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Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

2020; Nature Portfolio; Volume: 7; Issue: 1 Linguagem: Inglês

10.1038/s41597-020-00642-8

2052-4463

Solveig K. Sieberts, Thanneer M. Perumal, Minerva M. Carrasquillo, Mariet Allen, Joseph S. Reddy, Gabriel E. Hoffman, Kristen K. Dang, John Calley, Philip J. Ebert, James A. Eddy, Xue Wang, Anna K. Greenwood, Sara Mostafavi, Schahram Akbarian, Jaroslav Bendl, Michael S. Breen, Kristen Brennand, Leanne Brown, Andrew Browne, Joseph D. Buxbaum, Alexander W. Charney, Andrew Chess, Lizette Couto, Greg Crawford, Olivia Devillers, Bernie Devlin, Amanda Dobbyn, Enrico Domenici, Michele Filosi, Elie Flatow, Nancy Francoeur, John F. Fullard, Sergio Espeso‐Gil, Kiran Girdhar, Attila Gulyás-Kovács, Raquel E. Gur, Chang-Gyu Hahn, Vahram Haroutunian, Mads E. Hauberg, Laura M. Huckins, Rivky Jacobov, Yan Jiang, Jessica Johnson, Bibi Kassim, Yungil Kim, Lambertus Klei, Robin S. S. Kramer, Mario Lauria, Thomas Lehner, David A. Lewis, Barbara K. Lipska, Kelsey S. Montgomery, Royce Park, Chaggai Rosenbluh, Panagiotis Roussos, Douglas M. Ruderfer, Geetha Senthil, Hardik Shah, Laura Sloofman, Lingyun Song, Eli Stahl, Patrick Sullivan, Roberto Visintainer, Jiebiao Wang, Ying‐Chih Wang, Jennifer Wiseman, Eva Xia, Wen Zhang, Elizabeth Zharovsky, Laura Addis, Sadiya N. Addo, David Airey, Matthias Arnold, David A. Bennett, Yingtao Bi, Knut Biber, Colette Blach, Elizabeth Bradhsaw, Paul E. Brennan, Rosa Canet-Aviles, Sherry Cao, Anna Cavalla, Yooree Chae, William W. Chen, Jie Cheng, David Collier, Jeffrey L. Dage, Eric B. Dammer, J. Wade Davis, John B. Davis, Derek Drake, Duc M. Duong, Brian J. Eastwood, Michelle E. Ehrlich, Benjamin M. Ellingson, Brett W. Engelmann, Sahar Esmaeeli-Nieh, Daniel Felsky, Cory C. Funk, Chris Gaiteri, Sam Gandy, Fan Gao, O. Gileadi, Todd E. Golde, Shaun Grosskurth, Rishi R. Gupta, Alex Gutteridge, Vahram Haroutunian, Basavaraj Hooli, Neil Humphryes-Kirilov, Koichi Iijima, Corey James, Paul Jung, Rima Kaddurah‐Daouk, Gabi Kastenmüller, Hans‐Ulrich Klein, Markus P. Kummer, Pascale N. Lacor, James J. Lah, Emma Laing, Allan I. Levey, Yupeng Li, Samantha Lipsky, Yushi Liu, Jiang Liu, Zhandong Liu, Gregory Louie, Tao Lu, Yiyi Ma, Yasuji Y. Matsuoka, Vilas Menon, Bradley B. Miller, Thomas P. Misko, J Mollon, Kelsey S. Montgomery, Sumit Mukherjee, Scott Noggle, Ping‐Chieh Pao, Tracy Young Pearce, Neil Pearson, Michelle A. Penny, Vladislav Petyuk, Nathan D. Price, Danjuma Quarless, Brinda Ravikumar, Janina S. Ried, Cara Ruble, Heiko Runz, Andrew J. Saykin, Eric E. Schadt, James Scherschel, Nicholas T. Seyfried, Joshua Shulman, Phil Snyder, Holly Soares, Gyan Srivastava, Henning Stöckmann, Mariko Taga, Shinya Tasaki, Jessie Tenenbaum, Li‐Huei Tsai, Aparna Vasanthakumar, Astrid Wachter, Yaming Wang, Hong Wang, Minghui Wang, Christopher D. Whelan, Charles C. White, Kara Woo, Paul Wren, Jessica Wu, Hualin Simon Xi, Bruce A. Yankner, Steven G. Younkin, Lei Yu, Maria I. Zavodszky, Wenling Zhang, Guoqiang Zhang, Bin Zhang, Jun Zhu, Larsson Omberg, Mette A. Peters, Benjamin A. Logsdon, Philip L. De Jager, Nilüfer Ertekin‐Taner, Lara M. Mangravite,

RNA Research and Splicing

Abstract The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis- eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).