Statistical Methods for Systems Genetics
Module IN2344
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of WS 2016/7
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
| available module versions | |
|---|---|
| SS 2021 | WS 2016/7 |
Basic Information
IN2344 is a semester module in English language at Master’s level which is offered irregular.
This module description is valid to SS 2022.
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 150 h | 60 h | 5 CP |
Content, Learning Outcome and Preconditions
Content
- Introduction to human genetics
- Quantitative genetics / GWAS
- Multiple hypothesis testing
- Population structure
- Transcriptomics
- Metabolomics
- Enrichment algorithms
- Causal inference
- Regularized linear models
- Network models
- Multi-OMICS data integration
- Survival models
- Quantitative genetics / GWAS
- Multiple hypothesis testing
- Population structure
- Transcriptomics
- Metabolomics
- Enrichment algorithms
- Causal inference
- Regularized linear models
- Network models
- Multi-OMICS data integration
- Survival models
Learning Outcome
At the end of the module students understand / are able to practically implement
- the challenges of complex trait genetics
- statistical models for QTL mapping and GWAS
- methods for adjustment for multiple testing
- linear mixed models to deal with population structure
- experimental techniques to measure gene expression
- algorithms for transcriptome quantification from NGS
- efficient algorithms for expression QTL analysis
- methods of metabolome quantification
- algorithms based on gene sets
- statistical concepts for causal inference such as Mendelian randomization
- regularized linear models and its applications in genetics
- network inference methods such as Graphical Gaussian models
- the application of graphical models for the integration of multiple OMICS data sets
- statistics of survival models
- the challenges of complex trait genetics
- statistical models for QTL mapping and GWAS
- methods for adjustment for multiple testing
- linear mixed models to deal with population structure
- experimental techniques to measure gene expression
- algorithms for transcriptome quantification from NGS
- efficient algorithms for expression QTL analysis
- methods of metabolome quantification
- algorithms based on gene sets
- statistical concepts for causal inference such as Mendelian randomization
- regularized linear models and its applications in genetics
- network inference methods such as Graphical Gaussian models
- the application of graphical models for the integration of multiple OMICS data sets
- statistics of survival models
Preconditions
IN0018 Discrete Probability Theory, Basics in biology / genetics, Data analysis and visualization in R
Courses, Learning and Teaching Methods and Literature
Learning and Teaching Methods
The lectures will introduce the concepts. In the programming excercises students will practice the tools to perform the analyses. For each excercise a short report has to be written.
Media
Weekly posted exercises online, slides, live demonstrations in the exercises
Literature
- Civelek, M., & Lusis, A. J. (2014). Systems genetics approaches to understand complex traits. Nature Reviews Genetics, 15(1), 34–48. http://doi.org/10.1038/nrg3575
- Krishnarao Appasani, Genome-Wide Association Studies, Cambridge University Press
- more literature will be announced in the lecture.
- Krishnarao Appasani, Genome-Wide Association Studies, Cambridge University Press
- more literature will be announced in the lecture.
Module Exam
Description of exams and course work
The understanding of the mathematical concepts from the lecture will be assessed in a written exam (120 min). In addition the ability of the students to explain and summarize the biological context and the experimental methods will be evaluated in the exam.
Exam Repetition
The exam may be repeated at the end of the semester.