Statistical association networks as complex phenotypes : new methods and applications
File(s)
Author(s)
Valcarcel Salamanca, Beatriz
Type
Thesis
Abstract
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.
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.
Version
Open Access
Date Issued
2013-04
Date Awarded
2013-11
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
de Iorio, Maria
Jarvelin, Marjo-Riitta
Ebbels, Timothy
Sponsor
Economic and Social Research Council (Great Britain)
Grant Number
ES/H016058/1
Publisher Department
School of Public Health
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)