This course provides a comprehensive, concept driven journey through the mathematical and conceptual foundations of population genetics. It begins by refreshing the Hardy-Weinberg model as the baseline against which evolutionary change is measured. Students learn how equilibrium genotype frequencies arise in the absence of evolutionary forces, setting the stage for understanding how real populations depart from these expectations.

Building on this foundation, the class turns to fitness and directional selection, exploring how differences in reproductive success shift allele frequencies through time. Students develop a quantitative intuition for the rate of selection to see how directional selection plays out across generations. The course then introduces selection and dominance coefficients, connecting classical population genetics to modern genomic signals of adaptation such as hard selective sweeps and the tradeoffs associated with positive selection.

The second unit focuses on balancing selection, beginning with a conceptual comparison between the classic and balance schools of thought and continuing through empirical. Students contrast the assumptions of balancing models with those of positive selection and then move into the mathematical derivation and interpretation of overdominance. A deeper exploration of marginal overdominance follows, highlighting how genotype specific fitness maintains genetic diversity.

The class further examines mutation selection balance and then migration selection balance, both central to understanding how gene flow and mutation interact with natural selection. Computational simulations and genomic data are used to illustrate the detectable consequences of these interacting forces.

The third part of the course introduces the concept of linkage disequilibrium and the effects of linked selection, including Hill-Robertson interference. The course turns to genetic drift and the Wright Fisher model. The course then examines effective population size and mutation drift balance, connecting theoretical models to real world genomic signals of drift. We also discuss F-statistics and conclude with simulations and inference approaches for detecting selection, drift, and population structure in genomic data.
 

Throughout the semester, students blend mathematical reasoning, biological interpretation, and computational exploration to develop a modern understanding of how evolutionary forces shape genetic variation in natural populations.