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.

RETRACTED ARTICLE: Detection and replication of epistasis influencing transcription in humans

This article was retracted on 11 August 2021

Matters Arising to this article was published on 11 August 2021

This article has been updated

Abstract

Epistasis is the phenomenon whereby one polymorphism’s effect on a trait depends on other polymorphisms present in the genome. The extent to which epistasis influences complex traits1 and contributes to their variation2,3 is a fundamental question in evolution and human genetics. Although often demonstrated in artificial gene manipulation studies in model organisms4,5, and some examples have been reported in other species6, few examples exist for epistasis among natural polymorphisms in human traits7,8. Its absence from empirical findings may simply be due to low incidence in the genetic control of complex traits2,3, but an alternative view is that it has previously been too technically challenging to detect owing to statistical and computational issues9. Here we show, using advanced computation10 and a gene expression study design, that many instances of epistasis are found between common single nucleotide polymorphisms (SNPs). In a cohort of 846 individuals with 7,339 gene expression levels measured in peripheral blood, we found 501 significant pairwise interactions between common SNPs influencing the expression of 238 genes (P < 2.91 × 10−16). Replication of these interactions in two independent data sets11,12 showed both concordance of direction of epistatic effects (P = 5.56 × 10−31) and enrichment of interaction P values, with 30 being significant at a conservative threshold of P < 9.98 × 10−5. Forty-four of the genetic interactions are located within 5 megabases of regions of known physical chromosome interactions13 (P = 1.8 × 10−10). Epistatic networks of three SNPs or more influence the expression levels of 129 genes, whereby one cis-acting SNP is modulated by several trans-acting SNPs. For example, MBNL1 is influenced by an additive effect at rs13069559, which itself is masked by trans-SNPs on 14 different chromosomes, with nearly identical genotype–phenotype maps for each cistrans interaction. This study presents the first evidence, to our knowledge, for many instances of segregating common polymorphisms interacting to influence human traits.

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: Replication of genotype–phenotype maps in two independent populations.
Figure 2: Q-Q plots of interaction P values from replication data sets.
Figure 3: Discovery and replication of epistatic networks.

Accession codes

Accessions

Gene Expression Omnibus

Data deposits

Gene expression data is available at the Gene Expression Omnibus under accession code GSE53195.

Change history

References

  1. Carlborg, O. & Haley, C. S. Epistasis: too often neglected in complex trait studies? Nature Rev. Genetics 5, 618–625 (2004)

    CAS  Article  Google Scholar 

  2. Hill, W. G., Goddard, M. E. & Visscher, P. M. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 4, e1000008 (2008)

    Article  Google Scholar 

  3. Crow, J. F. On epistasis: why it is unimportant in polygenic directional selection. Phil. Trans. R. Soc. B 365, 1241–1244 (2010)

    Article  Google Scholar 

  4. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010)

    ADS  CAS  Article  Google Scholar 

  5. Bloom, J. S., Ehrenreich, I. M., Loo, W. T., Lite, T.-L. V. & Kruglyak, L. Finding the sources of missing heritability in a yeast cross. Nature 234–237 (2013)

  6. Carlborg, O., Jacobsson, L., Ahgren, P., Siegel, P. & Andersson, L. Epistasis and the release of genetic variation during long-term selection. Nature Genetics 38, 418–420 (2006)

    CAS  Article  Google Scholar 

  7. Strange, A. et al. A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1. Nature Genetics 42, 985–990 (2010)

    CAS  Article  Google Scholar 

  8. Evans, D. M. et al. Interaction between ERAP1 and HLA-B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA-B27 in disease susceptibility. Nature Genetics 43, (2011)

  9. Cordell, H. J. Detecting gene–gene interactions that underlie human diseases. Nature Rev. Genetics 10, 392–404 (2009)

    CAS  Article  Google Scholar 

  10. Hemani, G., Theocharidis, A., Wei, W. & Haley, C. EpiGPU: exhaustive pairwise epistasis scans parallelized on consumer level graphics cards. Bioinformatics 27, 1462–1465 (2011)

