Population Genetics and Mathematical Models for Genomic Data

Understanding how genetic variation spreads and evolves within populations is essential for modern biology, medicine, and bioinformatics. This text-based course introduces the foundational concepts of population genetics and guides you through mathematical models used to interpret genomic data. What you'll learn: Understand key terms and core principles of allele frequencies, genetic drift, and natural selection. Explore Hardy-Weinberg equilibrium and mathematical extensions for complex evolutionary scenarios. Analyze population structure using principal component analysis and admixture modeling concepts. Apply mathematical models to infer demographic history and gene flow from genomic datasets. Evaluate nucleotide diversity, linkage disequilibrium, and selective sweeps using modern genomic metrics. You will begin with essential terminology and theoretical foundations before advancing to mathematical frameworks and computational concepts for modern population genomics. Designed for beginners in bioinformatics and life sciences, no prior background in population genetics is required. Start reading today to build a strong foundation in population genetics and genomic data modeling.