School of Agriculture, Food and Ecosystem Sciences - Theses
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ItemGenetic variation in the bovine myostatin gene and its effect on muscularity( 2010)Retail beef yield (RBY) refers to the amount of saleable meat from an animal, and is an economically important trait for the beef industry. The gross components that determine RBY are skeletal muscle, bone and fat content: Skeletal muscle is the most abundant tissue and has high economic value. The propensity to develop muscle mass is heritable and it is controlled by many genes. Genetic variation within these genes can influence differences in muscle mass. The studies within this thesis, further investigated the effect of genetic variation in the myostatin (MSTN) gene on muscle mass in cattle. MSTN was selected as a candidate for further investigation because of its important role in muscle development. Previous studies have found loss-of-function mutations in MSTN that are responsible for extreme increases in muscle mass, known as double muscling, and a high level of genetic variability. The primary objective of this thesis was to determine if MSTN polymorphisms which have not been implicated in double muscling, are also contributing to variation in muscularity. Genetic variation in bovine MSTN was examined in a sub-population of Angus cattle. Eighteen polymorphisms were identified and the haplotype diversity was inferred. From the phased haplotypes the extent of linkage disequilibrium between the polymorphisms was determined, which provided a tag SNP genotyping strategy using six MSTN polymorphisms. Large cattle populations (Herds A to E) were genotyped at each of the tag SNP sites. Haplotypes were inferred from the genotypic data, and were then used to test associations with quantitative indices for muscularity. The results from Herd A used as the discovery population showed that haplotypes 2 and 7 had a moderate effect on eye muscle area relative to double muscling heterozygotes and haplotypes 8 and 9 had a small effect. Similar trends were observed for these haplotypes in the validation population compromising Herds B to E. The effect of the MSTN haplotypes was also examined for other carcass, meat quality and feed efficiency traits. Significant associations were found for almost all traits examined, but their effects could not be proven as casual and requires further validation. Differences in gene expression between the MSTN haplotypes were also investigated to provide biological support for the quantitative associations. The data from this study did not provide convincing support for the quantitative associations, which may have been biased by measurements between assays, differences in splicing or because the differences in eye muscle area are not due to MSTN. The effect of the MSTN splice variant warrants further investigation since a favourable role has been demonstrated previously in sheep A historical perspective on recent selection pressure for double mutations has also been provided. The time to the most recent common ancestor was investigated for multiple MSTN mutations responsible for double muscling. The long regions of haplotype homozygosity that were found associated with the double muscling mutations indicate the time to the most recent common ancestor occurred is consistent with the first reports of double muscling approximately 200 years ago. The studies within this thesis have further contributed to the elucidation of the biology of muscling. It has been demonstrated that other MSTN polymorphisms, not implicated in double muscling, are also contributing to variation in muscle mass. This knowledge will add value to current methods for estimating the genetic merit of beef cattle and may offer more practical genomic selection alternative to improve muscling in beef cattle.
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ItemAspects of statistical modelling for genomic selection( 2010)The research reported in this thesis investigated aspects of statistical models used for genomic selection. The importance of, and, interest in genomic selection is driven by the desire to increase the rate of genetic gain for commercially important traits. Genomic selection could increase the rate of genetic gain by increasing the accuracy of selection through the inclusion of DNA markers. Multiple methods and models have been proposed for implementing genomic selection. All methods have to overcome the problem that the number of DNA markers (p) is typically much larger than the number of phenotypic records (n) i.e. the p>n problem. One approach to this problem is to use Bayesian Inference which allows for an oversaturated model. Two simulation studies and a large data study were undertaken to gain a comprehensive understanding of what makes a robust and accurate Bayesian prediction model. Results from the simulation studies indicated that the match between the assumed QTL distribution and the true QTL distribution had an effect on the accuracy of the direct genomic values (DGV) produced by the different Bayesian models. Some of the models producing accurate DGV were computationally demanding. Subsequently, a novel Bayesian model using Stochastic Search Variable Selection (SSVS) for genomic selection was developed (Bayes SSVS). This model was demonstrated to produce accurate DGV and be computationally efficient. In contrast to the results from simulated studies, the results from a real dairy cattle data study showed a general equality in the accuracy of prediction across the various Bayesian models including Bayes SSVS. The exception was for traits with atypical genetic architectures such as fat percentage in milk where Bayes SSVS and other model selection approaches performed better than other approaches assuming that all markers equally contributed to the total genetic variation. The thesis also sought to explore the potential of genomic selection for improving novel traits that have been traditionally very difficult to select for. Energy Balance (EB) is a minimally recorded trait as the cost and measurement logistics mean it can only recorded on experimental farms. Using EB as a case study, it was demonstrated that genomic selection could provide the opportunity to select for EB and other minimally recorded through the accurate prediction of DGV. Additionally, selection for EB could be a valuable tool in finding a balance between production and non- production traits. Another attractive feature of some of the Bayesian models for genomic selection is they can be used to map QTL. Consequently, the establishment of significance when using multi-locus models for genome wide association studies was explored using a permutation testing approach. Three examples demonstrated that the permutation testing approach could correctly identify QTL. Two specialised approaches, permuting within strata, are presented. One approach accounted for a structured pedigree satisfying the condition of exchangeability. The second approach enabled the identification of secondary moderate QTL in the presence of a major QTL. The effect of the number of permutations needed was also examined; confirming previous results. This method was shown to provide accurate identification of QTL when compared with current approaches.