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Title: Computational genomic analyses of long-lived mammals to study variation in cancer resistance, longevity and life history
Author: Keane, Michael
ISNI:       0000 0004 7658 1411
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2018
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Little is known about the genetic and molecular mechanisms responsible for the great differences in mammalian longevity and life history. One potential source of novel insights is based on comparative analyses of the genomes of species which exhibit extreme longevity and an extended life history. As such, this work describes the results obtained from the analysis of the bowhead whale, naked mole rat (NMR) and human genomes, each of which are exceptionally long-lived compared to closelyrelated species. The bowhead whale genome was analysed with a focus on identifying genes with evidence of positive selection and proteins with unique amino acid residues when compared with other mammals. A number of genes that have previously been associated with cancer and ageing were found to exhibit evidence of positive selection on the bowhead lineage. In addition, bowheadspecific alterations in proteins linked to sensory perception of sound and size and development were also identified which are of potential relevance due to the phenotypic divergence of the bowhead whale associated with these traits. The analysis of the NMR assembly attempted to identify genes with a signal of positive selection by comparing synonymous and non-synonymous substitution rates. While positive selection on NMR genes has previously been analysed, we found additional signals of selection in several which have not previously been reported, including in regions of p53 and the hyaluronan receptors CD44 and HMMR. Finally, while the previous analyses focused on coding sequences, it is also likely that much of the genetic basis for the variation in longevity is to be found in non-coding regions of the genome. In order to assess this hypothesis, human data from both genome wide association studies (GWAS) and annotated 3'UTR sequences was analysed in order to identify genes with signals of molecular adaptation which correlate with trait divergence. The GWAS meta-analysis identified genes from a specific pathway which has previously been shown to regulate the timing of growth and development. The genes identified in the 3'UTR analysis were slightly below the level of statistical significance indicating that greater statistical power, most likely in the form of including sequences from addition species, is necessary. Overall, the results obtained offer novel insights regarding the molecular adaptations by which longevity and life history evolve and identify numerous genes which could be prioritised for future studies including potential functional characterisation. Furthermore, all the data and results generated have been made available on customised online portals in order to allow easy access to the scientific community and facilitate further research into these long-lived species.
Supervisor: Jones, Andy ; De Magalhaes, Joao Pedro Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral