Statistical Methods for Systems Genetics

Module IN2344

Credit: 5 ECTS.

Room (lecture and exercise): 01.11.018

Lecture: Tuesdays, 14:00 - 16:00

Exercise: Tuesdays, 16:00 - 18:00

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



- 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