[Dataset] Data for the course "Population Genomics" at Aarhus University

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