25-29 November 2024

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 intended for graduate students, postdocs or any researcher interested that has no or relatively little knowledge  in population genomics. A main objective is to learn this field following a learning-by-doing strategy. Participants are expected to have a basic background in genetics/genomics and evolutionary biology and to have already used a programming language. In the event, a participant has absolutely no prior knowledge in a programming language but would be interested to join the course, we strongly encourage them to read online R tutorials before this Physalia course (e.g. https://rforcats.net/). Lectures will provide more context and information for participants wishing to further hone their analytical skills.

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- and long-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.