Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease

Abstract

Genetic determinants of cognition are poorly characterized, and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here we performed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities, including memory. Using exome sequence data from 6,871 trios, we found that M3 genes were also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease–associated genes in the developed human brain, and provide empirical support for a convergent gene-regulatory network influencing cognition and neurodevelopmental disease.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Gene coexpression network analysis.
Figure 2: Enrichment of nsDNM from patients with neurodevelopmental disease.
Figure 3: Graphical representation of the M3 coexpression network and its relationship to neurodevelopmental disease.

Accession codes

Accessions

Gene Expression Omnibus

References

  1. Deary, I.J., Johnson, W. & Houlihan, L.M. Genetic foundations of human intelligence. Hum. Genet. 126, 215–232 (2009).

    PubMed  Google Scholar 

  2. Davies, G. et al. Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol. Psychiatry 16, 996–1005 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Plomin, R., Haworth, C.M., Meaburn, E.L., Price, T.S. & Davis, O.S. & Wellcome Trust Case Control Consortium 2. Common DNA markers can account for more than half of the genetic influence on cognitive abilities. Psychol. Sci. 24, 562–568 (2013).

    PubMed  Google Scholar 

  4. Davies, G. et al. Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53949). Mol. Psychiatry 20, 183–192 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Hill, W.D. et al. Human cognitive ability is influenced by genetic variation in components of postsynaptic signalling complexes assembled by NMDA receptors and MAGUK proteins. Transl. Psychiatry 4, e341 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Christoforou, A. et al. GWAS-based pathway analysis differentiates between fluid and crystallized intelligence. Genes Brain Behav. 13, 663–674 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Carroll, J. Human cognitive abilities: A survey of factor-analytic studies (Cambridge University Press, 1993).

  8. Plomin, R. & Deary, I.J. Genetics and intelligence differences: five special findings. Mol. Psychiatry 20, 98–108 (2015).

    CAS  PubMed  Google Scholar 

  9. Trzaskowski, M. et al. DNA evidence for strong genome-wide pleiotropy of cognitive and learning abilities. Behav. Genet. 43, 267–273 (2013).

    PubMed  PubMed Central  Google Scholar 

  10. Trzaskowski, M., Shakeshaft, N.G. & Plomin, R. Intelligence indexes generalist genes for cognitive abilities. Intelligence 41, 560–565 (2013).

    PubMed  PubMed Central  Google Scholar 

  11. Kahn, R.S. & Keefe, R.S.E. Schizophrenia is a cognitive illness: time for a change in focus. JAMA Psychiatry 70, 1107–1112 (2013).

    PubMed  Google Scholar 

  12. Doherty, J.L. & Owen, M.J. Genomic insights into the overlap between psychiatric disorders: implications for research and clinical practice. Genome Med. 6, 29 (2014).

    PubMed  PubMed Central  Google Scholar 

  13. Helmstaedter, C. & Witt, J.-A. Clinical neuropsychology in epilepsy: theoretical and practical issues. Handb. Clin. Neurol. 107, 437–459 (2012).

    PubMed  Google Scholar 

  14. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    PubMed  PubMed Central  Google Scholar 

  15. Li, J.Z. et al. Circadian patterns of gene expression in the human brain and disruption in major depressive disorder. Proc. Natl. Acad. Sci. USA 110, 9950–9955 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Nithianantharajah, J. et al. Synaptic scaffold evolution generated components of vertebrate cognitive complexity. Nat. Neurosci. 16, 16–24 (2013).

    CAS  PubMed  Google Scholar 

  17. Bayés, A. et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat. Neurosci. 14, 19–21 (2011).

    PubMed  Google Scholar 

  18. Bayés, A. et al. Comparative study of human and mouse postsynaptic proteomes finds high compositional conservation and abundance differences for key synaptic proteins. PLoS One 7, e46683 (2012).

