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Genetic Epidemiology of Infectious Diseases in Livestock

The course is cancelled.
NOVA PhD course of 3 ECTS, organised by Dirk Jan de Koning, Swedish University of Agricultural Sciences (SLU).
Dates and location: 7-11 June 2016 in Uppland, Sweden.

Photo: 
Janne Karin Brodin
Course Description
The course focuses on the collection, curation, phenotypic analysis, modelling and genetic analysis of field data on infectious diseases in livestock. Field data on infectious diseases in livestock is very noisy and incomplete by its very nature and the course will clearly outline all the challenges related to handling these data and how to make meaningful inferences.

Programme Outline
The course will consist of a series of lectures (approximately 5 hours spread over the day), exercises and discussions. Participants will work in groups and data labs during the day to get hands on experience of the topics covered. Students will present results of the group work. R will be used for exercises.

Learning Outcomes
After the course students should have an understanding of the challenges for genetic modelling of disease resistance using filed data. They will be able to evaluate the potential impact of genetic selection for increased resistance. Students will be able to assess the potential for genetic studies on disease resistance in livestock using field data and interpret the results of such studies in the context of population specific parameters. Furthermore they will understand and be able to apply the basic principles of epidemiological modelling.

Pre-/Post-Campus Assignments

  • Prepare a 10 minute talk about their own research to be given during the course.
  • The course will inevitably be of quite a numerical nature and students will be expected to study some background papers:
    • Stephen C. Bishop*, Andrea B. Doeschl-Wilson and John A. Woolliams Uses and implications of field disease data for livestock genomic and genetics studies. Front. Genet., 22 June 2012 http://dx.doi.org/10.3389/fgene.2012.00114
    • Preventive Veterinary Medicine Volume 113, Issue 3, Pages 279-338, 15 February 2014
    • Making valid causal inferences from observational data, Pages 281-297, Wayne Martin
    • The data – Sources and validation, Pages 298-303, Ulf Emanuelson, Agneta Egenvall
    • Bias—Is it a problem, and what should we do?, Pages 331-337, Ian R. Dohoo
    • The analysis—Hierarchical models: Past, present and future, Pages 304-312, Henrik Stryhn, Jette Christensen
  • Also, students are expected to have a working knowledge of R before the course and we will make some training tools for R available online prior to the course.

Evaluation Elements
The course will be pass or fail on the basis of in-course assessments. A written assessment on day 1 to get an overview of how well the literature has been studied and what is the starting knowledge. Each student will prepare and briefly present a poster describing their research in the context of the course. Further assessments will be made on the active participation of the students in computer exercises and plenary discussions.

Pedagogical Approach
We test the students’ knowledge at the beginning of the week and adapt the teaching as needed. We have a varied teaching approach including lectures, practical and discussions. The students can contribute actively through the poster presentations and the discussions of how their research topic can be influenced by what they have learned during the course. All lecturers are very experienced at teaching at postgraduate level to an international audience.

Estimated Workload
100 hours, consisting of:

  • 20 hours lectures
  • 15 hours computer practical
  • 7-8 hours discussions and presentations
  • 10 hours pre-course poster preparation
  • about 48 hours literature study before and during the course

Prerequisite Knowledge
The course is primarily targeted at PhD in animal breeding and quantitative genetics. However, PhD students with a background in veterinary science with a strong interest and aptitude in modelling are also encouraged to attend.

Students are expected to have a working knowledge of R before the course.

Admission
Admission for NOVA courses is handled by the course organiser/ the NOVA member institution organising the course. Please see the links in the margin for more information.

Published 7. August 2015 - 9:26 - Updated 20. May 2016 - 9:50

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