Genomic data analysis: populations, patients and pipelines
Genomic data analysis: populations, patients and pipelines
Methods for the ascertainment of genotype data have become more cost efficient by orders of magnitude with the use of high-density genotyping arrays and the advent of next generation sequencing (NGS). The resulting deluge of data has required ever advancing analytical approaches in order for the maximal information to be gleaned from these extensive data.
In this work, many application of NGS to clinical research are discussed. This includes the application of targeted gene sequencing to a cohort of 83 patients with chronic kidney disease, whole-exome investigations of eight families with cleft lip/palate phenotypes, as well as five cases where analytical lessons can be learned from exome sequenced cases harbouring pathogenic variants refractory to identification. Additionally, a novel QC tool for the unambiguous tracking of samples undergoing exome sequencing is presented.
Furthermore, work is presented investigating the linkage disequilibrium (LD) patterns in populations applying the Malecot-Morton model. We demonstrate that array genotyping is insufficient for the accurate determination of ne LD patterns in the human genome, with whole-genome sequencing providing more representative LD maps. Finally, we apply similar methods to Gallus gallus, generating the highest resolution maps of LD presented to date, showing that the patterns are highly discordant between commercial lines, and define features associated with recombination.
Overall, we highlight the diversity of ways in which genetic data can be utilised effectively in the age of genomic `big data', and present tools which may be of benefit to other researchers utilising these technologies
Pengelly, Reuben
af97c0c1-b568-415c-9f59-1823b65be76d
October 2015
Pengelly, Reuben
af97c0c1-b568-415c-9f59-1823b65be76d
Ennis, Sarah
7b57f188-9d91-4beb-b217-09856146f1e9
Collins, Andrew
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
Gibson, Jane
855033a6-38f3-4853-8f60-d7d4561226ae
Pengelly, Reuben
(2015)
Genomic data analysis: populations, patients and pipelines.
University of Southampton, Faculty of Medicine, Doctoral Thesis, 216pp.
Record type:
Thesis
(Doctoral)
Abstract
Methods for the ascertainment of genotype data have become more cost efficient by orders of magnitude with the use of high-density genotyping arrays and the advent of next generation sequencing (NGS). The resulting deluge of data has required ever advancing analytical approaches in order for the maximal information to be gleaned from these extensive data.
In this work, many application of NGS to clinical research are discussed. This includes the application of targeted gene sequencing to a cohort of 83 patients with chronic kidney disease, whole-exome investigations of eight families with cleft lip/palate phenotypes, as well as five cases where analytical lessons can be learned from exome sequenced cases harbouring pathogenic variants refractory to identification. Additionally, a novel QC tool for the unambiguous tracking of samples undergoing exome sequencing is presented.
Furthermore, work is presented investigating the linkage disequilibrium (LD) patterns in populations applying the Malecot-Morton model. We demonstrate that array genotyping is insufficient for the accurate determination of ne LD patterns in the human genome, with whole-genome sequencing providing more representative LD maps. Finally, we apply similar methods to Gallus gallus, generating the highest resolution maps of LD presented to date, showing that the patterns are highly discordant between commercial lines, and define features associated with recombination.
Overall, we highlight the diversity of ways in which genetic data can be utilised effectively in the age of genomic `big data', and present tools which may be of benefit to other researchers utilising these technologies
Text
Reuben Pengelly.pdf
- Other
More information
Published date: October 2015
Organisations:
University of Southampton, Human Development & Health
Identifiers
Local EPrints ID: 397102
URI: http://eprints.soton.ac.uk/id/eprint/397102
PURE UUID: 25216740-e62e-4c73-8698-1eb7075c0a8b
Catalogue record
Date deposited: 19 Jul 2016 13:04
Last modified: 15 Mar 2024 03:48
Export record
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics