Browsing by Author "Hancock, Carolyn Elizabeth."

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Computer simulation of marker-assisted selection utilizing linkage disequilibrium.
(2006) Keildson, Sarah.; Hancock, Carolyn Elizabeth.
The face of animal breeding has changed significantly over the past few decades. Traditionally, the genetic improvement of both plant and animal species focussed on the selective breeding of individuals with superior phenotypes, with no precise knowledge of the genes controlling the traits under selection. Over the past few decades, however, advances in molecular genetics have led to the identification of genetic markers associated with genes controlling economically important traits, which has enabled breeders to enhance the genetic improvement of breeding stock through linkage disequilibrium marker-assisted selection. Since the integration of marker-assisted selection into breeding programmes has not been widely documented, it is important that breeders are able to evaluate the advantages and disadvantages of marker-assisted selection, in comparison to phenotypic selection, prior to the implementation of either selection strategy. Therefore, this investigation aimed to develop deterministic simulation models that could accurately demonstrate and compare the effects of phenotypic selection and marker-assisted selection, under the assumption of both additive gene action and complete dominance at the loci of interest. Six computer models were developed using Microsoft Excel, namely 'Random Mating,' 'Phenotypic Selection,' 'Marker-Assisted Selection,' 'Selection with Dominance,' 'Direct Selection' and 'Indirect selection.' The 'Random Mating' model was firstly used to determine the effects of linkage disequilibrium between two loci in a randomly mating population. The 'Phenotypic Selection' and 'Marker-Assisted Selection' models focused primarily on examining and comparing the response to these two selection strategies over five generations and their consequent effect on genetic variation in a population when the QTL of interest exhibited additive gene action. In contrast, the 'Selection with Dominance' model investigated the efficiency of phenotypic selection and marker-assisted selection under the assumption of complete dominance at the QTL under selection. Finally, the 'Direct Selection and 'Indirect Selection' models were developed in order to mimic the effects of marker assisted selection on two cattle populations utilizing both a direct and indirect marker respectively. The simulated results showed that, under the assumption of additive gene action, marker-assisted selection was more effective than phenotypic selection in increasing the population mean, when linkage disequilibrium was present between the marker locus and the QTL under selection and the QTL captured more than 80% of the trait variance. The response to both selection strategies was shown to decrease over five generations due to the decrease in genetic variation associated with selection. When the QTL under selection was assumed to display complete dominance, however, marker-assisted selection was markedly more effective than phenotypic selection, even when a minimal amount of linkage disequilibrium was present in the population and the QTL captured only 60% of the trait variance. The results obtained in this investigation were successful in simulating the theoretical expectations of markerassisted selection. The computer models developed in this investigation have potential applications in both the research and agricultural sectors. For example, the successful application of a model developed in this investigation to a practical situation that simulated markerassisted selection, was demonstrated using data from two Holstein cattle populations. Furthermore, the computer models that have been developed may be used in education for the enhancement of students understanding of abstract genetics concepts such as linkage disequilibrium and marker-assisted selection.
Development of computer models of different selection strategies on poultry egg production.
(2003) De Guisti, Jonathan.; Hancock, Carolyn Elizabeth.
Poultry have many behavioural, structural and biological features that are ideal for domestication and for meat and egg production (Appleby et al., 1992). Because of the importance of poultry meat and eggs to the human population, breeders and farmers are always looking for ways of improving these traits. Artificial selection is the primary method of trait improvement, and involves selecting individuals with the highest breeding values as parents in each generation. There are a number of different methods of artificial selection, including: individual selection, between family selection, within family selection, family-index selection and index selection. In order to maintain a good response to selection breeders are constantly striving to improve the effectiveness and accuracy of the different methods of artificial selection for traits of economic importance. One method of achieving this goal is the use of computer models. Computer models can be used to simulate selection strategies and to predict what strategy will be the most appropriate for the improvement of a particular trait. This is important as all traits are influenced by many different genetic and environmental factors (Falconer and Mackay, 1996). This investigation was designed to compare the effectiveness of five different artificial selection strategies, namely individual selection, between family selection, within family selection, family index selection and index selection. Five computer models were developed using Microsoft Excel 2000 and these models were then used to compare the efficiencies of the five selection strategies for four different traits. The selection techniques were applied to an artificially, randomly generated population of 500 chickens. The four traits were egg weight with a heritability of 0.51, egg production with a heritability of 0.22, age at first egg with a heritability of 0.41 and body weight with a heritability of 0.55. Firstly, each of these traits were selected for independently using the first four selection methods and secondly the traits were selected for two at a time using index selection. The most significant results obtained from the single trait simulations were that for all traits family-index selection produced the best response to selection in the initial generations and between family selection produced the best response in the later generations. The traits with a higher heritability (egg weight and body weight) responded better to individual selection than they did to within family selection and between family selection in the initial generations. However, within family selection and between family selection proved to be more effective for traits with a low heritability such as egg production. Individual selection and family-index selection resulted in a very rapid decline in the standard deviation of all the traits. Between family selection resulted in the slowest drop in the standard deviation of all the traits, which is why this technique produced the best responses to selection in the later generations. The impact of the correlations between the economically important traits were evident from the results of index selection. For example, egg production is negatively correlated with egg weight making it difficult to gain a correlated response in both these traits simultaneously. Furthermore, egg