Genetic variability in the regulation of gene expression in ten regions of the human brain (original) (raw)

Accession codes

Primary accessions

Gene Expression Omnibus

Referenced accessions

Gene Expression Omnibus

References

  1. International Parkinson's Disease Genomics Consortium (IPDGC) & Wellcome Trust Case Control Consortium 2 (WTCCC2). A two-stage meta-analysis identifies several new loci for Parkinson's disease. PLoS Genet. 7, e1002142 (2011).
  2. Hollingworth, P. et al. Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease. Nat. Genet. 43, 429–435 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  3. Hamshere, M.L. et al. Genome-wide significant associations in schizophrenia to ITIH3/4, CACNA1C and SDCCAG8, and extensive replication of associations reported by the Schizophrenia PGC. Mol. Psychiatry 18, 708–712 (2013).
    Article CAS PubMed Google Scholar
  4. Hardy, J. & Singleton, A. Genomewide association studies and human disease. N. Engl. J. Med. 360, 1759–1768 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  5. Visscher, P.M., Brown, M.A., McCarthy, M.I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  6. Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008).
    Article CAS PubMed Google Scholar
  7. Moffatt, M.F. et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature 448, 470–473 (2007).
    Article CAS PubMed Google Scholar
  8. Jellinger, K. Recent Developments in Parkinson's Disease 33–36 (Raven, 1986).
  9. Hyman, B.T., Van Hoesen, G.W., Damasio, A.R. & Barnes, C.L. Alzheimer's disease: cell-specific pathology isolates the hippocampal formation. Science 225, 1168–1170 (1984).
    Article CAS PubMed Google Scholar
  10. Wang, E.T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).
    Article CAS PubMed PubMed Central Google Scholar
  11. Li, Q., Lee, J.A. & Black, D.L. Neuronal regulation of alternative pre-mRNA splicing. Nat. Rev. Neurosci. 8, 819–831 (2007).
    Article CAS PubMed Google Scholar
  12. Norris, A.D. & Calarco, J.A. Emerging roles of alternative pre-mRNA splicing regulation in neuronal development and function. Front. Neurosci. 6, 122 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  13. Myers, A.J. et al. A survey of genetic human cortical gene expression. Nat. Genet. 39, 1494–1499 (2007).
    Article CAS PubMed Google Scholar
  14. Heinzen, E.L. et al. Tissue-specific genetic control of splicing: implications for the study of complex traits. PLoS Biol. 6, e1 (2008).
    Article CAS PubMed Google Scholar
  15. 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 PubMed PubMed Central Google Scholar
  16. 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 CAS PubMed PubMed Central Google Scholar
  17. Liu, C. et al. Whole-genome association mapping of gene expression in the human prefrontal cortex. Mol. Psychiatry 15, 779–784 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  18. Kang, H.J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  19. Colantuoni, C. et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519–523 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  20. Hernandez, D.G. et al. Integration of GWAS SNPs and tissue specific expression profiling reveal discrete eQTLs for human traits in blood and brain. Neurobiol. Dis. 47, 20–28 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  21. Kim, S., Cho, H., Lee, D. & Webster, M.J. Association between SNPs and gene expression in multiple regions of the human brain. Transl. Psychiatry 2, e113 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  22. Zou, F. et al. Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants. PLoS Genet. 8, e1002707 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  23. Ramasamy, A. et al. Resolving the polymorphism-in-probe problem is critical for correct interpretation of expression QTL studies. Nucleic Acids Res. 41, e88 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  24. Trabzuni, D. et al. Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. J. Neurochem. 119, 275–282 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  25. Zeller, T. et al. Genetics and beyond–—the transcriptome of human monocytes and disease susceptibility. PLoS ONE 5, e10693 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  26. Fairfax, B.P. et al. Genetics of gene expression in primary immune cells identifies cell type–specific master regulators and roles of HLA alleles. Nat. Genet. 44, 502–510 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  27. Grundberg, E. et al. Mapping _cis_- and _trans_-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  28. Albert, F.W., Treusch, S., Shockley, A.H., Bloom, J.S. & Kruglyak, L. Genetics of single-cell protein abundance variation in large yeast populations. Nature 506, 494–497 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  29. Schorge, S., van de Leemput, J., Singleton, A., Houlden, H. & Hardy, J. Human ataxias: a genetic dissection of inositol triphosphate receptor (ITPR1)-dependent signaling. Trends Neurosci. 33, 211–219 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  30. Nixon, R.A. The role of autophagy in neurodegenerative disease. Nat. Med. 19, 983–997 (2013).
