Recent advances in ’omics technologies and the development of new computational techniques have greatly contributed to the identification of factors influencing the onset and progression of many common diseases. Yet, despite this great success, it is unlikely that the independent analysis of these data will elucidate the complex web of mechanisms involved in disease development. To enhance our knowledge of disease aetiology, new approaches for linking the large amount of available data need to be developed. As a step towards this goal, the aim of this thesis is to investigate and develop novel statistical methods for the integrative analysis of ’omic data. In particular, in this project we analyse genomic and metabolomic data in relation to the health outcomes in three study populations. To investigate how genetic and metabolic variables act as risk factors in the development of complex disorders, we have developed three novel analytical methodologies, namely ’Differential Network’, ’GEMINi: GEnome Metabolome Integrated Network analysis’ and ’Variance and Covariance regression’ and illustrate their use on real data sets. The results demonstrate the applicability of the new methodologies to identify key molecular changes undetectable with standard approaches. The approaches introduce here have the potential of providing insight into the biological basis of phenotypic variation and aid the generation of new hypotheses about molecular control and regulation in the context of systems biology.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:634074 |
| Date | January 2013 |
| Creators | Valcarcel Salamanca, Beatriz |
| Contributors | de Iorio, Maria : Jarvelin, Marjo-Riitta : Ebbels, Timothy |
| Publisher | Imperial College London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10044/1/18678 |
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