TY - JOUR
T1 - Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood
AU - Qi, Ting
AU - eQTLGen Consortium
AU - Wu, Yang
AU - Zeng, Jian
AU - Zhang, Futao
AU - Xue, Angli
AU - Jiang, Longda
AU - Zhu, Zhihong
AU - Kemper, Kathryn
AU - Yengo, Loic
AU - Zheng, Zhili
AU - Agbessi, Mawussé
AU - Ahsan, Habibul
AU - Alves, Isabel
AU - Andiappan, Anand
AU - Awadalla, Philip
AU - Battle, Alexis
AU - Beutner, Frank
AU - Jan Bonder, Marc
AU - Boomsma, Dorret
AU - Christiansen, Mark
AU - Claringbould, Annique
AU - Deelen, Patrick
AU - Esko, Tõnu
AU - Favé, Marie Julie
AU - Franke, Lude
AU - Frayling, Timothy
AU - Gharib, Sina
AU - Gibson, Gregory
AU - Hemani, Gibran
AU - Jansen, Rick
AU - Kähönen, Mika
AU - Kalnapenkis, Anette
AU - Kasela, Silva
AU - Kettunen, Johannes
AU - Kim, Yungil
AU - Kirsten, Holger
AU - Kovacs, Peter
AU - Krohn, Knut
AU - Kronberg-Guzman, Jaanika
AU - Kukushkina, Viktorija
AU - Kutalik, Zoltan
AU - Lee, Bernett
AU - Lehtimäki, Terho
AU - Loeffler, Markus
AU - Marigorta, Urko M.
AU - Metspalu, Andres
AU - Milani, Lili
AU - Müller-Nurasyid, Martina
AU - Nauck, Matthias
AU - Ring, Susan
PY - 2018/6/11
Y1 - 2018/6/11
N2 - Understanding 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 b = 0.70 for cis-eQTLs and r ^ 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.
AB - Understanding 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 b = 0.70 for cis-eQTLs and r ^ 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.
UR - http://www.scopus.com/inward/record.url?scp=85048415370&partnerID=8YFLogxK
U2 - 10.1038/s41467-018-04558-1
DO - 10.1038/s41467-018-04558-1
M3 - Article (Academic Journal)
C2 - 29891976
AN - SCOPUS:85048415370
SN - 2041-1723
VL - 9
JO - Nature Communications
JF - Nature Communications
M1 - 2282
ER -
