Statistical Methods for Systems Genetics
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
IN2344 is a semester module in English language at Master’s level which is offered irregular.
This module description is valid to SS 2022.
Content, Learning Outcome and Preconditions
- Quantitative genetics / GWAS
- Multiple hypothesis testing
- Population structure
- Enrichment algorithms
- Causal inference
- Regularized linear models
- Network models
- Multi-OMICS data integration
- 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
Courses, Learning and Teaching Methods and Literature
Learning and Teaching Methods
- Krishnarao Appasani, Genome-Wide Association Studies, Cambridge University Press
- more literature will be announced in the lecture.
Description of exams and course work
The exam may be repeated at the end of the semester.