| Department of Large Animal Sciences | |||||||||||||||
| Earliest Possible Year | MSc. 1 year | ||||||||||||||
| Duration | One block | ||||||||||||||
| Credits | 7.5 (ECTS) | ||||||||||||||
| Course Level | MSc | ||||||||||||||
| Examination | Final Examination oral examination Some Aid allowed All aids are allowed during the ½ hour of preparation, but only an A4-page with own notes can be brought to the examination Description of Examination: An individual oral examination is held at the end of the course; 1/2 hour preparation followed by 1/2 hour examination. Each student will draw a topic at random from the curriculum and will get the opportunity to present this topic initially. Afterwards, questions will be asked about this topic and finally the discussion will be broadened to include other aspects of the curriculum. Weight: 100% 7-point scale, internal examiner | ||||||||||||||
| Requirement For Attending Exam | Approval of at least 3 assignments | ||||||||||||||
| Organisation of Teaching | Lectures, theoretical exercises, and practical computer exercises. | ||||||||||||||
| Block Placement | Block 2 Week Structure: A | ||||||||||||||
| Teaching Language | English | ||||||||||||||
| Optional Prerequisites | 210006 Statistical Data Analysis 2 240056 210004 Mathematics and Planning Knowledge of multivariate statistics, matrix algebra, basic computer programming and basic genetics are assumed. "Matematik og modeller" (210012) can replace "Matematik og planlægning". | ||||||||||||||
| Restrictions | None | ||||||||||||||
| Course Contents | |||||||||||||||
| Initially, the necessary genetic theory including Ideal populations, gene and genopypic frequencies, Hardy-Weinberg equilibrium, linkage equilibrium, genetic drift, mutation, migration, relationship and indbreeding will be introduced. Thereafter mathematical and statistical preconditions will be introduced in order to make sure that all students have a basic understanding of topics like vector and matrix operations (i.e., linear algebra), random numbers, Bayes' theorem, distributions (including multivariate and conditional), etc. The duration of this part is between 2 and 3 weeks. Afterwards, the lectures move towards quantitative genetics. Subjects are means, variances and covariances, mixed inheritance model and the infinitessimal model. Different inheritance modes, as dominance and epistasis, are also considered. The effects of selection will be illustrated in simple cases and by computer exercises. Later on, real populations will be in focus. The centre of attention will be prediction of breeding values and response to selection that can be predicted, estimated and used in designing and evaluating selection experiments. Various statistical models that can handle a powerful range of real problems will be presented. | |||||||||||||||
| Teaching And Learning Methods | |||||||||||||||
| Lectures, theoretical exercises, and practical computer exercises. In connection with lectures and exercises, the students are expected to participate actively in mutual discussions. Part 1 will be supported mainly by theoretical problem solving exercises. The last part of the course will be supported by both theoretical problem solving exercises and computer exercises. | |||||||||||||||
| Learning Outcome | |||||||||||||||
| After completing the course students should be able to participate in designing selection experiments, use methods for genetic analyses of qualitative and quantitative traits and evaluate the results. Moreover the students should be able to evaluate the future implementation of results from the DNA-technology. Knowledge: - Fully understand the quantitative and population genetic theory which forms the basis of both animal and plant breeding in industrialised and developing countries. - Understand the concepts of genetic drift, selection, mutation, migration, resemblance between relatives, linkage and inbreeding. Skills: - Is able to apply statistical models (e.g., various Best Linear Unbiased Prediction models) to predict genetic response to selection, breeding values or genetic parameters simultaneous with estimation of non-genetic effects for small data sets. Competencies: - Is able to evaluate methods and models used in quantitative genetics and interpret their results. | |||||||||||||||
| Course Litterature | |||||||||||||||
| Lynch, M. & Walsh, B. (1998) Genetics and analysis of quantitative traits, Sinauer. Handouts and scientific papers. | |||||||||||||||
| Course Coordinator | |||||||||||||||
| Thomas Mark, thm@life.ku.dk, Department of Basic Animal and Veternary Sciences/Genetics & Bioinformatics, Phone: 35332890 | |||||||||||||||
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