Syllabus database for doctoral courses
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Syllabus database for doctoral courses
SYLLABI FOR DOCTORAL COURSES
| Swedish title | Grundläggande statistisk genetik och datavisualisering |
|---|---|
| English title | Fundamentals of Statistical Genetics and Data Visualization |
| Course number | 5308 |
| Credits | 1.5 |
| Responsible KI department | Institutionen för medicin, 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. |
| Grading | Passed /Not passed |
| Established by | The Committee for Doctoral Education |
| Established | 2021-09-16 |
| 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 https://uppmax.uu.se/. 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: • https://www.nature.com/articles/s41576-020-0265-5 • https://pubmed.ncbi.nlm.nih.gov/32818441/ • https://pubmed.ncbi.nlm.nih.gov/29789686/ • https://www.nature.com/articles/s41588-018-0183-z • https://www.nature.com/articles/s41467-020-14452-4 • https://pubmed.ncbi.nlm.nih.gov/34135084/ • https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1215-1 • https://link.springer.com/article/10.1186/s12859-015-0857-9 • https://www.nature.com/articles/nrg3433 • https://www.nature.com/articles/nrc3721 • https://journals.sagepub.com/doi/full/10.1177/1177932219899051 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: • https://ggplot2.tidyverse.org/index.html |
Course responsible |
Natalia Rivera Institutionen för medicin, Solna 0700302763 natalia.rivera@ki.se Center of Molecular Medicine, L8:05 17176 Srockholm |
| Contact person |
Natalia Rivera Institutionen för medicin, Solna 0700302763 natalia.rivera@ki.se Center of Molecular Medicine, L8:05 17176 Srockholm Xia Jiang Institutionen för klinisk neurovetenskap xia.jiang@ki.se |
