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Predicting mutation outcome from early stochastic variation in genetic interaction partners


Many mutations, including those that cause disease, only have a detrimental effect in a subset of individuals. The reasons for this are usually unknown, but may include additional genetic variation and environmental risk factors1. However, phenotypic discordance remains even in the absence of genetic variation, for example between monozygotic twins2, and incomplete penetrance of mutations is frequent in isogenic model organisms in homogeneous environments3,4. Here we propose a model for incomplete penetrance based on genetic interaction networks5,6. Using Caenorhabditis elegans as a model system, we identify two compensation mechanisms that vary among individuals and influence mutation outcome. First, feedback induction of an ancestral gene duplicate differs across individuals, with high expression masking the effects of a mutation. This supports the hypothesis that redundancy is maintained in genomes to buffer stochastic developmental failure7. Second, during normal embryonic development we find that there is substantial variation in the induction of molecular chaperones such as Hsp90 (DAF-21). Chaperones act as promiscuous buffers of genetic variation8,9, and embryos with stronger induction of Hsp90 are less likely to be affected by an inherited mutation. Simultaneously quantifying the variation in these two independent responses allows the phenotypic outcome of a mutation to be more accurately predicted in individuals. Our model and methodology provide a framework for dissecting the causes of incomplete penetrance. Further, the results establish that inter-individual variation in both specific and more general buffering systems combine to determine the outcome inherited mutations in each individual.

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Figure 1: Genetic interactions provide a general model for incomplete penetrance.
Figure 2: Early inter-individual variation in the induction of ancestral gene duplicates predicts the outcome of inherited mutations.
Figure 3: Inter-individual variation in chaperone induction predicts the outcome of a mutation.
Figure 4: Simultaneous quantification of inter-individual variation in two buffering systems accurately predicts the outcome of a mutation.


  1. 1

    Badano, J. L. & Katsanis, N. Beyond Mendel: an evolving view of human genetic disease transmission. Nature Rev. Genet. 3, 779–789 (2002)

    CAS  Article  Google Scholar 

  2. 2

    Baranzini, S. E. et al. Genome, epigenome and RNA sequences of monozygotic twins discordant for multiple sclerosis. Nature 464, 1351–1356 (2010)

    ADS  CAS  Article  Google Scholar 

  3. 3

    Horvitz, H. R. & Sulston, J. E. Isolation and genetic characterization of cell-lineage mutants of the nematode Caenorhabditis elegans. Genetics 96, 435–454 (1980)

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

    Gartner, K. A third component causing random variability beside environment and genotype. A reason for the limited success of a 30 year long effort to standardize laboratory animals? Lab. Anim. 24, 71–77 (1990)

    CAS  Article  Google Scholar 

  5. 5

    Lehner, B., Crombie, C., Tischler, J., Fortunato, A. & Fraser, A. G. Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nature Genet. 38, 896–903 (2006)

    CAS  Article  Google Scholar 

  6. 6

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

    ADS  CAS  Article  Google Scholar 

  7. 7

    Nowak, M. A., Boerlijst, M. C., Cooke, J. & Smith, J. M. Evolution of genetic redundancy. Nature 388, 167–171 (1997)

    ADS  CAS  Article  Google Scholar 

  8. 8

    Rutherford, S. L. & Lindquist, S. Hsp90 as a capacitor for morphological evolution. Nature 396, 336–342 (1998)

    ADS  CAS  Article  Google Scholar 

  9. 9

    Queitsch, C., Sangster, T. A. & Lindquist, S. Hsp90 as a capacitor of phenotypic variation. Nature 417, 618–624 (2002)

    ADS  CAS  Article  Google Scholar 

  10. 10

    Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002)

    ADS  CAS  Article  Google Scholar 

  11. 11

    Wernet, M. F. et al. Stochastic spineless expression creates the retinal mosaic for colour vision. Nature 440, 174–180 (2006)

    ADS  CAS  Article  Google Scholar 

  12. 12

    Chang, H. H., Hemberg, M., Barahona, M., Ingber, D. E. & Huang, S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 453, 544–547 (2008)

