The role of 3D human genome architecture in mutability - from predicting penetrance/gene fusions to discovering novel schizophrenia-associated variants

Buxton, D., 2017. The role of 3D human genome architecture in mutability - from predicting penetrance/gene fusions to discovering novel schizophrenia-associated variants. PhD, Nottingham Trent University.

PhD Thesis Daniel Buxton CORRECTED.pdf - Published version

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We have become very familiar with the genome being represented as a one dimensional sequence of the four nucleobases – cytosine, guanine, adenine and thymine. However, in reality this chain folds and is densely packed into the nucleus of eukaryotic cells in a three-dimensional (3D) setting, meaning that pairs of otherwise remote areas of the genome can come into close proximity in 3D space. It is thought that the expression of target genes is influenced by remotely acting regulatory elements, such as enhancers, which are often located several kilobases away from the genes they target.

In our studies we hypothesised that communication between widely spaced genomic elements is facilitated by the spatial organisation of chromosomes that bring genes and their regulatory elements in close spatial proximity. We explored this hypothesis in three distinct contexts:(1) reduced/incomplete penetrance, where disease genotypes do not always induce the expected phenotype; (2) gene fusion events, known to be frequent in cancer; (3) schizophrenia, a complex brain disorder. Whilst previous studies acknowledged the role of polygenic activity in these genetic diseases and phenomena, they did not integrate this idea into existing detection/prediction techniques. Our analysis addressed this oversight by transforming traditionally one-dimensional studies into a contextually relevant, 3D setting.

We utilised data describing the 3D structure of the human genome, alongside prior knowledge of various diseases and genetic phenomena, to predict novel genomic regions of association. Our approaches incorporated network, statistical and computational methods to identify where these regions of interest lie. Identified regions were investigated further to ascertain biological properties, such as an enriched presence of mutations, functionally relevant genes, regulatory elements, or all of the above. Whilst existing approaches tend to fixate on only these static properties, our studies also focused on the communication of otherwise remote regions by creating 3D interaction networks that describe the spatial proximities of genomic fragments. The most important units of such networks were identified via centrality measures and statistical testing, followed by subsequent biological interrogation of so-called candidate regions. This method ultimately confirmed whether regions were genuinely disease-associated via polygenic activity, or not.

A total of 35 novel schizophrenia candidate regions were identified using our approach, 22 of which contained polymorphisms with prior schizophrenia association; most variants found were shown to influence gene expression specifically in brain tissues. We were also successful in showing that cancer-causing gene fusion events are catalysed by paired fusion gene-containing fragments (of lengths 1 megabase and 100 kilobases) sharing small 3D neighbourhoods, particularly for genes residing on different chromosomes. Our transformation of existing approaches into 3D studies has therefore elucidated features and properties of genetic disease and cancer that were otherwise unknown or overlooked.

Item Type: Thesis
Creators: Buxton, D.
Date: December 2017
Rights: This work is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed in the owner(s) of the Intellectual Property Rights.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 03 Dec 2018 11:49
Last Modified: 03 Dec 2018 11:49

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