SummaryHere we report the generation and analysis of genome-wide exon-level transcriptome data from 16 brain regions comprising the cerebellar cortex, mediodorsal nucleus of the thalamus, striatum, amygdala, hippocampus, and 11 areas of the neocortex. The dataset was generated from 1,340 tissue samples collected from one or both hemispheres of 57 postmortem human brains, spanning from embryonic development to late adulthood and representing males and females of multiple ethnicities. We also performed genotyping of 2.5 million SNPs and assessed copy number variations for all donors. Approximately 86% of protein-coding genes were found to be expressed using stringent criteria, and over 90% of these were differentially regulated at the whole transcript or exon level across regions and/or time. The majority of these spatiotemporal differences occurred before birth, followed by an increase in the similarity among regional transcriptomes during postnatal lifespan. Genes were organized into functionally distinct co-expression networks, and sex differences were present in gene expression and exon usage. Finally, we demonstrate how these results can be used to profile trajectories of genes associated with neurodevelopmental processes, cell types, neurotransmitter systems, autism, and schizophrenia, as well as to discover associations between SNPs and spatiotemporal gene expression. This study provides a comprehensive, publicly available dataset on the spatiotemporal human brain transcriptome and new insights into the transcriptional foundations of human neurodevelopment.

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (OR=1.11, P=5.7×10−15), which persisted after excluding loci implicated in previous studies (OR=1.07, P=1.7 ×10−6). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 ×10−11) and neurobehavioral phenotypes in mouse (OR = 1.18, P= 7.3 ×10−5). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by non-allelic homologous recombination.

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.

In lightly anaesthetized or awake adult mice using millisecond timescale voltage sensitive dye imaging, we show that a palette of sensory-evoked and hemisphere-wide activity motifs are represented in spontaneous activity. These motifs can reflect multiple modes of sensory processing including vision, audition, and touch. Similar cortical networks were found with direct cortical activation using channelrhodopsin-2. Regional analysis of activity spread indicated modality specific sources such as primary sensory areas, and a common posterior-medial cortical sink where sensory activity was extinguished within the parietal association area, and a secondary anterior medial sink within the cingulate/secondary motor cortices for visual stimuli. Correlation analysis between functional circuits and intracortical axonal projections indicated a common framework corresponding to long-range mono-synaptic connections between cortical regions. Maps of intracortical mono-synaptic structural connections predicted hemisphere-wide patterns of spontaneous and sensory-evoked depolarization. We suggest that an intracortical monosynaptic connectome shapes the ebb and flow of spontaneous cortical activity.

Major depressive disorder (MDD), one of the most frequently encountered forms of mental illness and a leading cause of disability worldwide1, poses a major challenge to genetic analysis. To date no robustly replicated genetic loci have been identified 2, despite analysis of more than 9,000 cases3. Using low coverage genome sequence of 5,303 Chinese women with recurrent MDD selected to reduce phenotypic heterogeneity, and 5,337 controls screened to exclude MDD, we identified and replicated two genome-wide significant loci contributing to risk of MDD on chromosome 10: one near the SIRT1 gene (P-value = 2.53×10−10) the other in an intron of the LHPP gene (P = 6.45×10−12). Analysis of 4,509 cases with a severe subtype of MDD, melancholia, yielded an increased genetic signal at the SIRT1 locus. We attribute our success to the recruitment of relatively homogeneous cases with severe illness.

Purpose: The dismal outcome of esophageal cancer patients highlights the need for novel prognostic biomarkers, such as microRNAs (miRNA). Although recent studies have established the role of miRNAs in esophageal carcinoma, a comprehensive multicenter study investigating different histologic types, including squamous cell carcinoma (SCC) and adenocarcinoma with or without Barrett's, is still lacking. Experimental Design: miRNA expression was measured in cancerous and adjacent noncancerous tissue pairs collected from 100 adenocarcinoma and 70 SCC patients enrolled at four clinical centers from the United States, Canada, and Japan. Microarray-based expression was measured in a subset of samples in two cohorts and was validated in all available samples. Results: In adenocarcinoma patients, miR-21, miR-223, miR-192, and miR-194 expression was elevated, whereas miR-203 expression was reduced in cancerous compared with noncancerous tissue. In SCC patients, we found elevated miR-21 and reduced miR-375 expression levels in cancerous compared with noncancerous tissue. When comparing cancerous tissue expression between adenocarcinoma and SCC patients, miR-194 and miR-375 were elevated in adenocarcinoma patients. Significantly, elevated miR-21 expression in noncancerous tissue of SCC patients and reduced levels of miR-375 in cancerous tissue of adenocarcinoma patients with Barrett's were strongly associated with worse prognosis. Associations with prognosis were independent of tumor stage or nodal status, cohort type, and chemoradiation therapy. Conclusions: Our multicenter-based results highlight miRNAs involved in major histologic types of esophageal carcinoma and uncover significant associations with prognosis. Elucidating miRNAs relevant to esophageal carcinogenesis is potentially clinically useful for developing prognostic biomarkers and identifying novel drug targets and therapies.

To evaluate the utility of transcript profiling for prediction of protein expression levels, we compared profiles across the NCI-60 cancer cell panel, which represents nine tissues of origin. For that analysis, we present here two new NCI-60 transcript profile data sets (A based on Affymetrix HG-U95 and HG-U133A chips; Affymetrix, Santa Clara, CA) and one new protein profile data set (based on reverse-phase protein lysate arrays). The data sets are available online at http://discover.nci.nih.gov in the CellMiner program package. Using the new transcript data in combination with our previously published cDNA array and Affymetrix HU6800 data sets, we first developed a ''consensus set'' of transcript profiles based on the four different microarray platforms. Using that set, we found that 65% of the genes showed statistically significant transcript-protein correlation, and the correlations were generally higher than those reported previously for panels of mammalian cells. Using the predictive analysis of microarray nearest shrunken centroid algorithm for functional prediction of tissue of origin, we then found that (a) the consensus mRNA set did better than did data from any of the individual mRNA platforms and (b) the protein data seemed to do somewhat better (P = 0.027) on a gene-for-gene basis in this particular study than did the consensus mRNA data, but both did well. Analysis based on the Gene Ontology showed protein levels of structure-related genes to be well predicted by mRNA levels (mean r = 0.71). Because the transcript-based technologies are more mature and are currently able to assess larger numbers of genes at one time, they continue to be useful, even when the ultimate aim is information about proteins. [Mol Cancer Ther 2007;6(3):820 -32]