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| Course Description |
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| Course Name |
: |
Estimation of Genetic Parameters |
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| Course Code |
: |
ZO-546 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Fall (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
: |
Prof.Dr. ZEYNEL CEBECİ
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| Learning Outcomes of the Course |
: |
Understands genetic parameters and their importance in animal breeding
Learns the estimation methods of heritability, and calculates the parameters using these methods
Estimates repeatability
Estimates phenotypic and genetic correlations
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| Mode of Delivery |
: |
Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
This course aims to teach the estimation methods of genetic parameters such as heritability, repeatability, genetic and phenotypic correlations, and how to analyze the data to estimate genetic parameters. |
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| Course Contents |
: |
This course covers the topics on methods for the estimation of components of variance and co-variance with specific focus given to their application to heritability, repeatability and genetic correlation estimation. |
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| Language of Instruction |
: |
Turkish |
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| Work Place |
: |
Classroom |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Genetic parameters and their usage in animal breeding |
None |
None |
|
2 |
Heritability and estimation methods |
None |
None |
|
3 |
Estimaton of heritability from selection and crossbreeding experimental data |
Reading articles and related textbook chapters on artificial selection and selection methods |
Reading |
|
4 |
Estimaton of heritability from data on animal relatives |
None |
None |
|
5 |
Repeatability and estimation methods |
Reading the related chapters of correlation and regression in statistics texbooks |
Reading |
|
6 |
Phenotypic correlations and estimation methods |
None |
None |
|
7 |
Genetic correlations and estimation methods |
None |
None |
|
8 |
Midterm exam |
None |
None |
|
9 |
Estimation of genetic parameters with DFREML software |
Setting up DFREML software and reading the users guide |
Exercise, Reading |
|
10 |
Simulation and creating artifical population data |
Creating artificial data in R |
Exercise |
|
11 |
Estimation of genetic parameters for milk yield |
Reading an article on genetic parameters estimation for milk yield |
Reading |
|
12 |
Estimation of genetic parameters for meat yield |
Reading an article on genetic parameters estimation for meat yield |
Reading |
|
13 |
Correlations between two traits |
None |
None |
|
14 |
Term project presentation and discussion |
None |
None |
|
15 |
Final exam preparation |
None |
None |
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16/17 |
Final exam |
None |
None |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Rao, A.R. & V.K. Bathia. Estimation of Genetic Parameters in Statistical Methods for Agricultural Research. http://www.iasri.res.in/ebook/EB_SMAR/e-book_pdf%20files/Manual%20III/19-animal_breed_tech.pdf
Krishnan, T. Variance Components Analysis in Statistical Methods for Agricultural Research. http://www.iasri.res.in/ebook/EB_SMAR/e-book_pdf%20files/Manual%20III/19-animal_breed_tech.pdf
van der Werf J. Estimation of Genetic Parameters. http://www-personal.une.edu.au/~jvanderw/Estimation_of_Variance_Components.pdf
B. Walsh (2003). Basic Designs for Estimation of Genetic Parameters. http://www.rni.helsinki.fi/~boh/Teaching/Selection/qgen3.pdf
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| |
| Required Course Material(s) |
J. Bento, S. Ferraz & R. K. Johnson (1993). Animal Model Estimation of Genetic Parameters.http://goo.gl/O9tvK
R.Thompson, S.Brotherstone & I. M.S White (2005). Estimation of quantitative genetic parameters. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1569516/
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
60 |
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Homeworks/Projects/Others |
5 |
40 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
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100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
At the end of this programme, the students improve and deepen their knowledge in the field of Animal Science by building on the knowledge and competence acquired at the undergraduate level and can employ interdisciplinary interaction in their field of study. |
3 |
|
2 |
The students interpret and generate new information and theories in specific fields related to Animal Science using the theoretical and practical knowledge at masters level. Also, they can reveal the cause-effect relationship regarding the problems in their field of study and employ scientific research methods to generate possible solutions. |
3 |
|
3 |
The students independently identify potential problems and carry out research studies aiming at solutions in the field of Animal Science. Also, they investigate and develop strategic approaches for potential problems that may arise related to the particular studies. |
3 |
|
4 |
The students access and compile information about the latest developments and fundamental sources in the particular field and reach a new synthesis by evaluating and interpreting the existing research. They can make use of this acquired knowledge to practice the profession effectively and follow the improving implementations in the field. |
4 |
|
5 |
The students use information in the field of Animal Science, through compiling, interpreting and synthesising it, in order to make social contributions. They make evaluations by creating a plan and framework and taking specific total quality criteria into consideration. They use the skills and knowledge acquired in the field of Animal Science in joint projects with other disciplines. |
3 |
|
6 |
The students discuss and pass on the acquired knowledge based on their work in the field by making written and oral presentations. They have speaking and writing competence in at least one foreign language at a level that enables them to keep up with the requirements of the age. They express their ideas clearly using the tools of information and communication technologies. |
0 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
13 |
3 |
39 |
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Out of Class Study (Preliminary Work, Practice) |
5 |
10 |
50 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
5 |
10 |
50 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
3 |
3 |
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Final Exam |
1 |
3 |
3 |
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Total Workload: | 145 |
| Total Workload / 25 (h): | 5.8 |
| ECTS Credit: | 6 |
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