Molecular epidemiology

The course is an elective course within the Master’s Programme of Applied Biostatistics (M2STA). The course can be also offered as a free-standing course on advanced level.

The entry requirements of the course include a professional degree/Bachelor's degree of at least 180 credits in health sciences, natural sciences, economics, or engineering, and a course in statistics or quantitative methods of at least 7,5 credits. Further, R-programming of at least 2 credits or equivalent, a course in regression analysis of at least 2 credits or equivalent, English B/English 6 or equivalent, and Matematik 3b/3c or equivalent are required.

The course provides an introduction to key concepts, methods, and applications within molecular epidemiology. Modern medical research often relies on analyses of large‑scale molecular data, an area that requires specialized statistical approaches. The course gives an overview of the fundamentals of cell biology with a focus on DNA, RNA, and protein expression, which are necessary for analyzing and interpreting results.

The course presents examples of the connections between genotype, protein, and phenotype, as well as the various roles of proteins in cellular functions. It focuses on how genetic analyses such as genome‑wide association studies (GWAS) and Mendelian randomization (MR) can be used as tools for epidemiological research and causal inference. Furthermore, the course covers biomarkers and large‑scale analyses of molecular data (omics).

On successful completion of the course the student will be able to:

Knowledge and understanding

• Describe relevant concepts and processes in cell biology related to heredity and protein expression
• Explain how genetic variation and expression can be linked to disease
• Explain principles of, and differences between, statistical analysis of single biomarkers and large‑scale analysis of omics data

Competence and skills

• Perform Mendelian randomization based on available summary data
• Interpret results from GWAS and Mendelian randomization

Judgement and approach

• Reflect on different aspects of large‑scale studies, including genetic studies, in relation to data collection, ethics, and interpretation.
• Discuss the strengths and limitations of genetic studies for causal inference.

The teaching consists of lectures, practical computer‑based sessions, interactive discussions, and seminars.

Language of instruction: English

The course is examined through:

• an individual written on‑site exam (3 credits)
• active participation in one mandatory seminar and one mandatory computer exercise (1.0 credits)

Completion of failed mandatory components will be offered and must be carried out according to the teacher’s instructions.

If a student who has been failed twice for the same examination element wishes to change examiner before the next examination session, such a request is to be granted unless there are specific reasons to the contrary (Chapter 6 Section 22 HF).

If a student has received a certificate of disability study support from the University of Gothenburg with a recommendation of adapted examination and/or adapted forms of assessment, an examiner may decide, if this is consistent with the course’s intended learning outcomes and provided that no unreasonable resources would be needed, to grant the student adapted examination and/or adapted forms of assessment.

If a course has been discontinued or undergone major changes, the student must be offered at least two examination sessions in addition to ordinary examination sessions. These sessions are to be spread over a period of at least one year but no more than two years after the course has been discontinued/changed. The same applies to placement and internship (VFU) except that this is restricted to only one further examination session.

If a student has been notified that they fulfil the requirements for being a student at Riksidrottsuniversitetet (RIU student), to combine elite sports activities with studies, the examiner is entitled to decide on adaptation of examinations if this is done in accordance with the Local rules regarding RIU students at the University of Gothenburg.

The grading scale comprises: Pass with distinction (VG), Pass (G) and Fail (U). The mandatory components — the seminar and the computer exercise (1.0 credits) — are graded on a Pass/Fail scale, while the individual written exam (3 credits) is graded as Pass with Distinction, Pass, or Fail.

To receive a grade of Pass for the course, the student must obtain a Pass on the seminar and the computer exercise as well as on the written exam. To receive a grade of Pass with Distinction for the course, the student must obtain a Pass on the seminar and the computer exercise and a grade of Pass with Distinction on the individual written exam.

The course evaluation is carried out in the form of an anonymous questionnaire. A compilation of the questionnaire is done by the course coordinator. The results of and possible changes to the course will be shared with students who participated in the evaluation and students who are starting the course.

Each student needs access to a laptop (at least 8 GB RAM recommended) with R and an integrated development environment (such as RStudio or Positron) installed. The expected prior knowledge in statistics includes an understanding of the principles of statistical hypothesis testing, the construction of confidence intervals, the concept of p-value, and solid knowledge of linear regression.