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.

Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights

Abstract

Genome-wide association studies (GWAS) have identified over 100 risk loci for schizophrenia, but the causal mechanisms remain largely unknown. We performed a transcriptome-wide association study (TWAS) integrating a schizophrenia GWAS of 79,845 individuals from the Psychiatric Genomics Consortium with expression data from brain, blood, and adipose tissues across 3,693 primarily control individuals. We identified 157 TWAS-significant genes, of which 35 did not overlap a known GWAS locus. Of these 157 genes, 42 were associated with specific chromatin features measured in independent samples, thus highlighting potential regulatory targets for follow-up. Suppression of one identified susceptibility gene, mapk3, in zebrafish showed a significant effect on neurodevelopmental phenotypes. Expression and splicing from the brain captured most of the TWAS effect across all genes. This large-scale connection of associations to target genes, tissues, and regulatory features is an essential step in moving toward a mechanistic understanding of GWAS.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Fig. 1: Schematic of the TWAS approach.
Fig. 2: Schizophrenia TWAS associations and polygenic effects.
Fig. 3: Chromatin TWAS associations compared with top eSNP–cQTL associations.
Fig. 4: Chromatin and schizophrenia TWAS association at PPP2R3C.
Fig. 5: Chromatin and schizophrenia TWAS association at KLC1.
Fig. 6: Suppression of endogenous mapk3 rescues the microcephaly and neuronal-proliferation phenotypes induced by overexpression of wild-type KCTD13.

Similar content being viewed by others

References

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

    Article  PubMed Central  CAS  Google Scholar 

  2. Price, A. L., Spencer, C. C. & Donnelly, P. Progress and promise in understanding the genetic basis of common diseases. Proc. R. Soc. B 282, 20151684 (2015).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  3. Soldner, F. et al. Parkinson-associated risk variant in distal enhancer of α-synuclein modulates target gene expression. Nature 533, 95–99 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Grubert, F. et al. Genetic control of chromatin states in humans involves local and distal chromosomal interactions. Cell 162, 1051–1065 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).

    Article  CAS  PubMed  Google Scholar 

  9. Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kichaev, G. et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 10, e1004722 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Won, H.-H. et al. Disproportionate contributions of select genomic compartments and cell types to genetic risk for coronary artery disease. PLoS Genet. 11, e1005622 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Degner, J. F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390–394 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. McVicker, G. et al. Identification of genetic variants that affect histone modifications in human cells. Science 342, 747–749 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kasowski, M. et al. Extensive variation in chromatin states across humans. Science 342, 750–752 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kilpinen, H. et al. Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription. Science 342, 744–747 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Waszak, S. M. et al. Population variation and genetic control of modular chromatin architecture in humans. Cell 162, 1039–1050 (2015).

    Article  CAS  PubMed  Google Scholar 

  19. Taudt, A., Colomé-Tatché, M. & Johannes, F. Genetic sources of population epigenomic variation. Nat. Rev. Genet. 17, 319–332 (2016).

    Article  CAS  PubMed  Google Scholar 

  20. Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Moyerbrailean, G. A. et al. Which genetics variants in DNase-seq footprints are more likely to alter binding? PLoS Genet. 12, e1005875 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    Article  CAS  PubMed  Google Scholar 

  25. Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wright, F. A. et al. Heritability and genomics of gene expression in peripheral blood. Nat. Genet. 46, 430–437 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Li, Y. I. et al. RNA splicing is a primary link between genetic variation and disease. Science 352, 600–604 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Nicolae, D. L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012). S1–S3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Nica, A. C. et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 6, e1000895 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Purcell, S. M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

    Article  CAS  PubMed  Google Scholar 

  35. Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Palla, L. & Dudbridge, F. A fast method that uses polygenic scores to estimate the variance explained by genome-wide marker panels and the proportion of variants affecting a trait. Am. J. Hum. Genet. 97, 250–259 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Daetwyler, H. D., Villanueva, B. & Woolliams, J. A. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3, e3395 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  PubMed Central  CAS  Google Scholar 

  39. Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Ryan, J. & Saffery, R. Crucial timing in schizophrenia: role of DNA methylation in early neurodevelopment. Genome Biol. 15, 495 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Akbarian, S. et al. The PsychENCODE project. Nat. Neurosci. 18, 1707–1712 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. van de Geijn, B., McVicker, G., Gilad, Y. & Pritchard, J. K. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 1061–1063 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Reilly, S. K. et al. Evolutionary changes in promoter and enhancer activity during human corticogenesis. Science 347, 1155–1159 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Golzio, C. et al. KCTD13 is a major driver of mirrored neuroanatomical phenotypes of the 16p11.2 copy number variant. Nature 485, 363–367 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Maillard, A. M. et al. The 16p11.2 locus modulates brain structures common to autism, schizophrenia and obesity. Mol. Psychiatry 20, 140–147 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. McCarthy, S. E. et al. Microduplications of 16p11.2 are associated with schizophrenia. Nat. Genet. 41, 1223–1227 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Migliavacca, E. et al. A potential contributory role for ciliary dysfunction in the 16p11.2 600kb BP4–BP5 pathology. Am. J. Hum. Genet. 96, 784–796 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Föcking, M. et al. Proteomic and genomic evidence implicates the postsynaptic density in schizophrenia. Mol. Psychiatry 20, 424–432 (2015).

