Interpreting disease genetics using functional genomics

It is evident that many complex diseases have a genetic background. By performing genome-wide association studies in the last five years, many genetic variants have now been found that contribute to this genetic background. However, for many of these variants, it is unclear how they function and eventually cause disease. To develop future therapies for these diseases, it is very important that the function of these variants is investigated.

In this thesis, we show that many disease-associated variants affect the activity (transcription) of genes in many different ways, which indicates that a large fraction of disease-associated variants have regulatory consequences. Additionally, we show that the genes that are affected by these variants don't necessarily need to be located near the genetic variant. Some variants affect genes that are located at great genetic distances away from the variant, or sometimes on completely different chromosomes. We show that these effects can form functional networks that describe specific disease processes. As such, we have determined an important fraction of the functional consequences of these variants. Additionally, in this thesis, we present several computational methods for increasing the statistical power to identify associations between genotypes and gene expression levels.