    CAS  Article  Google Scholar 

  11. Metspalu, A. The Estonian Genome Project. Drug Dev. Res. 62, 97–101 (2004)

    CAS  Article  Google Scholar 

  12. Fehrmann, R. S. N. et al. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genetics 7, e1002197 (2011)

    CAS  Article  Google Scholar 

  13. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009)

    ADS  CAS  Article  Google Scholar 

  14. Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012)

    CAS  Article  Google Scholar 

  15. Weinreich, D. M., Delaney, N. F., Depristo, M., a & Hartl, D. L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006)

    ADS  CAS  Article  Google Scholar 

  16. Breen, M. S., Kemena, C., Vlasov, P. K., Notredame, C. & Kondrashov, F. a Epistasis as the primary factor in molecular evolution. Nature 490, 535–538 (2012)

    ADS  CAS  Article  Google Scholar 

  17. Weir, B. S. Linkage disequilibrium and association mapping. Annu. Rev. Genomics Hum. Genet. 9, 129–142 (2008)

    CAS  Article  Google Scholar 

  18. Hemani, G., Knott, S. & Haley, C. An evolutionary perspective on epistasis and the missing heritability. PLoS Genet. 9, e1003295 (2013)

    CAS  Article  Google Scholar 

  19. Marchini, J., Donnelly, P. & Cardon, L. R. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nature Genet. 37, 413–417 (2005)

    CAS  Article  Google Scholar 

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

    ADS  CAS  Article  Google Scholar 

  21. Schadt, E. E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003)

    ADS  CAS  Article  Google Scholar 

  22. Powell, J. E. et al. Congruence of additive and non-additive effects on gene expression estimated from pedigree and SNP data. PLoS Genet. 9, e1003502 (2013)

    CAS  Article  Google Scholar 

  23. Powell, J. E. et al. The Brisbane Systems Genetics Study: genetical genomics meets complex trait genetics. PLoS ONE 7, e35430 (2012)

    ADS  CAS  Article  Google Scholar 

  24. Preininger, M. et al. Blood-informative transcripts define nine common axes of peripheral blood gene expression. PLoS Genet. 9, e1003362 (2013)

    CAS  Article  Google Scholar 

  25. Cockerham, C. C. An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics 39, 859–882 (1954)

    CAS  Article  Google Scholar 

  26. Ho, T. H. et al. Muscleblind proteins regulate alternative splicing. EMBO J. 23, 3103–3112 (2004)

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  28. Hoffman, M. M., Buske, O., Wang, J. & Weng, Z. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nature Methods 9, 473–476 (2012)

    CAS  Article  Google Scholar 

  29. Lan, X. et al. Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages. Nucleic Acids Res. 40, 7690–7704 (2012)

    CAS  Article  Google Scholar 

  30. Rieder, D., Trajanoski, Z. & McNally, J. G. Transcription factories. Front. Genet. 3, 221 (2012)

    Article  Google Scholar 

  31. Medland, S. E. et al. Common variants in the trichohyalin gene are associated with straight hair in Europeans. Am. J Hum. Genet. 85, 750–755 (2009)

    CAS  Article  Google Scholar 

  32. Aulchenko, Y. S., Ripke, S., Isaacs, A. & van Duijn, C. M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007)

    CAS  Article  Google Scholar 

  33. Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nature Genet. 43, 519–525 (2011)

    CAS  Article  Google Scholar 

  34. Westra, H.-J. et al. MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects. Bioinformatics 27, 2104–2111 (2011)

    CAS  Article  Google Scholar 

  35. Williams, D. A. Improved likelihood ratio tests for complete contingency tables. Biometrika 63, 33–37 (1976)

    MathSciNet  Article  Google Scholar 

  36. Álvarez-Castro, J. M., Le Rouzic, A. & Carlborg, O. How to perform meaningful estimates of genetic effects. PLoS Genet. 4, e1000062 (2008)

