Population Genetics is centred around understanding genetic variation.
Towards this end, population geneticists are interested in the fundamental processes of mutation, migration, recombination, selection and drift.
In this lab session, we’ll be touching on most of these and covering a range of common foundation analyses to understand the evolutionary history of a SNP dataset. The goals are for you to get a feel of a basic population genetics workflow through a whistle-stop tour of methods. We could spend an entire day on each step of the analysis, but given we only have 3 hours we’ll be learning a little about a few things as opposed to a deep-dive on one thing.
It’s expected that there are a range of abilities in the cohort, and consequently some may progress quicker than others. At the end of each section there are additional exercises that are optional for anyone who is working quickly.
It’s also important to learn how to troubleshoot errors and problems
on your own! Remember that for command-line programs we can usually type
--help
, or in R type ?my_function
, to see
documentation.
We’ll be working with a simulated dataset that includes a number of conditions and events that we will uncover over the course of the session. The workshop is designed around the idea of building up an understanding of a SNP dataset that we may have limited a-priori knowledge of, which can be common for example when working with sequencing data from natural populations. Starting with exploratory summary analyses, we will generate and test hypotheses to explore evolutionary questions.
Let’s pretend our data comes from a study into the a hypothetical, yet MIRACULOUS, conservation success story of Scottish Wild Cats spreading south into England from their Highlands refugia. In this study, samples were collected from a number of individuals from the ancestral Highlands population (p0) and at four further locations moving south through the Scottish borders and into England, at Kielder Forest (p1), Leeds (p2), Norfolk (p3), and London (p4).