Course catalogue doctoral education - VT22

  • Application can be done between 2021-10-15 and 2021-11-15
Application closed
Title Fundamentals of Statistical Genetics and Data Visualization
Course number 5308
Programme Cell Biology and Genetics
Language English
Credits 1.5
Date 2022-03-14 -- 2022-03-18
Responsible KI department Department of Medicine, Solna
Specific entry requirements Knowledge of basic statistics; knowledge of logistic and linear regression; familiarity of R software and scripting in R; familiarity with UNIX commands; knowledge in epidemiology equivalent to the course Epidemiology I; Introduction to Epidemiology or corresponding courses.
Purpose of the course The course aims to enable doctoral students and postdocs to acquire an understanding of statistical genetics in complex diseases based on theory and practical examples. The course will focus on teaching fundamental principles in genetic epidemiology and genomic data analysis.
The course will be conducted in the classroom (or online) along with assigned times for practical exercises and will use the UPPMAX platform, the Uppsala Multidisciplinary Center for Advanced Computational Science. This is a national resource and platform for high-performance computing Students will get an UPPMAX account, which will facilitate computational analyses and implementation of course activities and practicals. Computational tools and software are readily available in UPPMAX.
Intended learning outcomes The intended learning outcomes (ILOs) include to be able to:
1. Describe statistical methods for genetic studies
2. Explain new and old practices in the design and execution of computational genetic studies and integration of gene expression data
3. Differentiate and apply different methods for computational genetics
4. Develop programming skills and critical thinking to conduct problem-solving solutions using genetic data
Contents of the course Topics to be covered:
• Association studies and meta-analysis
• Principal component analysis (PCA)
• Expression quantitative trait loci (eQTLs)
• Computational methods for gene x environmental (GxE) interactions
• Methods and estimation of polygenic risk scores (PRS)
• Methods and application of Mendelian randomization (MR)
Teaching and learning activities Teaching and Learning Activities (TLAs) include:
1. Pre-reading and notes based on past and current statistical methods followed by group discussion
2. Presentation of independent project based on a past or current statistical method
3. In pairs, propose an idea to solve a biological question of your choice – use whiteboard or brainstorming techniques
4. Create a systematic protocol for executing the idea proposed in step 3. Present your data analysis plan and genomic data to be investigated. Provide an interpretation of the results and defend why your approach is appropriate for how to tackle the biological question of your choice.
Compulsory elements Students absent during the course elements are asked to perform computational exercises and practicals independently. Students will then submit data analysis interpretation in writing.
Examination Assessments tasks (AT) include:
1. Daily quizzes
2. Summarize discussion on past and current methods
3. In a group of 3, write a critical assessment of each group member’ presentation
4. Present your idea in one PowerPoint slide
5. Present the abstract for your project and analysis protocol
Literature and other teaching material Recent articles, recommended online textbooks, and websites.

Articles of interest:

Recommended textbooks:
• An introduction to statistical genetic data analysis by Melinda C. Mills, Nicola Barban, Felix C. Tropf.
• A statistical approach to genetic epidemiology: with access to e-learning platform by Friedrich Pahlke, Ziegler, Andreas, 1966-; Koonig, Inke R., Weinheim an der Bergstrasse, Germany : WILEY-VCH Verlag GmbH & Co.; Second edition.; 2010
• Statistical Human Genetics: Methods and Protocols, Walker, John M; Elston, Robert C. New York, NY: Springer New York; 2nd ed. 2017; 2017
• The R Book, Crawley, Michael J Hoboken: Wiley; 2. Aufl.; 2012
• Statistics and Data with R: An Applied Approach Through Examples. Cohen, Yosef; Cohen, Jeremiah Y. New York: Wiley; 1. Aufl.; 1st ed.; 2008
• Computational Biology: A Practical Introduction to BioData Processing and Analysis with Linux, MySQL, and R
Wünschiers, Röbbe. Berlin, Heidelberg: Springer Berlin / Heidelberg; 2nd ed. 2013; 2013
• Modern epidemiology, Rothman, Kenneth J.; Greenland, Sander, Lash, Timothy L., Philadelphia : Wolters Kluwer Health/Lippincott Williams & Wilkins; Third edition; 2008

Links for plotting in R:
Number of students 8 - 30
Selection of students Selection will be based on 1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation), 2) start date of doctoral studies (priority given to earlier start date)
More information The course will be held full-time during week 11 (March 14-18, 2022) Special invited lecturers include: Professor Suzanne Leal (Department of Neurology at Columbia University and Director of the Center for Statistical Genetics, and Senior Research Associate at The Rockefeller University, USA), Professor Michael Nothnagel (University of Cologne, Cologne Center for Genomics, Department of Statistical Genetics and Bioinformatics, Germany), Dr. Stephen Burguess (MRC Biostatistics Unit, Cambridge, UK), and Dr. Bogdan Pasaniuc (Associate Professor, Computational Medicine, UCLA, USA).
Additional course leader
Latest course evaluation Not available
Course responsible Natalia Rivera
Department of Medicine, Solna

Center of Molecular Medicine, L8:05

Contact person Natalia Rivera
Institutionen för medicin, Solna

Center of Molecular Medicine, L8:05


Xia Jiang
Institutionen för klinisk neurovetenskap