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Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/14595
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dc.contributor.authorGondro, Cedricen
dc.contributor.authorPorto-Neto, Laercio Ren
dc.contributor.authorLee, S Hen
local.source.editorEditor(s): Cedric Gondro, Julius van der Werf, Ben Hayesen
dc.date.accessioned2014-04-08T11:23:00Z-
dc.date.issued2013-
dc.identifier.citationGenome-Wide Association Studies and Genomic Predictions, p. 1-17en
dc.identifier.isbn9781627034470en
dc.identifier.isbn9781627034463en
dc.identifier.urihttps://hdl.handle.net/1959.11/14595-
dc.description.abstractIn recent years R has become de facto statistical programming language of choice for statisticians and it is also arguably the most widely used generic environment for analysis of high-throughput genomic data. In this chapter we discuss some approaches to improve performance of R when working with large SNP datasets.en
dc.languageenen
dc.publisherHumana Pressen
dc.relation.ispartofGenome-Wide Association Studies and Genomic Predictionsen
dc.relation.ispartofseriesMethods in Molecular Biologyen
dc.relation.isversionof1en
dc.titleR for Genome-Wide Association Studiesen
dc.typeBook Chapteren
dc.identifier.doi10.1007/978-1-62703-447-0_1en
dc.subject.keywordsQuantitative Genetics (incl Disease and Trait Mapping Genetics)en
local.contributor.firstnameCedricen
local.contributor.firstnameLaercio Ren
local.contributor.firstnameS Hen
local.subject.for2008060412 Quantitative Genetics (incl Disease and Trait Mapping Genetics)en
local.subject.seo2008970106 Expanding Knowledge in the Biological Sciencesen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolAnimal Scienceen
local.profile.schoolAnimal Scienceen
local.profile.emailcgondro2@une.edu.auen
local.output.categoryB1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20130829-122010en
local.publisher.placeNew York, United States of Americaen
local.identifier.totalchapters26en
local.format.startpage1en
local.format.endpage17en
local.series.issn1940-6029en
local.series.issn1064-3745en
local.series.number1019en
local.contributor.lastnameGondroen
local.contributor.lastnamePorto-Netoen
local.contributor.lastnameLeeen
dc.identifier.staffune-id:cgondro2en
dc.identifier.staffune-id:lportoneen
local.profile.orcid0000-0003-0666-656Xen
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:14810en
dc.identifier.academiclevelAcademicen
local.title.maintitleR for Genome-Wide Association Studiesen
local.output.categorydescriptionB1 Chapter in a Scholarly Booken
local.relation.urlhttp://trove.nla.gov.au/version/198468706en
local.search.authorGondro, Cedricen
local.search.authorPorto-Neto, Laercio Ren
local.search.authorLee, S Hen
local.uneassociationUnknownen
local.year.published2013en
local.subject.for2020310506 Gene mappingen
local.subject.seo2020280102 Expanding knowledge in the biological sciencesen
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