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.

  • Original Article
  • Published:

Risk alleles of genes with monoallelic expression are enriched in gain-of-function variants and depleted in loss-of-function variants for neurodevelopmental disorders

Molecular Psychiatry volume 22, pages 1785–1794 (2017)Cite this article

Subjects

Abstract

Over 3000 human genes can be expressed from a single allele in one cell, and from the other allele—or both—in neighboring cells. Little is known about the consequences of this epigenetic phenomenon, monoallelic expression (MAE). We hypothesized that MAE increases expression variability, with a potential impact on human disease. Here, we use a chromatin signature to infer MAE for genes in lymphoblastoid cell lines and human fetal brain tissue. We confirm that across clones MAE status correlates with expression level, and that in human tissue data sets, MAE genes show increased expression variability. We then compare mono- and biallelic genes at three distinct scales. In the human population, we observe that genes with polymorphisms influencing expression variance are more likely to be MAE (P<1.1 × 10−6). At the trans-species level, we find gene expression differences and directional selection between humans and chimpanzees more common among MAE genes (P<0.05). Extending to human disease, we show that MAE genes are under-represented in neurodevelopmental copy number variants (CNVs) (P<2.2 × 10−10), suggesting that pathogenic variants acting via expression level are less likely to involve MAE genes. Using neuropsychiatric single-nucleotide polymorphism (SNP) and single-nucleotide variant (SNV) data, we see that genes with pathogenic expression-altering or loss-of-function variants are less likely MAE (P<7.5 × 10−11) and genes with only missense or gain-of-function variants are more likely MAE (P<1.4 × 10−6). Together, our results suggest that MAE genes tolerate a greater range of expression level than biallelic expression (BAE) genes, and this information may be useful in prediction of pathogenicity.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  1. Savova V, Vigneau S, Gimelbrant AA . Autosomal monoallelic expression: genetics of epigenetic diversity? Curr Opin Gene Dev 2013; 23: 642–648.

    Article  CAS  Google Scholar 

  2. Nag A, Savova V, Fung HL, Miron A, Yuan GC, Zhang K et al. Chromatin signature of widespread monoallelic expression. Elife 2013; 2: e01256.

    Article  Google Scholar 

  3. Nag A, Vigneau S, Savova V, Zwemer LM, Gimelbrant AA . Chromatin signature identifies monoallelic gene expression across mammalian cell types. G3 2015; 5: 1713–1720.

    Article  CAS  Google Scholar 

  4. Pereira JP, Girard R, Chaby R, Cumano A, Vieira P . Monoallelic expression of the murine gene encoding Toll-like receptor 4. Nat Immunol 2003; 4: 464–470.

    Article  CAS  Google Scholar 

  5. Wu H, Luo J, Yu H, Rattner A, Mo A, Wang Y et al. Cellular resolution maps of x chromosome inactivation: implications for neural development, function, and disease. Neuron 2014; 81: 103–119.

    Article  CAS  Google Scholar 

  6. Gendrel AV, Attia M, Chen CJ, Diabangouaya P, Servant N, Barillot E et al. Developmental dynamics and disease potential of random monoallelic gene expression. Dev Cell 2014; 28: 366–380.

    Article  CAS  Google Scholar 

  7. Gimelbrant A, Hutchinson JN, Thompson BR, Chess A . Widespread monoallelic expression on human autosomes. Scienc 2007; 318: 1136–1140.

    Article  CAS  Google Scholar 

  8. Eckersley-Maslin MA, Thybert D, Bergmann JH, Marioni JC, Flicek P, Spector DL . Random monoallelic gene expression increases upon embryonic stem cell differentiation. Dev Cell 2014; 28: 351–365.

    Article  CAS  Google Scholar 

  9. Kindt AS, Navarro P, Semple CA, Haley CS . The genomic signature of trait-associated variants. BMC Genomics 2013; 14: 108.

