Letter | Published:

Detection and replication of epistasis influencing transcription in humans

Nature volume 508, pages 249253 (10 April 2014) | Download Citation

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.

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Accessions

Gene Expression Omnibus

Data deposits

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

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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

Author notes

    • Grant W. Montgomery
    • , Peter M. Visscher
    •  & Joseph E. Powell

    These authors contributed equally to this work.

Affiliations

  1. Queensland Brain Institute, University of Queensland, Brisbane, Queensland 4072, Australia

    • Gibran Hemani
    • , Konstantin Shakhbazov
    • , Allan F. McRae
    • , Jian Yang
    • , Peter M. Visscher
    •  & Joseph E. Powell
  2. University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, Queensland 4072, Australia

    • Gibran Hemani
    • , Konstantin Shakhbazov
    • , Allan F. McRae
    • , Peter M. Visscher
    •  & Joseph E. Powell
  3. Department of Genetics, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands

    • Harm-Jan Westra
    •  & Lude Franke
  4. Estonian Genome Center, University of Tartu, Tartu 51010, Estonia

    • Tonu Esko
    •  & Andres Metspalu
  5. Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA

    • Tonu Esko
  6. Divisions of Endocrinology, Children's Hospital, Boston, Massachusetts 02115, USA

    • Tonu Esko
  7. Queensland Institute of Medical Research, Brisbane, Queensland 4006, Australia

    • Anjali K. Henders
    • , Nicholas G. Martin
    •  & Grant W. Montgomery
  8. School of Biology and Centre for Integrative Genomics, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

    • Greg Gibson

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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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Gibran Hemani.

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    Supplementary Information

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DOI

https://doi.org/10.1038/nature13005

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