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Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex

Donna M. Werling, Sirisha Pochareddy, Jinmyung Choi, Joon‐Yong An, Brooke Sheppard, Minshi Peng, Zhen Li, Claudia Dastmalchi, Gabriel Santpere, André M. M. Sousa, Andrew T.N. Tebbenkamp, Navjot Kaur, Forrest O. Gulden, Michael S. Breen, Lindsay Liang, Michael C. Gilson, Xuefang Zhao, Shan Dong, Lambertus Klei, A. Ercüment Çiçek, Joseph D. Buxbaum, Homa Adle‐Biassette, Jean‐Léon Thomas, Kimberly A. Aldinger, Diana R. O’Day, Ian A. Glass, Noah Zaitlen, Michael E. Talkowski, Kathryn Roeder, Matthew W. State, Bernie Devlin, Stephan Sanders, Nenad Šestan

2020Cell Reports154 citationsDOIOpen Access PDF

Abstract

Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both "constant" and "temporal-predominant" eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology.

Topics & Concepts

TranscriptomePrefrontal cortexRNABiologyGenomeComputational biologyGeneticsHuman genomeGeneNeuroscienceGene expressionCognitionBioinformatics and Genomic NetworksGenetic Associations and EpidemiologySingle-cell and spatial transcriptomics
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