    Article CAS PubMed Google Scholar
  31. Gruber, A.R., Fallmann, J., Kratochvill, F., Kovarik, P. & Hofacker, I.L. AREsite: a database for the comprehensive investigation of AU-rich elements. Nucleic Acids Res. 39, D66–D69 (2011).
    Article CAS PubMed Google Scholar
  32. Pankratz, N. et al. Meta-analysis of Parkinson's disease: identification of a novel locus, RIT2. Ann. Neurol. 71, 370–384 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  33. van Es, M.A. et al. Genome-wide association study identifies 19p13.3 (UNC13A) and 9p21.2 as susceptibility loci for sporadic amyotrophic lateral sclerosis. Nat. Genet. 41, 1083–1087 (2009).
    Article CAS PubMed Google Scholar
  34. Augustin, I., Rosenmund, C., Sudhof, T.C. & Brose, N. Munc13-1 is essential for fusion competence of glutamatergic synaptic vesicles. Nature 400, 457–461 (1999).
    Article CAS PubMed Google Scholar
  35. Varoqueaux, F. et al. Total arrest of spontaneous and evoked synaptic transmission but normal synaptogenesis in the absence of Munc13-mediated vesicle priming. Proc. Natl. Acad. Sci. USA 99, 9037–9042 (2002).
    Article CAS PubMed PubMed Central Google Scholar
  36. Köhler, M. et al. Small-conductance, calcium-activated potassium channels from mammalian brain. Science 273, 1709–1714 (1996).
    Article PubMed Google Scholar
  37. McKay, J.D. et al. Lung cancer susceptibility locus at 5p15.33. Nat. Genet. 40, 1404–1406 (2008).
    Article CAS PubMed PubMed Central Google Scholar
  38. Thorgeirsson, T.E. et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature 452, 638–642 (2008).
    Article CAS PubMed PubMed Central Google Scholar
  39. Thorgeirsson, T.E. et al. Sequence variants at CHRNB3–CHRNA6 and CYP2A6 affect smoking behavior. Nat. Genet. 42, 448–453 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  40. Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).
  41. Liu, J.Z. et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat. Genet. 42, 436–440 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  42. Landi, M.T. et al. A genome-wide association study of lung cancer identifies a region of chromosome 5p15 associated with risk for adenocarcinoma. Am. J. Hum. Genet. 85, 679–691 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  43. Capasso, M. et al. Common variations in BARD1 influence susceptibility to high-risk neuroblastoma. Nat. Genet. 41, 718–723 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  44. Naj, A.C. et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease. Nat. Genet. 43, 436–441 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  45. Lambert, J.C. et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease. Nat. Genet. 41, 1094–1099 (2009).
    Article CAS PubMed Google Scholar
  46. Plagnol, V., Smyth, D.J., Todd, J.A. & Clayton, D.G. Statistical independence of the colocalized association signals for type 1 diabetes and RPS26 gene expression on chromosome 12q13. Biostatistics 10, 327–334 (2009).
    Article PubMed Google Scholar
  47. Millar, T. et al. Tissue and organ donation for research in forensic pathology: the MRC Sudden Death Brain and Tissue Bank. J. Pathol. 213, 369–375 (2007).
    Article CAS PubMed Google Scholar
  48. Beach, T.G. et al. The Sun Health Research Institute Brain Donation Program: description and experience, 1987–2007. Cell Tissue Bank 9, 229–245 (2008).
    Article PubMed PubMed Central Google Scholar
  49. Hawrylycz, M.J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  50. Roth, R.B. et al. Gene expression analyses reveal molecular relationships among 20 regions of the human CNS. Neurogenetics 7, 67–80 (2006).
    Article CAS PubMed Google Scholar
  51. Irizarry, R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).
    Article PubMed Google Scholar
  52. Nalls, M.A. et al. Imputation of sequence variants for identification of genetic risks for Parkinson's disease: a meta-analysis of genome-wide association studies. Lancet 377, 641–649 (2011).
    Article CAS PubMed Google Scholar
  53. Li, Y., Willer, C., Sanna, S. & Abecasis, G. Genotype imputation. Annu. Rev. Genomics Hum. Genet. 10, 387–406 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  54. Li, Y., Willer, C.J., Ding, J., Scheet, P. & Abecasis, G.R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).
    Article PubMed PubMed Central Google Scholar
  55. Coin, L.J. et al. cnvHap: an integrative population and haplotype-based multiplatform model of SNPs and CNVs. Nat. Methods 7, 541–546 (2010).
    Article CAS PubMed Google Scholar
  56. Shabalin, A.A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  57. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. A Stat. Soc. 57, 289–300 (1995).
    Google Scholar
  58. Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).
    Article PubMed PubMed Central Google Scholar
  59. Obenchain, V., Morgan, M. & Lawrence, M. R Package Version 1.4.8 (Bioconductor, 2012).
  60. Barbosa-Morais, N.L. et al. A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data. Nucleic Acids Res. 38, e17 (2010).
    Article CAS PubMed Google Scholar

