Statistics (Double Degree with the University of Bologna) BSc/LSc
Statistical Genetics STATS4074
- Academic Session: 2024-25
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 2
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
Short Description
This course introduces students to applications of probability and statistics in the field of modern genetics.
Timetable
2 1-hour lectures per week for 10 weeks
4 1-hour tutorials
Excluded Courses
STATS5011
Assessment
Examination (100%)
Main Assessment In: April/May
Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses
Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
Course Aims
to introduce some of the applications of probability and statistics in the field of genetics;
to provide applications of approaches to inference using likelihood in a variety of genetics setting;
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ describe basic notions of genetics;
■ prove, test and apply Hardy-Weinberg equilibrium and explain departures from it;
■ estimate gene frequencies, using a variety of methods, including the EM algorithm;
■ apply Bayes' theorem in order to solve problems in genetic counselling;
■ explain and use the coalescent process, as derived from the Wright-Fisher dynamics;
■ explain how the presence of recombination affects models of ancestry;
■ estimate evolutionary parameters using models of ancestry.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.