    PubMed  PubMed Central  Google Scholar 

  19. Bliss, T.V.P. & Collingridge, G.L. A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361, 31–39 (1993).

    CAS  PubMed  Google Scholar 

  20. Ramasamy, A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418–1428 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Kirov, G. et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol. Psychiatry 17, 142–153 (2012).

    CAS  PubMed  Google Scholar 

  23. Rossin, E.J. et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Smith, B.H. et al. Cohort profile: generation Scotland: Scottish family health study (GS:SFHS). The study, its participants and their potential for genetic research on health and illness. Int. J. Epidemiol. 42, 689–700 (2013).

    PubMed  Google Scholar 

  25. Deary, I.J., Gow, A.J., Pattie, A. & Starr, J.M. Cohort profile: the Lothian birth cohorts of 1921 and 1936. Int. J. Epidemiol. 41, 1576–1584 (2012).

    PubMed  Google Scholar 

  26. Liu, J.Z. et al.; AMFS Investigators. A versatile gene-based test for genome-wide association studies. Am. J. Hum. Genet. 87, 139–145 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Nam, D., Kim, J., Kim, S.-Y. & Kim, S. GSA-SNP: a general approach for gene set analysis of polymorphisms. Nucleic Acids Res. 38, W749–54 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Pletikos, M. et al. Temporal specification and bilaterality of human neocortical topographic gene expression. Neuron 81, 321–332 (2014).

    CAS  PubMed  Google Scholar 

  29. Kang, H.J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang, J., Duncan, D., Shi, Z. & Zhang, B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 41, W77–W83 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Satoh, J., Kawana, N. & Yamamoto, Y. ChIP-Seq data mining: remarkable differences in NRSF/REST target genes between human ESC and ESC-derived neurons. Bioinform. Biol. Insights 7, 357–368 (2013).

    PubMed  PubMed Central  Google Scholar 

  32. Moreno-De-Luca, A. et al. Developmental brain dysfunction: revival and expansion of old concepts based on new genetic evidence. Lancet Neurol. 12, 406–414 (2013).

    PubMed  PubMed Central  Google Scholar 

  33. Johnson, M.R. & Shorvon, S.D. Heredity in epilepsy: neurodevelopment, comorbidity, and the neurological trait. Epilepsy Behav. 22, 421–427 (2011).

    PubMed  Google Scholar 

  34. Petrovski, S., Wang, Q., Heinzen, E.L., Allen, A.S. & Goldstein, D.B. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 9, e1003709 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhu, X., Need, A.C., Petrovski, S. & Goldstein, D.B. One gene, many neuropsychiatric disorders: lessons from Mendelian diseases. Nat. Neurosci. 17, 773–781 (2014).

    CAS  PubMed  Google Scholar 

  36. Deciphering Developmental Disorders Study. Large-scale discovery of novel genetic causes of developmental disorders. Nature 519, 223–228 (2014).

  37. Wright, C.F. et al. Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data. Lancet 385, 1305–1314 (2015).

    PubMed  PubMed Central  Google Scholar 

  38. Sanders, S.J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. O'Roak, B.J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Rauch, A. et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380, 1674–1682 (2012).

    CAS  PubMed  Google Scholar 

  42. Gulsuner, S. et al. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 154, 518–529 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Xu, B. et al. De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat. Genet. 44, 1365–1369 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

  46. International League Against Epilepsy Consortium on Complex Epilepsies. Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies. Lancet Neurol. 13, 893–903 (2014).

  47. Li, H. et al. Candidate single-nucleotide polymorphisms from a genomewide association study of Alzheimer disease. Arch. Neurol. 65, 45–53 (2008).

    PubMed  Google Scholar 

  48. Rimfeld, K., Kovas, Y., Dale, P.S. & Plomin, R. Pleiotropy across academic subjects at the end of compulsory education. Sci. Rep. 5, 11713 (2015).

    PubMed  PubMed Central  Google Scholar 

  49. McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Johnson, M.R. et al. Systems genetics identifies Sestrin 3 as a regulator of a proconvulsant gene network in human epileptic hippocampus. Nat. Commun. 6, 6031 (2015).

