Clustering genes by function to understand disease phenotypes : code used in the analyses presented in the thesis with the same title

Andrews, T

Documentation:: Contains Perl & C algorithms used in the analysis as well as R scripts for producing figures. Does not include publicly available datasets and annotations required to replicate the analyses.

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APA Style

Andrews, T. (2015). Clustering genes by function to understand disease phenotypes : code used in the analyses presented in the thesis with the same title. University of Oxford.

MLA Style

Andrews, T. Clustering Genes by Function to Understand Disease Phenotypes : Code Used in the Analyses Presented in the Thesis with the Same Title. University of Oxford, 2015.

Chicago Style

Andrews, T. 2015. Clustering Genes by Function to Understand Disease Phenotypes : Code Used in the Analyses Presented in the Thesis with the Same Title. University of Oxford.
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Files:: ThesisCode.zip

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Authors/Creators

+ Andrews, T More by this author/creator

Institution:: University of Oxford
Division:: MSD
Department:: Physiology Anatomy & Genetics
Role:: Creator, Performer

+ Clarendon Scholarships More from this funder

Funding agency for:: Andrews, T

+ Natural Sciences and Engineering Research Council of Canada More from this funder

Funding agency for:: Andrews, T

+ Somerville College More from this funder

Funding agency for:: Andrews, T

Publisher:: University of Oxford
Publication date:: 2015

Pubs id:: 1480151
UUID:: uuid:3b33ea36-1644-4e55-bb3a-6796a064e802
Local pid:: pubs:1480151
Deposit date:: 2015-11-08
ARK identifier:: ark:/29072/ora_3b33ea3616444e55bb3a6796a064e802

Related item:: Gene Networks Underlying Convergent and Pleiotropic Phenotypes in a Large and Systematically-Phenotyped Cohort with Heterogeneous Developmental Disorders
Description:: Readily-accessible and standardised capture of genotypic variation has revolutionised our understanding of the genetic contribution to disease. Unfortunately, the corresponding systematic capture of patient phenotypic variation needed to fully interpret the impact of genetic variation has lagged far behind. Exploiting deep and systematic phenotyping of a cohort of 197 patients presenting with heterogeneous developmental disorders and whose genomes harbour de novo CNVs, we systematically applied a range of commonly-used functional genomics approaches to identify the underlying molecular perturbations and their phenotypic impact. Grouping patients into 408 non-exclusive patient-phenotype groups, we identified a functional association amongst the genes disrupted in 209 (51%) groups. We find evidence for a significant number of molecular interactions amongst the association-contributing genes, including a single highly-interconnected network disrupted in 20% of patients with intellectual disability, and show using microcephaly how these molecular networks can be used as baits to identify additional members whose genes are variant in other patients with the same phenotype. Exploiting the systematic phenotyping of this cohort, we observe phenotypic concordance amongst patients whose variant genes contribute to the same functional association but note that (i) this relationship shows significant variation across the different approaches used to infer a commonly perturbed molecular pathway, and (ii) that the phenotypic similarities detected amongst patients who share the same inferred pathway perturbation result from these patients sharing many distinct phenotypes, rather than sharing a more specific phenotype, inferring that these pathways are best characterized by their pleiotropic effects.
Related item:: Clustering genes by function to understand disease phenotypes
Description:: 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.
Related item:: Clustering of functionally-related genes impacts significantly on CNV-mediated disease
Description:: Clusters of functionally-related genes can be disrupted by a single copy-number variant (CNV). We demonstrate that the simultaneous disruption of multiple functionally-related genes is a frequent and significant characteristic of de novo CNVs in patients with developmental disorders (p = 1 x10-3). Using three different functional networks, we identified unexpectedly large numbers of functionally-related genes within de novo CNVs from two large independent cohorts of individuals with developmental disorders. The presence of multiple functionally-related genes was a significant predictor of a CNV's pathogenicity when compared to CNVs from apparently healthy individuals, and a better predictor than the presence of known disease or haplo-insufficient genes for larger CNVs. The functionally-related genes found in the de novo CNVs belonged to 70% of all clusters of functionally related genes found across the genome. De novo CNVs were more likely to affect functional clusters and affect them to a greater extent than benign CNVs (p = 6 x 10-4). Furthermore, such clusters of functionally-related genes are phenotypically-informative: different patients possessing CNVs that affect the same cluster of functionally-related genes exhibit more similar phenotypes than expected (p < 0.05). The spanning of multiple functionally-similar genes by single CNVs contributes substantially to how these variants exert their pathogenic effects.

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