Quantitative Approaches to Population Genetics

Topics

• Derive, compute, and interpret population genetic statistics grounded in coalescent and other evolutionary theories, and relate these quantities to observable patterns of genetic data.
• Apply population genetic models and inference frameworks to analyze genetic datasets, formulate and test evolutionary hypotheses, and draw evidence-based conclusions.
• Design and implement forward- and backward-in-time simulations (e.g., using tools such as SLiM and msprime) that incorporate key evolutionary processes, including mutation, recombination, migration, genetic drift, and natural selection.
• Conduct rigorous statistical analyses of simulated and empirical data, evaluate model fit, and interpret results in the context of evolutionary questions.
• Critically assess the assumptions, limitations, and appropriate use of analytical methods and published findings in population genetics.
• Communicate quantitative and evolutionary concepts effectively through clear written, oral, and visual scientific presentations.
• Apply population genetic theory to interpret patterns of genetic data.
• Explain how mutation, recombination, gene flow, and selection affect the coalescence process.
• Develop mathematical models to describe the genetic data-generating process.
• Construct the genotype likelihood under different models.
• Choose appropriate population genetic inference tools for analyzing different types of genetic data.
• Extract and use public domain genetic data and perform sensible tests on them.
• Develop population genetic simulations with popular simulators such as SLiM and msPrime.

Official roster learning outcomes