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The Genomics Workgroup of the National Advisory Mental Health Council (NAMHC) recently issued a set of recommendations for advancing the NIMH psychiatric genetics research program and prioritizing subsequent follow-up studies. The report emphasized the primacy of rigorous statistical support from properly designed, well-powered studies for pursuing genetic variants robustly associated with disease. Here we discuss the major points NIMH program staff consider when assessing research applications based on common and rare variants, as well as genetic syndromes, associated with psychiatric disorders. These are broad guiding principles for investigators to consider prior to submission of their applications. NIMH staff weigh these points in the context of reviewer comments, the existing literature, and current investments in related projects. Following the recommendations of the NAMHC, statistical strength and robustness of the underlying genetic discovery weighs heavily in our funding considerations as does the suitability of the proposed experimental approach. We specifically address our evaluation of applications motivated in whole, or in part, by an association between human DNA sequence variation and a disease or trait relevant to the mission of the NIMH.

The Genotype-Tissue Expression (GTEx) project dissects how genetic variation affects gene expression and splicing. Some human genetic variants affect the amount of RNA produced and the splicing of gene transcripts, crucial steps in development and maintaining a healthy individual. However, some of these changes only occur in a small number of tissues within the body. The Genotype-Tissue Expression (GTEx) project has been expanded over time, and, looking at the final data in version 8, Aguet et al. present a deep characterization of genetic associations and gene expression and splicing in 838 individuals over 49 tissues (see the Perspective by Wilson). This large study was able to characterize the details underlying many aspects of gene expression and provides a resource with which to better understand the fundamental molecular mechanisms of how genetic variants affect gene regulation and complex traits in humans. Science, this issue p. 1318; see also p. 1298 The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues. We thank the donors and their families for their generous gifts of organ donation for transplantation and tissue donations for the GTEx research project; the Genomics Platform at the Broad Institute for data generation; J. Struewing for support and leadership of the GTEx project; M. Khan and C. Stolte for the illustrations in Fig. 1; and R. Do, D. Jordan, and M. Verbanck for providing GWAS pleiotropy scores. Funding: This work was supported by the Common Fund of the Office of the Director, U.S. National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, NIA, NIAID, and NINDS through NIH contracts HHSN261200800001E (Leidos Prime contract with NCI: A.M.S., D.E.T., N.V.R., J.A.M., L.S., M.E.B., L.Q., T.K., D.B., K.R., and A.U.), 10XS170 (NDRI: W.F.L., J.A.T., G.K., A.M., S.S., R.H., G.Wa., M.J., M.Wa., L.E.B., C.J., J.W., B.R., M.Hu., K.M., L.A.S., H.M.G., M.Mo., and L.K.B.), 10XS171 (Roswell Park Cancer Institute: B.A.F., M.T.M., E.K., B.M.G., K.D.R., and J.B.), 10X172 (Science Care Inc.), 12ST1039 (IDOX), 10ST1035 (Van Andel Institute: S.D.J., D.C.R., and D.R.V.), HHSN268201000029C (Broad Institute: F.A., G.G., K.G.A., A.V.S., X.Li., E.T., S.G., A.G., S.A., K.H.H., D.T.N., K.H., S.R.M., and J.L.N.), 5U41HG009494 (F.A., G.G., and K.G.A.), and through NIH grants R01 DA006227-17 (University of Miami Brain Bank: D.C.M. and D.A.D.), Supplement to University of Miami grant DA006227 (D.C.M. and D.A.D.), R01 MH090941 (University of Geneva), R01 MH090951 and R01 MH090937 (University of Chicago), R01 MH090936 (University of North Carolina–Chapel Hill), R01MH101814 (M.M.-A., V.W., S.B.M., R.G., E.T.D., D.G.-M., and A.V.), U01HG007593 (S.B.M.), R01MH101822 (C.D.B.), U01HG007598 (M.O. and B.E.S.), U01MH104393 (A.P.F.), extension H002371 to 5U41HG002371 (W.J.K.), as well as other funding sources: R01MH106842 (T.L., P.M., E.F., and P.J.H.), R01HL142028 (T.L., Si.Ka., and P.J.H.), R01GM122924 (T.L. and S.E.C.), R01MH107666 (H.K.I.), P30DK020595 (H.K.I.), UM1HG008901 (T.L.), R01GM124486 (T.L.), R01HG010067 (Y.Pa.), R01HG002585 (G.Wa. and M.St.), Gordon and Betty Moore Foundation GBMF 4559 (G.Wa. and M.St.), 1K99HG009916-01 (S.E.C.), R01HG006855 (Se.Ka. and R.E.H.), BIO2015-70777-P, Ministerio de Economia y Competitividad and FEDER funds (M.M.-A., V.W., R.G., and D.G.-M.), la Caixa Foundation ID 100010434 under agreement LCF/BQ/SO15/52260001 (D.G.-M.), NIH CTSA grant UL1TR002550-01 (P.M.), Marie-Skłodowska Curie fellowship H2020 Grant 706636 (S.K.-H.), R35HG010718 (E.R.G.), FPU15/03635, Ministerio de Educación, Cultura y Deporte (M.M.-A.),R01MH109905, 1R01HG010480 (A.Ba.), Searle Scholar Program (A.Ba.), R01HG008150 (S.B.M.), 5T32HG000044-22, NHGRI Institutional Training Grant in Genome Science (N.R.G.), EU IMI program (UE7-DIRECT-115317-1) (E.T.D. and A.V.), FNS funded project RNA1 (31003A_149984) (E.T.D. and A.V.), DK110919 (F.H.), F32HG009987 (F.H.), Massachusetts Lions Eye Research Fund Grant (A.R.H.), Wellcome grant WT108749/Z/15/Z (P.F.), and European Molecular Biology Laboratory (P.F. and D.Z.). Peer Reviewed "Article signat per 1 autors/es del BSC membres del THE GTEX CONSORTIUM: Marta Mele Messeguer"

Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

The active debate about the return of incidental or secondary findings in research has primarily focused on return to research participants, or in some cases, family members. Particular attention has been paid to return of genomic findings. Yet, research may generate other types of findings that warrant consideration for return, including findings related to the pathology of donated biospecimens. In the case of deceased biospecimen donors who are also organ and/or tissue transplant donors, pathology incidental findings may be relevant not to family members, but to potential organ or tissue transplant recipients. This paper will describe the ethical implications of pathology incidental findings in the Genotype-Tissue Expression (GTEx) project, the process for developing a consensus approach as to if/when such findings should be returned, possible implications for other research projects collecting postmortem tissues and how the scenario encountered in GTEx fits into the larger return of results/incidental findings debate.

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from 12,627 genes across 32 normal human tissue types prepared by the GTEx project. Known and new tissue specific or enriched proteins (5,499) were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode highly tissue specific proteins. Discordance in the sites of RNA expression and protein detection also revealed potential sites of synthesis and action of protein signaling molecules. Overall, these results provide an extraordinary resource, and demonstrate that understanding protein levels can provide insights into metabolism, regulation, secretome, and human diseases.SummaryQuantitative proteome study of 32 human tissues and integrated analysis with transcriptome data revealed that understanding protein levels could provide in-depth knowledge to post transcriptional or translational regulations, human metabolism, secretome, and diseases.

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of relative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships. First, we developed a framework called Transcriptome Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the complex interplay between the regulation of splicing and transcription. We built TWNs for sixteen tissues, and found that hubs with isoform node neighbors in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome, and providing a set of candidate shared and tissue-specific regulatory hub genes. Next, we used a Bayesian biclustering model that identifies network edges between genes with co-expression in a single tissue to reconstruct tissue-specific networks (TSNs) for 27 distinct GTEx tissues and for four subsets of related tissues. Using both TWNs and TSNs, we characterized gene co-expression patterns shared across tissues. Finally, we found genetic variants associated with multiple neighboring nodes in our networks, supporting the estimated network structures and identifying 33 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships between genes in the human transcriptome, including tissue-specificity of gene co-expression, regulation of splicing, and the coordinated impact of genetic variation on transcription.

