Personal Genomics for Bioinformaticians

These online lecture notes are meant to accompany the UCSD graduate level course CSE284. It is currently a work in progress!

Most chapters contain a mix of text on core topics in medical and population genetics, tutorials on topics such as file formats and software packages that are likely to change over time, and links to hands on activities in the form of either interactive Jupyter notebooks or discussion questions.

Module 1 - Introduction

• Chapter 1: Introduction to the human genome
  • 1.1 The structure of DNA
  • 1.2 Organization of the human genome
  • 1.3 Genes and the central dogma
  • 1.4 The non-coding genome
  • 1.5 Genetic variation terminology
• Chapter 2: Overview of technologies for genome analysis
  • 2.1 Genotyping arrays
  • 2.2 Next-generation sequencing
  • 2.3 Long-read technologies
  • 2.4 Summary of genomics technologies
• Tutorial 1: file formats for describing genetic variation
  • T1.2 VCF files
  • T1.2 Plink files

Module 2 - Ancestry

• Chapter 3: Introduction to population genetics
  • 3.1 The four forces of evolution
  • 3.2 Types and patterns of mutations
  • 3.3 Genetic drift
  • 3.4 Selection
  • 3.5 Hardy-Weinberg Equilibrium and random mating
  • 3.6 Measuring population differentiation with Fst
  • 3.7 Recombination
  • Activity: Exploring the Wright-Fisher model of genetic drift
• Chapter 4: Global ancestry
  • 4.1 Global ancestry analysis with PCA
  • 4.2 Global ancestry analysis with ADMIXTURE
• Chapter 5: Relative finding
  • 5.1 Identity by descent
  • 5.2 Expected IBD of close relatives
  • 5.3 Computing expected IBD - plink method
  • 5.4 Computing expected IBD segment sharing
• Tutorial 2: Working with 1000 Genomes data and plink
  • T2.1 The 1000 Genomes Project dataset (and wrangling VCF files)
  • T2.2 Some tips and examples for using plink

Module 3 - From genotypes to phenotypes

• Chapter 7: GWAS for complex traits
  • 7.1 GWAS for quantitative traits
  • 7.2 Exploring GWAS association statistics
  • 7.3 Confounding factors in GWAS
  • 7.4 GWAS for case-control traits
  • 7.5 Linear mixed models
  • 7.6 Power to detect associations
• Chapter 8: Heritability
  • 8.1 Measuring heritability in related individuals
  • 8.2 Measuring SNP-based heritability using LMMs
• Chapter 9: Polygenic risk scores
  • 9.1 Clumping + Threshold (C+T) method
  • 9.2 Bayesian methods for polygenic risk scores

Appendix 1 - glossary and notation

• Chapter A1.1: Unix cheat sheet