Plenty of gene variants have been associated with disease, indicating widespread genetic heterogeneity, which leaves the molecular basis of complex diseases unclear. However, it is widely postulated that the products of genes whose mutations are implicated in the same disease function together in the same biological pathways and it is the disruption of these pathways that underlies the disease. Such pathways are not well defined and their identification could help elucidate disease mechanisms. To discern molecular pathways of relevance to complex disease, I have inferred functional associations between human genes from diverse data types and assessed these associations with a novel phenotype-based method. I could confirm the hypothesis that dysfunctions of genes associated with each other in terms of functional genomic and proteomic data tend to give rise to the same disease. Examining the functional association between disease-associated gene variants, I have found that genes implicated through de novo sequence variants are biased in their coding sequence length and that longer genes tend to cluster together in gene networks, leading to exaggerated p-values in functional studies. I have controlled for the confounding bias and, testing different data sources, found that an integrated phenotypic-linkage network offers superior power to detect functional associations among genes mutated in the same disease. Applying these methods to clinical phenotypes related to intellectual disability, I have observed an increased predictive potential in identifying genes associated with these phenotypes. I have also performed case–control association analyses of variants from an exome-sequencing study of Parkinson’s disease and tested the functional associations of the mutated genes. I have advanced a framework for the identification of biological pathways disrupted in complex disorders, also demonstrating the suitability of this method to functionally sub-cluster the gene variants underlying a complex disorder, with implications for the understanding of disease mechanisms.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:692865 |
| Date | January 2014 |
| Creators | Honti, Frantisek |
| Contributors | Webber, Caleb ; Ponting, Christopher P. |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:e7079080-a814-431a-badd-35080f5a2825 |
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