In silico identification of novel genetic factors associated with longevity in Drosophila

Hall, B., 2019. In silico identification of novel genetic factors associated with longevity in Drosophila. PhD, Nottingham Trent University.

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Abstract

To determine genetic factors causing variation in survival into old age, several genome-wide association studies (GWAS) have been carried out on panels of long-lived individuals. The findings from a number of these GWAS studies were somewhat inconclusive, owing to the small sample sizes investigated. It is for this reason that model organisms such as Drosophila melanogaster have become increasingly important in identifying genetic factors underlying longevity.

In this study we hypothesised that co-location of novel genes/genomic regions with genes, known to be associated with longevity, that share biological function with co-located genes, make them good candidates for novel genomic regions, linked to longevity. We further hypothesised that single nucleotide polymorphisms (SNPs) residing within these co-located regions may influence longevity either individually (when a SNP in one of these genes causes a particular phenotype) or collectively (when one or several SNPs in these regions occur in the same individual thus causing the phenotype). Summary statistics of datasets of SNPs generated by two GWAS (Burke et al., 2013; Ivanov et al., 2015) which include position of each SNP and a corresponding statistic (D or P- value) showing the strength of association with longevity were used in this study to guide the initial choice of genes/loci strongly associated with longevity.

First, a network approach was applied to predict novel genes/genomic regions/SNPs, playing a role in longevity, which integrated three-dimensional (3D) chromosome conformation data (Hi-C) and two GWAS datasets. Networks were created using genes/genomic regions, known to associate with longevity, as original nodes with additional nodes (regions) later added to these networks if they strongly interacted (i.e. came into close proximity as measured by the Hi-C data) with the original nodes. Various network measures were calculated, in order to identify important previously unknown regions. These previously unknown regions were further explored and longevity associated genes were found including Rim and Tpi with a 'long-lived' phenotype, and some newly found regions were observed to be common between both GWAS datasets. A human ortholog search of genes found in this analysis resulted in matches to human genes with functions related to lifespan. Subnetworks of these GWAS-based networks were sought for enrichment in GO terms and several genes with no previous association with longevity but enriched in longevity-related terms were identified.

Second, SNPs residing in non-coding regions, e.g. within transcription factor binding sites (TFBSs) recognised by transcription factors (TF) and borders between Topologically Associated Domains (TADs) were analysed. Each TF typically recognises a collection of often dissimilar DNA motifs. Here we hypothesised that TFs may recognise a certain structure, e.g. non-B DNA structures, rather than sequence motifs. Structures such as slipped, cruciform, triplexes and tetraplexes, formed on direct, inverted and mirrored repeats and G-quartets were considered and SNPs residing within these structures were analysed. For the study of SNPs in TAD borders we hypothesised that SNPs residing in these border regions may cause a severe disruption to the way in which regulation usually occurs within these TADs. We found that a significant proportion (~2%) of non-coding SNPs, reported in the DGRP GWAS dataset, resided in TAD border regions on the Drosophila genome, when compared to a match control dataset ((

Item Type: Thesis
Creators: Hall, B.
Date: September 2019
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
Depositing User: Linda Sullivan
Date Added: 28 May 2020 14:14
Last Modified: 28 May 2020 14:25
URI: http://irep.ntu.ac.uk/id/eprint/39906

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