    CAS  PubMed  Google Scholar 

  51. 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).

    PubMed  Google Scholar 

  52. Hardin, J., Mitani, A., Hicks, L. & VanKoten, B. A robust measure of correlation between two genes on a microarray. BMC Bioinformatics 8, 220 (2007).

    PubMed  PubMed Central  Google Scholar 

  53. Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2008).

    CAS  PubMed  Google Scholar 

  54. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    PubMed  PubMed Central  Google Scholar 

  55. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  56. North, B.V., Curtis, D. & Sham, P.C. A note on the calculation of empirical P values from Monte Carlo procedures. Am. J. Hum. Genet. 71, 439–441 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Therneau, T.M. & Ballman, K.V. What does PLIER really do? Cancer Inform. 6, 423–431 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Hebenstreit, D. et al. RNA sequencing reveals two major classes of gene expression levels in metazoan cells. Mol. Syst. Biol. 7, 497 (2011).

    PubMed  PubMed Central  Google Scholar 

  60. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. & Hattori, M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 32, D277–D280 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Wechsler, D. Wechsler Adult Intelligence Scale - third edition. (London: The Psychological Corporation, 1998).

  63. Nelson, H.E. & Willison, J. National Adult Reading Test (NART) Test Manual. (Windsor, NFER-Nelson, 1991).

  64. Wechsler, D. Wechsler Memory Scale III UK. (London: The Psychological Corporation, 1998).

  65. Lezak, M.D., Howieson, D.B., Bigler, E.D. & Tranel, D. Neuropsychological Assessment (Oxford University Press, 2004).

  66. Raven, J.C., Court, J.H. & Raven, J. Manual for Raven's Progressive Matrices and Vocabulary Scales (H.K. Lewis, 1977).

  67. Johnson, W., Bouchard, T.J., Krueger, R.F., McGue, M. & Gottesman, I.I. Just one g: consistent results from three test batteries. Intelligence 32, 95–107 (2004).

    Google Scholar 

  68. Johnson, W., Nijenhuis, J.T. & Bouchard, T.J. Still just 1 g: consistent results from five test batteries. Intelligence 36, 81–95 (2008).

    Google Scholar 

  69. Smith, B.H. et al. Generation Scotland: the Scottish Family Health Study; a new resource for researching genes and heritability. BMC Med. Genet. 7, 74 (2006).

    PubMed  PubMed Central  Google Scholar 

  70. Kerr, S.M. et al. Pedigree and genotyping quality analyses of over 10,000 DNA samples from the Generation Scotland: Scottish Family Health Study. BMC Med. Genet. 14, 38 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Pruitt, K.D. et al. The consensus coding sequence (CCDS) project: identifying a common protein-coding gene set for the human and mouse genomes. Genome Res. 19, 1316–1323 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Girard, S.L. et al. Increased exonic de novo mutation rate in individuals with schizophrenia. Nat. Genet. 43, 860–863 (2011).

    CAS  PubMed  Google Scholar 

  75. Girard, S.L. et al. Mutation burden of rare variants in schizophrenia candidate genes. PLoS One 10, e0128988 (2015).

    PubMed  PubMed Central  Google Scholar 

  76. de Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367, 1921–1929 (2012).

    CAS  PubMed  Google Scholar 

  77. Hamdan, F.F. et al. De novo mutations in moderate or severe intellectual disability. PLoS Genet. 10, e1004772 (2014).

    PubMed  PubMed Central  Google Scholar 

  78. Allen, A.S. et al. De novo mutations in epileptic encephalopathies. Nature 501, 217–221 (2013).

    CAS  PubMed  Google Scholar 

  79. EuroEPINOMICS-RES. Consortium, Epilepsy Phenome/Genome Project & Epi4K Consortium. De novo mutations in synaptic transmission genes including DNM1 cause epileptic encephalopathies. Am. J. Hum. Genet. 95, 360–370 (2014).

