Methods of detecting and investigating genetic variation, as well as its causes and consequences. Applications from medicine, forensics, and environmental biology are emphasized. Pre/co-requisite: BCOR 101.
Dates: May 22 - June 16, 2017; Pre/co-requisite: BCOR 101 or 102; Calculus and Statistics recommended
This course uses online presentations, analysis of biological data, readings of the primary literature and computer simulations to examine how population genetic data is collected, analyzed, presented, modeled and simulated, used in interdisciplinary applications and can be used to address societal problems. The course is divided into four modules: (1) Controlling infectious disease using population genetic analysis of insect disease vectors, (2) Dogs: A population genetic perspective, (3) Human genetic population structure: Where we came from and where we are going, (4) Tracking parasite transmission using population genetics.
The course is entirely online, every week a new module is available. Two assignments are due each week in addition to participation in online discussions, and a final exam. Students should expect to spend about 20 hours a week. The required materials for the course are: (1) A computer (Windows or Mac) capable of running freely available software (DnaSP, Mega, STRUCTURE, R statistical software) that will be installed as part of the lab exercises, and (2) Nielsen & Slatkin, 2013. An introduction to population genetics theory and applications. Sinauer. ISBN: 978-1-60535-153-7.
Each module includes evaluating population processes using online simulation tools, analyzing experimental data through laboratory exercises and online discussions. A detailed study guide will be provided for the final exam which will focus on material from the text book and primary literature and include interpreting data and explaining and describing population genetic processes. Modeling and simulation exercises include using pre-written computer programs to examine processes such as the effect of population size on genetic variation. Laboratory exercises include applying the process of science by analysis, evaluating, explaining and describing of experimental data.
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