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Selecting informative traits for multivariate quantitative trait locus mapping helps to gain optimal power

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dc.contributor.authorCheng, Riyan
dc.contributor.authorBorevitz, Justin
dc.contributor.authorDoerge, Rebecca
dc.date.accessioned2015-12-13T22:32:06Z
dc.date.issued2013
dc.date.updated2015-12-11T09:04:49Z
dc.description.abstractA major consideration in multitrait analysis is which traits should be jointly analyzed. As a common strategy, multitrait analysis is performed either on pairs of traits or on all of traits. To fully exploit the power of multitrait analysis, we propose va
dc.identifier.issn1943-2631
dc.identifier.urihttp://hdl.handle.net/1885/75427
dc.publisherGenetics Society of America
dc.sourceGenetics (online)
dc.titleSelecting informative traits for multivariate quantitative trait locus mapping helps to gain optimal power
dc.typeJournal article
local.bibliographicCitation.issue3
local.bibliographicCitation.lastpage691
local.bibliographicCitation.startpage683
local.contributor.affiliationCheng, Riyan, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationBorevitz, Justin, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationDoerge, Rebecca, Purdue University
local.contributor.authoruidCheng, Riyan, u5264559
local.contributor.authoruidBorevitz, Justin, u5083581
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor010402 - Biostatistics
local.identifier.absfor060412 - Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
local.identifier.absseo970106 - Expanding Knowledge in the Biological Sciences
local.identifier.ariespublicationf5625xPUB4615
local.identifier.citationvolume195
local.identifier.doi10.1534/genetics.113.155937
local.identifier.scopusID2-s2.0-84887061517
local.identifier.thomsonID000329874100004
local.type.statusPublished Version

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