  80. Kumar, P., Henikoff, S. & Ng, P.C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

    CAS  PubMed  Google Scholar 

  81. Adzhubei, I.A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge funding from UK Imperial National Institute for Health Research Biomedical Research Centre (M.R.J.), the UK Medical Research Council (M.R.J., E.P. and D.S.), The National Genome Research Network (NGFNplus: EMINet, grant 01GS08122; A.J.B.), EuroEpinomics (A.J.B.), UCB Pharma (M.R.J. and E.P.) and the Singapore Ministry of Health (E.P.). We thank the Lothian Birth Cohort 1936 research team for data collection and collation. The Lothian Birth Cohort 1936 is supported by Age UK (Disconnected Mind project). The work at The University of Edinburgh was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1). We acknowledge funding from the Biotechnology and Biological Sciences Research Council and MRC. Generation Scotland received core funding from the Chief Scientist Office of the Scottish Government Health Directorate CZD/16/6 and the Scottish Funding Council HR03006. Genotyping of the GS:SFHS samples was carried out by staff at the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the UK's Medical Research Council. We thank all the families who took part, the general practitioners and the Scottish School of Primary Care for their help in recruiting them, and the whole Generation Scotland team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, healthcare assistants and nurses. A.D.-D. was supported by a Marie Curie Intra European Fellowship within the 7th European Community Framework Programme.

Author information

Authors and Affiliations

Authors

Contributions

M.R.J. and E.P. conceived, designed and coordinated the study. K.S., P.S. and A.D.-D. carried out network and comparative genomics analyses. S.P. and O.J.L.R. carried out RVIS analysis. S.R.L. carried out GWAS-enrichment analysis with support from A.K., D.S. and W.D.H., A.D.-D. carried out enrichment analysis of neuropsychiatric de novo mutations with support from S.P. and K.S. M.R., A.V., M.F., L.B., T.R. and A.M.-M. provided technical support and contributed to methodology. M.M., P.F., B.D. and R.M.K. contributed mouse RNA-seq data. C.H., A.G. and A.J.B. contributed human hippocampus expression data and clinical information. I.J.D., W.D.H., G.D., S.E.H., C.H., D.J.P., B.H.S., S.P., L.J.H., J.M.S. and D.C.L. contributed GWAS data for the cognitive phenotypes. O.J.L.R. designed and implemented the web server. M.R.J. and E.P. wrote and revised the manuscript with input from K.S., S.R.L., A.D.-D., P.S., W.D.H. and I.J.D.

Corresponding authors

Correspondence to Michael R Johnson or Enrico Petretto.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Schematic overview of study design

We hypothesized that gene regulatory networks starting from the human hippocampus could be informative for genes and pathways relevant to diverse cognitive abilities and neurodevelopmental disease. Hippocampus gene-regulatory networks were inferred a priori, without reference to cognitive phenotypes using fresh-frozen hippocampus samples that had been surgically removed from 122 patients undergoing temporal lobectomy for epilepsy. Conservation of modules was then tested using distinct expression datasets from non-diseased post-mortem hippocampus samples (n=63) and healthy mouse hippocampus samples (n=100). From this, we identified 4 cross-species conserved hippocampal gene co- expression modules whose co-expression relationships are unrelated to epilepsy (M1, M3, M11 and M19). We then used independent gene expression datasets to show that these modules were conserved widely across the human cortex and expressed under tight developmental regulation during brain development. The modules were then integrated with GWAS data relating to four cognitive abilities (general fluid cognitive ability, processing speed, crystalized cognitive ability and verbal delayed recall) in two independent community cohorts (GS:SFHS and LBC1936), and de novo mutation data from parent-offspring trios across five neurodevelopmental disorders (ASD, SCZ, ID, EE, DDD) to reveal convergent gene co-expression modules for healthy human cognitive abilities and neurodevelopmental disease. Full details relating to datasets, experimental methods and references are provided in the manuscript. TLE=temporal lobe epilepsy; ASD=autism spectrum disorder; SCZ=schizophrenia; ID=intellectual disability; DDD=Deciphering Development Disorders Cohort (http://www.ddduk.org). UKBEC=UK Brain Expression Consortium (http://www.braineac.org). WES= whole-exome sequencing.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1 (PDF 838 kb)

Supplementary Methods Checklist (PDF 382 kb)

Supplementary Table 1: Details of temporal lobe epilepsy (TLE) patients.

