BIO416 Population Genetics
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Genetics and Bioengineering
Muhamed Adilović
Course Lecturer
Course Objectives
Introduce students to Population Genetics, focusing on main concepts including Hardy-Weinberg equilibrium, inbreeding, mutations, drift, selection, gene flow, and human population structure.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Human Population Genetics by John H. Relethford
Additional Literature
A Primer of Population Genetics and Genomics by Daniel L. Hartl Molecular population genetics by Hahn, Matthew WilliamTeaching Methods
Lectures
Presentations
Class Discussions
Problem-solving
Project.
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to the Course | |
| 2 | Genetic, Mathematical, and Anthropological Background | Chapter 1 |
| 3 | Hardy–Weinberg Equilibrium | Chapter 2 |
| 4 | Inbreeding | Chapter 3 |
| 5 | Mutation; Quiz | Chapter 4 |
| 6 | Problem solving | |
| 7 | Genetic Drift | Chapter 5 |
| 8 | Models of Natural Selection | Chapter 6 |
| 9 | Midterm | |
| 10 | Natural Selection in Human Populations | Chapter 7 |
| 11 | Gene Flow | Chapter 8 |
| 12 | Project Preparation and Discussion | |
| 13 | Human Population Structure and History; Quiz | Chapter 9 |
| 14 | Study of Quantitative Traits | Hartl, Chapter 8-9 |
| 15 | Review |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| BIO416.1 | Course | - | - | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Tuesday | 08:00 - 11:00 | A F1.33 | |
| Wednesday | 08:00 - 11:00 | A F1.33 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Midterm
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Quizes
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Project
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lectures
45 hours ⏳ (15 week × 3 h)
Quizes
10 hours ⏳ (2 week × 5 h)
Midterm
10 hours ⏳ (1 week × 10 h)
Project
20 hours ⏳ (2 week × 10 h)
Final
20 hours ⏳ (2 week × 10 h)
Home study
45 hours ⏳ (15 week × 3 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [BIO416] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Mar 03, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| BIO416 | Population Genetics | 3 | 0 | 6 | Tuesday 09:00-11:50 | |||||
| Prerequisite | Junior Standing | It is a prerequisite to | - | |||||||
| Lecturer | Muhamed Adilović | Office Hours / Room / Phone | Tuesday: 8:00-11:00 Wednesday: 8:00-11:00 |
|||||||
| madilovic@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | Introduce students to Population Genetics, focusing on main concepts including Hardy-Weinberg equilibrium, inbreeding, mutations, drift, selection, gene flow, and human population structure. | |||||||||
| Textbook | Human Population Genetics by John H. Relethford | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Lectures, Presentations, Class Discussions, Problem-solving, Project. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to the Course | |||||||||
| Week 2 | Genetic, Mathematical, and Anthropological Background | Chapter 1 | ||||||||
| Week 3 | Hardy–Weinberg Equilibrium | Chapter 2 | ||||||||
| Week 4 | Inbreeding | Chapter 3 | ||||||||
| Week 5 | Mutation; Quiz | Chapter 4 | ||||||||
| Week 6 | Problem solving | |||||||||
| Week 7 | Genetic Drift | Chapter 5 | ||||||||
| Week 8 | Models of Natural Selection | Chapter 6 | ||||||||
| Week 9 | Midterm | |||||||||
| Week 10 | Natural Selection in Human Populations | Chapter 7 | ||||||||
| Week 11 | Gene Flow | Chapter 8 | ||||||||
| Week 12 | Project Preparation and Discussion | |||||||||
| Week 13 | Human Population Structure and History; Quiz | Chapter 9 | ||||||||
| Week 14 | Study of Quantitative Traits | Hartl, Chapter 8-9 | ||||||||
| Week 15 | Review | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,4,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm | 1 | 21 | 1,2,3,4,5 | Not Allowed | |
| Quizes | 2 | 18 | 1,2,3,4,5 | Not Allowed | |
| Project | 1 | 21 | 1,2,3,4,5 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lectures | 3 | 15 | 45 | Quizes | 5 | 2 | 10 | |||
| Midterm | 10 | 1 | 10 | Project | 10 | 2 | 20 | |||
| Final | 10 | 2 | 20 | Home study | 3 | 15 | 45 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||