Download references

Acknowledgements

We are grateful to the Banner Sun Health Research Institute Brain and Body Donation Program of Sun City, Arizona for the provision of human biospecimens. The Brain and Body Donation Program is supported by the US National Institute of Neurological Disorders and Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson's Disease and Related Disorders), the National Institute on Aging (P30 AG19610 Arizona Alzheimer's Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer's Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson's Disease Consortium) and the Michael J. Fox Foundation for Parkinson's Research. We would like to thank AROS Applied Biotechnology AS company laboratories and Affymetrix for their input. H. Jonvik, L. Stanyer, J. Toombs and M. Gaskin provided invaluable assistance in helping with our computer infrastructure and in sample handling and databasing. We thank A. Pittman for discussions. This work was supported by the UK Medical Research Council (MRC) through the MRC Sudden Death Brain Bank (C.S.), a Project Grant (G0901254 to J.H. and M.E.W.) and Training Fellowship (G0802462 to M.R.). D.T. was supported by the King Faisal Specialist Hospital and Research Centre, Saudi Arabia. This work was also supported in part by the Intramural Research Program of the US National Institute on Aging, National Institutes of Health, Department of Health and Human Services; project ZO1 AG000947. We acknowledge support from the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' National Health Service (NHS) Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Author information

Author notes

  1. Adaikalavan Ramasamy, Daniah Trabzuni, Sebastian Guelfi and Luigi Ferrucci: These authors contributed equally to this work.
  2. Robert Johnson, Ronald Zielke, Mina Ryten and Michael E Weale: These authors jointly directed this work.

Authors and Affiliations

  1. Department of Medical & Molecular Genetics, King's College London, Guy's Hospital, London, UK
    Adaikalavan Ramasamy, Vibin Varghese, Paola Forabosco, Mina Ryten & Michael E Weale
  2. Department of Molecular Neuroscience, Reta Lila Weston Research Laboratories, University College London (UCL) Institute of Neurology, London, UK
    Adaikalavan Ramasamy, Daniah Trabzuni, Sebastian Guelfi, Rohan de Silva, John Hardy & Mina Ryten
  3. Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
    Daniah Trabzuni
  4. Department of Neuropathology, MRC Sudden Death Brain Bank Project, University of Edinburgh, Edinburgh, UK
    Colin Smith & Robert Walker
  5. School of Public Health, Faculty of Medicine, Imperial College London, London, UK
    Tisham De
  6. Institute of Molecular Bioscience, The University of Queensland, Brisbane St Lucia, Queensland, Australia
    Lachlan Coin
  7. Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
    Sampath Arepalli, Allissa Dillman, J Raphael Gibbs, Dena G Hernandez, Michael A Nalls, Bryan Traynor, Marcel van der Brug, Mark R Cookson & Andrew B Singleton
  8. Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, USA
    Luigi Ferrucci
  9. NICHD Brain and Tissue Bank for Developmental Disorders, University of Maryland Medical School, Baltimore, Maryland, USA
    Robert Johnson & Ronald Zielke
  10. Lymphocyte Cell Biology Unit, Laboratory of Immunology, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
    Dan L Longo
  11. Brain Resource Center, Johns Hopkins University, Baltimore, Maryland, USA
    Juan Troncoso
  12. ITGR Biomarker Discovery Group, Genentech, South San Francisco, California, USA
    Marcel van der Brug
  13. Research Resources Branch, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
    Alan Zonderman

