DOI: 10.5281/zenodo.7670839
title: [Dataset] Data for the course "Population Genomics" at Aarhus University

description: Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch. This course material is maintained by the health data science sandbox. This webpage shows the latest version of the course material.



	
 Data.tar.gz Contains the datasets and executable files for some of the softwares
	You can unpack by simply doing
	tar -zxf Data.tar.gz -C ./
	This will create a folder called Data with the uncompressed material inside
	
Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes (Note this environment is created on Ubuntu 22.10). You do this in the command line by
	

		
creating the folder Course_Env:  mkdir Course_Env
		
untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env
		
Activate the environment: conda activate ./Course_Env
		
Run the unpacking script (it can take quite some time to get it done): conda-unpack
	
	
	
Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the version above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.
	
environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands:
	

		
conda env create -f environment_with_args.yml -p ./Course_Env
		
conda activate ./Course_Env
	
	



 


The data is connected to the following repository: https://github.com/hds-sandbox/Popgen_course_aarhus. The original course material from Prof Kasper Munch is at https://github.com/kaspermunch/PopulationGenomicsCourse.


 


Description


The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.


The participants must at the end of the course be able to:



	
Identify an experimental platform relevant to a population genomic analysis.
	
Apply commonly used population genomic methods.
	
Explain the theory behind common population genomic methods.
	
Reflect on strengths and limitations of population genomic methods.
	
Interpret and analyze results of population genomic inference.
	
Formulate population genetics hypotheses based on data



The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.


Curriculum


The curriculum for each week is listed below. "Coop" refers to a set of lecture notes by Graham Coop that we will use throughout the course.


Course plan



	
Course intro and overview:
	

		
Coop chapters 1, 2, 3, Paper: Genome Diversity Project
	
	
	
Drift and the coalescent:
	

		
Coop chapter 4; Paper: Platypus
		
Exercise: Read mapping and base calling
	
	
	
Recombination:
	

		
Lecture: Review: Recombination in eukaryotes, Review: Recombination rate estimation
		
Exercise: Phasing and recombination rate
	
	
	
Population strucure and incomplete lineage sorting:
	

		
Lecture: Coop chapter 6, Review: Incomplete lineage sorting
		
Exercise: Working with VCF files
	
	
	
Hidden Markov models:
	

		
Lecture: Durbin chapter 3, Paper: population structure
		
Exercise: Inference of population structure and admixture
	
	
	
Ancestral recombination graphs:
	

		
Lecture: Paper: Approximating the ARG, Paper: Tree inference
		
Exercise: ARG dashboard exercises + Inference of trees along sequence
	
	
	
Past population demography:
	

		
Lecture: Coop chapter 4, Paper: PSMC, revisit Paper: Tree inference
		
Exercise: Inferring historical populations
	
	
	
Direct and linked selection:
	

		
Lecture: Coop chapters 12, 13, revisit Paper: Tree inference
	
	
	
Admixture:
	

		
Lecture: Review: Admixture, Paper: Admixture inference
		
Exercise: Detecting archaic ancestry in modern humans
	
	
	
Genome-wide association study (GWAS):
	

		
Lecture: Coop lecture notes 99-120
		
Exercise: GWAS quality control
	
	
	
Heritability:
	

		
Lecture: Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)
		
Exercise: Association testing
	
	
	
Evolution and disease:
	

		
Lecture:  Coop Lecture notes Sec. 11.0.1 (p217-221)
		
Exercise: Estimating heritability