    Article  CAS  Google Scholar 

  10. Stranger BE, Raj T . Genetics of human gene expression. Curr Opin Gene Dev 2013; 23: 627–634.

    Article  CAS  Google Scholar 

  11. Bryois J, Buil A, Evans DM, Kemp JP, Montgomery SB, Conrad DF et al. Cis and trans effects of human genomic variants on gene expression. PLoS Genet 2014; 10: e1004461.

    Article  Google Scholar 

  12. Henrichsen CN, Chaignat E, Reymond A . Copy number variants, diseases and gene expression. Hum Mol Genet 2009; 18: R1–R8.

    Article  CAS  Google Scholar 

  13. Nord AS, Roeb W, Dickel DE, Walsh T, Kusenda M, O'Connor KL et al. Reduced transcript expression of genes affected by inherited and de novo CNVs in autism. Eur J Hum Genet 2011; 19: 727–731.

    Article  CAS  Google Scholar 

  14. Gamazon ER, Nicolae DL, Cox NJ . A study of CNVs as trait-associated polymorphisms and as expression quantitative trait loci. PLoS Genet 2011; 7: e1001292.

    Article  CAS  Google Scholar 

  15. Prendergast JG, Chambers EV, Semple CA . Sequence-level mechanisms of human epigenome evolution. Genome Biol Evol 2014; 6: 1758–1771.

    Article  Google Scholar 

  16. Hernando-Herraez I, Prado-Martinez J, Garg P, Fernandez-Callejo M, Heyn H, Hvilsom C et al. Dynamics of DNA methylation in recent human and great ape evolution. PLoS Genet 2013; 9: e1003763.

    Article  CAS  Google Scholar 

  17. Miller JA, Ding SL, Sunkin SM, Smith KA, Ng L, Szafer A et al. Transcriptional landscape of the prenatal human brain. Nature 2014; 508: 199–206.

    Article  CAS  Google Scholar 

  18. Savova V, Patsenker J, Vigneau S, Gimelbrant AA . dbMAE: the database of autosomal monoallelic expression. Nucleic Acids Res 2016; 44: D753–D756.

    Article  CAS  Google Scholar 

  19. Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat Biotechnol 2010; 28: 1045–1048.

    Article  CAS  Google Scholar 

  20. Li B, Dewey CN . RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 2011; 12: 323.

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  22. Paulson J, Chen C-Y, Lopes-Ramos CM, Kuijjer ML, Platig J, Sonawane AR et al. Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data. bioRxiv 2016; Available at: https://doi.org/10.1101/081802.

  23. Hicks SC, Okrah K, Paulson JN, Quackenbush J, Irizarry RA, Corrada Bravo H . Smooth Quantile Normalization. bioRxiv 2016; doi: https://doi.org/10.1101/085175.

  24. Hulse AM, Cai JJ . Genetic variants contribute to gene expression variability in humans. Genetics 2013; 193: 95–108.

    Article  CAS  Google Scholar 

  25. Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C et al. Population genomics of human gene expression. Nat Genet 2007; 39: 1217–1224.

    Article  CAS  Google Scholar 

  26. Khan Z, Ford MJ, Cusanovich DA, Mitrano A, Pritchard JK, Gilad Y . Primate transcript and protein expression levels evolve under compensatory selection pressures. Science 2013; 342: 1100–1104.

    Article  CAS  Google Scholar 

  27. Jeffries AR, Collier DA, Vassos E, Curran S, Ogilvie CM, Price J . Random or stochastic monoallelic expressed genes are enriched for neurodevelopmental disorder candidate genes. PLoS ONE 2013; 8: e85093.

    Article  Google Scholar 

  28. Mowry BJ, Gratten J . The emerging spectrum of allelic variation in schizophrenia: current evidence and strategies for the identification and functional characterization of common and rare variants. Mo Psychiatry 2013; 18: 38–52.

    Article  CAS  Google Scholar 

  29. Stefansson H, Rujescu D, Cichon S, Pietilainen OP, Ingason A, Steinberg S et al. Large recurrent microdeletions associated with schizophrenia. Nature 2008; 455: 232–236.

    Article  CAS  Google Scholar 

  30. Murdoch JD, State MW . Recent developments in the genetics of autism spectrum disorders. Curr Opin Gene Dev 2013; 23: 310–315.

    Article  CAS  Google Scholar 

  31. Cooper GM, Coe BP, Girirajan S, Rosenfeld JA, Vu TH, Baker C et al. A copy number variation morbidity map of developmental delay. Nat Genet 2011; 43: 838–846.

    Article  CAS  Google Scholar 

  32. Conrad DF, Pinto D, Redon R, Feuk L, Gokcumen O, Zhang Y et al. Origins and functional impact of copy number variation in the human genome. Nature 2010; 464: 704–712.

    Article  CAS  Google Scholar 

  33. de Smith AJ, Tsalenko A, Sampas N, Scheffer A, Yamada NA, Tsang P et al. Array CGH analysis of copy number variation identifies 1284 new genes variant in healthy white males: implications for association studies of complex diseases. Hum Mol Genet 2007; 16: 2783–2794.

    Article  CAS  Google Scholar 

  34. Park H, Kim JI, Ju YS, Gokcumen O, Mills RE, Kim S et al. Discovery of common Asian copy number variants using integrated high-resolution array CGH and massively parallel DNA sequencing. Nat Genet 2010; 42: 400–405.

    Article  CAS  Google Scholar 

  35. Perry GH, Ben-Dor A, Tsalenko A, Sampas N, Rodriguez-Revenga L, Tran CW et al. The fine-scale and complex architecture of human copy-number variation. Am J Hum Genet 2008; 82: 685–695.

    Article  CAS  Google Scholar 

  36. Rosenfeld JA, Coe BP, Eichler EE, Cuckle H, Shaffer LG . Estimates of penetrance for recurrent pathogenic copy-number variations. Genet Med 2013; 15: 478–481.

    Article  CAS  Google Scholar 

  37. Stefansson H, Meyer-Lindenberg A, Steinberg S, Magnusdottir B, Morgen K, Arnarsdottir S et al. CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature 2014; 505: 361–366.

    Article  CAS  Google Scholar 

  38. Leitersdorf E, Chakravarti A, Hobbs HH . Polymorphic DNA haplotypes at the LDL receptor locus. Am J Hum Genet 1989; 44: 409–421.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Schizophrenia Working Group of the Psychiatric Genomics C. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014; 511: 421–427.

    Article  Google Scholar 

  40. Wright FA, Sullivan PF, Brooks AI, Zou F, Sun W, Xia K et al. Heritability and genomics of gene expression in peripheral blood. Nat Genet 2014; 46: 430–437.

    Article  CAS  Google Scholar 

  41. Myers AJ, Gibbs JR, Webster JA, Rohrer K, Zhao A, Marlowe L et al. A survey of genetic human cortical gene expression. Nat Genet 2007; 39: 1494–1499.

    Article  CAS  Google Scholar 

  42. Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai SL et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet 2010; 6: e1000952.

    Article  Google Scholar 

  43. Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 2011; 478: 519–523.

    Article  CAS  Google Scholar 

  44. Webster JA, Gibbs JR, Clarke J, Ray M, Zhang W, Holmans P et al. Genetic control of human brain transcript expression in Alzheimer disease. Am J Hum Genet 2009; 84: 445–458.

    Article  CAS  Google Scholar 

  45. De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 2014; 515: 209–215.

    Article  CAS  Google Scholar 

  46. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P et al. A method and server for predicting damaging missense mutations. Nat Methods 2010; 7: 248–249.

    Article  CAS  Google Scholar 

  47. Iossifov I, O'Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 2014; 515: 216–221.

    Article  CAS  Google Scholar 

  48. Lim ET, Raychaudhuri S, Sanders SJ, Stevens C, Sabo A, MacArthur DG et al. Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron 2013; 77: 235–242.

