Identification of regulatory SNPs and epistatic SNP pairs using deep learning and information theory

Abstract

In the last two decades, new technologies have made DNA genotyping and sequencing far more time and cost efficient. The resulting tremendous increase in the amount of available genomic data allows for a deeper understanding of the relationship between the genotype and the phenotype. In this thesis, I present two novel frameworks which analyze specific aspects of the relationship between the genotype and the phenotype, namely the identification of regulatory SNPs (rSNPs) as well as the detection of epistatic SNP pairs. Additional thanks go to the group of Prof. Wolfgang Link whose collaboration helped to facilitate my Master's thesis and first publication.Finally, I would like to thank my parents. They supported me during my studies and in my daily life and they have made it possible for me to concentrate fully on my studies. I dedicate this thesis to them.Last but not least, I thank my sister Jasmin who, despite her own work and studies, found the time to proofread my publications and this thesis. Contents x Material and methods