Quantitative evolutionary genetics

Quantitative genetics is a discipline born at the beginning of the 20th century to understand the heredity of continuous traits, i.e. the majority of traits of agronomic (yield...) 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 beginning of the 21st century, with the advent of genomics (a factor of scientific progress provided that all evolutionary problems are not reduced to the fiction of a few Mendelian alleles with a strong effect), and the return in force of alternative models of heredity (epigenetics) that go 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 view.

1) have a thorough understanding of basic concepts: heritabilities, additive/dominance genetic variance, selection gradients, response to selection, covariance 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 controlled crosses using linear models and data from natural populations using 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 base in Mendelian genetics and statistics is necessary, and a base in quantitative genetics (M1 module) is useful but not strictly necessary (upgrade at the first session for students of the Doctoral School with whom this module is shared).

Continuous assessment : 100%.