production is negatively correlated with age at first egg implying that early maturing birds will lay more eggs, however, these eggs will be lighter. The majority of the results obtained were to be expected. Family-index selection takes all the information about an individual's breeding value into account resulting in this method of selection consistently identifying the most desirable individuals being selected. It is therefore the preferred method of selection under all circumstances. It is, however, often not economically and practically efficient to incorporate this technique and the use of another method of selection usually proves to be more beneficial. Individual selection proved to be most effective when applied to traits with high heritabilities, due to the fact that this method selects individuals based on their own phenotypic values. For traits with a high heritability, an individual with a good phenotypic value will have a good breeding value. Between family selection and within family selection proved better for traits with lower heritabilities. For traits with a low heritability the phenotypic value of an individual is a poor indicator of its breeding value. Information from a number of relatives may thus improve the accuracy of prediction of the breeding value by accounting for the influence of environmental effects. The use of computer models to simulate the selection techniques proved very successful in illustrating the effectiveness of the different selection techniques under various genetic and environmental conditions. The models may also prove to be very effective from an educational perspective.
Identification and remediation of student difficulties with quantitative genetics.
(2006) Hancock, Carolyn Elizabeth.; Anderson, Trevor Ryan.
Genetics has been identified as a subject area which many students find difficult to comprehend. The researcher, who is also a lecturer at the University of KwaZulu-Natal, had noted over a number of years that students find the field of quantitative genetics particularly challenging. The aim of this investigation was two-fold. Firstly, during the diagnostic phase of the investigation, to obtain empirical evidence on the nature of difficulties and alternative conceptions that may be experienced by some students in the context of quantitative genetics. Secondly, to develop, implement and assess an intervention during the remediation phase of the study which could address the identified difficulties and alternative conceptions. The research was conducted from a human constructivist perspective using an action research approach. A mixed-method, pragmatic paradigm was employed. The study was conducted at the University of KwaZulu-Natal over four years and involved third-year students studying introductory modules in quantitative genetics. Empirical evidence of students' conceptual frameworks, student difficulties and alternative conceptions was obtained during the diagnostic phase using five research instruments. These included: free-response probes, multiple-choice diagnostic tests, student-generated concept maps, a word association study and student interviews. Data were collected, at the start and completion of the modules, to ascertain the status of students' prior knowledge (prior knowledge concepts), and what they had learnt during the teaching of the module (quantitative genetics concepts). Student-generated concept maps and student interviews were used to determine whether students were able to integrate their knowledge and link key concepts of quantitative genetics. This initial analysis indicated that many students had difficulty integrating their knowledge of variance and heritability, and could not apply their knowledge of quantitative genetics to the solution of practical problems. Multiple-choice diagnostic tests and interviews with selected students were used to gather data on student difficulties and alternative conceptions. The results suggested that students held five primary difficulties or alternative conceptions with respect to prior knowledge concepts: (1) confusion between the terms variation and variance; (2) inappropriate association of heterozygosity with variation in a population; (3) inappropriate association of variation with change; (4) inappropriate association of equilibrium with inbred populations and with values of zero and one; and, (5) difficulty relating descriptive statistics to graphs of a normal distribution. Furthermore, three major difficulties were detected with respect to students understanding of quantitative genetics concepts: (1) students frequently confused individual and population measures such as breeding value and heritability; (2) students confused the terms heritability and inheritance; and, (3) students were not able to link descriptive statistics such as variance and heritability to histograms. Students found the concepts of variance and heritability to be particularly challenging. A synthesis of the results obtained from the diagnostic phase indicated that many of the difficulties and alternative conceptions noted were due to confusion between certain terms and topics and that students had difficulty with the construction and interpretation of histograms. These results were used to develop a model of the possible source of students' difficulties. It was hypothesized and found that the sequence in which concepts are introduced to students at many South African universities could be responsible for difficulties and alternative conceptions identified during the study, particularly the inappropriate association of terms or topics. An intervention was developed to address the identified difficulties and alternative conceptions. This intervention consisted of a series of computer-based tutorials and concept mapping exercises. The intervention was then implemented throughout a third year introductory module in quantitative genetics. The effectiveness of the intervention was assessed using the multiple-choice diagnostic tests and interview protocols developed during the diagnostic phase. The knowledge of the student group who participated in the intervention (test group) was compared against a student group from the previous year that had only been exposed to conventional teaching strategies (control group). t-tests, an analysis of covariance and a regression analysis all indicated that the intervention had been effective. Furthermore, an inductive analysis of the student responses indicted that most students understanding of the concepts of variance, heritability and histograms was greatly improved. The concept maps generated by students during the remediation phase, and data from the student interviews, provided an indication of the nature and extent of the conceptual change which had occurred during the teaching of the module. The results showed that most of the conceptual change could be classified as conceptual development or conceptual capture and not conceptual exchange. Furthermore, it seemed that conceptual change had occurred when considered from an epistemological, ontological and affective perspective, with most students indicating that they felt they had benefited from all aspects of the intervention. The findings of this research strongly suggest an urgent need to redesign quantitative genetics course curricula. Cognisance should be taken of both the sequence and the manner in which key concepts are taught in order to enhance students' understanding of this highly cognitively demanding area of genetics.
