Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.711928
Title: Clustering genes by function to understand disease phenotypes
Author: Andrews, Tallulah
ISNI:       0000 0004 6061 7993
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2015
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Abstract:
Developmental disorders including: autism, intellectual disability, and congenital abnormalities are present in 3-8% of live births and display a huge amount of phenotypic and genetic heterogeneity. Traditionally, geneticists have identified individual monogenic diseases among these patients but a majority of patients fail to receive a clinical diagnosis. However, the genomes of these patients frequently harbour large copynumber variants (CNVs) but their interpretation remains challenging. Using pathway analysis I found significant functional associations for 329 individual phenotypes and show that 39% of these could explain the patients’ multiple co-morbid phenotypes; and multiple associated genes clustered within individual CNVs. I showed there was significantly more such clustering than expected by chance. In addition, the presence of a multiple functionally-related genes is a significant predictor of CNV pathogenicity beyond the presence of known disease genes and size of the CNV. This clustering of functionally-related genes was part of a broader pattern of functional clusters across the human genome. These genome-wide functional clusters showed tissuespecific expression and some evidence of chromatin-domain level regulation. Furthermore, many genome-wide functional clusters were enriched in segmental duplications making them prone to CNV-causing mutations and were frequently seen disrupted in healthy individuals. However, the majority of the time a pathogenic CNV affected the entire functional cluster, where as benign CNVs tended to affect only one or two genes. I also showed that patients with CNVs affecting the same functional cluster are significantly more phenotypically similar to each other than expected even if their CNVs do not affect any of the same genes. Lastly, I considered one of the major limitations in pathway analysis, namely ascertainment biases in functional information due to the prioritization of genes linked to human disease, and show how the modular nature of gene-networks can be used to identify and prioritize understudied genes.
Supervisor: Webber, Caleb ; Ponting, Chris Sponsor: Natural Sciences and Engineering Research Council of Canada
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.711928  DOI: Not available
Keywords: Computational Biology ; Human Genetics ; protein-protein interactions ; biological networks ; copy number variation
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