Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/51781
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dc.contributor.authorNi, Guiyanen
dc.contributor.authorMoser, Gerharden
dc.contributor.authorWray, Naomi Ren
dc.contributor.authorLee, S Hongen
dc.date.accessioned2022-04-28T03:09:55Z-
dc.date.available2022-04-28T03:09:55Z-
dc.date.issued2018-06-07-
dc.identifier.citationAmerican Journal of Human Genetics, 102(6), p. 1185-1194en
dc.identifier.issn1537-6605en
dc.identifier.issn0002-9297en
dc.identifier.urihttps://hdl.handle.net/1959.11/51781-
dc.description.abstractGenetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ∼150,000 individuals give a higher accuracy than LDSC estimates based on ∼400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.en
dc.languageenen
dc.publisherCell Pressen
dc.relation.ispartofAmerican Journal of Human Geneticsen
dc.titleEstimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihooden
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.ajhg.2018.03.021en
dc.identifier.pmid29754766en
dcterms.accessRightsBronzeen
dc.subject.keywordsGenetics & Heredityen
local.contributor.firstnameGuiyanen
local.contributor.firstnameGerharden
local.contributor.firstnameNaomi Ren
local.contributor.firstnameS Hongen
local.relation.isfundedbyNHMRCen
local.relation.isfundedbyNHMRCen
local.relation.isfundedbyARCen
local.relation.isfundedbyARCen
dc.contributor.corporatePsychiatric Genomics Consortium, Schizophrenia Working Group (PGC SCZ)en
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.emailgni@une.edu.auen
local.profile.emailslee38@une.edu.auen
local.output.categoryC1en
local.grant.number1080157en
local.grant.number1087889en
local.grant.numberDP160102126en
local.grant.numberFT160100229en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeUnited States of Americaen
local.format.startpage1185en
local.format.endpage1194en
local.peerreviewedYesen
local.identifier.volume102en
local.identifier.issue6en
local.access.fulltextYesen
local.contributor.lastnameNien
local.contributor.lastnameMoseren
local.contributor.lastnameWrayen
local.contributor.lastnameLeeen
dc.identifier.staffune-id:gnien
dc.identifier.staffune-id:slee38en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/51781en
local.date.onlineversion2018-05-10-
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleEstimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihooden
local.relation.fundingsourcenoteThis research is supported by the Australian National Health and Medical Research Council ( 1080157 , 1087889 ) and the Australian Research Council ( DP160102126 , FT160100229 ). This research has been conducted using the UK Biobank Resource. UK Biobank Research Ethics Committee (REC) approval number is 11/NW/0382. Our reference number approved by UK Biobank is 14575. GERA data came from a grant, the Resource for Genetic Epidemiology Research in Adult Health and Aging ( RC2 AG033067 ; Schaefer and Risch, PIs) awarded to the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and the UCSF Institute for Human Genetics. The RPGEH was supported by grants from the Robert Wood Johnson Foundation , the Wayne and Gladys Valley Foundation , the Ellison Medical Foundation , Kaiser Permanente Northern California , and the Kaiser Permanente National and Northern California Community Benefit Programs . The RPGEH and the Resource for Genetic Epidemiology Research in Adult Health and Aging are described in the GERA website (see Web Resources). This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the WTCCC data is available online. Funding for the WTCCC project was provided by the Wellcome Trust under awards 076113 , 085475 , and 090355 .en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.relation.grantdescriptionNHMRC/1080157en
local.relation.grantdescriptionNHMRC/1087889en
local.relation.grantdescriptionARC/DP160102126en
local.relation.grantdescriptionARC/FT160100229en
local.search.authorNi, Guiyanen
local.search.authorMoser, Gerharden
local.search.authorWray, Naomi Ren
local.uneassociationYesen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.identifier.wosid000434946200014en
local.year.available2018en
local.year.published2018en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/1e301121-c0f4-4382-9dce-a54d6f5dfc47en
local.subject.for2020310207 Statistical and quantitative geneticsen
local.subject.seo2020280118 Expanding knowledge in the mathematical sciencesen
local.profile.affiliationtypeUnknownen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
Appears in Collections:Journal Article
School of Environmental and Rural Science
School of Psychology
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