Letter

Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins

Received:
Accepted:
Published online:

Abstract

Understanding the genetic architecture of gene expression is an intermediate step in understanding the genetic architecture of complex diseases. RNA sequencing technologies have improved the quantification of gene expression and allow measurement of allele-specific expression (ASE). ASE is hypothesized to result from the direct effect of cis regulatory variants, but a proper estimation of the causes of ASE has not been performed thus far. In this study, we take advantage of a sample of twins to measure the relative contributions of genetic and environmental effects to ASE, and we find substantial effects from gene × gene (G×G) and gene × environment (G×E) interactions. We propose a model where ASE requires genetic variability in cis, a difference in the sequence of both alleles, but where the magnitude of the ASE effect depends on trans genetic and environmental factors that interact with the cis genetic variants.

  • Subscribe to Nature Genetics for full access:

    $59

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

    et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).

  2. 2.

    et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315, 848–853 (2007).

  3. 3.

    et al. Patterns of cis regulatory variation in diverse human populations. PLoS Genet. 8, e1002639 (2012).

  4. 4.

    et al. Detection and replication of epistasis influencing transcription in humans. Nature 508, 249–253 (2014).

  5. 5.

    et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 464, 773–777 (2010).

  6. 6.

    et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).

  7. 7.

    et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

  8. 8.

    et al. Monozygotic twins discordant for 18q21.2qter deletion detected by array CGH in amniotic fluid. Eur. J. Med. Genet. 56, 502–505 (2013).

  9. 9.

    et al. Adult monozygotic twins discordant for intra-uterine growth have indistinguishable genome-wide DNA methylation profiles. Genome Biol. 14, R44 (2013).

  10. 10.

    et al. A genome-wide association study of monozygotic twin-pairs suggests a locus related to variability of serum high-density lipoprotein cholesterol. Twin Res. Hum. Genet. 15, 691–699 (2012).

  11. 11.

    et al. Gene-nutrient interactions in the metabolic syndrome: single nucleotide polymorphisms in ADIPOQ and ADIPOR1 interact with plasma saturated fatty acids to modulate insulin resistance. Am. J. Clin. Nutr. 91, 794–801 (2010).

  12. 12.

    , & Exploring gene-environment relationships in cardiovascular disease. Can. J. Cardiol. 29, 37–45 (2013).

  13. 13.

    et al. Adiponectin gene variants are associated with insulin sensitivity in response to dietary fat consumption in Caucasian men. J. Nutr. 138, 1609–1614 (2008).

  14. 14.

    et al. ADIPOQ polymorphisms, monounsaturated fatty acids, and obesity risk: the GOLDN study. Obesity (Silver Spring) 17, 510–517 (2009).

  15. 15.

    1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  16. 16.

    , & A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

  17. 17.

    et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

  18. 18.

    & Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  19. 19.

    et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

  20. 20.

    , , & Joint genetic analysis of gene expression data with inferred cellular phenotypes. PLoS Genet. 7, e1001276 (2011).

  21. 21.

    , , & GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).

  22. 22.

    Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

  23. 23.

    et al. Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories. Nat. Biotechnol. 31, 1015–1022 (2013).

  24. 24.

    et al. Quantifying RNA allelic ratios by microfluidic multiplex PCR and sequencing. Nat. Methods 11, 51–54 (2014).

  25. 25.

    & Introduction to Quantitative Genetics (Longmans Green, 1996).

  26. 26.

    et al. Single-tissue and cross-tissue heritability of gene expression via identity-by-descent in related or unrelated individuals. PLoS Genet. 7, e1001317 (2011).

  27. 27.

    R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2008).

Download references

Acknowledgements

We thank the twins for their voluntary contribution to this project. This work has been funded by European Union Framework Programme 7 grant EuroBATS (259749), which also supports A.A.B., A.B., M.N.D., D.G., A.V. and T.D.S. A.A.B. is also supported by a grant from the South-Eastern Norway Health Authority (2011060). R.D. is supported by the Wellcome Trust (098051). The Louis-Jeantet Foundation, the Swiss National Science Foundation, the European Research Council (ERC) and the US National Institutes of Health/National Institute of Mental Health GTEx grant support E.T.D. T.D.S. is a National Institute of Health Research (NIHR) senior investigator and the holder of an ERC Advanced Principal Investigator award. J.B.R. and H.F.Z. are supported by the Canadian Institutes of Health Research, Fonds de Recherche Santé du Québec and the Quebec Consortium for Drug Discovery. Most computations were performed at the Vital-IT center for high-performance computing of the Swiss Institute of Bioinformatics (SIB; http://www.vital-it.ch/). The TwinsUK study was funded by the Wellcome Trust, European Community Framework Programme 7 (2007–2013), and the NIHR Clinical Research Facility at Guy's and St Thomas' National Health Service (NHS) Foundation Trust and the NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. SNP genotyping was performed by the Wellcome Trust Sanger Institute and National Eye Institute via US National Institutes of Health/Center for Inherited Disease Research (CIDR) funding.

Author information

Affiliations

  1. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.

    • Alfonso Buil
    • , Tuuli Lappalainen
    •  & Emmanouil T Dermitzakis
  2. Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland.

    • Alfonso Buil
    • , Tuuli Lappalainen
    •  & Emmanouil T Dermitzakis
  3. Swiss Institute of Bioinformatics, Geneva, Switzerland.

    • Alfonso Buil
    • , Tuuli Lappalainen
    •  & Emmanouil T Dermitzakis
  4. Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK.

    • Andrew Anand Brown
    •  & Richard Durbin
  5. NORMENT, KG Jebsen Center for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.

    • Andrew Anand Brown
  6. Department of Genetics, Stanford University, Stanford, California, USA.

    • Tuuli Lappalainen
  7. Department of Twin Research, King's College London, London, UK.

    • Ana Viñuela
    • , Matthew N Davies
    • , J Brent Richards
    • , Daniel Glass
    • , Kerrin S Small
    •  & Timothy D Spector
  8. Department of Medicine, McGill University, Montreal, Quebec, Canada.

    • Hou-Feng Zheng
    •  & J Brent Richards
  9. Department of Human Genetics, McGill University, Montreal, Quebec, Canada.

    • Hou-Feng Zheng
    •  & J Brent Richards
  10. Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada.

    • Hou-Feng Zheng
    •  & J Brent Richards

Authors

  1. Search for Alfonso Buil in:

  2. Search for Andrew Anand Brown in:

  3. Search for Tuuli Lappalainen in:

  4. Search for Ana Viñuela in:

  5. Search for Matthew N Davies in:

  6. Search for Hou-Feng Zheng in:

  7. Search for J Brent Richards in:

  8. Search for Daniel Glass in:

  9. Search for Kerrin S Small in:

  10. Search for Richard Durbin in:

  11. Search for Timothy D Spector in:

  12. Search for Emmanouil T Dermitzakis in:

Contributions

A.B., R.D., T.D.S. and E.T.D. conceived the study. A.B., A.A.B., A.V. and M.N.D. analyzed the data. T.L. and K.S.S. contributed experimental and technical support as well as discussion. D.G. contributed to sample collection. H.F.Z. and J.B.R. contributed technical support and analyzed data. A.B. prepared the manuscript, with contributions from A.A.B. and E.T.D. All authors read and approved the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Alfonso Buil or Emmanouil T Dermitzakis.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–12 and Supplementary Tables 1–5.

CSV files

  1. 1.

    Supplementary Data Set

    Data for the comparison between the RNA-seq and mmPCR techniques.