UMR AGAP - équipe DAAV (Diversité, adaptation et amélioration de la vigne)139 auteurs : Ardlie KG, Deluca DS, Segrè AV, Sullivan TJ, Young TR, Gelfand ET, Trowbridge CA, Maller JB, Tukiainen T, Lek M, Ward LD, Kheradpour P, Iriarte B, Meng Y, Palmer CD, Esko T, Winckler W, Hirschhorn JN, Kellis M, MacArthur DG, Getz G, Shabalin AA, Li G, Zhou YH, Nobel AB, Rusyn I, Wright FA, Lappalainen T, Ferreira PG, Ongen H, Rivas MA, Battle A, Mostafavi S, Monlong J, Sammeth M, Melé M, Reverter F, Goldmann JM, Koller D, Guigó R, McCarthy MI, Dermitzakis ET, Gamazon ER, Im HK, Konkashbaev A, Nicolae DL, Cox NJ, Flutre T, Wen X, Stephens M, Pritchard JK, Tu Z, Zhang B, Huang T, Long Q, Lin L, Yang J, Zhu J, Liu J, Brown A, Mestichelli B, Tidwell D, Lo E, Salvatore M, Shad S, Thomas JA, Lonsdale JT, Moser MT, Gillard BM, Karasik E, Ramsey K, Choi C, Foster BA, Syron J, Fleming J, Magazine H, Hasz R, Walters GD, Bridge JP, Miklos M, Sullivan S, Barker LK, Traino HM, Mosavel M, Siminoff LA, Valley DR, Rohrer DC, Jewell SD, Branton PA, Sobin LH, Barcus M, Qi L, McLean J, Hariharan P, Um KS, Wu S, Tabor D, Shive C, Smith AM, Buia SA, Undale AH, Robinson KL, Roche N, Valentino KM, Britton A, Burges R, Bradbury D, Hambright KW, Seleski J, Korzeniewski GE, Erickson K, Marcus Y, Tejada J, Taherian M, Lu C, Basile M, Mash DC, Volpi S, Struewing JP, Temple GF, Boyer J, Colantuoni D, Little R, Koester S, Carithers LJ, Moore HM, Guan P, Compton C, Sawyer SJ, Demchok JP, Vaught JB, Rabiner CA, Lockhart NC, Ardlie KG, Getz G, Wright FA, Kellis M, Volpi S, Dermitzakis ET.; Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.

Aging is one of the most important biological processes and is a known risk factor for many age-related diseases in human. Studying age-related transcriptomic changes in tissues across the whole body can provide valuable information for a holistic understanding of this fundamental process. In this work, we catalogue age-related gene expression changes in nine tissues from nearly two hundred individuals collected by the Genotype-Tissue Expression (GTEx) project. In general, we find the aging gene expression signatures are very tissue specific. However, enrichment for some well-known aging components such as mitochondria biology is observed in many tissues. Different levels of cross-tissue synchronization of age-related gene expression changes are observed, and some essential tissues (e.g., heart and lung) show much stronger “co-aging” than other tissues based on a principal component analysis. The aging gene signatures and complex disease genes show a complex overlapping pattern and only in some cases, we see that they are significantly overlapped in the tissues affected by the corresponding diseases. In summary, our analyses provide novel insights to the co-regulation of age-related gene expression in multiple tissues; it also presents a tissue-specific view of the link between aging and age-related diseases., National Cancer Institute (U.S.), National Human Genome Research Institute (U.S.), National Heart, Lung, and Blood Institute, National Institute on Drug Abuse (NIDA), National Institute of Mental Health (U.S.), National Institute of Neurological Disorders and Stroke (U.S.)

The NIH Common Fund GTEx project is designed to serve as a data and post-mortem tissue resource to the research community. The project is testing the role of genomic variation in altering gene expression across a wide array of tissues in a large number of human post-mortem donors. Both data and tissue samples are available to the research community for additional studies.

Summary induce myogenic precursors or to maintain and expand a population of already specified cells (see Munsterberg To understand how the skeletal muscle lineage is in- and Lassar, 1995, and references therein). We have duced during vertebrate embryogenesis, we have shown that myogenesis of explanted presegmental sought to identify the regulatory molecules that medi- plate mesoderm (isolated from a stage 10 chick embryo) ate induction of the myogenic regulatory factors MyoD requires two signals from axial tissues, one emanating and Myf-5. In this work, we demonstrate that either from the floor plate-notochord complex and the other signals from the overlying ectoderm or Wnt and Sonic from more dorsal regions of the neural tube (Mun- hedgehog signals can induce somitic expression of sterberg and Lassar, 1995). Consistent with these find- the paired box transcription factors, Pax-3 and Pax-7, ings, Emerson and colleagues recently reported that concomitant with expression of Myf-5 and prior to that signals from the notochord are required to initiate quail of MyoD. Moreover, infection of embryonic tissues in MyoD expression and that signals from the neural tube vitro with a retrovirus encoding Pax-3 is sufficient to are required to maintain MyoD expression in ovo (Pow- induce expression of MyoD, Myf-5, and myogenin in nall et al., 1996). both paraxial and lateral plate mesoderm in the ab- The muscle-promoting signal from the floor plate- sence of inducing tissues as well as in the neural tube. notochord complex can be mimicked by the signaling Together, these findings imply that Pax-3 may mediate molecule Sonic hedgehog (Shh), and the muscle-pro- activation of MyoD and Myf-5 in response to muscle- moting signal from the neural tube can be mimicked by inducing signals from either the axial tissues or overly- cell lines programmed to express either Wnt-1 or Wnt-3 ing ectoderm and identify Pax-3 as a key regulator of (Munsterberg et al., 1995; Stern et al., 1995). Because somitic myogenesis.

Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues., National Institutes of Health (U.S.) (US NIH to the Broad Institute of Harvard and MIT, R01 DA006227-17), National Institute of Drug Abuse, National Institute of Mental Health (U.S.), National Institute of Neurological Disorders and Stroke (U.S.)

The NIH Common Fund's Genotype-Tissue Expression (GTEx) project aims to study gene expression and regulation across multiple human tissues from approximately 1000 healthy normal postmortem donors. GTEx will provide valuable insights into gene regulation and its tissue specificity, to identify correlations between genetic variations and variations in gene expression levels as expression quantitative trait loci (eQTLs), and to help understand inherited susceptibility to disease. Initiated in 2010, the GTEx program has generated a large volume of data associated with each donor, including clinical and histopathological, as well as genotyping and gene expression data from whole genome sequencing, whole exome sequencing, expression array, and RNAseq data. The program has published research results in multiple scientific journals over the past year. The following public resources are available from the GTEx program: 1. GTEx Portal: an open access database of GTEx expression data and analysis results: http://www.gtexportal.org 2. Database of Genotypes and Phenotypes (dbGaP): controlled access of comprehensive GTEx clinical data and raw sequencing data: http://www.ncbi.nlm.nih.gov/gap 3. Access to residual GTEx biospecimens for research: http://www.gtexportal.org/home/samplesPage 4. SOPs and best practices from GTEx biospecimen collections: http://biospecimens.cancer.gov/resources/sops/library.asp 5. GTEx histological image viewer: http://biospecimens.cancer.gov/resources/tissue_image_library.asp 6. GTEx donors’ families website: a lay description of the GTEx project tailored to the GTEx donors’ families: http://www.genome.gov/gtex GTEx data can be used in combination with other data sets such as The Cancer Genome Atlas (TCGA) and genome-wide association studies (GWAS) to further explore the genetic causes and biology of cancer. The eQTL data from multiple tissues will help prioritize candidate genes within GWAS-associated loci; allow evaluation of tissue specificity of associated loci, and pinpoint target tissues for disease studies. Researchers are using GTEx resources to: study cancer heterogeneity by subtracting normal tissue expression; identify mutations in protein-truncating variants which may cause cancer; and explore gene activity from multiple donors across multiple tissue types to better understand the age/gender bias in cancer and other diseases. Citation Format: Ping Guan, Abhi Rao, Simona Volpi, Susan Koester, Helen M. Moore, GTEx consortium. Normal tissue and data resources for cancer research from the GTEx program. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4500.

In adult rats, layer 5 cortical neurons send axons through the corpus callosum to contralateral cortex or through the internal capsule to subcortical targets, but individual neurons reportedly do not have both connections. Here we confirm this adult separation and address whether it develops by extension of axon collaterals to both sets of targets with later elimination of one or the other (a phenomenon common in developing cortex) or by initially selective axon outgrowth. Retrograde tracers Fast Blue and Diamidino Yellow were injected in the subcortical path at the pyramidal decussation and in the contralateral cortex, respectively, of adult and newborn rats. In 16 adults, no cortical neurons were double-labeled, indicating that none project to both sites. In 17 neonates, hundreds of thousands of layer 5 neurons were single-labeled in each brain, but only one was double-labeled. In cases in which the injections to one of the two targets was delayed, again, no double-labeled cells were found. These results indicate that the connectional distinction found in adults is not achieved by the elimination of long transient callosal or subcortical collaterals. To determine if shorter transient collaterals are extended by callosal neurons into the internal capsule, i.e., the subcortical pathway, we injected DiI into one cortical hemisphere of aldehyde-fixed Embryonic Day (E)19 and E21 brains. Two types of axons are seen in the white matter of the cortex opposite the injection: those which turn and extend through the callosum and those which branch, sending collaterals to both ipsilateral and contralateral cortex. Rarely were collaterals seen to extend into or toward the internal capsule. [3H]Thymidine birthdating studies show that layer 5 callosal and subcortically projecting neurons are generated at the same stage of corticogenesis. Thus, from early stages of axon extension, callosal and subcortically projecting cells are distinct classes of neurons and, responding differentially to cues present in cortex, initiate growth toward class-specific and nonoverlapping sets of targets. We conclude that the distinction between the two projection classes in inherent to them and is likely to be determined at an early stage of cortical development, prior to neuronal migration from the neuroepithelium.

