The response to both natural and artificial selection critically depends on the additive genetic variances and covariances underlying the traits subject to selection. As a consequence, understanding the genetic basis of complex morphological, life-history, physiological, ornamental and behavioural traits is crucial if we are to understand their evolutionary potential, and the evolutionary process in general.
Quantitative genetics uses the phenotypic resemblance among related individuals to infer the role of genes and the environment in shaping phenotypic variation. Depending on the species, we can use data obtained from breeding experiments under controlled conditions (e.g. insects, plants), or from individual-based monitoring programs in the wild (e.g. birds and mammals). Especially the latter has benefited greatly from the application of animal model methodology, originally developed in animal breeding to identify individuals of high genetic merit. By simultaneously using the resemblance among all individuals in the pedigree, these methods provide more precise and accurate estimates of genetic and non-genetic variance components (heritabilities and genetic correlations). Furthermore, they allow for the estimation of individual-level genetic effects (breeding values), and thereby the inference of evolution.
In this course we will cover everything from basic quantitative genetic theory and statistics to advanced mixed model-based approaches. You will learn how to estimate genetic variances and covariances in wild and captive populations, and how to test for evolutionary change. Along the way, you will be exposed to a range of general statistical methods (including generalised and mixed models), the R packages MCMCglmm and ASReml-R in particular. Furthermore, we will discuss a number of landmark papers that have put the concepts and methods covered during the lectures and practicals into practice to address fundamental evolutionary questions. You are strongly encouraged to bring your own data (if you have them), which you will be able to work on during the course and which will allow you to put the theory into practice.
Graduate or postgraduate degree in any Biosciences discipline. All participants must have a personal computer (Windows, Macintosh), with webcam if possible, and a good internet connection.
While some knowledge of R (e.g. importing and manipulating data) is required, you do not need any previous experience with quantitative genetics and animal models.
- Welcome and introduction
- Lecture 1: Quantitative genetic theory.
- Mendelian basis of continuous traits.
- Different types of gene action.
- Additive and non-additive genetic variances.
- Breeding values.
- Lecture 2: Basic statistics.
- Lecture 3: Heritability and its estimation.
- Parent-offspring regression.
- Fullsib/halfsib analysis.
- Practical 1: Estimating heritability
- Simulate data on parents and offspring.
- Estimate heritabilities.
- Parent-offspring regression.
- Discussion of Practical 1: Estimating heritabilit
- Lecture 4: More quantitative genetic theory.
- Genetic correlations.
- Phenotypic plasticity.
- Genotype-environment interactions.
- Lecture 5: Mixed models.
- Practical 2: (Quantitative genetic) mixed models
- Fitting mixed models in lme4 and asreml-R.
- Mixed model analysis of halfsib data.
- Discussion of Practical 2: (Quantitative genetic) mixed models.
- Lecture 6: Pedigrees.
- Lecture 7: The animal model.
- Practical 3: The animal model.
- Fitting animal models in ASReml-R.
- Discussion of Practical 3: The animal model.
- Lecture 8: Selection and its response
- Artificial selection.
- Breeder’s equation.
- Natural selection.
- Evolutionary constraints.
- Predicting evolution.
- Lecture 9: Breeding values.
- True versus predicted breeding values.
- Genetic selection gradients.
- Temporal trends.
- Drift versus adaptive evolution
- Practical 4: Fitting animal models in MCMCglmm
- Bayesian statistics and priors.
- Estimating heritability.
- Quantifying (adaptive) evolution.
- Discussion Practical 4: Fitting animal models in MCMCglmm
- Lecture 10: Generalised linear models.
- Lecture 11: Generalised animal models.
- Lecture 12: Advanced topics.
- Course Fee
- Early bird (until July 31st, 2020):
- 480 € *
(384 € for Ambassador Institutions)
- Regular (after July 31st, 2020):
- 640 € *
(512 € for Ambassador Institutions)
- This includes course material (VAT included).
* Participants from companies/industry will have an extra charge of 100 €.