    ADS  CAS  Article  Google Scholar 

  13. 13

    Raj, A., Rifkin, S. A., Andersen, E. & van Oudenaarden, A. Variability in gene expression underlies incomplete penetrance. Nature 463, 913–918 (2010)

    ADS  CAS  Article  Google Scholar 

  14. 14

    Eldar, A. et al. Partial penetrance facilitates developmental evolution in bacteria. Nature 460, 510–514 (2009)

    ADS  CAS  Article  Google Scholar 

  15. 15

    Lehner, B. Genes confer similar robustness to environmental, stochastic, and genetic perturbations in yeast. PLoS ONE 5, e9035 (2010)

    ADS  Article  Google Scholar 

  16. 16

    Andachi, Y. Caenorhabditis elegans T-box genes tbx-9 and tbx-8 are required for formation of hypodermis and body-wall muscle in embryogenesis. Genes Cells 9, 331–344 (2004)

    CAS  Article  Google Scholar 

  17. 17

    Pocock, R., Ahringer, J., Mitsch, M., Maxwell, S. & Woollard, A. A regulatory network of T-box genes and the even-skipped homologue vab-7 controls patterning and morphogenesis in C. elegans. Development 131, 2373–2385 (2004)

    CAS  Article  Google Scholar 

  18. 18

    Baugh, L. R. et al. Synthetic lethal analysis of Caenorhabditis elegans posterior embryonic patterning genes identifies conserved genetic interactions. Genome Biol. 6, R45 (2005)

    Article  Google Scholar 

  19. 19

    Kafri, R., Bar-Even, A. & Pilpel, Y. Transcription control reprogramming in genetic backup circuits. Nature Genet. 37, 295–299 (2005)

    CAS  Article  Google Scholar 

  20. 20

    DeLuna, A., Springer, M., Kirschner, M. W. & Kishony, R. Need-based up-regulation of protein levels in response to deletion of their duplicate genes. PLoS Biol. 8, e1000347 (2010)

    Article  Google Scholar 

  21. 21

    Koh, K. & Rothman, J. H. ELT-5 and ELT-6 are required continuously to regulate epidermal seam cell differentiation and cell fusion in C. elegans. Development 128, 2867–2880 (2001)

    CAS  PubMed  Google Scholar 

  22. 22

    Bateson, W. Mendel’s Principles of Heredity (Cambridge Univ. Press, 1909)

    Book  Google Scholar 

  23. 23

    Ow, M. C. et al. The FLYWCH transcription factors FLH-1, FLH-2, and FLH-3 repress embryonic expression of microRNA genes in C. elegans. Genes Dev. 22, 2520–2534 (2008)

    CAS  Article  Google Scholar 

  24. 24

    Vavouri, T., Semple, J. I. & Lehner, B. Widespread conservation of genetic redundancy during a billion years of eukaryotic evolution. Trends Genet. 24, 485–488 (2008)

    CAS  Article  Google Scholar 

  25. 25

    Tischler, J., Lehner, B., Chen, N. & Fraser, A. G. Combinatorial RNA interference in Caenorhabditis elegans reveals that redundancy between gene duplicates can be maintained for more than 80 million years of evolution. Genome Biol. 7, R69 (2006)

    Article  Google Scholar 

  26. 26

    Lehner, B. Conflict between noise and plasticity in yeast. PLoS Genet. 6, e1001185 (2010)

    Article  Google Scholar 

  27. 27

    Waddington, C. H. Canalization of development and the inheritance of acquired characters. Nature 150, 563–565 (1942)

    ADS  Article  Google Scholar 

  28. 28

    Bobula, J. et al. Why molecular chaperones buffer mutational damage: a case study with a yeast Hsp40/70 system. Genetics 174, 937–944 (2006)

    CAS  Article  Google Scholar 

  29. 29

    Van Dyk, T. K. G. A. LaRossa RA. Demonstration by genetic suppression of interaction of GroE products with many proteins. Nature 342, 451–453 (1989)

