Variations of Genomic DNA in Three Types of Cattle - 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 8 Discussion 13 Conclusion 14 References 16 Abstract This report centers on variations of genomic DNA in Angus, Santa and Brahman. The study used Bovine50Kmap. According to the findings, some have SNP that have variations, and this contributes to increased productivity. Furthermore, the paper asserts that a high degree of genetic variability may lower productivity. Santa has greater variability as compared with the other two breeds that were consideration. Looking the genotypes expression changes of the three types of cattle helped to group them into different categories base on the variability. Introduction There are several theories regarding the improvement of productivity of beef and milk. But, most scientists have proved that that a combination of genes can influence productivity. However, majority of the cases are heredity. 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. This study centers on bovine genome analysis using R program is important. The current paper carries out analysis of the existing data on genomic DNA in Angus, Santa and Brahman. This attempt is aimed at trying to increase(Walker, 2010). Although, it is often been acknowledged that, a large percentage of beef and milk production is a result of a hereditary familial tendency, precise understanding of the basic genetic developments has been missing. Recent studies in molecular biology, nonetheless, have indicated that inherited genes will affect productivity. This paper will inspect analyse Genotype text files finding out the associations of this cattle. This will requires the setting up of an object class that is setupSNP. Method In our case we have a huge data that needs to be manipulated thus it has to be coded using a collection of SNPs. The functions for operation will use the setupSNP to prepares the data for manipulation. From the data that was available the following source code was used MyData=matrix (scan (file="angus.txt", what= integer (),sep="\t"),50000,1000,byrow=T) This helps in reading the file with Bovine Genome for Angus in the angus.txt this data will need to read line by line and this is done using the following R source code myData=readLines(con="angus.txt") This information is turned into matrix by manipulating it using the following R code myData=readChar(con="angus.txt",nchars=file. info ("angus.txt")$size,useBytes=T) Then, this information needs to be saved in the system for analysis and is done using the following code, save (myData,file="angus.bin") After the information has been save it can be worked in the existing file therefore it was loaded to the workspace using the following R code load ("angus.bin") Source code for Santa myData=readLines(con="santa.txt") This information is turned into matrix by manipulating it using the following R code myData=readChar(con="santa.txt",nchars=file. info ("santa.txt")$size,useBytes=T) Then, this information needs to be saved in the system for analysis and is done using the following code, save (myData,file="santa.bin") After the information has been save it can be worked in the existing file therefore it was loaded to the workspace using the following R code load ("santa.bin") Brahman myData=readLines(con="brahman.txt") This information is turned into matrix by manipulating it using the following R code myData=readChar(con="brahman.txt",nchars=file. info ("brahaman.txt")$size,useBytes=T) Then, this information needs to be saved in the system for analysis and is done using the following code, save (myData,file="brahman.bin") After the information has been save it can be worked in the existing file therefore it was loaded to the workspace using the following R code load ("brahman.bin") 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. MyData=matrix (scan (file="angus.txt", what= integer(),sep="\t"),50000,1000,byrow=T) myData=readLines(con="angus.txt") save (myData,file="angus.bin") load ("angus.bin") Results The main purpose was to study and understand the genetic variability of these three types of species Brahman, Santa and Angus. The results of had a common deletion polymorphisms, was done around SNP(ARS-BFGL-NGS-28540 ) using qPCR around the following genoplot images were obtained. Figure 1: genoplot image From the image it can be noted that each of the considered markers had SNP number that was considered. The darkened bars indicate the genetic variations while other does not present the genetic variability. The other image produced was the image showing Genome-wide plot smoothening. This is a shown below Figure 2: Genome-wide plot of FST and smoothed FST From the figure above we state when smoothening helps cattle with less fluctuations in Genotype (SNP to SNP) for cross breeding from SNP to SNP.. 