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PVS0147
Linear Models in Animal Breeding
The course will be based on theoretical lectures alternating with practical computer exercises, mainly in R. Three weeks before the course starts some pre‐course exercises will be given, including exercises in R and some questions on linear regression and
statistical theory. Students are expected to have completed a majority of these exercises before arriving to the course. One week before the course start there will be an online meeting for those who want to ask questions on the pre‐course exercises.
The course will include sections on linear regression, diagnostic tools for linear regression, linear regression using weighted least squares, maximum likelihood, a brief introduction to generalized linear models, linear mixed models, introduction to GWAS models, and genomic selection using gBLUP, short overviews of dispersion modelling, GLMM, and Bayesian alternatives.
statistical theory. Students are expected to have completed a majority of these exercises before arriving to the course. One week before the course start there will be an online meeting for those who want to ask questions on the pre‐course exercises.
The course will include sections on linear regression, diagnostic tools for linear regression, linear regression using weighted least squares, maximum likelihood, a brief introduction to generalized linear models, linear mixed models, introduction to GWAS models, and genomic selection using gBLUP, short overviews of dispersion modelling, GLMM, and Bayesian alternatives.
Syllabus and other information
Syllabus
PVS0147 Linear Models in Animal Breeding, 3.0 Credits
Subjects
Animal ScienceEducation cycle
Postgraduate levelGrading scale
Pass / Failed
Prior knowledge
Doctoral student experience or similar, with knowledge in animal science or veterinary medicine, or participant in a residency program in veterinary science. NOVA doctoral students, and thereafter BOVA and other doctoral students will be prioritized.Objectives
After completing the course the students shall be able to: Choose appropriate models for different analyses. Use diagnostic tools in R for linear regression. Perform simple genome wide association analysis. Describe and explain the basic principles of genomic selection. Find relevant information on more complex situations and analyses when needed.Content
The course will be based on theoretical lectures alternating with practical computer exercises, mainly in R. Three weeks before the course starts some pre‐course exercises will be given, including exercises in R and some questions on linear regression and statistical theory. Students are expected to have completed a majority of these exercises before arriving to the course. One week before the course start there will be an online meeting for those who want to ask questions on the pre‐course exercises. The course will include sections on linear regression, diagnostic tools for linear regression, linear regression using weighted least squares, maximum likelihood, a brief introduction to generalized linear models, linear mixed models, introduction to GWAS models, and genomic selection using gBLUP, short overviews of dispersion modelling, GLMM, and Bayesian alternatives.Additional information
Each participants should bring a laptop with R already installed.The students are expected to have some knowledge in linear regression and R basics before the course. These can be can be acquired through the pre‐course exercises.
Location: To be decided. Probably a conference venue north of Uppsala, e.g. Orsa Grönklitt.
In collaboration with NOVA University Network.
Responsible department
Department of Animal Breeding and Genetics