Authors

  1. Adaikalavan Ramasamy
  2. Daniah Trabzuni
  3. Sebastian Guelfi
  4. Vibin Varghese
  5. Colin Smith
  6. Robert Walker
  7. Tisham De
  8. Lachlan Coin
  9. Rohan de Silva
  10. Mark R Cookson
  11. Andrew B Singleton
  12. John Hardy
  13. Mina Ryten
  14. Michael E Weale

Consortia

UK Brain Expression Consortium

North American Brain Expression Consortium

Contributions

A.R.: statistical and computer analysis, data display, web tool implementation and manuscript drafting; D.T.: laboratory work and analysis, manuscript revision; S.G.: manuscript revision and web tool implementation; V.V.: web tool implementation; C.S.: neuropathological characterization; R.W.: brain dissection and documentation; T.D.: copy number variant (CNV) analysis; L.C.: supervision of CNV analysis; R.d.S.: study design; M.R.C.: data accrual for NABEC and manuscript revision; A.B.S.: data accrual for NABEC and manuscript revision; J.H.: study design, funding acquisition and manuscript revision; M.R.: study design, funding acquisition and manuscript drafting and revision; M.E.W.: statistical analysis, study design, funding acquisition and manuscript drafting and revision.

Corresponding authors

Correspondence toMina Ryten or Michael E Weale.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Summary of sample features and analyses.

(a) Summary of sample characteristics and demographic information. (b) Plots of principal component axes 1 and 3 of gene expression with each sample coloured on the basis of brain region of origin or individual of origin. Given the number of individuals (N = 134) no key is provided within the figure. CRBL: cerebellar cortex; FCTX: frontal cortex; HIPP: hippocampus; MEDU: the inferior olivary nucleus (sub-dissected from the medulla); OCTX: occipital cortex; PUTM: putamen (at the level of the anterior commissure); SNIG: substantia nigra; TCTX: temporal cortex; THAL: thalamus (at the level of the lateral geniculate nucleus); WHMT: intralobular white matter. (c) Outline of the methods and analyses performed in this study.

Supplementary Figure 2 Comparison of _cis_-eQTL-based and expression-based clustering of gene expression

Rows relate to expression IDs in all panels. (a) Heatplot depicting membership of each expression ID within each of the ten cis-eQTL clusters identified in Figure 1a (labelled according to the brain region it is most associated with). Expression IDs are ordered according to cis-eQTL-based clustering (as depicted in Figure 1a). (b) Heatplot depicting gene expression in all ten brain regions. Expression IDs are ordered according to cis-eQTL-based clustering (as depicted in Figure 1a). (c) Heatplot depicting membership of each expression ID within each of the ten cis-eQTL clusters identified in Figure 1a (labelled according to the brain region it is most associated with). Expression IDs are ordered according to expression-based clustering (as depicted in Figure 1b). (d) Heatplot depicting gene expression in all ten brain regions. Expression IDs are ordered according to expression-based clustering (as depicted in Figure 1b). CRBL: cerebellar cortex; FCTX: frontal cortex; TCTX: temporal cortex; HIPP: hippocampus; MEDU: the inferior olivary nucleus (sub-dissected from the medulla); OCTX: occipital cortex; PUTM: putamen (at the level of the anterior commissure); SNIG: substantia nigra; THAL: thalamus (at the level of the lateral geniculate nucleus); WHMT: intralobular white matter.

Supplementary Figure 3 The effect of normalization approaches on _cis_-eQTL signals.

(a) Comparison of the regression coefficients for cis-eQTL analysis when all tissues are normalized together vs. when each tissue is normalized separately. (b) Comparison of the p-value of regression coefficients for cis-eQTL analysis when all tissues are normalized together vs. when each tissue is normalized separately.

Supplementary information

Rights and permissions

About this article

Cite this article

Ramasamy, A., Trabzuni, D., Guelfi, S. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain.Nat Neurosci 17, 1418–1428 (2014). https://doi.org/10.1038/nn.3801

Download citation