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


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 (

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Figure 1: A global view of the PFC transcriptome.
Figure 2: Reversal of fetal expression changes in infancy and ageing.
Figure 3: Genetic control of PFC gene expression.
Figure 4: The genome produces a consistent molecular architecture in PFC.

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Accession codes


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


  1. Schadt, E. E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003)

    Article  ADS  CAS  Google Scholar 

  2. Morley, M. et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004)

    Article  ADS  CAS  Google Scholar 

  3. Stranger, B. E. et al. Genome-wide associations of gene expression variation in humans. PLoS Genet. 1, e78 (2005)

    Article  Google Scholar 

  4. Myers, A. J. et al. A survey of genetic human cortical gene expression. Nature Genet. 39, 1494–1499 (2007)

    Article  CAS  Google Scholar 

  5. Heinzen, E. L. et al. Tissue-specific genetic control of splicing: implications for the study of complex traits. PLoS Biol. 6, e1 (2008)

    Article  Google Scholar 

  6. Gibbs, J. R. et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 6, e1000952 (2010)

    Article  Google Scholar 

  7. Liu, C. et al. Whole-genome association mapping of gene expression in the human prefrontal cortex. Mol. Psychiatry 15, 779–784 (2010)

    Article  CAS  Google Scholar 

  8. Webster, J. A. et al. Genetic control of human brain transcript expression in Alzheimer disease. Am. J. Hum. Genet. 84, 445–458 (2009)

    Article  CAS  Google Scholar 

  9. Johnson, M. B. et al. Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron 62, 494–509 (2009)

    Article  CAS  Google Scholar 

  10. Somel, M. et al. MicroRNA, mRNA, and protein expression link development and aging in human and macaque brain. Genome Res. 20, 1207–1218 (2010)

    Article  CAS  Google Scholar 

  11. Oldham, M. C. et al. Functional organization of the transcriptome in human brain. Nature Neurosci. 11, 1271–1282 (2008)

    Article  CAS  Google Scholar 

  12. Torkamani, A., Dean, B., Schork, N. J. & Thomas, E. A. Coexpression network analysis of neural tissue reveals perturbations in developmental processes in schizophrenia. Genome Res. 20, 403–412 (2010)

    Article  CAS  Google Scholar 

  13. Luo, L. & O’Leary, D. D. Axon retraction and degeneration in development and disease. Annu. Rev. Neurosci. 28, 127–156 (2005)

    Article  CAS  Google Scholar 

  14. Deo, M., Yu, J. Y., Chung, K. H., Tippens, M. & Turner, D. L. Detection of mammalian microRNA expression by in situ hybridization with RNA oligonucleotides. Dev. Dyn. 235, 2538–2548 (2006)

    Article  CAS  Google Scholar 

  15. Gao, F. B. Context-dependent functions of specific microRNAs in neuronal development. Neural Develop. 5, 25 (2010)

    Article  Google Scholar 

  16. Coolen, M. & Bally-Cuif, L. MicroRNAs in brain development and physiology. Curr. Opin. Neurobiol. 19, 461–470 (2009)

    Article  CAS  Google Scholar 

  17. Delaloy, C. et al. MicroRNA-9 coordinates proliferation and migration of human embryonic stem cell-derived neural progenitors. Cell Stem Cell 6, 323–335 (2010)

    Article  CAS  Google Scholar 

  18. Loerch, P. M. et al. Evolution of the aging brain transcriptome and synaptic regulation. PLoS ONE 3, e3329 (2008)

    Article  ADS  Google Scholar 

  19. Lu, T. et al. Gene regulation and DNA damage in the ageing human brain. Nature 429, 883–891 (2004)

    Article  ADS  CAS  Google Scholar 

  20. Blalock, E. M. et al. Incipient Alzheimer’s disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc. Natl Acad. Sci. USA 101, 2173–2178 (2004)

    Article  ADS  CAS  Google Scholar 

  21. Colantuoni, C., Henry, G., Zeger, S. & Pevsner, J. SNOMAD (Standardization and NOrmalization of MicroArray Data): web-accessible gene expression data analysis. Bioinformatics 18, 1540–1541 (2002)

    Article  CAS  Google Scholar 

  22. Leek, J. T. & Storey, J. D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, 1724–1735 (2007)

    Article  CAS  Google Scholar 

  23. Lipska, B. K. et al. Critical factors in gene expression in postmortem human brain: Focus on studies in schizophrenia. Biol. Psychiatry 60, 650–658 (2006)

    Article  CAS  Google Scholar 

  24. Zhang, Y. et al. Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information. BMC Med. Genomics 3, 1 (2010)

    Article  ADS  MathSciNet  Google Scholar 

  25. De, S., Zhang, Y., Garner, J. R., Wang, S. A. & Becker, K. G. Disease and phenotype gene set analysis of disease based gene expression in mouse and human. Physiol. Genomics 42A, 162–167 (2010)

    Article  CAS  Google Scholar 

  26. Daniel, V. C. et al. A primary xenograft model of small-cell lung cancer reveals irreversible changes in gene expression imposed by culture in vitro. Cancer Res. 69, 3364–3373 (2009)

    Article  CAS  Google Scholar 

  27. Schaeffer, E. M. et al. Androgen-induced programs for prostate epithelial growth and invasion arise in embryogenesis and are reactivated in cancer. Oncogene 27, 7180–7191 (2008)

    Article  CAS  Google Scholar 

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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.

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Authors and Affiliations



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.

Corresponding author

Correspondence to Joel E. Kleinman.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

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). (PDF 390 kb)

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. (PDF 835 kb)

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. (ZIP 3414 kb)

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Colantuoni, C., Lipska, B., Ye, T. et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519–523 (2011).

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