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. This was obtained as follows; The frequency distribution obtained was as follows Figure 3: frequency Distribution the frequency distribution would reflect the process variability. The values would be clustered close to the process average, but some of the values would vary somewhat from the mean. If we turn to the process and take samples and compute the mean in each sample, we would discover that these values also vary, just as the individual values varied. They too would have a distribution of values. This has that there is variability in genes when whole-genome SNP association analysis was done. This indicated some inconsistency in genotype when considering common deletion polymorphisms. The purpose was to find genomic variation information that could be used to find economical genetic traits in cattle. The graph below shows output from R package which was performed and consumed a lot of time. From the graph it can be noted that PC1 SNP index is higher as compared to the other three . When applying GP to calculate variances that are application in case, evolution of the initial data into many generations which requires more time to compute the answers. In the calculation to save time derive 6 was employed which simplified the equation and obtain the results s for model. The level of complexity of the analysis was simplified by the use of drive 6 which simplified the mathematical equation as well as providing optimum coefficient which was applied in the analysis. The same text was analyzed using genetic programming to obtain optimum values for regression coefficient, the model was found to have an error of 9.6. The model performed 1% better than the benchmark for the planned investigation. From the investigation general programming appeared to give less complex and comparable information with certain degree of accuracy. Discussion In the study Bovine50Kmap was analyzed for Angus, Santa, and Brahman with the intention of providing resources for cross breeding to produce economically viable cattle. Genetic variation analysis showed that there some genotypes that were variable. The genome in Angus appears have high quality beef which will require the cross breeding. Therefore this paper fills a gap in the literature of genetic variability by investigating the Angus, Brahman and Santa by looking SNP. It is the first analysis the connection between these animals. In the study, SNP was used to identify useful phenotypes in determining meat productivity and grading(Walker, 2010). As can be seen from Figure1 of genoplot image, the four markers of text has been plotted to represent the genetic variation as shown by R parkage. Theoretically, in a there is genetic variation in every species when compared to another. The frequencies of ARS-BFGL-NGS-28540 is almost similar to that of ARS-BFGL-NGS-119910 . The big fluctuation and vagaries from a strict average value for genes could be due to the fact that since there may be crossing breeding of the same in the past. Further, the data used for plotting the graph is differentiated by a few frequencies, while the change in corresponding genes is greater by a few magnitudes. This analysis has provided provide insight on how genes function thus they have to be significantly enriched to improve productivity. The gene functions enriched in nearby genes include the multicellular organism process, regulation of biological quality, and cell morphogenesis(Walker, 2010). This analysis provides estimated results of expected functions, so additional study of function consequences between actual phenotypes should be carried out. From the linear SNP index plot, it is better to discuss an observation in the plot that would be of interest to analyst. Most of the genotype were classified as ARS-BFGL-NGS-2085, ARS-BFGL-NGS-119910, ARS-BFGL-NGS-28540, Hapmap54252-rs29018304, , ARS-BFGL-NGS-28540, Hapmap54252-rs29018304, ARS-BFGL-NGS-119910. Figure three provides evidence of the dramatic variation in the all markers and common deletions. Conclusion In summary, genetic variations in Brahman, Angus and Santa were successfully validated using R software variance analysis. The report of bovine50Kmap provides genome-wide analysis that will be used in future. The analysis was done on Brahman, Angus and Santa because of their productivity in provision of meat. Is there genetic variation in Brahman, Angus and Santa species that provides beef. From the result we verified that ARS-BFGL-NGS-119910 dominant and the in Santa while ARS-BFGL-NGS-28540 is dominant is Angus. We can also conclude that the results are concurrent with the results obtained by other researchers. The small amount of current genetic data on Santa has made the analysis on rapid-cycling varieties highly useful to gaining an understanding of these highly valuable cattle. For each SNP that was provided had its own model with provided P-values for variability (Walker, 2010). From this analysis it is possible to provide a number of suggestions to help improve the process of making decisions from these data output as well as application of both geneplot and SNP index. References Gnatenko D., Dunn J., McCorkle S., Weissmann, D., Perrotta, P. & Bahou W., 2003. Transcript profiling of human platelets using microarray and serial analysis of gene expression. Blood. Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A. & Vingron, M., 2002. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002;18 Suppl 1:S96-104. Irizarry RA & Gautier L., 2003. The Analysis of Gene Expression Data: Methods and Software. New York: Springer. Tusher V, Tibshirani R & Chu G., 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci Walker, J. 2010. Methods in Molecular Biology- Genome-Wide Association Studies and Genomic Prediction. London: Springer Science Read More