    Article  CAS  Google Scholar 

  49. Huang N, Lee I, Marcotte EM, Hurles ME . Characterising and predicting haploinsufficiency in the human genome. PLoS Genet 2010; 6: e1001154.

    Article  Google Scholar 

  50. Samocha KE, Robinson EB, Sanders SJ, Stevens C, Sabo A, McGrath LM et al. A framework for the interpretation of de novo mutation in human disease. Nat Genet 2014; 46: 944–950.

    Article  CAS  Google Scholar 

  51. Johnson MB, Kawasawa YI, Mason CE, Krsnik Z, Coppola G, Bogdanovic D et al. Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron 2009; 62: 494–509.

    Article  CAS  Google Scholar 

  52. Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F et al. An integrated encyclopedia of DNA elements in the human genome. Nature 2012; 489: 57–74.

    Article  CAS  Google Scholar 

  53. Jeffries AR, Perfect LW, Ledderose J, Schalkwyk LC, Bray NJ, Mill J et al. Stochastic choice of allelic expression in human neural stem cells. Stem Cells 2012; 30: 1938–1947.

    Article  Google Scholar 

  54. Coe BP, Witherspoon K, Rosenfeld JA, van Bon BW, Vulto-van Silfhout AT, Bosco P et al. Refining analyses of copy number variation identifies specific genes associated with developmental delay. Nat Genet 2014; 46: 1063–1071.

    Article  CAS  Google Scholar 

  55. Olson H, Shen Y, Avallone J, Sheidley BR, Pinsky R, Bergin AM et al. Copy number variation plays an important role in clinical epilepsy. Ann Neurol 2014; 75: 943–958.

    Article  CAS  Google Scholar 

  56. Savova V, Chun S, Sohail M, McCole RB, Witwicki R, Gai L et al. Genes with monoallelic expression contribute disproportionately to genetic diversity in humans. Nat Genet 2016; 48: 231–237.

    Article  CAS  Google Scholar 

  57. Kriventseva EV, Tegenfeldt F, Petty TJ, Waterhouse RM, Simao FA, Pozdnyakov IA et al. OrthoDB v8: update of the hierarchical catalog of orthologs and the underlying free software. Nucleic Acids Res 2015; 43: D250–D256.

    Article  CAS  Google Scholar 

  58. Zwemer LM, Zak A, Thompson BR, Kirby A, Daly MJ, Chess A et al. Autosomal monoallelic expression in the mouse. Genome Biol 2012; 13: R10.

    Article  CAS  Google Scholar 

  59. Love MI, Huber W, Anders S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15: 550.

    Article  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge helpful discussion from Drs Artem Artemov, Rahul Deo, Aditi Deshpande, Ophir Klein, Aoife McLysaght, Andrey Mironov, Ludmila Pawlikowska, Clara Pereira, Jonathan Pritchard, Erika Yeh and Noah Zaitlen. We acknowledge funding from R21MH105745 (Weiss/Gimelbrant).

Author information

Authors and Affiliations

  1. Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    V Savova, S Vinogradova, D Pruss & A A Gimelbrant

  2. Department of Genetics, Harvard Medical School, Boston, MA, USA

    V Savova, S Vinogradova, D Pruss & A A Gimelbrant

  3. Department of Psychiatry and Institute for Human Genetics, University of California San Francisco, Langley Porter Psychiatric Institute, Nina Ireland Lab, San Francisco, CA, USA

    L A Weiss

Authors
  1. V Savova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S Vinogradova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D Pruss
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A A Gimelbrant
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L A Weiss
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to A A Gimelbrant or L A Weiss.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on the Molecular Psychiatry website

Supplementary information

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Savova, V., Vinogradova, S., Pruss, D. et al. Risk alleles of genes with monoallelic expression are enriched in gain-of-function variants and depleted in loss-of-function variants for neurodevelopmental disorders. Mol Psychiatry 22, 1785–1794 (2017). https://doi.org/10.1038/mp.2017.13

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/mp.2017.13

This article is cited by

Search

Advanced search

Quick links