Anglia Ruskin Research Online (ARRO)
Browse
- No file added yet -

Identification of novel genes associated with longevity in Drosophila melanogaster - a computational approach

Download (1.67 MB)
journal contribution
posted on 2023-07-26, 14:49 authored by Bethany S. Hall, Yvonne A. Barnett, Jonathan J. Crofts, Nadia Chuzhanova
Despite a growing number of studies on longevity in Drosophila, genetic factors influencing lifespan are still poorly understood. In this paper we propose a conceptually new approach for the identification of novel longevity-associated genes and potential target genes for SNPs in non-coding regions by utilizing the knowledge of co-location of various loci, governed by the three-dimensional architecture of the Drosophila genome. Firstly, we created networks between genes/genomic regions harboring SNPs deemed to be significant in two longevity GWAS summary statistics datasets using intra- and inter-chromosomal interaction frequencies (Hi-C data) as a measure of co-location. These networks were further extended to include regions strongly interacting with previously selected regions. Using various network measures, literature search and additional bioinformatics resources, we investigated the plausibility of genes found to have genuine association with longevity. Several of the newly identified genes were common between the two GWAS datasets and these possessed human orthologs. We also found that the proportion of non-coding SNPs in borders between topologically associated domains is significantly higher than expected by chance. Assuming co-location, we investigated potential target genes for non-coding SNPs. This approach therefore offers a stepping stone to identification of novel genes and SNP targets linked to human longevity.

History

Refereed

  • Yes

Volume

11

Issue number

23

Page range

11244-11267

Publication title

Aging

ISSN

1945-4589

Publisher

Impact Journals

File version

  • Published version

Language

  • eng

Legacy posted date

2019-12-06

Legacy creation date

2019-12-06

Legacy Faculty/School/Department

Faculty of Science & Engineering

Usage metrics

    ARU Outputs

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC