Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/9233
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGoodswen, Stephen Jamesen
dc.contributor.authorGondro, Cedricen
dc.contributor.authorWerf, Julius Van Deren
dc.contributor.authorKadarmideen, Hajaen
dc.date.accessioned2012-01-17T12:23:00Z-
dc.date.created2010en
dc.date.issued2011-
dc.identifier.urihttps://hdl.handle.net/1959.11/9233-
dc.description.abstractThe aftermath of the Human Genome Project has generated new revolutionary techniques and equipment such as high throughput measurement tools for collecting biological information. One notable tool is a microarray that can be used to genotype thousands of single nucleotide polymorphisms (SNPs) in one run. The main aim of the thesis was to implement a bioinformatics approach to transform biological data generated from high-throughput SNP genotyping into useful information. One of the main outcomes from whole genome association studies (WGAS) is a subset of statistically significant SNPs and a major challenge to a researcher is minimising false positive rates while maintaining the power to identify true positive associations. The need for a SNP annotation tool to assist a researcher in making informed judgments as to whether a significant SNP could be a causal variant or in linkage disequilibrium (LD) with a causal variant, provided the motivation to develop FunctSNP. The thesis describes the development of FunctSNP, which is an R package that provides the user interface to custom built species-specific databases. These local relational databases contain SNP data together with functional annotations extracted from online resources. The databases are scheduled for automatic creation or updated periodically by a suite of Perl scripts called dbAutoMaker. The thesis also describes the development of dbAutoMaker. The use of FunctSNP is illustrated with a livestock example. WGAS relies on a natural phenomenon of linkage disequilibrium between SNP markers and causal variants. For WGAS to be applied successfully there is a need to understand the extent and distribution of LD across the entire genome in a population. The need to know how LD (and haplotype diversity) varies from one region or population to another provided the motivation to develop SNPpattern. The thesis describes the development of SNPpattern, which is the collective name for a suite of Perl scripts essentially designed to group, count, and compare SNP allele patterns of various block sizes. Differences in SNP allele block frequency are used as a measure of haplotype diversity within and between groups. The use of SNPpattern is illustrated on sheep breeds.en
dc.languageenen
dc.titleHaplotype metrics and functional data mining from high-throughput SNP genotyping: A bioinformatics approachen
dc.typeThesis Masters Researchen
dc.subject.keywordsQuantitative Genetics (incl Disease and Trait Mapping Genetics)en
local.contributor.firstnameStephen Jamesen
local.contributor.firstnameCedricen
local.contributor.firstnameJulius Van Deren
local.contributor.firstnameHajaen
local.subject.for2008060412 Quantitative Genetics (incl Disease and Trait Mapping Genetics)en
local.subject.seo2008830301 Beef Cattleen
dcterms.RightsStatementCopyright 2010 - Stephen James Goodswenen
dc.date.conferred2011en
local.thesis.degreelevelMasters researchen
local.thesis.degreenameMaster of Scienceen
local.contributor.grantorUniversity of New Englanden
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.emailsgoodswe@une.edu.auen
local.profile.emailcgondro2@une.edu.auen
local.profile.emailjvanderw@une.edu.auen
local.profile.emailhkadarmi@une.edu.auen
local.output.categoryT1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune_thesis-20101018-131533en
local.title.subtitleA bioinformatics approachen
local.contributor.lastnameGoodswenen
local.contributor.lastnameGondroen
local.contributor.lastnameWerfen
local.contributor.lastnameKadarmideenen
dc.identifier.staffune-id:sgoodsween
dc.identifier.staffune-id:cgondro2en
dc.identifier.staffune-id:jvanderwen
dc.identifier.staffune-id:hkadarmien
local.profile.orcid0000-0003-0666-656Xen
local.profile.orcid0000-0003-2512-1696en
local.profile.roleauthoren
local.profile.rolesupervisoren
local.profile.rolesupervisoren
local.profile.rolesupervisoren
local.identifier.unepublicationidune:9424en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleHaplotype metrics and functional data mining from high-throughput SNP genotypingen
local.output.categorydescriptionT1 Thesis - Masters Degree by Researchen
local.relation.urlhttp://www.aaabg.org/proceedings18/files/goodswen454.pdfen
local.relation.urlhttp://www.kongressband.de/wcgalp2010/assets/html/0947.htmen
local.thesis.borndigitalyesen
local.search.authorGoodswen, Stephen Jamesen
local.search.supervisorGondro, Cedricen
local.search.supervisorWerf, Julius Van Deren
local.search.supervisorKadarmideen, Hajaen
local.uneassociationYesen
local.year.conferred2011en
Appears in Collections:Thesis Masters Research
Files in This Item:
6 files
File Description SizeFormat 
Show simple item record

Page view(s)

2,124
checked on Mar 7, 2023

Download(s)

2
checked on Mar 7, 2023
Google Media

Google ScholarTM

Check


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.