To broaden our understanding of human neurodevelopment, we profiled transcriptomic and epigenomic landscapes across brain regions and/or cell types for the entire span of prenatal and postnatal development. Integrative analysis revealed temporal, regional, sex, and cell type–specific dynamics. We observed a global transcriptomic cup-shaped pattern, characterized by a late fetal transition associated with sharply decreased regional differences and changes in cellular composition and maturation, followed by a reversal in childhood-adolescence, and accompanied by epigenomic reorganizations. Analysis of gene coexpression modules revealed relationships with epigenomic regulation and neurodevelopmental processes. Genes with genetic associations to brain-based traits and neuropsychiatric disorders (including MEF2C, SATB2, SOX5, TCF4, and TSHZ3) converged in a small number of modules and distinct cell types, revealing insights into neurodevelopment and the genomic basis of neuropsychiatric risks.

All data used in the manuscript are publicly available (see URLs). GTEx and GERA data can be accessed by application to dbGaP. CommonMind data are available through formal application to NIMH. ADGC phase 2 summary statistics used for validation are available through NIAGADS portal (see URLs) with accession number NG00076.

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average 39% improvement in imputation accuracy and generated effective imputation models for an average 120% more genes. We then describe a summary statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene-trait association. We applied our method, called UTMOST, to multiple genome wide association results and demonstrate its advantages over single-tissue strategies.

The prefrontal cortex (PFC) and its connections with the mediodorsal thalamus are crucial for cognitive flexibility and working memory 1 and are thought to be altered in disorders such as autism 2,3 and schizophrenia 4,5 . Although developmental mechanisms that govern the regional patterning of the cerebral cortex have been characterized in rodents [6][7][8][9] , the mechanisms that underlie the development of PFCmediodorsal thalamus connectivity and the lateral expansion of the PFC with a distinct granular layer 4 in primates 10,11 remain unknown. Here we report an anterior (frontal) to posterior (temporal), PFC-enriched gradient of retinoic acid, a signalling molecule that regulates neural development and function [12][13][14][15] , and we identify genes that are regulated by retinoic acid in the neocortex of humans and macaques at the early and middle stages of fetal development. We observed several potential sources of retinoic acid, including the expression and cortical expansion of retinoic-acid-synthesizing enzymes specifically in primates as compared to mice. Furthermore, retinoic acid signalling is largely confined to the prospective PFC by CYP26B1, a retinoic-acid-catabolizing enzyme, which is upregulated in the prospective motor cortex. Genetic deletions in mice revealed that retinoic acid signalling through the retinoic acid receptors RXRG and RARB, as well as CYP26B1-dependent catabolism, are involved in proper molecular patterning of prefrontal and motor areas, development of PFC-mediodorsal thalamus connectivity, intra-PFC dendritic spinogenesis and expression of the layer 4 marker RORB. Together, these findings show that retinoic acid signalling has a critical role in the development of the PFC and, potentially, in its evolutionary expansion.The PFC reaches its greatest complexity in anthropoid primates (monkeys and apes), which appear to uniquely have many prefrontal areas that cover the entire anterior two-thirds of the frontal lobe and a well-defined granular layer 4 10,11 . Previous analyses revealed that the transcriptomic differences between neocortical areas are most prominent in primates during the middle stages of fetal development, corresponding to post-conception weeks (PCW) 13 to 24 (hereafter referred to as 'mid-fetal') 16,17 -a crucial period for neuronal specification and the initial assembly of neocortical neural circuits 18 . Thus, we hypothesized that the molecular processes that govern the development and evolutionary diversification of the PFC could be revealed by differential regional gene expression analysis of the primate mid-fetal neocortex. Fetal frontal upregulation of RA-related genesUsing human BrainSpan RNA-sequencing (RNA-seq) data 17 , we screened for genes that are differentially upregulated in the mid-fetal frontal lobe. The mid-fetal data consisted of tissue-level samples ranging in age from PCW 16 to 22, which included four prospective PFC areas (medial, mPFC or MFC; orbital, oPFC or OFC; dorsolateral, dlPFC or DFC; and ventrolateral, vlPFC or VFC) and the primar...

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