Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

dc.contributor.authorQi T
dc.contributor.authorWu Y
dc.contributor.authorZeng J
dc.contributor.authorZhang FT
dc.contributor.authorXue AL
dc.contributor.authorJiang LD
dc.contributor.authorZhu ZH
dc.contributor.authorKemper K
dc.contributor.authorYengo L
dc.contributor.authorZheng ZL
dc.contributor.authoreQTLGen Consortium
dc.contributor.authorMarioni RE
dc.contributor.authorMontgomery GW
dc.contributor.authorDeary IJ
dc.contributor.authorWray NR
dc.contributor.authorVisscher PM
dc.contributor.authorMcRae AF
dc.contributor.authorYang J
dc.contributor.organizationfi=sydäntutkimuskeskus|en=Cardiovascular Medicine (CAPC)|
dc.contributor.organizationfi=tyks, vsshp|en=tyks, varha|
dc.contributor.organization-code1.2.246.10.2458963.20.35734063924
dc.converis.publication-id32113458
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/32113458
dc.date.accessioned2022-10-27T11:45:46Z
dc.date.available2022-10-27T11:45:46Z
dc.description.abstractUnderstanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (r(b)). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples ((r) over cap (b) = 0.70 for ciseQTLs and (r) over cap (b) = 0.78 for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes.
dc.identifier.eissn2041-1723
dc.identifier.jour-issn2041-1723
dc.identifier.olddbid171952
dc.identifier.oldhandle10024/155046
dc.identifier.urihttps://www.utupub.fi/handle/11111/29576
dc.identifier.urnURN:NBN:fi-fe2021042719380
dc.language.isoen
dc.okm.affiliatedauthorRaitakari, Olli
dc.okm.affiliatedauthorDataimport, tyks, vsshp
dc.okm.discipline3111 Biomedicineen_GB
dc.okm.discipline3111 Biolääketieteetfi_FI
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA1 ScientificArticle
dc.publisherNATURE PUBLISHING GROUP
dc.publisher.countryUnited Kingdomen_GB
dc.publisher.countryBritanniafi_FI
dc.publisher.country-codeGB
dc.relation.articlenumberARTN 2282
dc.relation.doi10.1038/s41467-018-04558-1
dc.relation.ispartofjournalNature Communications
dc.relation.volume9
dc.source.identifierhttps://www.utupub.fi/handle/10024/155046
dc.titleIdentifying gene targets for brain-related traits using transcriptomic and methylomic data from blood
dc.year.issued2018

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