In this course, we explore how genomic data can be used to study evolutionary processes in natural populations. Together we will examine questions related to selection, population structure, gene flow, introgression, and demographic history, and learn how these processes shape patterns of genomic variation.
We introduce the main types of genomic data used in population genomics and walk through the basic bioinformatics steps needed to prepare and manage datasets. During the course you will learn how to perform common analyses such as population structure inference, genome scans for selection, genotype–environment association analyses, demographic history inference, and genome-wide association studies (GWAS). We also examine how genomic variation relates to environmental variables using landscape genomics approaches.
The course combines lectures with computer-based practical sessions where you will work with real genomic datasets. We demonstrate commonly used bioinformatics workflows and analytical tools, and you will apply them through guided exercises and independent assignments. In addition, we read and discuss selected scientific papers that apply population genomic methods. As part of the coursework, you will analyze genomic data and prepare a short report or project based on your results.
To take this course, you should have completed Introduction to Population Genetics and Molecular Ecology, Fundamentals of Bioinformatics, and Data Wrangling, or have equivalent knowledge. You are expected to be familiar with basic genomic data and standard bioinformatics workflows. Basic experience with R and/or Python and some familiarity with command-line environments (Unix/Linux) is also recommended, as these tools are commonly used in population genomic analyses.
By the end of the course, you should feel comfortable preparing genomic datasets, carrying out key population genomic analyses, and interpreting the results in an evolutionary context.
- Opettaja: Juan Galarza Pavia