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.

  • Letter
  • Published:

Common genetic variants influence human subcortical brain structures

Abstract

The highly complex structure of the human brain is strongly shaped by genetic influences1. Subcortical brain regions form circuits with cortical areas to coordinate movement2, learning, memory3 and motivation4, and altered circuits can lead to abnormal behaviour and disease2. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume5 and intracranial volume6. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10−33; 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

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

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Common genetic variants associated with subcortical volumes and the ICV.
Figure 2: Effect of rs945270 on KTN1 expression and putamen shape.

Similar content being viewed by others

References

  1. Blokland, G. A., de Zubicaray, G. I., McMahon, K. L. & Wright, M. J. Genetic and environmental influences on neuroimaging phenotypes: a meta-analytical perspective on twin imaging studies. Twin Res. Hum. Genet. 15, 351–371 (2012)

    Article  Google Scholar 

  2. Kravitz, A. V. et al. Regulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry. Nature 466, 622–626 (2010)

    Article  CAS  ADS  Google Scholar 

  3. Poldrack, R. A. et al. Interactive memory systems in the human brain. Nature 414, 546–550 (2001)

    Article  CAS  Google Scholar 

  4. Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J. & Frith, C. D. Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Nature 442, 1042–1045 (2006)

    Article  CAS  ADS  Google Scholar 

  5. Stein, J. L. et al. Identification of common variants associated with human hippocampal and intracranial volumes. Nature Genet. 44, 552–561 (2012)

    Article  CAS  ADS  Google Scholar 

  6. Ikram, M. A. et al. Common variants at 6q22 and 17q21 are associated with intracranial volume. Nature Genet. 44, 539–544 (2012)

    Article  CAS  Google Scholar 

  7. Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010)

    Article  CAS  ADS  Google Scholar 

  8. Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature Genet. 42, 937–948 (2010)

    Article  CAS  Google Scholar 

  9. van der Sluis, S., Posthuma, D. & Dolan, C. V. TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLoS Genet. 9, e1003235 (2013)

    Article  CAS  Google Scholar 

  10. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014)

  11. Kumar, J., Yu, H. & Sheetz, M. P. Kinectin, an Essential Anchor for Kinesin-Driven Vesicle Motility. Science 267, 1834–1837 (1995)

    Article  CAS  ADS  Google Scholar 

  12. Hamasaki, T., Goto, S., Nishikawa, S. & Ushio, Y. A role of netrin-1 in the formation of the subcortical structure striatum: repulsive action on the migration of late-born striatal neurons. J. Neurosci. 21, 4272–4280 (2001)

    Article  CAS  Google Scholar 

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

    Article  CAS  ADS  Google Scholar 

  14. Motoyama, N. et al. Massive cell death of immature hematopoietic cells and neurons in Bcl-x-deficient mice. Science 267, 1506–1510 (1995)

    Article  CAS  ADS  Google Scholar 

  15. Itoh, K. et al. Apoptosis in the basal ganglia of the developing human nervous system. Acta Neuropathol. 101, 92–100 (2001)

    CAS  PubMed  Google Scholar 

  16. Scannevin, R. H. & Huganir, R. L. Postsynaptic organization and regulation of excitatory synapses. Nature Rev. Neurosci. 1, 133–141 (2000)

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. 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)

    Article  CAS  Google Scholar 

  19. Bis, J. C. et al. Common variants at 12q14 and 12q24 are associated with hippocampal volume. Nature Genet. 44, 545–551 (2012)

    Article  CAS  Google Scholar 

  20. Deans, M. R. et al. Control of neuronal morphology by the atypical cadherin Fat3. Neuron 71, 820–832 (2011)

    Article  CAS  Google Scholar 

  21. Stefansson, H. et al. A common inversion under selection in Europeans. Nature Genet. 37, 129–137 (2005)

    Article  CAS  Google Scholar 

  22. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nature Methods 9, 215–216 (2012)

    Article  CAS  Google Scholar 

  23. Ziebarth, J. D., Bhattacharya, A. & Cui, Y. CTCFBSDB 2.0: a database for CTCF-binding sites and genome organization. Nucleic Acids Res. 41, D188–D194 (2013)

    Article  CAS  Google Scholar 

  24. 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  Google Scholar 

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

    Article  CAS  Google Scholar 

  26. Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nature Genet. 45, 1238–1243 (2013)

    Article  CAS  Google Scholar 

  27. Toyoshima, I. & Sheetz, M. P. Kinectin distribution in chicken nervous system. Neurosci. Lett. 211, 171–174 (1996)

    Article  CAS  Google Scholar 

  28. Zhang, X. et al. Kinectin-mediated endoplasmic reticulum dynamics supports focal adhesion growth in the cellular lamella. J. Cell Sci. 123, 3901–3912 (2010)

