Course Syllabus

The course has two objectives: (a) to discuss problems in population genomics, and (b) to describe the algorithmic methods for analyzing population genetics data.  Lecture 1 will provide a short introduction.

Population Genomics Topics

  • Sources of variation
  • HW equilibrium
  • Linkage & Linkage Disequilibium
  • Coalescent theory
  • Recombination/Ancestral Recombination Graph
  • Selection
  • Haplotypes/Haplotype phasing
  • Population sub-structure
  • Structural variation
  • Extrachromosomal DNA and complex SVs
  • (Possible) Tandem Repeats

Algorithmic/Statistical principles

  • Perfect Phylogeny
  • Coalescent simulation
  • Algorithms for selection
  • Linear programming
  • Stochastic iteration
  • Graph algorithms (max flow)
  • Markov Chain Monte Carlo
  • Principle Component Analysis
  • Statistical Hypothesis testing

Grading:

  • 3 Assignments (15% each)
      • Late Penalty (5% off after each 24 hours from the deadline). The dates below are tentative. Final dates will always be on the course home page.
    • A1: 1/8-1/19
    • A2: 1/21-2/5
    • A3: 2/6-2/21
  • Final exam (20%)
    • 3/16/2026 (10 am – 6 pm PST take home)
  • Project (30%)
    • Goal is to get started on research (MS comprehensive exam)
    • In class presentations in week 10
    • Report due 3/13
  • Participation (5% mostly in February/March)
    • Points will be awarded at the instructor's discretion
    • Awarded for attendance, class participation, OR Piazza participation.
    • Asking  homework/exam/project related questions does not count.
    • However, (non-anonymously) answering student questions  or asking questions that provide new insights will be awarded points.
    • The instructors will flag good questions.