Documentation for the 2019 Workshop on Computational Population Genetics

This is the documentation for the Workshop held during January 22-24, 2019 at the Max-Planck-Institute for the Science of Human History (MPI-SHH) in Jena, organised by Stephan Schiffels (MPI-SHH), Choongwon Jeong (MPI-SHH) and Benjamin Peter (MPI-EVA). Important contributions came from Kay Prüfer and Cosimo Posth!

Contents:

• Setting up
  • The Computational Infrastructure for this Workshop
  • Basic Usage
  • Notebooks
  • Working with Bash
  • Back to Notebooks
  • Some Plotting with Python
• Bam Files
  • samtools: Converting, Filtering SAM and BAM Files
  • Bash programming
  • Basic programming in AWK
  • Calculating coverage with samtools
  • EXTRA: Genotype calling from bam files
• Principal Components Analysis (PCA)
  • Genotype Data
  • How PCA works
  • Preparing the parameter file
  • Population lists vs. Projection
  • Running smartPCA
  • Plotting modern populations
  • Adding ancient individuals
• F Statistics and Treemix
• Admixture modelling with qpWave and qpAdm
  • Overview: summarizing a matrix of F4 statistics
  • Data
  • Preparing “left” and “right” populations
  • Preparing the parameter file
  • Running the programs
  • Reading the qpWave log file
  • Reading the qpAdm log file
• Estimating Admixture times with Alder
  • Overview
  • Data
  • Preparing the parameter file
  • Running the programs
  • Reading the main output file (weighted LD table)
  • Reading the log file
  • Plotting weighted LD decay curve in R
• Estimating Admixture Graphs with qpGraph
  • Overview method
  • Data
  • Preparing the parameter file
  • Preparing the graph topology
  • Running the program
  • Reading the output files
  • Adding new groups to the scaffold graph
  • Continuing to fit new groups
  • Test the robustness of the graph topology
  • Adding admixture edges
• MSMC
  • Data
  • Generating consensus sequences for each sample
  • Combining samples
  • Running MSMC2 for estimating the effective population size
  • Estimating population separation history
  • Plotting in Python