Reliable phenotypic data at
* the ‘macro’ level such as plant’s fitness, flowering time, bacterial colony growth or
* the ‘micro’ level such as cellular response or pathogen colonization within tissues or
* the ‘molecular’ level such as gene expression levels or metabolite profiling;
are required for in-depth genetic studies.
Due to the unique particularity to possibly replicate the same genotype in time and space, the phenotypic data of plants and microbes can be analyzed with strong power and accounting for variability.
The practical impossibility to replicate analyses with higher animals, including humans, creates strong constraints on the reliability of data and requires the use of sophisticated models to deal with inter-individual variability.
The design of experiments is all about learning as much as possible from the smallest amount of data.
Nowadays, the combination of high-throughput methods to characterize phenotypes and rigorous experimental designs opens up unprecedented opportunities. However, it is very common to see undervalued data and incomplete results due to a lack of implementation of the right designs.
During this course, the fundamentals of determining the effects of given factors or covariates on a phenotypic trait will be given. This includes how to evaluate controlled and uncontrolled variability using the appropriate experimental design. The course is mostly practical-driven and aims to provide the skills to use these methodologies in future professional activities.