    ADS  CAS  Article  Google Scholar 

  30. 30

    Tokuriki, N. & Tawfik, D. S. Chaperonin overexpression promotes genetic variation and enzyme evolution. Nature 459, 668–673 (2009)

    ADS  CAS  Article  Google Scholar 

  31. 31

    Abramoff, M. D., Magelhaes, P. J. & Ram, S. J. Image processing with ImageJ. Biophoton. Int. 11, 36–42 (2004)

    Google Scholar 

  32. 32

    Wolf, D. E., Samarasekera, C. & Swedlow, J. R. Quantitative analysis of digital microscope images. Methods Cell Biol. 81, 365–396 (2007)

    Article  Google Scholar 

  33. 33

    Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941 (2005)

    CAS  Article  Google Scholar 

  34. 34

    Brenner, S. The genetics of Caenorhabditis elegans. Genetics 77, 71–94 (1974)

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Kelly, W. G., Xu, S., Montgomery, M. K. & Fire, A. Distinct requirements for somatic and germline expression of a generally expressed Caernorhabditis elegans gene. Genetics 146, 227–238 (1997)

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Mitani, S. Genetic regulation of mec-3 gene expression implicated in the specification of the mechanosensory neuron cell types in Caenorhabditis elegans. Dev. Growth Differ. 37, 551–557 (2003)

    Article  Google Scholar 

  37. 37

    Praitis, V., Casey, E., Collar, D. & Austin, J. Creation of low-copy integrated transgenic lines in Caenorhabditis elegans. Genetics 157, 1217–1226 (2001)

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Wilm, T., Demel, P., Koop, H. U., Schnabel, H. & Schnabel, R. Ballistic transformation of Caenorhabditis elegans. Gene 229, 31–35 (1999)

    CAS  Article  Google Scholar 

  39. 39

    Yamaguchi, Y., Murakami, K., Furusawa, M. & Miwa, J. Germline-specific antigens identified by monoclonal antibodies in the nematode Caenorhabditis elegans. Dev. Growth Differ. 25, 121–131 (1983)

    Article  Google Scholar 

  40. 40

    Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nature Methods 5, 877–879 (2008)

    CAS  Article  Google Scholar 

  41. 41

    Sage, D., Neumann, F. R., Hediger, F., Gasser, S. M. & Unser, M. Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics. IEEE Trans. Image Process. 14, 1372–1383 (2005)

    ADS  Article  Google Scholar 

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This work was funded by grants from the European Research Council, Institució Catalana de Recerca i Estudis Avançats, Ministerio de Ciencia e Innovación Plan Nacional BFU2008-00365, Agència de Gestió d’juts Universitaris i de Recerca, ERASysBio+, the European Molecular Biology Organization Young Investigator Programme, the EMBL-CRG Systems Biology Program, by a Formación de Personal Investigador–Ministerio de Ciencia e Innovación fellowship to A.B. and by a Beatriu de Pinós Fellowship to M.O.C. We thank I. Hope, V. Ambros and S. Kim for providing strains. Additional strains were obtained from the Caenorhabditis Genetics Center, which is funded by the National Institutes of Health National Center for Research Resources. We thank T. Zimmermann and R. García from the CRG Advanced Light Microscopy Unit for advice and assistance, J. Miwa and Y. Yamaguchi for 608F antibody, J. Semple for providing complementary DNA clones, A. Marchetti and R. García-Verdugo for technical assistance, J. Tischler and C. Kiel for advice on single-molecule fluorescence in situ hybridization and western blotting, respectively, and L. Serrano, M. Isalan and J. Semple for comments on the manuscript.

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A.B. performed all experiments, developed the method and analysed the data; M.O.C. demonstrated that increased chaperone activity can suppress mutation outcome in C. elegans; A.B. and B.L. designed experiments, conceived the model and wrote the manuscript.

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Correspondence to Ben Lehner.

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The authors declare no competing financial interests.

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Burga, A., Casanueva, M. & Lehner, B. Predicting mutation outcome from early stochastic variation in genetic interaction partners. Nature 480, 250–253 (2011).

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