We demonstrate in rat neocortex that the distinct laminar arrangements of the apical dendrites of two classes of layer 5 projection neurons, callosal and corticotectal, do not arise de novo, but are generated later in development from a common tall pyramidal morphology. Neurons of each class initially elaborate an apical dendrite in layer 1. Layer 5 callosal neurons later lose the segments of their apical dendrite superficial to layer 4, generating their characteristic short pyramidal morphology. The apical dendrite of layer 5 callosal neurons later lose the segments of their apical dendrite superficial to layer 4, generating their characteristic short pyramidal morphology. The apical dendrite of layer 5 callosal neurons is actively eliminated, rather than passively displaced, as superficial cortical layers expand. Corticotectal neurons and callosal neurons superficial to layer 5 maintain their apical dendrite to layer 1. Therefore, this selective dendritic loss occurs in a neuron class-specific manner and, within the callosal population, in a lamina-specific manner. Based on our additional observations and other studies, this phenomenon can be extended to other types of cortical projection neurons. These findings show that selective dendritic elimination plays a major role in shaping the functional architecture characteristic of the adult cortex.

The internal capsule and corpus callosum are the two major efferent axonal pathways of the mammalian neocortex. Previous studies have shown that the first cortical axons to grow through the internal capsule, the pathway from cortex to its subcortical targets, are extended by subplate neurons, which are the earliest generated neurons in the neocortex. Here, we characterize the origin of the first axons to project through the other major efferent pathway of the cortex, the midline corpus callosum, which connects the two cortical hemispheres. Using anterograde Dil tracing, we show that cortical axons first cross the midline through the nascent corpus callosum at E17. Retrograde Dil labeling from medial cortex at E18 reveals that these axons originate from a discrete group of neurons in medial (presumptive cingulate) cortex. These early callosal cells have complex morphologies with highly branched dendrites and later appear to take on a pyramidal form characteristic of callosal neurons in deep layers of cingulate cortex. 3H-thymidine birthdating demonstrates that these cells are predominantly generated on E14, making them among the earliest generated neurons in this cortical region. Injections of retrograde tracers in one cortical hemisphere at late embryonic or early postnatal ages result in substantial numbers of neurons labeled in the ipsilateral subplate, but only a few neurons labeled in the contralateral subplate. Thus, subplate neurons do not pioneer or ever project in significant numbers through the corpus callosum. We conclude that the two major efferent pathways from cortex, the corpus callosum and the internal capsule, are pioneered by developmentally and spatially distinct populations of early generated cortical neurons.

Publisher Summary This chapter summarizes the recent studies on the development of two distinct classes of efferent projection neurons in the mammalian cortex: the callosal and subcortically projecting. The cortex consists of six major layers which are distinguishable on the grounds of both cellular morphology and connectivity. The two major layer 5 efferent projection classes of the neocortex, the callosal and the subcortical, are distinct populations of projection neurons generated at the same time in development. The evidence suggests that the distinction between the two classes is determined early in development, either before or around the time that the neurons are generated. The projection class phenotype could be determined by cell-cell interactions such as those hypothesized to occur in the vertebrate retina. Alternatively, projection class could be determined by lineage dependent mechanisms. Lineage has been shown to be an important determinant of glial cell phenotype in cortex and is correlated with the differentiation of neurons into pyramidal and non-pyramidal morphologies. Only direct analysis of the role of lineage in determining cortical projection phenotype allows one to distinguish between these two mechanisms. These determination events are then played out during normal development as the two projection classes differentiate, elaborating their specific axonal and, later, dendritic phenotypes.

The mammalian neocortex processes visual, auditory, and somatic sensation, giving rise to perception and initiatingvoluntarymotor responses.Thesefunctions are performed through an intricate set of neural circuits within the neocortex, as well as through connections between the cortex and other parts of the brain. Neural scientists have long been intrigued with these connections, as indicated in the following passage written a century ago by Santiago Ramon y Cajal.

Comprehensive atlases of gene expression in the mouse brain will provide a great resource for neurobiology., The completion of the Allen Brain Atlas generated a great deal of press interest and enthusiasm from the research community. What does it do, and what other complementary resources increase its functionality?

The identification of the transcription factors involved in regulating axon pathfinding is far from complete. One important task for the near future of this field is to identify more transcriptional regulators of axon pathfinding. As we learn more about the nature of these transcription factors, it will be of interest to determine the level of control they exert over the pathway choices of an axon. That is, how directly is transcriptional regulation involved in the dynamic expression patterns that have been demonstrated for certain outgrowth and pathfinding related molecules? Expression of some surface proteins on axons is regulated with exquisite spatial and temporal detail at important decision points during axon outgrowth. For example, in the embryonic rat spinal cord, commissural neurons express TAG-1 while they are extending in the circumferential pathway toward the ventral midline. After the axons cross the midline, they turn abruptly and project in the longitudinal plane. Crossing the midline is associated with the downregulation of TAG-1 on the axons and upregulation of another cell-surface glycoprotein, L1. L1 is specifically expressed on the contralateral axon segment but is never expressed on the ipsilateral segment, cell bodies or dendrites of commissural neurons (Dodd et al. 1988xDodd, J, Morton, S.B, Karagogeos, D, Yamamoto, M, and Jessell, T.M. Neuron. 1988; 1: 105–116Abstract | Full Text PDF | PubMed | Scopus (504)See all ReferencesDodd et al. 1988). Likewise, in the grasshopper and in Drosophila, certain axonal glycoproteins are also selectively expressed in discrete segments of individual axons (2xBastiani, M.J, Harrelson, A.L, Snow, P.M, and Goodman, C.S. Cell. 1987; 48: 745–755Abstract | Full Text PDF | PubMed | Scopus (217)See all References, 10xPatel, N.H, Snow, P.M, and Goodman, C.S. Cell. 1987; 48: 975–988Abstract | Full Text PDF | PubMed | Scopus (356)See all References).In each of these cases, it is not known whether the switch in glycoprotein expression is cell autonomous or whether it is induced by external cues. If it occurs in response to an environmental stimulus the question remains, do transcription factors promote transcription of new message in anticipation of, or in response to the encounter with the stimulus? The latter case implies that a signal is sent from the growth cone back to the cell body altering transcription in the nucleus. However, some growth cones can navigate normally in vivo for several hours after being disconnected from their somas (e.g.,Harris et al. 1987xHarris, W.A, Holt, C.E, and Bonhoeffer, F. Development. 1987; 101: 123–133PubMedSee all ReferencesHarris et al. 1987). Indeed, the distance from the growth cone to the nucleus can be quite large, raising the question of whether this retrograde signaling mechanism is feasible given the current understanding of axonal transport and signaling mechanisms. Thus, transcriptional regulation of pathfinding cues is more likely to occur in advance of guidance cue encounters by growth cones. In this scenario, encounters with axon guidance cues would induce local changes in axonal proteins by one of two methods: either by post-translational alterations in proteins already present in the growth cone or by activating translation of previously transcribed messages located in growth cones or distal axons. Elucidating the signal transduction pathways activated in response to external guidance cues will tell us whether one or both of these mechanisms are at work.The recent identification of transcriptional regulators of axonal projection is an exciting advance that raises interesting questions about the role of transcriptional regulation in axon pathfinding. Primary among them is how tightly regulated are the pathfinding choices of an axon? Does there exist a single transcription factor that can turn on the expression of all of the various receptors and intracellular signaling molecules necessary for an axon to read a particular environment and the positive and negative cues that contribute to guide the growth cone to its appropriate target? Are there combinations of such factors that vary for slightly different projection subtypes (such as suggested in vertebrate motor neurons)? And how much feedback regulation of transcription is plausible from the environment encountered by the growth cone itself? An important challenge will be to determine the genes that are regulated by pathfinding-related transcription factors. This will help to shed light on the precise role of transcriptional regulation in axon pathfinding and will surely lead to the identification of new important players in the regulation of pathway choice and target recognition.

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes., Phenotypic variation and diseases are influenced by factors such as genetic variants and gene expression. Here, Barbeira et al. develop S-PrediXcan to compute PrediXcan results using summary data, and investigate the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes.