion of shared 1) behaviours and 2) alleles between numerous individuals. Pasted below ('propshared' function) is what I have now and and works very well for calculating the proportion of shared behaviours where the data is formatted with each column as a behaviour and each row an individual. Microsatellite genotypes are formatte...

Hello R-help community, I have another question about filtering datasets. Please consider the following "toy" data matrix example, called "x" for simplicity. There are 20 different individuals ("ID"), with information about the alleles (A,T, G, C) at six different loci ("Locus1" - "Locus6") for each of these 20 individuals. At any single locus (e.g., "Locus1" or "Locus2", ... or "Locus6"), the individuals have either one allele (from the set of A,T,C,G) or one other allele (fro...

...urce 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 (unordered allele pairs) and haplotypes (ordered allele pairs). Genotypes and haplotypes can be annotated with chromosome, locus, gene, and marker information. Utility functions compute genotype and allele frequencies, flag homozygotes or heterozygotes, flag allele carriers of certain alleles, count the number of...

...urce 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 (unordered allele pairs) and haplotypes (ordered allele pairs). Genotypes and haplotypes can be annotated with chromosome, locus, gene, and marker information. Utility functions compute genotype and allele frequencies, flag homozygotes or heterozygotes, flag allele carriers of certain alleles, count the number of...

Hello, I have two data frames, X and Y, with two columns each and different numbers of rows. # creation of data frame X Loc1.alleles <- c(1,5,6,7,8) Loc1.Freq <- c(0.35, 0.15, 0.05, 0.10, 0.35) Loc1 <- cbind( Loc1.alleles,Loc1.Freq) X <- data.frame(Loc1) #creation of data frame Y Loc2.alleles <- c(1,4,6,8) Loc2.Freq <- c(0.35, 0.35, 0.10, 0.20) Loc2 <- cbin...

Hello, In a case control study how to calculate the risk allele or score allele. Regards GRR [[alternative HTML version deleted]]

...t can be sophisticated enough to set the number of cluster between two or three within the array. These genotyping arrays are either 48 samples x 48 assays, 96x96, or 192x24 and it would be nice if it could accommodate any range of samples and assays. the data headings from the csv are: ID,Assay,Allele Y,Allele X,Name,Type,Auto,Confidence,Final,Converted,Allele Y,Allele X where Allele Y and Allele X are the plotted values and the vectors within the data.frame are 9216 (96x96) long. So what would be the recommended package for moving to a more quantifiable method of genotyping using cluster ana...

Dear R user: I am studying the allele data of two populations. the following is the data: a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 pop1 0.0217 0.0000 0.0109 0.0435 0.0435 0.0000 0.0109 0.0543 0.1739 0.0761 0.1413 0.1522 0.1087 0.0870 0.0435 0.0217 0.0109 pop2 0.0213 0.0213 0.0000 0.0000 0.0000...

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

...dentify SNPs. One is based on resequencing the genome and aligning the reads to the sequenced genome to identify SNPs (data available for 44 individuals). Another is based on SNP array with selected loci (30000 loci, 870 individuals). I want to compare the results from the resequencing based minor allele frequency and Array based minor allele frequency. What statistical test should I use? My table looks like Chromosome position Major/minor Resequencing SNP array chromosome 1 198432 C/T 75/13 1460/280...

Dear R listers, I have a data file looks like the following: Testing marker: s_1 --------------------------------------------- Allele df(0) -LnLk(0) df(T) -LnLk(T) ChiSq p 3 7995 29320.30 7994 29311.85 16.90 4e-05 (2229/8000 probands) Testing marker: s_2 --------------------------------------------- Allele df(0) -LnLk(0) df(T) -LnLk(T) ChiSq p 3 7995 29339.25 7994...

...ile we have many probands and most of their mothers we only have about 50% of the trios being complete. I have been running tdt and trio.types. It appears as if it is ignoring the duos. Sometimes a duo can be informative. For instance Father ..missing Mother 1/2 Proband 1/1 This duo shows that for allele 2, this was clearly a case where 2 was untransmitted. Yet I do not think this family counts toward the output that is generated. Am I correct? How do I use R to do TDT analysis where duo's are used if they are informative? If you want further details... I made a subset that I called MessWit...

...[1] "genotype" "factor" Now when I issue the command: > summary(founders[,59]) I get: Error in attr(retval, "which") <- which : attempt to set an attribute on NULL In addition: Warning message: $ operator is deprecated for atomic vectors, returning NULL in: x$allele.names Clearly, I am missing something. What am I missing? -- Farrel Buchinsky [[alternative HTML version deleted]]

...hen it does, it short circuits the entire loop. Nothing gets written to Resultdt. > Resultdt<-lapply(PGWide[,240:389], tdt) Error in rep.default(1, nrow(U)) : rep() incorrect type for second argument In addition: Warning messages: 1: 1 misinheritances in: phase.resolve(g.cs, g.mr, g.fr, as.allele.pair = TRUE, allow.ambiguous = (parent == 2: 1 misinheritances in: phase.resolve(g.cs, g.mr, g.fr, as.allele.pair = TRUE, allow.ambiguous = (parent == 3: 4 misinheritances in: phase.resolve(g.cs, g.mr, g.fr, as.allele.pair = TRUE, allow.ambiguous = (parent == It is very laborious working out...

...ics 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,] 2 0 [2,] 1 1 [3,] 2 0 [4,] 0 2 [5,] 2 0 [6,] 2 0 [7,] 2 0 [8,] 2 0 [9,] 0 2 [10,] NA NA > allele.count(g2) list() If I print the objects I get this > g1 [1] "D/D" "D/I" "D/D" &q...

#Hello, # Sorry, my last letter contained an error this code should work #I have three data frames, X,Y and Z with two columns each and different numbers of rows. # creation of data frame X X.alleles <- c(1,5,6,7,8) X.Freq <- c(0.35, 0.15, 0.05 , 0.10, 0.35) Loc1 <- cbind( X.alleles,X.Freq) X <- data.frame(Loc1) #creation of data frame Y Y.alleles <- c(1,4,6,8) Y.Freq <- c(0.35, 0.35, 0.10, 0.20 ) Loc2 <- cbind(Y.alleles, Y....