Course description:

A combination of observation and mathematics is used to study the processes that cause allele frequency change within and among populations including mutation, natural selection, genetic drift, and migration, while taking account of the mechanism of Mendelian inheritance and the effects of population structure.

Learning outcomes:

1. Learn to measure genetic diversity at the population level. This is typically the dependent variable in population genetics.
2. Learn to quantify mutation rate, selectin intensity, genetic drift, inbreeding coefficient and migration. These are typical independent variables
3. Construct models between the dependent and the independent variables and choose the best model based on likelihood ratio test and information-theoretic indices
4. gain an understanding of how the mechanisms of Mendelian particulate inheritance and the processes of mutation, migration, mating, natural selection, and genetic drift shape allele frequency change in populations (i.e. their evolution)
5. explore and understand how the use of simple mathematical models and simulations can enhance our understanding of biological problems
6. learn a set of skills that will enable you to think critically and to quantitatively address a variety of problems in evolutionary genetics
7. To acquire skills and experience in applying scientific methods to problem-solving, e.g., how to formulate hypotheses, derive predictions and test these predictions.
8. To develop skills in scientific communication.

Teaching methods:

Lectures

Reference books:

• Felsenstein, J. 2019. Theoretical Population Genetics (An excellent source of population genetics insights)
• Hamilton, Matthew B. Population Genetics . Chichester, UK;: Wiley-Blackwell, 2009. (It is on library reserve, and should have many used copies to go around)

Software notes:

• We will use EXCEL and R for simulation and statistical estimation. This will give you all the mathematical and statistical details about simulation and estimation. In particular, we will use EXCEL's Solver function and R's optimize (for one-dimension minimization/maximization) and optim (for multi-dimensional minimization/maximization). If you click 'Data' tab in EXCEL and does not see Solver to the right of the ribbon, click
• File | Options | Add-ins | Excel Add-ins | Go
• and check the 'Analysis Toolpak' and 'Solver', and click OK.

Evaluation:

• 30% - Six problem sets (5% each) These can be done in groups of up to three students and you are encouraged to do so. Please hand in a single copy in PDF file, with all students names and ID numbers. Except under exceptional circumstances all students in groups will receive the same grade. Make sure to show your work. LATE ASSIGNMENTS WILL BE PENALIZED 20% PER DAY (OR PART THEREOF) UNLESS YOU MAKE PRIOR ARRANGEMENTS WITH THE COURSE INSTRUCTOR. To reduce marking load, a subset of randomly chosen questions may be graded from each assignment but all will be taken up in class as practice.
• 30% - Slide-embedded assignments.
• 40% - Final exam.

Marking disputes: If you feel an error has been made in grading, you have 10 working days from the date of grade-release to request a regrading. Beyond 10 working days the mark is considered final.

When a regrading occurs, the new mark will stand whether it is higher or lower, so there is no guarantee your mark will increase. Simple adding mistakes unfortunately happen on occasion given the volume of exams we have to mark and such cases are easy to correct. However, disputes over partial marks on partially correct answers will not always work out in your favour and should be reserved for cases in which you feel you have a strong justification. When requesting a regrading, you must provide an explanation as to what you feel the issue is (i.e. why you think you deserve a higher grade). The more specific this is the better your chance of success.

Attendance:

As per uOttawa policy, to ensure they succeed in all courses of their program of study, students have the responsibility to participate in the various learning and assessment activities for this course.

Plagiarism

Plagiarism is the act of passing off someone else's words or ideas as your own. Plagiarism is a major academic offence (see the University of Ottawa regulation on academic fraud) and is also illegal. We uphold the law. It is critical that you understand what constitutes plagiarism and how you can avoid it. For more information, consult the Student's Guide on Academic Integrity from the Student Academic Success Service.

Instructor:

Prof. Xuhua Xia, Gendron 278.
E-mail: xxia@uottawa.ca
Phone: 562-5800 ext 6886
Office hour: Friday 2:00-5:00 pm (or email me to arrange an alternative time).