Course code BIN301

BIN301 Genomic and pedigree-based prediction of genetic value

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Showing course contents for the educational year starting in 2020 .

Course responsible: Tormod Ådnøy
Teachers: Jørgen Ødegård, Morten Lillemo, Theodorus Hendrikus Elisabeth Meuwissen
ECTS credits: 10
Faculty: Faculty of Biosciences
Teaching language: EN
(NO=norsk, EN=Engelsk)
Limits of class size:
50
Teaching exam periods:
This course starts in Autumn parallel. This course has teaching/evaluation in Autumn parallel.
Course frequency: Annually
First time: 2020H
Course contents:
Prediction of genetic value using mixed model theory - use of genomic data - use of pedigree data. Modeling of environmental, and genotype by environmental effects. Multitrait predictions. Prediction of binary traits. Accuracy and biases of predictions of genetic value. Introduction to estimation of variance components. Examples from plant and animal breeding.
Learning outcome:

Students will have an overview over state-of-the-art predictions of genetic value for animal and plant populations, and their relevance in breeding programs.

After the course students should be able to predict genetic values, and thus phenotypes, based on genomic and family information (SNP- and G-BLUP, and pedigree based BLUP). Students can judge whether there is more information in inbred lines, crosses between lines, outbreeding individuals; and how to assess accuracy and biases in particular cases. The students will be able to address complications for genetic predictions such as genotype*environmental interactions, binary traits, multitrait predictions, predictions where few major genes next to many small genes determine the trait. The students will be able to predict genetic value at MSc and PhD thesis level and obtain the know-how to apply predictions in practice. 

Learning activities:
Lectures and computer exercises. Problem-based learning is applied in combination with computer-based exercises. Presentation of predicted genetic values of own data set.
Teaching support:
Teacher assistance linked to data labs and lectures.
Syllabus:
Lecture notes and selected papers. Exercises, notes, programs, and other material will be made available on the course web site.
Prerequisites:

STAT200 Regression, or similar course in statistical linear models.

General breeding (HFA200, or AQB200) and similar in plant science, or similar courses.

Recommended prerequisites:
Matrix and vector manipulation (linear algebra). Acquaintance with MS Excel and R or other computer programming.
Mandatory activity:
Presentation of own term paper for the other students, and participation at such presentations. 
Assessment:
Handed in term paper with A-F grading, and external sensor.
Nominal workload:
300 hours, including individual study, exercises and participation and presentation in discussion groups/lectures.
Reduction of credits:

HFA301

5 ECTS reduction for students with HFA301.

Type of course:
Discussion groups / lectures: 2 hours per week. Datalab / exercises: 2 hours per week.
Note:
This is a major revision of HFA301 as taught up to 2019. Genomic information now has a major focus, and plant breeding is included.
Examiner:
An external examiner assesses the term paper.
Examination details: Langsgående vurdering: A - E / Ikke bestått