Network-Based Methods for the Analysis of Next Generation Sequencing Data in Human Genetic Disease

Student thesis: Doctoral ThesisDoctor of Philosophy


Next generation sequencing generates a large quantity of sequence data which has the potential to be highly informative when evaluated using appropriate analytical methods. One of the key aims of human genetic disease studies is to use such methods to help identify sequence variants having some phenotypic effect. In the past few years, whole exome sequencing in particular has been used to identify single variants that cause many monogenic diseases. However, monogenic diseases in which genetic heterogeneity plays a role present a more difficult problem because different affected individuals in a study may not carry disease-causing mutations in the same gene.
A major focus of my work is to develop and implement algorithms to identify disease-causing variants in such diseases. In particular I make use of functional information, such as that encoded by interaction networks, to prioritise genes for follow-up analysis. In this thesis I present two different analysis tools designed for this purpose. Simulated datasets are constructed to demonstrate the utility of these tools and test their performance under varying conditions.
The tools are applied to a whole exome sequencing study for a genetically-heterogeneous monogenic disease (Adams-Oliver syndrome) with the aim of generating novel hypotheses regarding disease aetiology. This work also allows comparison and exploration of the challenges facing network-based methods in practice. The tools are also applied to a study of families exhibiting atypically strong recurrence of a complex disorder (Crohn’s disease), testing the hypothesis that one or a small number of rare highly-penetrant variants might be implicated in each family. In this way it is proposed that the application of network-based methods to next generation sequencing data can help to describe disease mechanisms that move beyond monogenic diseases and towards more complex genetic architectures.
Date of Award2015
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorReiner Schulz (Supervisor), Rebecca Oakey (Supervisor) & Michael Simpson (Supervisor)

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