This 5-day course will cover population genetics data analysis using the software Migrate. Students will learn to operate the software to infer gene flow and population divergence model using sequence data and microsatellite marker data and compare and choose among different population genetics models. The course has two major components: a theoretical and a hands-on component where participants will use the software on their own computers. In the theoretical section, we will review coalescence theory and discuss concepts of genetic drift, gene flow and migration, population divergence estimation, Bayesian inference, Markov chain Monte Carlo methods, and Model selection. In the practical section, students will learn to operate the program, set options, define population models, compare these models, and also learn to assess whether the *migrate*-analyses were run sufficiently well to guarantee that the results are meaningful.
Last Up- Dated – N/A
Duration – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
Assumed quantitative knowledge
We assume basic knowledge of population genetics, for example, the small primer by *John Gillespie*, **a concise guide to population genetics** [John Hopkins University Press] would be perfect preparation. Some basic knowledge of probability theory will be beneficial but is not required to understand the concepts of Bayesian inference, because we will introduce the essential mathematical concepts.
Assumed computer background
We will use command style based program execution (*Migrate* does not have a Graphical User interface) therefore UNIX experience is desirable but not essential because, during the first day of the workshop, we will give an introduction to UNIX-style shell commands.
Equipment and software requirements
A laptop computer with the latest operating system installed and with the possibility to start a command-line tool; on Macintosh laptops, we will use the *Terminal.app*, and on Windows 10 (64bit) laptops we will use the Linux *bash* shell. If you have another system, please let us know before the workshop, we will be able to help you to install a viable command-line tool.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK email@example.com
Attendees will need to install/update R/RStudio and various additional R packages.
This can be done on Macs, Windows, and Linux.
R – https://cran.r-project.org/
RStudio – https://www.rstudio.com/products/rstudio/download/
PLEASE READ – CANCELLATION POLICY
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact firstname.lastname@example.org.
If you are unsure about course suitability, please get in touch by email to find out more email@example.com
Day 1 – Approx. 7 hours
Module 1: Introduction to population genetics and population genetic inference using DNA sequences: What do we estimate? Why do we estimate these parameters?
Module 2: Basic Coalescence theory, mutation models
Module 3: **Guided Hands-on** Commandline tools and preparation of data
Day 2 – Approx. 7 hours
Module 4: Bayesian inference and Markov chain Monte Carlo
Module 5: **Guided Hands-on** Introduction to Migrate
Day 3 – Approx. 7 hours
Module 6: **Guided Hands-on** Gene flow analysis; discussion of setting up specific population models, in-depth examination of quality assessment of results
Module 7: **Guided Hands-on** Population divergence analysis;
Day 4 – Approx. 7 hours
Module 8: Model selection introduction
Module 9: **Guided Hands-on** Tutorial on how to differentiate among population models, and how to choose among the models.
Day 5 – Approx. 7 hours
Module 10: Parallelizing *migrate* for cluster computer or multicore systems.
Module 11: Things to come, glimpse into the ‘fractional coalescent’ with a tutorial.
Works at – University of Helsink
Teaches – Multivariate analysis of ecological communities in R with the VEGAN package (VGNR03)
Antoine is a plant community ecologist working as a postdoctoral researcher at the University of Helsinki and as a postdoctoral fellow at the Institute of Botany of the Academy of the Czech Republic. Antoine holds a degree in Conservation Biology from the University of Paris-Sud-Orsay, and from the Natural History Museum of Paris, he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity with a particular focus on the forest and Arctic vegetation. Antoine has taught community ecology, plant ecology and evolution, linear and multivariate statistics assisted on R.