Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/3854
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dc.contributor.authorLee, Sang Hongen
dc.contributor.authorVan Der Werf, Julius Hermanen
dc.contributor.authorKinghorn, Brianen
dc.date.accessioned2009-12-16T11:16:00Z
dc.date.issued2008en
dc.identifier.citationBMC Bioinformatics, v.9 (189)en
dc.identifier.issn1471-2105en
dc.identifier.urihttps://hdl.handle.net/1959.11/3854en
dc.description.abstractBackground: Haplotype reconstruction is important in linkage mapping and association mapping of quantitative trait loci (QTL). One widely used statistical approach for haplotype reconstruction is simulated annealing (SA), implemented in SimWalk2. However, the algorithm needs a very large number of sequential iterations, and it does not clearly show if convergence of the likelihood is obtained. Results: An evolutionary algorithm (EA) is a good alternative whose convergence can be easily assessed during the process. It is feasible to use a powerful parallel-computing strategy with the EA, increasing the computational efficiency. It is shown that the EA can be ~4 times faster and gives more reliable estimates than SimWalk2 when using 4 processors. In addition, jointly updating dependent variables can increase the computational efficiency up to ~2 times. Overall, the proposed method with 4 processors increases the computational efficiency up to ~8 times compared to SimWalk2. The efficiency will increase more with a larger number of processors. Conclusion: The use of the evolutionary algorithm and the joint updating method can be a promising tool for haplotype reconstruction in linkage and association mapping of QTL.en
dc.languageenen
dc.publisherBioMed Central Ltden
dc.relation.ispartofBMC Bioinformaticsen
dc.titleUsing and evolutionary algorithm and parallel computing for haplotyping in a general complex pedigree with multiple marker locien
dc.typeJournal Articleen
dc.identifier.doi10.1186/1471-2105-9-189en
dc.subject.keywordsQuantitative Genetics (incl Disease and Trait Mapping Genetics)en
local.contributor.firstnameSang Hongen
local.contributor.firstnameJulius Hermanen
local.contributor.firstnameBrianen
local.subject.for2008060412 Quantitative Genetics (incl Disease and Trait Mapping Genetics)en
local.subject.seo2008830310 Sheep - Meaten
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolAdministrationen
local.profile.emailslee38@une.edu.auen
local.profile.emailjvanderw@une.edu.auen
local.profile.emailbkinghor@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordpes:6641en
local.publisher.placeUnited Kingdomen
local.peerreviewedYesen
local.identifier.volume9en
local.identifier.issue189en
local.contributor.lastnameLeeen
local.contributor.lastnameVan Der Werfen
local.contributor.lastnameKinghornen
dc.identifier.staffune-id:slee38en
dc.identifier.staffune-id:jvanderwen
dc.identifier.staffune-id:bkinghoren
local.profile.orcid0000-0003-2512-1696en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:3949en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleUsing and evolutionary algorithm and parallel computing for haplotyping in a general complex pedigree with multiple marker locien
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.description.statisticsepubsVisitors: 219<br />Views: 223<br />Downloads: 0en
local.search.authorLee, Sang Hongen
local.search.authorVan Der Werf, Julius Hermanen
local.search.authorKinghorn, Brianen
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