Course Details
This online course in quantitative genetics and molecular breeding is hosted by the Forest Molecular Genetics (FMG) Programme at the University of Pretoria and by the African Centre for Crop Improvement (ACCI) at the University of KwaZulu Natal (UKZN).
This course syllabus is designed for postgraduate students in plant and animal breeding as well as professionals working in the forestry sector. The syllabus has been modified and adapted from the graduate course “Advanced Topics in Quantitative Genetics and Breeding” that Prof Fikret Isik co-developed at North Carolina State University since 2012.
The course has two main sections that will be presented in two semester periods in 2023. The Theory and the Concept Module (March to June) and the Molecular Breeding Module (August to November). The main delivery method will be online course meetings (2-3 lecture hours per week).
The first meeting will be on Friday 10 March 2023, to discuss the overview and format of the course. Feedback from participants is welcomed. Lectures will start during the week of 20 March 2023.
During the course of each semester, an in-person intensive (3-day) training workshop will be offered to course participants to emphasize and reinforce the practical aspects of the course. The training workshops will be presented in a hybrid (online and in-person) format, but in-person attendance will be encouraged.
Instructor: Prof Fikret Isik, a Fulbright scholar and visiting Professor at University of Pretoria (UP), and University of KwaZulu-Natal (UKZN). Guest lecturers from UKZN and the UP will also be included.
Course prerequisite(s): Introductory statistics and quantitative/population genetics courses at 3rd year level (part of the BSc Biological Sciences degree programme) will be helpful but are not required. Some R programming skills will be helpful. The course will start with an introduction to R. In preparation for the course, Dr Nanette Christie is also hosting online R training courses.
Register for "R Basics" or the more advanced "Using R" course:
Alternatively, contact nanette.christie@up.ac.za to make personal arrangements.
By taking both modules of the course participants will be able to learn about:
-
The concepts of linear mixed models pertinent to plant and animal breeding
-
Introduction to state-of-the-art in genome-wide DNA marker analysis towards quantitative genetic dissection and molecular breeding
-
Computing skills to analyze large genetic data from different genotyping platforms
-
The theory of genetic and genomic relationships and estimation methods
-
Concept and application of genetic data for predictions of genetic merit of individuals
-
Genomic selection strategies
Course Scope
1
QUANTITATIVE GENETICS
(15 weeks + workshop)
-
Basic statistical machinery
-
A brief introduction to matrix algebra
-
Heritability concept
-
Review of quantitative genetics
-
Genetic variances and resemblance
-
Linear mixed models
-
Covariance structures in mixed models
-
Breeding values (sire and animal models)
-
Genetic values
Workshop 1 (mid-May)
-
Introduction to ASReml-R
-
Prediction of breeding and genetic values
-
Multi-environmental trials and GxE
-
Multivariate models and selection index
2
PLANT MOLECULAR BREEDING
(15 weeks + workshop)
-
Multi trait selection response
-
Analysis of categorical traits
-
Exploratory marker data analysis
-
Using SNP Markers for quality control
-
Imputation of missing genotypes
-
Genetic/genomic relationships
-
Genomic selection
Workshop 2 (mid-Sept)
-
Calculation of genomic relationships
-
Using SNP Markers for quality control
-
Genomic prediction of breeding values
-
Genomic selection methods
3
ASSESSMENTS
Reading Assignments:
Students are expected to read the latest literature on each topic. Some papers will be discussed during the class time. The following textbook is recommended: Isik, Fikret, James Holland, and Christian Maltecca. Genetic data analysis for plant and animal breeding. Vol. 400. Cham, Switzerland: Springer International Publishing, 2017.
Exercises & Assignments:
We plan to have assignments covering the major topics as outlined in the course organization. Assignments are typically due the week after the respective lectures. Hands-on data analysis is expected from the assignments. The assignment problems will be selected from real breeding programs. No exams are planned. Assignments will be self-graded based on the solutions provided by the instructor.