26-30 May 2025

The Physalia course on population genomics offers a comprehensive five-day program designed to introduce participants to key concepts and techniques in the field. Through a hands-on approach, attendees will delve into different topics including basic bioinformatics, population structure, introgression, demographic modeling, genome scanning, and landscape genomics. The first day will be dedicated to learning essential skills for handling large short-read sequencing data and that will be used during the rest of the week. The course aims at providing a global overview of population genomics methods and their applications. Participants will gain fundamental knowledge (~ 2 hours/day) and practical experience (~3 hours/day) advance their research in this rapidly evolving field. Participants will be strongly encouraged to further engage with the practical exercises after each session and to discuss with the instructors regarding any issues encountered.

This course is designed for graduate students, postdocs, or researchers with little to no prior experience in population genomics. The primary objective is to learn the field through a hands-on, learning-by-doing approach.
Participants should have a basic understanding of genetics/genomics and evolutionary biology, as well as prior experience with a programming language. Familiarity with UNIX-based command line tools and R is required.
To ensure you can follow the course effectively, we recommend that participants without prior experience in R complete this R tutorial and those without knowledge of the UNIX command line complete this UNIX tutorial before attending.

Graham Coop: “Population and Quantitative Genetics” course at University of California, Davis (CC-BY)

Thibault Leroy & Quentin Rougemont (2020) Book chapter: Introduction to population genomics methods. In: Molecular Plant Taxonomy.

Yann X. C. Bourgeois & Ben H. Warren (2021). An overview of current population genomics methods for the analysis of whole-genome resequencing data in eukaryotes. Molecular Ecology, 30, 6036–6071. 

Monday. 2-7 pm Berlin time
Round-table to discuss participants’ interests and biological models.
Lecture: Basic bioinformatics, introducing notions related to the handling of large sequencing data. Quality Control, read trimming, mapping and SNP calling for both short-read sequencing data.  

Tuesday. 2-7 pm Berlin time
Inference of population structure and introgression.
Lecture: Concepts, methods, assumptions and main pitfalls regarding structure inference and detection of introgression.

Workshop: Analysis of empirical data: detection of clusters with supervised and unsupervised methods, detecting introgression and introgressed regions.

Wednesday. 2-7 pm Berlin time
Demographic modeling methods.
Lecture: Rationale of demographic inference and modeling, range of application, advantages and disadvantages. Introduction to Markovian coalescent-, composite likelihood- and Approximate Bayesian Computation-based methods.

Workshop: Reconstructing past changes in effective population sizes and comparison of different demographic scenarios.

Thursday. 2-7 pm Berlin time
Genome-scans for association and selection.
Lecture: Genome-Wide Association Study (GWAS), selective sweep detection, differentiation, and whole-genome scanning for Genotype-Environment association (GEA). Brief introduction to advanced methods: machine/deep learning, Ancestral Recombination Graphs.

Workshop: Application of GWAS and GEA to empirical data. Detection of selective sweeps.

Friday. 2-7 pm Berlin time
Landscape genomics.
Lecture: Methods to test isolation-by-distance, isolation-by-environment. Inference of population structure in space. Identification of geographical and environmental barriers to gene flow.

Workshop: Detection of deviations from strict isolation-by-distance. Testing landscape resistance to gene flow. Reintroducing some major concepts (e.g. population structure, GEA) in a spatial context to summarize the physalia course as a whole.