Semester course; 3 lecture hours. 3 credits. Prerequisite: STAT/BIOS 543. Theoretical and empirical analyses of how demographic and evolutionary processes influence neutral and adaptive genetic variation within populations.
Population genetics is concerned with understanding the mechanisms influencing genetic material's movement through space and time. To understand the operation of evolution itself, you must have a working knowledge of population genetics. It is, so to speak, where the rubber meets the road, and the concepts contained within the field of population genetics have relevance to other sub-disciplines such as population biology, population ecology, evolutionary biology, evolutionary ecology, conservation genetics, landscape genetics, and forensic science.
The field of population genetics has a history punctuated with periods of intense interest interspersed with periods of mind-numbing stagnation. Despite, or perhaps due to, the isochronal development in the theory underlying this field, the foundations laid down by Fisher, Haldane, and Wright during the early decades of the 20th century continue to influence empirical validation.
To understand data analysis in population genetics, we should recognize a generic iterative workflow, such as the one depicted below.
- Collect: Getting data from an external source into a format you can use is often the most time-consuming step in the analysis. The content of this class will provide training in data import from local, online, and database sources.
- Visualize: Visualizing data is key to understanding. In the image below, notice that the variables X and Y in all the displayed data sets have equivalent means, standard deviations, and correlation up to 2 decimal places! We will emphasize visualization, both static and dynamic, throughout this class.
- Transform: More than pulling data into your analysis ecosystem is required. Often, the data needs to be reformatted and reconfigured before it is actually usable.
- Model: Applying models to subsets of data is often the step that takes the least time and effort. However, applying a model to data is not the endpoint. The model must be visualized, and, often, the underlying data or derivate data must be transformed and submitted to subsequent models.
- Communicate: The effort we put into research and analyses is only possible when we effectively communicate our data and findings to a broad audience. Here, we will focus on developing effective data communication strategies and formats.
This course aims to help you develop your data skills and a foundational understanding upon which subsequent courses will build. The overarching goal is to develop a working knowledge of the R statistical computing language and enough proficiency to import raw data and then iterate through the visualization, manipulation, and analysis steps to create output that is easily communicated to a scientific audience.
The content of this course is built upon the following general student learning objectives (SLO):
- SLO 1: Identify, manipulate, analyze, and summarize data associated with marker-based genetic data.
- SLO 2: Explain and demonstrate how microevolutionary processes impact genetic structure and diversity.
- SLO 3: Create graphical and tabular displays of various population genetic data of suitable quality for inclusion in published manuscripts.
This course has a total of 10 independent learning modules. Each of these will have content necessary to introduce the concept, some background information on the process/processes, an example manuscript for discussion, a longer narrative document with data and worked examples, an in-class activity for group work, and one or more assessment tools.
This course has the following components.
In-Class: All in-class activities will be treated as group work. On occasion, this material will be turned in, though it is intended to serve mostly as in-person practice activities.
Homework: Homework assignments will be conducted in RLinks to an external site.. Depending on the module, you may be asked to submit your work online (as R code or as a Markdown document) OR as a physical document. A rubric will be provided.
Synthesis: At the end of the course, you will individually perform a larger synthetic analysis of a provided data set. You will be asked to analyze the data and discuss salient population genetic characteristics of the data set.
I will give you sufficient time to complete these items. Science is not done by "sitting in a room and taking an exam." Routinely, you will have a full week to complete each assignment. I ask that all homework be done individually, though you may collaborate "in general terms" on content and approaches.
Grading The grade for this course is based upon the totality of the points gained for all assignments, as well as a single large data analysis synthesis at the end of the semester.
Grades will be determined using the normal 10% scale:
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A (>= 90%),
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B (>= 80% & < 90%),
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C (>= 70% & < 80%),
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D (>= 60% & < 70%), and
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F (< 60%).
All percentages are concrete, scores will be rounded to the nearest integer, and no extra credit will be given.
All of the content in this class is given as take-home assignments and tests. You will have a full seven days to complete and submit the work. The intention here is to provide you with more than sufficient time to complete the task. We do not rush data analysis, and no practitioner sits in a room with a clock over their shoulder telling them to hurry up. That said, you must not put off doing the work until the end; it may take some iteration (see image above).
On the due date for each deliverable, I will post the answers so you can check your work. After the answers are posted, no points will be awarded for late work.
If you have an emergency, sickness, or professional reason to be missing a week's worth of class (e.g., presentations at a scientific meeting, etc.), please let me know beforehand so we can work out a solution.
All content is provided in the form of slides, handouts, and video content. Much of the work in this class will be conducted during the in-class session. As such, you must attend the class session if you intend to receive the content. Data analysis is a hands-on experience, and the more you do it, the more efficient you will become.
Note that the specifics of this Course Syllabus may be changed at any time during the semester. You will be responsible for abiding by any such changes that are communicated to you via email, course announcement, and/or posting in the course discussion forums.
Students should visit http://go.vcu.edu/syllabusLinks to an external site.Links to an external site. and thoroughly review all of the listed syllabus statement information. The full university syllabus statement includes information such as safety, registration, the VCU Honor Code, student conduct, withdrawal from courses, and more.