Introduction

Population Genetics Course Structure

Population genetics is the study of microevolutionary processes (both neutral and adaptive) and how they impact allele and genotype frequencies. This course is designed help you master the application of quantitative population genetic analysis techniques as they are applied to real-world data sets.

This course has three main learning objectives (see below) and will be divided into self-contained learning modules that will reinforce one or more of these objectives.

Course Learning Outcomes (CLOs)

Learning objectives may be applied at several heirarchical levels within this course. Overall, the course has specific objectives that are a de facto statement of what you should expect to get from the content of this class. If you look to your individual degree Program Learning Outcomes, you should see how these course-level objectives map directly onto those outcomes.

Let’s start with a definition. In this text, all definitions will be styled as follows:

Learning Objective

Learning Objectives are explicit statements that define the knowledge, skills, and abilities you will demonstrate upon successful completion of this course. All assessments are designed to directly measure your achievement of these objectives, ensuring alignment between what you practice, what you’re evaluated on, and what you ultimately master.

This course has the following CLOs:

CLO1: Evolutionary Consequences of Population Genetic Processes (Bloom ~2.5):

* Primary verb: Explain/Predict   
* Assessment vehicle: Pre-class quizzes (10% of course grade)   
* Description: Maintained throughout course with exponentially increasing content sophistication but consistent cognitive operation

CLO2: Applied Population Genetic Analysis (Bloom ~4.0-5.0):

* Primary verb: Execute/Interpret  
* Assessment vehicle: In-class activities (20% of course grade)  
* Description: R skills applied to progressively complex genetic phenomena through simulation and data analysis

CLO3: Evidence-Based Communication (Bloom ~5.5-6.0):

* Primary verb: Generate/Highlight  
* Assessment vehicle: Methods & Results papers (70% of course grade: 17.5% × 3 + 25%)  
* Description: Progressive technical development culminating in integrated publication-quality scientific arguments

Course Modules

This course is partitioned into the following four self-contained, though sequential, learning modules.

Module 1: The R Ecosystem

Establishing the technical foundation for quantitative analysis in population genetics. Students will master R programming fundamentals, data visualization using the grammar of graphics, and reproducible scientific documentation. These skills are prerequisite for all subsequent evolutionary analyses.

Module Bloom Spread¹: ~3.8-4.0

Analogy Summary:: The process of learning these foundational skills is like learning to prepare a sophisticated meal. MLO1 teaches you ingredient preparation—correctly identifying your ingredients (data types), organizing them into appropriate containers (data frames/lists), and cleaning or transforming them (Tidyverse verbs) before cooking can begin. MLO2 focuses on presentation—using the “Grammar of Graphics” (rules of plating) to ensure the final dish is attractive and conveys information effectively. MLO3 combines everything into the recipe and plating—documenting every step (code chunks) and integrating narrative (text), results (inline R output), tables, and presentation (visualizations) into a final, shareable, verifiable format (Quarto document). Just as a chef must master ingredients, presentation, and documentation to create reproducible haute cuisine, you must master these three objectives to produce professional, reproducible data analysis.

Module 2: Single Population Processes

Microevolutionary processes occurring within a single population: genetic drift, mutation, inbreeding, pedigrees, and paternity analysis. CLO1 (Evolutionary Consequences) enters here as students begin analyzing genetic phenomena.

Module Bloom Spread: ~4.2-4.5

Analogy Summary: Learning these three objectives is like becoming a complete aviation professional. MLO1 teaches you to read your instruments—checking whether the plane is in equilibrium (Hardy-Weinberg) and diagnosing deviations (inbreeding coefficient F). MLO2 teaches you to pilot through changing weather conditions, using simulations to model how quickly drift, mutation, and mating systems change your trajectory and predict where you’ll be 100 generations from now. MLO3 makes you the expert crash investigator who reconstructs flight paths backward through generations (pedigree analysis) and determines causation from evidence (forensic paternity assessment using likelihood ratios). Just as pilots must master static instruments, dynamic modeling, and accident investigation, population geneticists must master equilibrium testing, evolutionary simulation, and forensic analysis.

