Pathway Analyisis of GWAS data

Background: For many human traits it is of interest to discover the genetic variants that have an impact on the trait. Current large scale GWAS studies have uncovered a multitude of genomic regions that associate with traits of interest such as disease susceptibility. To make biological sense of these results, the genomic regions have to be mapped to genes, which in turn have to be mapped to biological processes. Pathway analysis is one method to achieve these goals. However, there is no clear consensus in the scientific community on how to do this in a standardized fashion.

Goal: The goal of this project is to evaluate a new method that we have developed in house which is supposed to take into account an additional bias not well accounted for by previous methods.

Mathematical tools: The students will learn some statistical principles related to the field of statistical genetics and also pathway analysis. No prior knowledge in statistics is necessary but It would be advantageous if high-school math was not completely forgotten.

Biological or Medical aspects: Our main data sets will be GWAS-data for blood lipid levels. Blood lipid levels are a major biomarker for cardiovascular problems and the genetics of them is of great interest to the medical community.

Supervisor: David Lamparter

Students: Rosanne Miles, Anthony Sonrel, Alain Pulfer, Stefan Milosavljevic

Documents: Script for the first theoretical part of our course [[1]] and summary of the entire course in one page [[2]]