Investigation of the application of best linear prediction for breeding and clonal production purposes in a Eucalyptus grandis population.
(2006) Louw, Andrea Kate.; Hancock, Carolyn Elizabeth.
The genus Eucalyptus has been planted extensively throughout the world in tropical and subtropical regions, primarily because of its economic importance and use in wood and pulp production. Due to the growing demands for timber, forestry companies need to increase the productivity of available forest land. The genetic improvement of forest trees through selection and breeding involves a lengthy process of scientifically controlled trials focused on short-term and long-term goals using breeding and production populations. This investigation focused on the use of Best Linear Prediction (BLP) and its application to: (1) the prediction of genetic gains for a breeding population and, (2) the selection of superior individuals for clonal production of E. grandis. A CSIR dataset for a 20-year-old progeny trial involving 90 open-pollinated families was obtained. Four traits, namely, diameter at breast height (DBH), stem form, splitting and density were identified for use in this investigation. Relevant data were extracted and a file termed, Dataset created. Dataset was edited, standardized and corrected for the fixed effect of replication using SAS® procedures. Precise and accurate population parameter estimates are fundamental in determining breeding strategies and thus, heritabilities of each trait and phenotypic correlations between traits in Dataset were estimated using SAS® procedures. DBH was found to have the highest heritability (0.600), followed by density (0.492). The estimated heritability for stem form was 0.401 and splitting had the lowest heritability at 0.214. A high positive phenotypic correlation of 0.83 was estimated between DBH and stem form. The phenotypic correlations between other traits were close to zero. An index provides a weighted score for individuals, which takes all relevant information into account and allows individuals or families to be chosen for breeding and production purposes. Consequently, Best Linear Prediction (BLP) of individual breeding values were calculated using MATGEN® (2003). Thereafter, BLP values were used to determine the rankings of individual trees for 15 different selection indices. In order to determine the effect of selection on the change in the population mean of a trait, the breeding population's response to selection was predicted and compared across three selection strategies, namely: (1) individual selection, (2) single-trait index selection, and (3) multiple-trait index selection. The top 8% of individuals in the breeding population were selected for and the genetic gains were predicted. It was found that the response to selection was greatest when using individual selection. Furthermore, DBH had the best selection response for all three strategies as compared to the other traits under investigation. Fifteen indices, considering different numbers and choice of traits, were compared for commonality among rankings of the top 30 individuals. Two methods, namely, (1) a rank-correlation matrix and (2) a manual assessment, were used. The commonality between indices showed that a simple index, considering two traits (DBH and density) was equally effective (93%) in identifying genetically-superior individuals as the more complex index that considered four traits. Furthermore, it was possible to select for only three traits (DBH, splitting, density) and identify the same top 30 individuals as using the index that considered four traits. The researcher's goal was to find the most desirable individuals in the population to be used for production purposes, such as clonal forestry. Consequently, various selection options, specifying certain trait requirements, were used to select superior individuals for use in production and deployment. The "commercial selection" option was the only option successful in obtaining an individual that met the required criteria for the four traits in the population of 475 individuals. The results suggested that breeders should consider large populations and only a few important traits in order to obtain a greater number of individuals suitable for mass propagation in clonal forestry. In order to further investigate the effect of population size on the number of individuals suitable for clonal forestry, a hypothetical population was generated. This was accomplished using between family and within family standard deviation values obtained from Dataset. The large hypothetical population of 1000 individuals produced twelve individuals suitable for production purposes, as opposed to only one in the real population of 475 individuals. This result further indicates that a larger population provides a greater number of individuals appropriate for use in production and deployment. This investigation successfully addressed the aims by: (1) calculating individual breeding values (BLP) and ranking individuals, (2) predicting the breeding population's response to selection, according to three strategies, for the four traits under investigation, and (3) identifying superior individuals for use in commercial clonal forestry. As the work of tree breeders is aimed at improving the growth and quality of trees by increasing the frequency of desirable genotypes in the population, further research could focus on (1) the effect of different sets of economic weightings on index rankings in a population and (2) the influence that population structure has on the optimal genetic gains obtained.