    Article  Google Scholar 

  37. Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013)

    ADS  CAS  Article  Google Scholar 

  38. Stormo, G. D. DNA binding sites: representation and discovery. Bioinformatics 16, 16–23 (2000)

    CAS  Article  Google Scholar 

  39. Ho Sui, S. J. et al. oPOSSUM: identification of over-represented transcription factor binding sites in co-expressed genes. Nucleic Acids Res. 33, 3154–3164 (2005)

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to the volunteers for their participation in these studies. We thank B. Hill, C. Haley and L. Ronnegard for discussions and comments. This work could not have been completed without access to high performance GPGPU compute clusters. We acknowledge iVEC for the use of advanced computing resources located at iVEC@UWA (http://www.ivec.org), and the Multi-modal Australian Sciences Imaging and Visualisation Environment (MASSIVE) (http://www.massive.org.au). We also thank J. Carroll and I. Porebski from the Queensland Brain Institute Information Technology Group for HPC support. The University of Queensland group is supported by the Australian National Health and Medical Research Council (NHMRC) grants 389892, 496667, 613601, 1010374 and 1046880, the Australian Research Council (ARC) grant (DE130100691), and by National Institutes of Health (NIH) grants GM057091 and GM099568. The QIMR researchers acknowledge funding from the Australian National Health and Medical Research Council (grants 241944, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 496688 and 552485), and the National Institutes of Health (grants AA07535, AA10248, AA014041, AA13320, AA13321, AA13326 and DA12854). We thank A. Caracella and L. Bowdler for technical assistance with the micro-array hybridisations. The CHDWB study funding support from the Georgia Institute of Technology Research Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The Fehrmann study was supported by grants from the Celiac Disease Consortium (an innovative cluster approved by the Netherlands Genomics Initiative and partly funded by the Dutch Government (grant BSIK03009)), the Netherlands Organization for Scientific Research (NWO-VICI grant 918.66.620, NWO-VENI grant 916.10.135 to L.F.), the Dutch Digestive Disease Foundation (MLDS WO11-30), and a Horizon Breakthrough grant from the Netherlands Genomics Initiative (grant 92519031 to L.F.). This project was supported by the Prinses Beatrix Fonds, VSB fonds, H. Kersten and M. Kersten (Kersten Foundation), The Netherlands ALS Foundation, and J.R. van Dijk and the Adessium Foundation. The research leading to these results has received funding from the European Communitys Health Seventh Framework Programme (FP7/2007-2013) under grant agreement 259867. The EGCUT study received targeted financing from the Estonian Government SF0180142s08, Center of Excellence in Genomics (EXCEGEN) and University of Tartu (SP1GVARENG). We acknowledge EGCUT technical personnel, especially V. Soo and S. Smit. Data analyses were carried out in part in the High Performance Computing Center of University of Tartu.

Author information

Authors and Affiliations

Authors

Contributions

G.H., J.E.P., P.M.V. and G.W.M. conceived and designed the study. G.H., J.E.P., K.S., H.-J.W. and J.Y. performed the analysis. T.E. and A.M. provided the EGCUT data. A.K.H., A.F.M., G.W.M., N.G.M. and J.E.P. provided the BSGS data. G.G. provided the CHDWB data. H.-J.W. and L.F. provided the Fehrmann data. G.H. and J.E.P. wrote the manuscript with the participation of all authors.

Corresponding author

Correspondence to Gibran Hemani.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1038/s41586-021-03766-y

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-17 and Supplementary Tables 1-5. (PDF 1070 kb)

PowerPoint slides

About this article

Cite this article

Hemani, G., Shakhbazov, K., Westra, HJ. et al. RETRACTED ARTICLE: Detection and replication of epistasis influencing transcription in humans. Nature 508, 249–253 (2014). https://doi.org/10.1038/nature13005

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature13005

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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