Statistical Methods for System Genetics
Module: IN2344
Credit: 5 ECTS.
Room (lecture and exercise): MW 0250, Ludwig-Prandtl-Hörsaal (5502.EG.250)
Lecturer: Matthias Heinig, Julien Gagneur, Gabi Kastenmueller, Michael Menden, Paolo Casale, Elefteria Zeggini
Lecture: Thursdays, 14:00 - 15:30, room MW 0250, Ludwig-Prandtl-Hörsaal (5502.EG.250)
Exercise: Thursdays, 15:30 - 17:00, room MW 0250, Ludwig-Prandtl-Hörsaal (5502.EG.250)
Lecture Language: English
Prerequiste (recommended):
- Basics in biology / genetics
- Data analysis and visualization in R
- Basics of statistics and probability
Intended Learning Outcomes: 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
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