    Article  PubMed  CAS  Google Scholar 

  49. Sibley, C. R., Blazquez, L. & Ule, J. Lessons from non-canonical splicing. Nat. Rev. Genet. 17, 407–421 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Nelson, C. E. et al. In vivo genome editing improves muscle function in a mouse model of Duchenne muscular dystrophy. Science 351, 403–407 (2016).

    Article  CAS  PubMed  Google Scholar 

  51. Li, Y. I. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151–158 (2018).

    Article  CAS  PubMed  Google Scholar 

  52. Chen, C.-Y. et al. Improved ancestry inference using weights from external reference panels. Bioinformatics 29, 1399–1406 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B. & Eskin, E. Identifying causal variants at loci with multiple signals of association. Genetics 198, 497–508 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Jordan, D. M. et al. Identification of cis-suppression of human disease mutations by comparative genomics. Nature 524, 225–229 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge M. Gandal, B. van de Geijn, A. Ko, P.-R. Loh, L. O’Connor, P. Pajukanta, and N. Zaitlen for helpful discussions. This research was funded by NIH grants F32GM106584 (A.G.), R01GM105857 (A.L.P.), R01MH109978 (A.L.P.), R01MH107649 (B.M.N.), R01MH105472 (G.E.C. and P.F.S.), R01HG009120 (B.P.), U01 MH103339-03S2 (D.H.G.), and R01 MH110927-02 (D.H.G.). H.K.F. was supported by the Fannie and John Hertz Foundation. The project described was also supported by award no. T32GM007753 from the National Institute of General Medical Sciences.This study was supported by a P50MH094268 grant (to N.K.). N.K. is suported as a distinguished Jean and George Brumley Professor. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health. We are grateful to the CommonMind Consortium and the PsychENCODE Consortium for making data publicly and readily available. Data were generated as part of the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company Limited; F. Hoffman-La Roche Ltd.; and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1-MH-075916, P50M096891, P50MH084053S1, R37MH057881, R37MH057881S1, HHSN271201300031C, AG02219, AG05138, and MH06692. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimers Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories, and the NIMH Human Brain Collection Core. CMC Leadership: P. Sklar, J. Buxbaum (Icahn School of Medicine at Mount Sinai), B. Devlin, D. Lewis (University of Pittsburgh), R. Gur, C.-G. Hahn (University of Pennsylvania), K. Hirai, H. Toyoshiba (Takeda Pharmaceuticals Company Limited), E. Domenici, L. Essioux (F. Hoffman-La Roche Ltd.), L. Mangravite, M. Peters (Sage Bionetworks), T. Lehner, and B. Lipska (NIMH). Data were generated as part of the PsychENCODE Consortium, supported by U01MH103339, U01MH103365, U01MH103392, U01MH103340, U01MH103346, R01MH105472, R01MH094714, R01MH105898, R21MH102791, R21MH105881, R21MH103877, and P50MH106934 awarded to S. Akbarian (Icahn School of Medicine at Mount Sinai), G. Crawford (Duke), S. Dracheva (Icahn School of Medicine at Mount Sinai), P. Farnham (USC), M. Gerstein (Yale), D. Geschwind (UCLA), T. M. Hyde (LIBD), A. Jaffe (LIBD), J. A. Knowles (USC), C. Liu (UIC), D. Pinto (Icahn School of Medicine at Mount Sinai), N. Sestan (Yale), P. Sklar (Icahn School of Medicine at Mount Sinai), M. State (UCSF), P. Sullivan (UNC), F. Vaccarino (Yale), S. Weissman (Yale), K. White (University of Chicago), and P. Zandi (JHU).

Author information

Authors and Affiliations

Authors

Consortia

Contributions

A.G., B.P., and A.L.P. designed the study. A.G., N.M., H.W., H.K.F., and Y.R. conducted analyses. M.K., L.S., A.S., G.E.C., D.H.G., N.K., and P.F.S. conducted and supervised experiments. The Psychiatric Genomics Consortium, S.M., B.M.N., R.A.O., M.C.O., and P.F.S. collected the data. A.G., B.P., and A.L.P. wrote the paper.

Corresponding authors

Correspondence to Alexander Gusev, Bogdan Pasaniuc or Alkes L. Price.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–39, Supplementary Tables 1, 2, 4–18 and 20–25, and Supplementary Note

Life Sciences Reporting Summary

Supplementary Tables 3 and 19

Supplementary Tables 3 and 19

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gusev, A., Mancuso, N., Won, H. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet 50, 538–548 (2018). https://doi.org/10.1038/s41588-018-0092-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-018-0092-1

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