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Course Description |
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Course Name |
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Estimation Methods of Breeding Values |
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Course Code |
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ZO-553 |
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Course Type |
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Optional |
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Level of Course |
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Second Cycle |
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Year of Study |
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1 |
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Course Semester |
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Spring (16 Weeks) |
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ECTS |
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6 |
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Name of Lecturer(s) |
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Prof.Dr. ZEYNEL CEBECİ |
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Learning Outcomes of the Course |
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Understands the principles and fundementals of articifial selection. Understands breeding value (genetic value) and its importance. Understands estimation methods and algorithms on breeding value. Knows and uses the software on quantitative genetics. Determines and defines objectives of selection programmes in animal breeding. Writes projects for selection programmes related with milk and meat traits.
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Mode of Delivery |
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Face-to-Face |
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Prerequisites and Co-Prerequisites |
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None |
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Recommended Optional Programme Components |
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None |
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Aim(s) of Course |
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The objective of this course is to teach and apply the estimation methods of breeding values for selection in animal breeding programs. |
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Course Contents |
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This course includes the topics on introduction to quantitative genetics, maximising response to selection, selection methods, estimation of breeding values and development of breeding programs. |
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Language of Instruction |
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Turkish |
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Work Place |
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Classroom |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
Objectives of selection in animal breeding |
Reading the chapter on selection in the "animal breeding" textbook by O. Düzgüneş. |
Reading |
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2 |
Introduction to artificial selection methods |
None |
None |
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3 |
Definition and importance of breeding values |
None |
None |
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4 |
Breeding value estimation methods |
None |
None |
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5 |
Single trait selection and breeding value estimation |
None |
None |
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6 |
Multiple traits selection and breeding value estimation |
None |
None |
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7 |
Introduction to index method for multiple traits |
None |
None |
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8 |
Midterm exam |
None |
None |
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9 |
Direct update method |
None |
None |
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10 |
Applications of direct update method in R |
Working with simulated data in R |
Exercise |
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11 |
BLUP Method |
None |
None |
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12 |
Comparison of the methods and reliability |
Graphics and descriptive analysis for comparision of reliabilities of breeding values which are computed from simulated data. |
Exercise |
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13 |
Applications of breeding value estimation with DFREML 1 |
None |
None |
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14 |
Applications of breeding value estimation with DFREML 2 |
None |
None |
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15 |
Final exam preperation |
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) |
Kinghorn, B.P., Van der Werf, J. & Ryan, M.(2000) (eds.). Animal Breeding: Use of New Technologies. Post Graduate Foundation in Veterinary Science, University of Sydney ( ISBN: 0 646 38713 8) .
van der Werf J (2003). Lecture notes on Quantitative Genetics. http://www-personal.une.edu.au/~jvanderw/325TOC.htm
van der Werf J (2003). Teaching Material used for Sheep Genetics Training at UNE. http://www-personal.une.edu.au/~jvanderw/IndustryTraining_Material.htm
Falconer, D.S. & Mackay, T.F.C. (1996) Introduction to Quantitative Genetics (4th Ed.), Longman.
Willis, M.B. (1991) Introduction to Practical Animal Breeding, Blackwell.
Christensen, K. (2012). Animal Genetics, Section of Genetics, Bioinformatics and Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark
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| Required Course Material(s) |
Potocnik, K., Krsnik, J., Štepec, M. & Gorjanc, G (2007). "Comparison between methods for estimation of breeding values for longevity in Slovenian Holstein population". Interbull Bulletin 38.p.55-60.
Estimation of Breeding Values (http://www.holstein-dhv.de/estimation_of_breeding_values.html)
GENUP - Computer aided learning for quantitative genetics. University of New England, Australia. http://www-personal.une.edu.au/~bkinghor/genup.htm
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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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
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60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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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 |
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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. |
2 |
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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 |
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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. |
3 |
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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. |
5 |
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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 |
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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 |
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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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