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

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

2020Scientific Data330 citationsDOIOpen Access PDF

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).

Topics & Concepts

Meta-analysisNeuroscienceCerebellumBiologyMedicineInternal medicineBioinformatics and Genomic NetworksAdvanced Neuroimaging Techniques and ApplicationsGene expression and cancer classification