Module 3: Population Subdivision

Microevolutionary processes occurring across subdivided populations: genetic diversity, F-statistics, population structure, migration dynamics, and the Wahlund Effect. Increased methodological sophistication with emphasis on distinguishing real patterns from sampling artifacts.

Module Bloom Spread: ~4.7-5.0

Analogy Summary: The progression across these three MLOs is like learning professional cartography. MLO1 teaches you to partition total topographic variation into hierarchical components—local hills and valleys (within-population variation, FIS) versus mountain ranges (among-population variation, FST)—while applying appropriate surveying corrections for unequal sampling (rarefaction). MLO2 develops your ability to distinguish real landscape features from surveying artifacts: Is that apparent cliff genuine population structure, or a methodological illusion from merging incompatible datasets (Wahlund Effect)? You model how migration acts like rivers connecting regions over time. MLO3 makes you the professional cartographer who measures distances using appropriate metrics (Euclidean vs. evolutionary distance), decomposes spatial variation (AMOVA), and selects effective visualizations to communicate complex spatial genetic relationships. Just as accurate maps require understanding terrain, detecting artifacts, and choosing appropriate projections, accurate population genetics requires hierarchical thinking, artifact detection, and thoughtful visualization.

Module 4: Selection

Natural selection and quantitative genetics as capstone integration. Students synthesize selection with all non-selective forces (drift, mutation, migration, inbreeding), evaluate evolutionary potential through heritability, and compare foundational evolutionary frameworks. Highest cognitive demand and integration requirement.

Module Bloom Spread: ~5.3-5.6

Analogy Summary: The progression across these three MLOs is like learning engineering design under real-world constraints. MLO1 teaches you to model force dynamics—understanding how selection shapes populations over time with different possible outcomes (stable equilibria like heterosis, unstable equilibria like heterozygote disadvantage, or deterministic fixation), similar to how engineers model structural responses to different load patterns. MLO2 requires you to evaluate design feasibility by partitioning material properties (phenotypic variance) into usable components (additive genetic variance = heritability) and calculating whether your materials can achieve required performance (breeder’s equation: R = h²S). This is where you integrate all course content to evaluate real-world evolutionary potential. MLO3 completes your training by requiring you to engineer under complexity—integrating multiple simultaneous forces (selection interacting with drift, mutation, migration, inbreeding), evaluating empirical evidence from multiple data modalities, and comparing competing design philosophies (Fisher vs. Wright). Just as engineers must understand forces, evaluate materials, and design under multiple constraints, evolutionary biologists must model selection, assess heritability, and integrate multiple processes to predict adaptation.

Course-Level Bloom Spread Progression

Module	Content Focus	Bloom Spread	Weight	Pattern Type
1	R Ecosystem (Tools)	3.8-4.0	17.5%	Sequential Mastery (within)
2	Single Population	4.2-4.5	17.5%	Sequential Mastery (within)
3	Subdivision	4.7-5.0	17.5%	Sequential Mastery (within)
4	Selection & Integration	5.3-5.6	25.0%	Sequential Mastery (within)
All	All Processes	3.8 → 5.6	100%	Convergent Integration

Fractal Pedagogical Structure

The content within and across learning modules share a similar fractal structure.

Within each module: Type 1 Sequential Mastery (quizzes → activities → paper)
Across modules: Type 4 Convergent Integration (all developmental arcs converge at Module 4)
Grade weighting: Ensures papers drive cognitive demand while activities provide scaffolding

Bloom spread is metric that quantifies cognitive complexity in course design by aggregating Bloom’s taxonomy classifications from individual assessments through learning objectives to course outcomes. This framework enables temporal analysis of cognitive demand progression across a semester, revealing developmental arcs rather than simple linear progression. See here for an overview of this metric.↩︎