Quantitative evolutionary genetics

Quantitative genetics is a discipline that emerged in the early 20th century to understand the heredity of continuous traits, i.e. the majority of traits of agronomic interest (yield, etc.) or evolutionary interest (life-history traits, morphology). It is therefore an essential tool for understanding, modeling and predicting natural or artificial selection, and the evolution of natural systems or cultivated plants/animals. Its relevance is more relevant than ever at the start of the 21st century, with the advent of genomics (a factor of scientific progress, provided we don't reduce every evolutionary problem to the fiction of a few Mendelian alleles with a strong effect), and the return in force of alternative models of heredity (epigenetics) going beyond the sequence-centric vision inherited from classical molecular biology.

The aim of the module is to provide a culture of quantitative genetics sufficient to (i) understand the classical foundations of the discipline, manipulate the key quantities (genetic variances, heritabilities, genetic correlations) and the statistical techniques for estimating these parameters (ii) understand the power of this technique for posing and understanding fundamental or applied evolutionary problems (agronomic improvement) (iii) understand how this formalization of heredity fits in with the classical Mendelian vision.

1) in-depth knowledge of basic concepts: heritabilities, additive/dominant genetic variance, selection gradients, response to selection, covariances between relatives

2) understand QTL and genetic association techniques (GWAS) 2) know how to construct a protocol to estimate variance components and heritabilities in an experimental design

3) know how to analyze data from crosses controlled by linear models and data from natural populations via the animal model.

4) be able to understand how quantitative genetics can be used to address evolutionary questions, and to understand articles that do so.

A grounding in Mendelian genetics and statistics is required, and a grounding in quantitative genetics (M1 module) is useful but not strictly necessary (students from the Doctoral School, with whom this module is shared, will need to be brought up to speed at the first session).

100% continuous assessment