Estimation of Genetic Variability between Angus, Brahman, and Santa Populations - Lab Report Example

Estimation of Genetic Variability between Populations Name of Student Name of University Date Table of Contents Abstract 3 Introduction 4 Method 4 Results 6 Discussion 9 Conclusion 11 References 12 Abstract The purpose of this paper is to analysis genetic variability of Angus, Brahman and Santa with the intention of recommending the best cattle for breeding. According to the findings, some have SNP that have variations, and this contributes to increased productivity. DNA markers were for the three types of cattle under analysis. The information was analyzed using R package. The results showed genetic variability among the three breeds in that Angus had high productivity genome transferred than the other two breeds. The analysis shows that ARS-BFGL-NGS-95821 genotype group had greater influence in Santa than the other breeds. The genotypes expression changes in the Angus, Brahman and Santa helped to group them into different categories in terms of productivity Introduction In cattle production it is important for the farmer to know which breed can be closed to increase productivity. This is done by monitoring the increase genetic variability of different breeds. Thus, genetic variations of cattle or beef cows are critical in determining the amount of milk or beef that can be obtained from crossing breeding them. However genetic variability depends on environmental changes which affect production. We have low additive genetic variance and non additive genetic variances in reproductive cattle. This means reproductive traits cannot under any circumstances through close breeding. Thus, genetic variations of cattle or beef cows are critical in determining the amount of milk or beef that can be obtained from crossing breeding them. Method The four txt files that is Bovine50Kmap-1.txt, angus.txt, santa.txt and, brahman.txt transferred to a saved in folder called population. Since they were files with large data were coded using a collection of SNPs. The data was loaded and read using Data= read.table("Bovine50Kmap-1.txt ",header=F, Sep=" ") dim(Bovine50Kmap-1.txt) Data= read.table("brahman.txt ",header=F, Sep=" ") dim(Bovine50Kmap-1.txt) Data= read.table("santa.txt ",header=F, Sep=" ") dim(Bovine50Kmap-1.txt) Data= read.table("angus.txt ",header=F, Sep=" ") dim(Bovine50Kmap-1.txt) >75000 100 In order to identify each line the following code is used Data=readLines(con="angus.txt") The extracted SNP/angus are 0 1 2 0 0 2 0 0 1 0 1 1 2 0 2 0 1 0 1 2 This information is turned into matrix by manipulating it using the following R code Data=readChar(con="angus.txt",nchars=file. Data=readChar(con="santa.txt",nchars=file Data=readChar(con="brahman.txt",nchars=file info ("angus.txt")$size,useBytes=T) info ("santa.txt")$size,useBytes=T) info ("brahman.txt")$size,useBytes=T) The data is saved using the following R code as , save (Data,file="angus.bin") save (Data,file="brahman.bin") save (Data,file="santa.bin") This data is loaded into the working space using the following R code load ("angus.bin") load ("santa.bin") load ("brahman.bin") After it has been loaded we set up setupSNP’ and start to work by comparing the frequencies of the SNP among the animals under consideration that is Santa, Angus and Brahman. Results Looking at the SNPs, BTA-116372-no-rs, BTA-35325-no-rs, BTA-35325-no-rs, BTA-81684-no-rs, BTA-78330-no-rs, BTA-84344-no-rs, BTA-77299-no-rs, BTA4 (219) and BTA-82879-no-rs proved to be the most influence in the breeds. While BTA-85701-no-rs, BTA-89302-no-rs and BTA-89311-no-rs proved to be less significant SNPs was in the breeds. Significant SNP markers detected During the simulation the following were detected BTA-116372-no-rs -- During the simulation this trait was only detected in Angus and Brahman while in Santa it was not found. This had high concentration of Hapmap51889 in this animals while the while Santa had high concentration of Hapmap26982 BTA-35325-no-rs have shown dominance in Santa and Angus populations but not in the Brahman. However, this SNS consisted traits that had overlapped as shown by the output. There two traits that were observed which overlapped showing the variability in the populations. In the Angus with BTA-35325-no-rs had SNP markers of   ARS-BFGL-NGS-21633 dominance. BTA-52266-no-rs, BTA-38976-no-rs and BTA-50450-no-rs - The results of the analysis showed that this SNP had ARS-BFGL-NGS-28540 markers and were only available in the Angus population as compared to other breeds. This implies that this were influential to breed as compared to their breeds. BTA-38976-no-rs and BTA-50450-no-rs - this is associated with the Santa breed as compared to other Angus and Braham. SNP markers linked to the BTA-38976-no-rs are ARS-BFGL-BAC-31737 and ARS-BFGL-NGS-15716. BTA-56833-no-rs- we had 15 significant SNP markers associated with SNP and 6 were validated in both the Brahman and Angus populations. However, four SNPs were associated with Santa were validated in the area. Association analysis To depict the possible correlation in between populations, model is set on a basic assumption that the there is serial correlation in the genes. Running correlation using R software it was found that there was some correlation between genetics of the Angus, Santa and Brahman. Thus, it can be said there is association of the three type’s populations. Analysis will be carried out to determine the relationship between the various genes. Errors in the analysis can be minimized by quality control of the survey and are generally within acceptable tolerances. However, gross errors do sometimes occur and these need to be eliminated either by quality control procedures at the data input stage or during the model calibration procedure. This leads us to graph for heritability genes validation using quantitative PCR of R package. The scatter plot for BTA-08335-no-rs showed a positive and strong linear relationship among the Angus and Santa. To test this condition, a Pearson’s correlation was carried out. It was noticed that relationship between genome of samples selected of Angus and Santa populations was considerably correlated (r = -.960, N = 50, p Read More