    Article  CAS  Google Scholar 

  29. Cohen, M. X., Schoene-Bake, J. C., Elger, C. E. & Weber, B. Connectivity-based segregation of the human striatum predicts personality characteristics. Nature Neurosci. 12, 32–34 (2009)

    Article  CAS  Google Scholar 

  30. Parent, A. & Hazrati, L. N. Functional anatomy of the basal ganglia. I. The cortico-basal ganglia-thalamo-cortical loop. Brain Res. Brain Res. Rev. 20, 91–127 (1995)

    Article  CAS  Google Scholar 

  31. Patenaude, B., Smith, S. M., Kennedy, D. N. & Jenkinson, M. A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56, 907–922 (2011)

    Article  Google Scholar 

  32. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002)

    Article  CAS  Google Scholar 

  33. Morey, R. A. et al. Scan-rescan reliability of subcortical brain volumes derived from automated segmentation. Hum. Brain Mapp. 31, 1751–1762 (2010)

    PubMed  PubMed Central  Google Scholar 

  34. 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  Google Scholar 

  35. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nature Genet. 44, 955–959 (2012)

    Article  CAS  Google Scholar 

  36. Abecasis, G. R., Cherny, S. S., Cookson, W. O. & Cardon, L. R. Merlin-rapid analysis of dense genetic maps using sparse gene flow trees. Nature Genet. 30, 97–101 (2002)

    Article  CAS  Google Scholar 

  37. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010)

    Article  CAS  Google Scholar 

  38. Nyholt, D. R. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am. J. Hum. Genet. 74, 765–769 (2004)

    Article  CAS  Google Scholar 

  39. Boker, S. et al. OpenMx: an open source extended structural equation modeling framework. Psychometrika 76, 306–317 (2011)

    Article  MathSciNet  Google Scholar 

  40. Walters, R., Bartels, M. & Lubke, G. Estimating variance explained by all variants in meta-analysis with heterogeneity. Behav. Genet. 43, 543 (2013)

    Google Scholar 

  41. So, H. C., Li, M. & Sham, P. C. Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study. Genet. Epidemiol. 35, 447–456 (2011)

    Article  Google Scholar 

  42. Li, M. X., Gui, H. S., Kwan, J. S. & Sham, P. C. GATES: a rapid and powerful gene-based association test using extended Simes procedure. Am. J. Hum. Genet. 88, 283–293 (2011)

    Article  CAS  Google Scholar 

  43. Li, M. X., Kwan, J. S. & Sham, P. C. HYST: a hybrid set-based test for genome-wide association studies, with application to protein-protein interaction-based association analysis. Am. J. Hum. Genet. 91, 478–488 (2012)

    Article  CAS  Google Scholar 

  44. 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  Google Scholar 

  45. 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  Google Scholar 

  46. Gutman, B. A. et al. Maximizing power to track Alzheimer’s disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features. Neuroimage 70, 386–401 (2013)

    Article  Google Scholar 

  47. Gutman, B. A., Wang, Y. L., Rajagopalan, P., Toga, A. W. & Thompson, P. M. Shape matching with medial curves and 1-d group-wise registration. In 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). 716–719 (2012)

  48. Schumann, G. et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol. Psychiatry 15, 1128–1139 (2010)

    Article  CAS  Google Scholar 

  49. Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010)

    Article  CAS  Google Scholar 

  50. Cheung, I. et al. Developmental regulation and individual differences of neuronal H3K4me3 epigenomes in the prefrontal cortex. Proc. Natl Acad. Sci. USA 107, 8824–8829 (2010)

    Article  CAS  ADS  Google Scholar 

  51. Maunakea, A. K. et al. Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature 466, 253–257 (2010)

    Article  CAS  ADS  Google Scholar 

  52. Boyle, A. P. et al. High-resolution mapping and characterization of open chromatin across the genome. Cell 132, 311–322 (2008)

    Article  CAS  Google Scholar 

  53. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011)

    Article  CAS  ADS  Google Scholar 

  54. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999)

    Article  CAS  Google Scholar 

  55. Hager, R., Lu, L., Rosen, G. D. & Williams, R. W. Genetic architecture supports mosaic brain evolution and independent brain-body size regulation. Nat. Commun. 3, 1079 (2012)

    Article  ADS  Google Scholar 

  56. Schmucker, D. & Chen, B. Dscam and DSCAM: complex genes in simple animals, complex animals yet simple genes. Genes Dev. 23, 147–156 (2009)

    Article  CAS  Google Scholar 

  57. Brunet, A., Datta, S. R. & Greenberg, M. E. Transcription-dependent and -independent control of neuronal survival by the PI3K-Akt signaling pathway. Curr. Opin. Neurobiol. 11, 297–305 (2001)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Funding sources for contributing sites and acknowledgments of contributing consortia authors can be found in Supplementary Note 3.

Author information

Authors and Affiliations

Authors