Temporal dynamics and genetic control of transcription in the human prefrontal cortex

  • Nature volume 478, pages 519523 (27 October 2011)
  • doi:10.1038/nature10524
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Previous investigations have combined transcriptional and genetic analyses in human cell lines1,2,3, but few have applied these techniques to human neural tissue4,5,6,7,8. To gain a global molecular perspective on the role of the human genome in cortical development, function and ageing, we explore the temporal dynamics and genetic control of transcription in human prefrontal cortex in an extensive series of post-mortem brains from fetal development through ageing. We discover a wave of gene expression changes occurring during fetal development which are reversed in early postnatal life. One half-century later in life, this pattern of reversals is mirrored in ageing and in neurodegeneration. Although we identify thousands of robust associations of individual genetic polymorphisms with gene expression, we also demonstrate that there is no association between the total extent of genetic differences between subjects and the global similarity of their transcriptional profiles. Hence, the human genome produces a consistent molecular architecture in the prefrontal cortex, despite millions of genetic differences across individuals and races. To enable further discovery, this entire data set is freely available (from Gene Expression Omnibus: accession GSE30272; and dbGaP: accession phs000417.v1.p1) and can also be interrogated via a biologist-friendly stand-alone application (http://www.libd.org/braincloud).

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Gene Expression Omnibus

Data deposits

The entire data set has been deposited in Gene Expression Omnibus under accession number GSE30272 and dbGaP under accession number phs000417.v1.p1 and can also be interrogated at http://www.libd.org/braincloud.


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We thank the families who donated tissue to make this study possible. We also thank the Offices of the Chief Medical Examiner of the District of Columbia, and of the Commonwealth of Virginia, Northern District, and the National Institute of Child and Health Development Brain and Tissue Bank for their collaboration. We thank R. McKay, N. Schork, F. McMahon and S. Zeger for their consultations on many issues, L. Marchionni for his assistance in assembling functional gene groups, as well as A. Deep-Soboslay, L. B. Bigelow, L. Wang, R. Buerlein, H. Choxi, V. Imamovic, Y. Snitkovsky, J. D. Paltan-Ortiz, J. Sirovatka, K. Becker, E. Lehrman and R. Vakkalanka for their contributions to this work.

Author information

Author notes

    • Carlo Colantuoni
    •  & Barbara K. Lipska

    These authors contributed equally to this work.


  1. Section on Neuropathology, Clinical Brain Disorders Branch, Genes, Cognition and Psychosis Program, IRP, NIMH, NIH, Bethesda, Maryland 20892, USA

    • Carlo Colantuoni
    • , Barbara K. Lipska
    • , Tianzhang Ye
    • , Thomas M. Hyde
    • , Ran Tao
    • , Mary M. Herman
    • , Daniel R. Weinberger
    •  & Joel E. Kleinman
  2. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA

    • Carlo Colantuoni
    • , Jeffrey T. Leek
    •  & Elizabeth A. Colantuoni
  3. Illuminato Biotechnology, Inc., Baltimore, Maryland 21211, USA

    • Carlo Colantuoni
  4. The Lieber Institute for Brain Development, Johns Hopkins University Medical Center, Baltimore, Maryland 21205, USA

    • Carlo Colantuoni
    • , Thomas M. Hyde
    •  & Daniel R. Weinberger
  5. Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA

    • Abdel G. Elkahloun


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C.C., design of the study, data exploration and analysis, writing of manuscript; B.K.L., design of the study, preparation of samples, data analysis, writing of the paper; T.Y., data analysis and web tool construction; T.M.H., brain collection, diagnosis, dissection (primary); writing/editing and commentary on analysis (secondary); planning experiment (primary); R.T., genotyping; J.T.L., surrogate variable analysis methods and code, statistical consultation; E.A.C., linear model methods, statistical consultation; A.G.E., microarray experiments; M.M.H., tissue characterization and micro/macro neuropathology; D.R.W., design and planning of the study, writing of manuscript; J.E.K., experimental design, characterization of specimens, data analysis and writing/editing.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Joel E. Kleinman.

Supplementary information

PDF files

  1. 1.

    Supplemental Figure 1

    Part 1 uses the dimension reducing MDS representation from Fig. 1C in order to visualize the possible effects of additional covariates on the expression data. Parts 2 and 3 are 3-dimensional extensions of the 2-dimensional analyses presented in Fig. 1 C and D (MDS and PCA, respectively).

  2. 2.

    Supplemental Figure 2

    Part 1 shows an analysis related to that in Fig. 4. While all genetic polymorphisms are used to look for an association with global transcriptional distance in Fig. 4, here only SNPs involved in significant SNP:expression associations are included in the genetic distance. Again no association is found. Part 2 depicts one negative and two additional positive controls for the analytical framework used in Fig. 4.

Zip files

  1. 1.

    Supplementary Tables

    This file contains Supplementary Tables 1-8 comprising: Table 1 Correlations of expression trajectories (slope of expression change across age) across the different age stages analyzed here, p-values, and N’s are also included, as are different filtering criteria; Table 2 List of all microarray probes showing BOTH significant change with age during fetal development AND significant change with age during infancy; Table 3 Full listing of functional gene groups enriched in each of the four patterns of expression described in Fig. 2A; Table 4 Individual gene details for synaptic and axonal genes highlighted in Fig. 2A; Table 5 Listing of individual genes found in the fetal:aging and fetal:AD expression trajectory reversals; Table 6 Listing of individual SNP:expression associations which reached genome-wide significance in the analysis including all subjects as well as analyses including only African American and only Caucasian subjects; Table 7 Demographic and tissue resource details for all subjects in the collection and Table 8 Taqman qPCR verification of microarray expression profiles.


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