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https://hdl.handle.net/1959.11/51781
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DC Field | Value | Language |
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dc.contributor.author | Ni, Guiyan | en |
dc.contributor.author | Moser, Gerhard | en |
dc.contributor.author | Wray, Naomi R | en |
dc.contributor.author | Lee, S Hong | en |
dc.date.accessioned | 2022-04-28T03:09:55Z | - |
dc.date.available | 2022-04-28T03:09:55Z | - |
dc.date.issued | 2018-06-07 | - |
dc.identifier.citation | American Journal of Human Genetics, 102(6), p. 1185-1194 | en |
dc.identifier.issn | 1537-6605 | en |
dc.identifier.issn | 0002-9297 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/51781 | - |
dc.description.abstract | Genetic 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.language | en | en |
dc.publisher | Cell Press | en |
dc.relation.ispartof | American Journal of Human Genetics | en |
dc.title | Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.1016/j.ajhg.2018.03.021 | en |
dc.identifier.pmid | 29754766 | en |
dcterms.accessRights | Bronze | en |
dc.subject.keywords | Genetics & Heredity | en |
local.contributor.firstname | Guiyan | en |
local.contributor.firstname | Gerhard | en |
local.contributor.firstname | Naomi R | en |
local.contributor.firstname | S Hong | en |
local.relation.isfundedby | NHMRC | en |
local.relation.isfundedby | NHMRC | en |
local.relation.isfundedby | ARC | en |
local.relation.isfundedby | ARC | en |
dc.contributor.corporate | Psychiatric Genomics Consortium, Schizophrenia Working Group (PGC SCZ) | en |
local.profile.school | School of Environmental and Rural Science | en |
local.profile.school | School of Environmental and Rural Science | en |
local.profile.email | gni@une.edu.au | en |
local.profile.email | slee38@une.edu.au | en |
local.output.category | C1 | en |
local.grant.number | 1080157 | en |
local.grant.number | 1087889 | en |
local.grant.number | DP160102126 | en |
local.grant.number | FT160100229 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.publisher.place | United States of America | en |
local.format.startpage | 1185 | en |
local.format.endpage | 1194 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 102 | en |
local.identifier.issue | 6 | en |
local.access.fulltext | Yes | en |
local.contributor.lastname | Ni | en |
local.contributor.lastname | Moser | en |
local.contributor.lastname | Wray | en |
local.contributor.lastname | Lee | en |
dc.identifier.staff | une-id:gni | en |
dc.identifier.staff | une-id:slee38 | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:1959.11/51781 | en |
local.date.onlineversion | 2018-05-10 | - |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood | en |
local.relation.fundingsourcenote | This 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.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.relation.grantdescription | NHMRC/1080157 | en |
local.relation.grantdescription | NHMRC/1087889 | en |
local.relation.grantdescription | ARC/DP160102126 | en |
local.relation.grantdescription | ARC/FT160100229 | en |
local.search.author | Ni, Guiyan | en |
local.search.author | Moser, Gerhard | en |
local.search.author | Wray, Naomi R | en |
local.uneassociation | Yes | en |
local.atsiresearch | No | en |
local.sensitive.cultural | No | en |
local.identifier.wosid | 000434946200014 | en |
local.year.available | 2018 | en |
local.year.published | 2018 | en |
local.fileurl.closedpublished | https://rune.une.edu.au/web/retrieve/1e301121-c0f4-4382-9dce-a54d6f5dfc47 | en |
local.subject.for2020 | 310207 Statistical and quantitative genetics | en |
local.subject.seo2020 | 280118 Expanding knowledge in the mathematical sciences | en |
local.profile.affiliationtype | Unknown | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
Appears in Collections: | Journal Article School of Environmental and Rural Science School of Psychology |
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