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Learning Your Data: Data Processing of Genetic Data

Overview

This workshop provides a comprehensive introduction to the foundational quality control measures and analyses in population genetics. You'll learn to work with genetic data through the complete pipeline from raw VCF files to publication-ready analyses. The workshop covers essential topics including genome builds, liftover procedures, basic quality control, Principal Component Analysis (PCA), and Genome-Wide Association Studies (GWAS). By the end of this workshop, you'll understand how to:

  • Perform genomic coordinate liftover between different genome builds
  • Apply standard quality control filters to genetic data
  • Conduct population structure analysis using PCA
  • Run basic GWAS analyses and interpret Manhattan plots
  • Process and standardize genetic datasets for downstream analysis

Requirements

Knowledge Requirements

  • Basic understanding of genetics and genomics concepts
  • Familiarity with Python programming (beginner to intermediate level)
  • Basic statistics knowledge (helpful but not required)

Required Python Packages

The following packages will be used in this workshop:

  • scikit-allel - For genetic data manipulation and analysis
  • pandas - Data manipulation and analysis
  • numpy - Numerical computing
  • matplotlib - Data visualization
  • seaborn - Statistical data visualization
  • scikit-learn - Machine learning tools (for PCA)
  • scipy - Scientific computing
  • pysam - Reading/writing genomic data files
  • pyliftover - Genomic coordinate conversion

Command-line installation instructions: pip install scikit-allel pandas numpy matplotlib seaborn scikit-learn scipy pysam pyliftover

Required Dataset

  • Attached to the GitHub repository
  • Tarball of this Tutorial's dataset and the GRCh38 FASTA can be downloaded in the first cell of the Tutorial_Lesson.ipynb

Outline

Workshop Structure

All Excercises can be found in the Jupyter Notebook labeled Tutorial_Lesson.ipynb

  1. Introduction to Genetic Data Processing
  • Understanding genome builds and their importance
  • Introduction to VCF (Variant Call Format) files
  1. Genomic Coordinate Liftover
  • Exercise: Liftover Tutorial - Converting genomic coordinates between different genome builds
  • Understanding coordinate systems and reference genomes
  1. Reference Genome Alignment
  • Exercise: Reference Alignment - Aligning variants to the reference genome
  • Quality control of genomic variants
  • Filtering invalid and ambiguous variants
  • Handling strand flips and palindromic SNPs
  1. Variant Quality Control
  • Exercise: Applying standard quality control filters
  • Missing data filtering
  • Hardy-Weinberg Equilibrium testing
  • Minor Allele Frequency (MAF) filtering
  1. Population Structure Analysis
  • Exercise: Conducting Principal Component Analysis on genetic data
  • Understanding population stratification
  • Visualizing genetic ancestry and population clusters
  1. Genome-Wide Association Studies (GWAS)
  • Exercise: Running basic association tests
  • Statistical testing for trait associations
  • Creating and interpreting Manhattan plots
  • Understanding genome-wide significance thresholds

References

Workshop material created by Benjamin Kaufman, PhD Student in Human Genetics Workshop created as part of the McGill Initiative in Computational Medicine