Evolutionary inference from population genomic data is a cornerstone of modern research across many applied fields, including conservation biology, ancient DNA, agriculture, genetic genealogy, and ancestry inference. This course provides a comprehensive introduction to the theory and practice of evolutionary demographic inference, focusing on two broad classes of population genomic data: frequency-based data (e.g. SNPs, structural variants, site frequency spectra) and haplotype-based data (e.g. whole genomes and haplotype alignments).

Participants will explore key methodological frameworks for inferring population history, structure, and gene flow, including method-of-moments estimators, site frequency spectrum (SFS)-based inference, isolation-with-migration models, and Sequentially Markovian Coalescent (SMC) approaches. The course combines lectures with hands-on practical sessions using real and simulated data.

This course is intended for graduate students, postdoctoral researchers, and other researchers interested in population genomics and statistical inference, with a particular focus on introducing model-based demographic inference from NGS data. Participants are expected to have a basic background in population genetics, and previous programming or scripting experience is required. We strongly encourage participants without prior command-line experience to complete a Bash tutorial before the course begins.

Monday — 2:00–8:00 PM (Berlin time)

• Overview of NGS data types: short- and long-read sequencing, whole genomes, RAD-seq, PoolSeq, ultra-conserved elements

• Introduction to core population genetics concepts

• Lab:

  • Introduction to Unix and R

  • Estimation of population genetic summary statistics (genetic diversity, differentiation, population structure, Tajima’s D, runs of homozygosity)

Tuesday — 2:00–8:00 PM (Berlin time)

• Introduction to population genomics and coalescent theory

• Definition and computation of the site frequency spectrum

• Lab:

  • Simulation of SFS under different demographic scenarios using msprime, tskit, and stdpopsim

  • Computation of SFS from simulated and real datasets

Wednesday — 2:00–8:00 PM (Berlin time)

• Designing demographic models using CoalMiner

• Demographic inference using fastsimcoal27

• Lab:

  • Demographic inference from real data

  • Model testing and comparison

  • Bootstrapping and estimation of confidence intervals

Thursday — 2:00–8:00 PM (Berlin time)

• Inference of demographic history from haplotypic data

• Island models and isolation-with-migration frameworks

• Lab:

  • Demographic inference using PSMC and MSMC2

  • Demographic inference using IMa3 (M-mode)

Friday — 2:00–8:00 PM (Berlin time)

• Comparative discussion of SFS-based vs haplotype-based inference

• Practical considerations: missing data, model complexity, identifiability, ancient DNA, low-coverage data

• Overview of additional methods (e.g. dadi, Ks-based inference)

• Lab:

  • Demographic inference using IMa3 (L-mode)

  • Ghost population inference with GhostBuster

1- Population Genomics - ONLINE, 16-20 March

2 - Deep Learning in Population Genomics & Phylogeography - ONLINE, 23-26 March

3 - Conservation Genomics - ONLINE, 7-10 April

Cancellation Policy:

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.