Computational Modeling for System Genetics
Module: CIT4230001
Credit: 6 ECTS
Room (lecture and exercise): Seminarroom Taurus 1, Galileo (8120.EG.002)
Lecturer: Matthias Heinig, Julien Gagneur, Gabi Kastenmueller, Michael Menden, Paolo Casale, Elefteria Zeggini
Lecture: Thursdays, 14:00 - 15:00, semiar room Taurus 1, Galileo (8120.EG.002)
Exercise: Thursdays, 15:00 - 17:00, semiar room Taurus 1, Galileo (8120.EG.002)
Lecture Language: English
Prerequisite (recommended):
- Basics in biology / genetics
- Data analysis and visualization in R
- Familiar with our primer in statistics, probability, and the multivariate Gaussian (Being able to do the exercise) available here: drive.google.com/drive/folders/18s1LU1ccxmxUVLOZXO7a1QYgYuRrRnpW
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
- regularized linear models and its applications in genetics
- linear mixed models to deal with population structure
- experimental techniques to measure gene expression
- efficient algorithms for expression QTL analysis
- statistical concepts for causal inference such as Mendelian randomization
- network inference methods such as Graphical Gaussian models and application to omics data (metabolome, transcriptome)
- Apply some of the above-mentioned techniques on an actual problem from systems genetics. Evaluate model performance, calibration and provide biological interpretation of its application to real data.
Content:
This is a two-part-module: (1) Eight lectures introduce basics of systems genetics, and statistical models employed. The Eight lectures are supported with tutorials in R or python. In addition, we will provide a script covering statistical concepts required for the lectures that we expect the students to be familiar with before the start of the lectures.
This is followed by (2) an eight-week hands-on-project which will be suggested and supervised by lab members from the lecturers. This way, you will be given a unique opportunity to work on ongoing research projects currently addressed by the respective lab.
During the lectures, the following topics will be covered:
- Introduction to human genetics and genome-wide association studies (GWAS)
- Population structure
- Polygenic risk score
- Gene-mapping and variant fine-mapping
- Gene expressions QTLs (eQTLs)
- Causal inference with omics data (metabolomics, transcriptomics, etc.)
- Omics approaches for rare diseases
Over these lectures, various machine learning methods are introduced including:
- Linear regression and hypothesis testing
- Linear mixed models
- Regularized linear models
- Multiple testing correction
- Graphical Gaussian models