Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/18891
Full metadata record
DC FieldValueLanguage
dc.contributor.authorVisscher, Peter Men
dc.contributor.authorHemani, Gibranen
dc.contributor.authorVinkhuyzen, Anna A Een
dc.contributor.authorChen, Guo-Boen
dc.contributor.authorLee, Sang Hongen
dc.contributor.authorWray, Naomi Ren
dc.contributor.authorGoddard, Michael Een
dc.contributor.authorYang, Jianen
dc.date.accessioned2016-04-18T11:19:00Z-
dc.date.issued2014-
dc.identifier.citationPLoS Genetics, 10(4), p. 1-10en
dc.identifier.issn1553-7404en
dc.identifier.issn1553-7390en
dc.identifier.urihttps://hdl.handle.net/1959.11/18891-
dc.description.abstractWe have recently developed analysis methods (GREML) to estimate the genetic variance of a complex trait/disease and the genetic correlation between two complex traits/diseases using genome-wide single nucleotide polymorphism (SNP) data in unrelated individuals. Here we use analytical derivations and simulations to quantify the sampling variance of the estimate of the proportion of phenotypic variance captured by all SNPs for quantitative traits and case-control studies. We also derive the approximate sampling variance of the estimate of a genetic correlation in a bivariate analysis, when two complex traits are either measured on the same or different individuals. We show that the sampling variance is inversely proportional to the number of pairwise contrasts in the analysis and to the variance in SNP-derived genetic relationships. For bivariate analysis, the sampling variance of the genetic correlation additionally depends on the harmonic mean of the proportion of variance explained by the SNPs for the two traits and the genetic correlation between the traits, and depends on the phenotypic correlation when the traits are measured on the same individuals. We provide an online tool for calculating the power of detecting genetic (co)variation using genome-wide SNP data. The new theory and online tool will be helpful to plan experimental designs to estimate the missing heritability that has not yet been fully revealed through genome-wide association studies, and to estimate the genetic overlap between complex traits (diseases) in particular when the traits (diseases) are not measured on the same samples.en
dc.languageenen
dc.publisherPublic Library of Scienceen
dc.relation.ispartofPLoS Geneticsen
dc.titleStatistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samplesen
dc.typeJournal Articleen
dc.identifier.doi10.1371/journal.pgen.1004269en
dcterms.accessRightsGolden
dc.subject.keywordsQuantitative Genetics (incl. Disease and Trait Mapping Genetics)en
dc.subject.keywordsGenomicsen
dc.subject.keywordsAnimal Breedingen
local.contributor.firstnamePeter Men
local.contributor.firstnameGibranen
local.contributor.firstnameAnna A Een
local.contributor.firstnameGuo-Boen
local.contributor.firstnameSang Hongen
local.contributor.firstnameNaomi Ren
local.contributor.firstnameMichael Een
local.contributor.firstnameJianen
local.subject.for2008060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics)en
local.subject.for2008060408 Genomicsen
local.subject.for2008070201 Animal Breedingen
local.subject.seo2008970106 Expanding Knowledge in the Biological Sciencesen
local.subject.seo2008970107 Expanding Knowledge in the Agricultural and Veterinary Sciencesen
local.subject.seo2008970111 Expanding Knowledge in the Medical and Health Sciencesen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.emailslee38@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20160402-123319en
local.publisher.placeUnited States of Americaen
local.identifier.runningnumbere1004269en
local.format.startpage1en
local.format.endpage10en
local.peerreviewedYesen
local.identifier.volume10en
local.identifier.issue4en
local.access.fulltextYesen
local.contributor.lastnameVisscheren
local.contributor.lastnameHemanien
local.contributor.lastnameVinkhuyzenen
local.contributor.lastnameChenen
local.contributor.lastnameLeeen
local.contributor.lastnameWrayen
local.contributor.lastnameGoddarden
local.contributor.lastnameYangen
dc.identifier.staffune-id:slee38en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:19092en
dc.identifier.academiclevelAcademicen
local.title.maintitleStatistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samplesen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorVisscher, Peter Men
local.search.authorHemani, Gibranen
local.search.authorVinkhuyzen, Anna A Een
local.search.authorChen, Guo-Boen
local.search.authorLee, Sang Hongen
local.search.authorWray, Naomi Ren
local.search.authorGoddard, Michael Een
local.search.authorYang, Jianen
local.uneassociationUnknownen
local.year.published2014en
local.subject.for2020310506 Gene mappingen
local.subject.for2020310509 Genomicsen
local.subject.for2020300305 Animal reproduction and breedingen
local.subject.seo2020280102 Expanding knowledge in the biological sciencesen
Appears in Collections:Journal Article
Files in This Item:
2 files
File Description SizeFormat 
Show simple item record

SCOPUSTM   
Citations

231
checked on Jan 20, 2024

Page view(s)

1,150
checked on Jan 21, 2024
Google Media

Google ScholarTM

Check

Altmetric


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