New phenotyping techniques are revolutionizing the field of plant sciences in general and plant breeding in particular. How can phenotyping data from platforms and high throughput devices be analysed and incorporated into statistical genetic models in plant breeding? This course will teach how to get the most out of new types of phenotypic data and integrate these data into genetic analyses. The course will deal with pre-processing of new phenotypic data (image pre-processing, spatial and longitudinal modelling) and show the integration of new phenotypic traits into QTL and genomic prediction models. You will also learn how to relate field and platform data. Examples and exercises will use real data from phenotyping platforms and field experiments.
The statistical analysis techniques will be presented in lectures by experts from Wageningen University & Research and University of Queensland. The lectures will be complemented by hands-on computer training using R.
The course will be structured as follows:
09.00-12.30: Introduction phenotyping techniques
13.30-17.00: Single platform experiments
09.00-12.30: Longitudinal and multi-trait data
13.30-17.30: Multi-platform and multi-environment data
Drinks at de Spot (Orion building)
09.00-12.30: Integrating phenotyping data into QTL and GWAS models
13.30-17.00: Genomic prediction and phenotyping & further developments
The course aims at students, researchers and breeders interested in incorporating new phenotyping data into genetic analyses. Some familiarity with models for QTL mapping, GWAS and genomic prediction is required.
After the course you should be able to use R to analyze field and platform experiments and integrate platform and phenotyping device data into genetic analyses of plant breeding experiments.
Registration is closed
*Note: if you are a PhD or MSc at WUR and would like us to send the invoice directly to your reseach group, send an email to daniela.bustoskorts (at) wur.nl with the name of your group and project code. In that case, you don´t need to fill in the form below.