Population Genetics
Foundations of Genetic Variation and Evolutionary Change
Course Overview
This course introduces the principles used to describe genetic variation within and among populations. It examines allele and genotype frequencies, Hardy-Weinberg equilibrium, mating systems, genetic drift, migration, mutation, selection, linkage disequilibrium, and population history.
Core Topics
- Genetic data and diversity: distinguish alleles, genotypes, haplotypes and phase; calculate frequencies and diversity measures in Practicals 1–4.
- Population change: compare mutation, migration, recombination, drift and selection models in Practicals 5–7.
- Statistical inference and equilibrium: assess HWE proportions and distinguish deterministic balance from stationary variation in Practicals 8–9.
- Applications: connect the models to conservation choices, DNA-profile probabilities, molecular clocks and genotype-specific phenotype probabilities in Practicals 10–13.
An HWE test can detect incompatibility with its sampling and null model; a departure alone cannot identify its cause. Practical 14 explores hitchhiking and sweep interpretation; Practical 15 builds a neutral genealogy and connects ancestry to sampled variation. These worked models do not qualify a genomic sweep-detection or demographic-inference workflow.
Learning Objectives
After completing the course, students should be able to:
- Describe genetic variation using allele, genotype, and haplotype frequencies.
- Explain and apply the Hardy-Weinberg principle.
- Explain how mutation, migration, drift, recombination, and selection affect populations.
- Interpret inbreeding, linkage disequilibrium, and population structure.
- Connect population-genetics models with biological and genomic applications.
Prerequisites
- Basic genetics and Mendelian inheritance
- Introductory probability and statistics
- Basic familiarity with R
Course Materials
- Shared course map — topic overview and learning routes across Population Genetics and Quantitative Genetics.
- Population Genetics slides
- Narrated Population Genetics slides
Practicals
Practical 1: Understanding genotype data — allele coding, identity alignment and haplotype phase.
Practical 2: Allele frequencies and Hardy–Weinberg expectations — called denominators, observed heterozygosity and model expectations.
Practical 3: Population structure, mating and inbreeding — mating rules, identity by descent and population-mixture effects.
Practical 4: Measuring genetic diversity — heterozygosity, sample correction and nucleotide diversity.
Practical 5: Mutation, migration and linkage disequilibrium — deterministic expectations, haplotypes, admixture and recombination.
Practical 6: Genetic drift in finite populations — neutral sampling, diversity loss and fixation.
Practical 7: Selection, fitness and dominance — selection recursions, balancing selection and finite realizations.
Practical 8: Testing Hardy–Weinberg proportions — fitted expectations, chi-square approximation and exact conditional inference.
Practical 9: Mutation, selection and drift at balance — deterministic equilibria, stationary distributions and mutation-model assumptions.
Practical 10: Conservation genetics and management choices — contributions, kinship, marker diversity and resource constraints.
Practical 11: DNA profiles and match probabilities — multi-allelic genotypes, population structure and evidence interpretation.
Practical 12: Sequence divergence and the molecular clock — comparable sites, substitution distances, rate units and calibration uncertainty.
Practical 13: Disease alleles, penetrance and population risk — genotype frequencies, phenotype probabilities, inbreeding and conditional risk.
Practical 14: Selective sweeps and genetic hitchhiking — linked neutral variation, recombination and initial backgrounds.
Practical 15: Coalescent history and sampled variation — backwards ancestry, mutation and demographic assumptions.
Interactive Apps
The repository includes the following R/Shiny app source files for local use. These apps are not currently configured for deployment.
Using the Materials
Read the slides for a conceptual overview, then use a practical to work through assumptions, equations and interpretation. Treat R as a virtual laboratory: reproduce the example, predict the effect of changing one parameter, then compare that prediction with the result. Attempt the exercises before opening the solutions.
For independent study and exam preparation, use the shared course map to choose a route and explain the population, denominator, life cycle and sampling model behind each calculation. Project analyses need their own data checks and model assessment before teaching code is adapted.
Course Hub
Return to the gteach teaching-materials hub.