Values in the table are actual numbers with percentages indicated in brackets. Genome-wide expression data were generated from snap frozen (liquid nitrogen) whole human hippocampus samples surgically resected en bloc. All samples were resected at a single neurosurgical centre. Clinical data recorded for each patient included; date of birth, gender handedness, age at epilepsy onset, laterality of TLE as determined by imaging and ictal EEG criteria, operation date, age at operation, pre-operative seizure type/s and frequency, antiepilepsy drug (AED) therapy and pathology. All patients underwent detailed neuropsychological testing prior to surgery (see text for details). (XLSX 10 kb)

Supplementary Table 2: Probe annotations and gene lists for the WGCNA-inferred human surgical hippocampus gene co-expression modules.

Clusters are arranged by name. Probes that are not part of any cluster are listed at the end. See Methods for details relating to data generation and network construction. (XLSX 1079 kb)

Supplementary Table 3: Summary statistics for each of the 24 human hippocampal co-expression networks.

*, average of pairwise biweight correlations between expression of all genes in the module. **, proportion of genes from the module represented in replication dataset (for mouse brain expression, only one-to-one orthologues were considered). ***, P-values for the significance of the co-expression relationships in post-mortem human hippocampus and healthy mouse hippocampus were calculated by comparing the average topological overlap for network genes to the average connectivity in 10,000 randomly sampled networks with the same size (see Methods for details). Bold, significant after Bonferroni correction for the number of modules (24) (P=0.002). Boxed modules are preserved across all three hippocampus datasets (i.e., human surgical, human post-mortem and mouse). (XLSX 48 kb)

Supplementary Table 4: Conservation of co-expression for M1 and M3 genes in occipital cortex, cerebellum, temporal cortex, frontal cortex.

Expression data from UK Brain Expression Consortium (Ramasamy, A., Trabzuni, D., Guelfi, S., Varghese, V., Smith, C., Walker, R., … Weale, M. E. (2014). Genetic variability in the regulation of gene expression in ten regions of the human brain. Nature Neuroscience, 17(10), 1418–1428. doi:10.1038/nn.3801). Significance of conservation of co-expression relationships was assessed by empirical P-values, which were calculated by comparing the strength of gene co-expression between the module genes and 10,000 randomly sampled networks of the same size. P-values passing Bonferroni correction for the number of modules and brain regions analyzed are highlighted in bold. (XLSX 48 kb)

Supplementary Table 5: Functional enrichments for the 24 hippocampal gene co-expression networks.

For each module (M1-M24) identified by WGCNA in human hippocampus the functional enrichment statistics were calculated with respect to a background set of 11,837 microarray- expressed probes (representing 9,616 protein coding genes, see Methods) used to infer the networks. The WebGestalt tool (Wang et al. Nucleic Acids Res. 2013 41, W77–83) was used to test the enrichment for (GO) categories (biological processes, cellular components and molecular functions) and pathways (“KEGG” and “Pathway common” databases) in module genes. In all cases, only significant enrichments are reported (Benjamini-Hochberg (BH) corrected P<0.05); colours represent the significance of the enrichment. (XLSX 127 kb)

Supplementary Table 6: Detailed annotation of Module 3 genes from UNIPROT function and Entrez gene summary.

Bold highlights genes with function potentially relating to neural activity. (XLSX 72 kb)

Supplementary Table 7: Enrichment of association of M1 and M3 with non-cognitive phenotype.

Waist-hip ratio (n=15,234 subjects), fasting glucose homeostasis (n=69,395 subjects), glucose challenge homeostasis (n=242,530 subjects), systolic blood pressure (n=46,186 subjects) and diastolic blood pressure (n=69,395 subjects). *, Number of genes in the module with ≥1 genotyped SNP within the transcription start and end positions of the gene (NCBI36, hg18). **, P-value for enrichment of association. (XLSX 51 kb)

Supplementary Table 8: Results of RVIS analysis for the 24 modules identified in the human hippocampus.

RVIS, Residual Variation Intolerance Score (Petrovski et al. PLoS Genet 2013) was assessed as described in Supplementary Methods. Grey, modules preserved in the mammalian hippocampus and unrelated to epilepsy (Fig. 1a). The Mann-Whitney U test (2-tailed) was used to compare the distribution of genic RVIS scores for each module to the distribution from the rest of the protein-coding genes outside the 24 modules and expressed in the human hippocampus (n=8,414 with CCDS) (see Methods for additional details). Sample estimate (difference in medians) represents the direction of enrichment, where negative values reflect enrichment for intolerant genes (and vice versa positive values reflect enrichment for tolerant genes). Bold, significant overrepresentation of genes intolerant to genetic variation (alpha adjusted for 24 modules, p<0.002). The four cross-species conserved hippocampal modules are highlighted in grey. (XLSX 11 kb)

Supplementary Table 9: Enrichment of neurodevelopmental de novo mutations.

Enrichment for each module is tested in two ways. Firstly, we adopt a binomial exact test, BET (two-tail) to assess whether there is excess single variant de novo mutations among cases as compared with what is expected based on the protein-coding real-estate that the collection of genes in a module takes up from the entire CCDS release 17 exome and on a length model in which DNMs occur randomly in all genes, proportional to length. The Ratio Obs/Exp is the ratio between the number of observed DNMs overlapping with the module and the expected number based on this model. Subsequently, we adopt a Fisher's exact test, FET (two-tail) to empirically compare the rates of overlap between case- and control-ascertained de novo mutations for the module. See Methods for additional details. Significant P-values are highlighted in bold. Empirical P-values of enrichment test for reported single nucleotide variant (SNV) DNMs reflecting loss-of-function DNM (only nonsense and splice-site mutations), predicted deleterious DNM (loss-of-function DNM and missense DNM with SIFT score < or = 0.05 and Polyphen2 score > or = 0.5), non synonymous DNM (all missense, nonsense and splice-site mutations), and synonymous DNM. *Subset of module genes that are CCDS release 17 genes, as filtered for DNM comparisons. Autism spectrum disorder (ASD), epileptic encephalopathy (EE), intellectual disability (ID), schizophrenia (SCZ), Combined = ASD+EE+ID+SCZ combined, Deciphering Developmental Disorders (DDD). The DNMs of the DDD study were not combined with the other neurospychiatric disorders as some of the patients of the DDD study present with congenital abnormalies without neuropsychiatric features. Background genes are all expressed genes in the surgical hippocampus (n=9,616), including those genes that contribute to the individual modules. (XLSX 60 kb)

Supplementary Table 10: Enrichment of association of M1 and M3 with phenotypes relating to the Psychiatric Genomics Consortium (PGC) traits.

Attention deficit-hyperactivity disorder (ADHD), bipolar disorder (BP), major depressive disorder (MDD) and schizophrenia (SCZ), as well as GWAS data relating to common forms of epilepsy (EPI) from the International League Against Epilepsy (ILAE) Consortium on Complex Epilepsies and Alzheimer's disease (AD) from the GenADA study (see manuscript for details and GWAS references). Bold, significant GWAS enrichment after Bonferroni correction for the number of modules and phenotypes tested. * Genes in the module with ≥1 genotyped SNP within the transcription start and end positions of the gene (NCBI36, hg18). ** Empirical P-value for enrichment of association determined by 100,000 bootstrap samples. (XLSX 56 kb)

Supplementary Code

R code for the main analytical functions. (TXT 8 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Johnson, M., Shkura, K., Langley, S. et al. Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease. Nat Neurosci 19, 223–232 (2016). https://doi.org/10.1038/nn.4205

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4205

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing