Dynamic genetic architecture of yeast response to environmental perturbation shed light on origin of cryptic genetic variation

Autoři: Yanjun Zan aff001;  Örjan Carlborg aff001
Působiště autorů: Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden aff001
Vyšlo v časopise: Dynamic genetic architecture of yeast response to environmental perturbation shed light on origin of cryptic genetic variation. PLoS Genet 16(5): e32767. doi:10.1371/journal.pgen.1008801
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008801


Cryptic genetic variation could arise from, for example, Gene-by-Gene (G-by-G) or Gene-by-Environment (G-by-E) interactions. The underlying molecular mechanisms and how they influence allelic effects and the genetic variance of complex traits is largely unclear. Here, we empirically explored the role of environmentally influenced epistasis on the suppression and release of cryptic variation by reanalysing a dataset of 4,390 haploid yeast segregants phenotyped on 20 different media. The focus was on 130 epistatic loci, each contributing to segregant growth in at least one environment and that together explained most (69–100%) of the narrow sense heritability of growth in the individual environments. We revealed that the epistatic growth network reorganised upon environmental changes to alter the estimated marginal (additive) effects of the individual loci, how multi-locus interactions contributed to individual segregant growth and the level of expressed genetic variance in growth. The estimated additive effects varied most across environments for loci that were highly interactive network hubs in some environments but had few or no interactors in other environments, resulting in changes in total genetic variance across environments. This environmentally dependent epistasis was thus an important mechanism for the suppression and release of cryptic variation in this population. Our findings increase the understanding of the complex genetic mechanisms leading to cryptic variation in populations, providing a basis for future studies on the genetic maintenance of trait robustness and development of genetic models for studying and predicting selection responses for quantitative traits in breeding and evolution.

Klíčová slova:

Evolutionary genetics – Genetic loci – Genetic networks – Genetic polymorphism – Interaction networks – Phenotypes – Population genetics – Quantitative trait loci


1. Mackay TFC. Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nat Rev Genet. 2014;15: 22–33. doi: 10.1038/nrg3627 24296533

2. Cordell HJ. Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum Mol Genet. 2002; doi: 10.1093/hmg/11.20.2463 12351582

3. Carlborg Ö, Haley CS. Epistasis: Too often neglected in complex trait studies? Nature Reviews Genetics. 2004. pp. 618–625. doi: 10.1038/nrg1407 15266344

4. Gibson G, Dworkin I. Uncovering cryptic genetic variation. Nat Rev Genet. 2004;5: 681–690. doi: 10.1038/nrg1426 15372091

5. Des Marais DL, Hernandez KM, Juenger TE. Genotype-by-Environment Interaction and Plasticity: Exploring Genomic Responses of Plants to the Abiotic Environment. Annu Rev Ecol Evol Syst. 2013;44: 5–29. doi: 10.1146/annurev-ecolsys-110512-135806

6. Thomas D. Gene–environment-wide association studies: emerging approaches. Nat Rev Genet. 2010;11: 259–272. doi: 10.1038/nrg2764 20212493

7. Elias AA, Robbins KR, Doerge RW, Tuinstra MR. Half a Century of Studying Genotype × Environment Interactions in Plant Breeding Experiments. Crop Sci. 2016;56: 2090. doi: 10.2135/cropsci2015.01.0061

8. Forsberg SKG, Carlborg Ö. On the relationship between epistasis and genetic variance heterogeneity. J Exp Bot. Oxford University Press; 2017;68: 5431–5438. doi: 10.1093/jxb/erx283 28992256

9. Carlborg O, Jacobsson L, Ahgren P, Siegel P, Andersson L. Epistasis and the release of genetic variation during long-term selection. Nat Genet. 2006;38: 418–420. doi: 10.1038/ng1761 16532011

10. Juenger TE, Sen S, Stowe KA, Simms EL. Epistasis and genotype-environment interaction for quantitative trait loci affecting flowering time in Arabidopsis thaliana. Genetica. 2005;123: 87–105. doi: 10.1007/s10709-003-2717-1 15881683

11. Sasaki E, Zhang P, Atwell S, Meng D, Nordborg M. Missing G x E Variation Controls Flowering Time in Arabidopsis thaliana. PLOS Genet. 2015;11: e1005597. doi: 10.1371/journal.pgen.1005597 26473359

12. Smith AN, Miller L-A, Radice G, Ashery-Padan R, Lang RA. Stage-dependent modes of Pax6-Sox2 epistasis regulate lens development and eye morphogenesis. Development. 2009;136: 3377–3377. doi: 10.1242/dev.043802

13. Kerwin RE, Feusier J, Muok A, Lin C, Larson B, Copeland D, et al. Epistasis × environment interactions among Arabidopsis thaliana glucosinolate genes impact complex traits and fitness in the field. New Phytol. 2017;215: 1249–1263. doi: 10.1111/nph.14646 28608555

14. Hou J, van Leeuwen J, Andrews BJ, Boone C. Genetic Network Complexity Shapes Background-Dependent Phenotypic Expression. Trends Genet. 2018;34: 578–586. doi: 10.1016/j.tig.2018.05.006 29903533

15. Mullis MN, Matsui T, Schell R, Foree R, Ehrenreich IM. The complex underpinnings of genetic background effects. Nat Commun. 2018;9: 3548. doi: 10.1038/s41467-018-06023-5 30224702

16. Lee JT, Taylor MB, Shen A, Ehrenreich IM. Multi-locus Genotypes Underlying Temperature Sensitivity in a Mutationally Induced Trait. Siegal ML, editor. PLOS Genet. 2016;12: e1005929. doi: 10.1371/journal.pgen.1005929 26990313

17. Bhatia A, Yadav A, Zhu C, Gagneur J, Radhakrishnan A, Steinmetz LM, et al. Yeast Growth Plasticity Is Regulated by Environment-Specific Multi-QTL Interactions. G3 Genes|Genomes|Genetics. 2014;4: 769–777. doi: 10.1534/g3.113.009142 24474169

18. Yadav A, Sinha H. Gene-gene and gene-environment interactions in complex traits in yeast. Yeast. 2018;35: 403–416. doi: 10.1002/yea.3304 29322552

19. Yadav A, Dhole K, Sinha H. Differential regulation of cryptic genetic variation shapes the genetic interactome underlying complex traits. Genome Biol Evol. 2016;8: 3559–3573. doi: 10.1093/gbe/evw258 28172852

20. Flynn KM, Cooper TF, Moore FBG, Cooper VS. The Environment Affects Epistatic Interactions to Alter the Topology of an Empirical Fitness Landscape. Fay JC, editor. PLoS Genet. 2013;9: e1003426. doi: 10.1371/journal.pgen.1003426 23593024

21. Li C, Zhang J. Multi-environment fitness landscapes of a tRNA gene. Nat Ecol Evol. 2018;2: 1025–1032. doi: 10.1038/s41559-018-0549-8 29686238

22. de Vos MGJ, Poelwijk FJ, Battich N, Ndika JDT, Tans SJ. Environmental Dependence of Genetic Constraint. Hoekstra HE, editor. PLoS Genet. 2013;9: e1003580. doi: 10.1371/journal.pgen.1003580 23825963

23. Remold SK, Lenski RE. Pervasive joint influence of epistasis and plasticity on mutational effects in Escherichia coli. Nat Genet. 2004;36: 423–426. doi: 10.1038/ng1324 15072075

24. Lee JT, Coradini AL V., Shen A, Ehrenreich IM. Layers of Cryptic Genetic Variation Underlie a Yeast Complex Trait. Genetics. 2019;211: 1469–1482. doi: 10.1534/genetics.119.301907 30787041

25. Zhu C-T, Ingelmo P, Rand DM. G×G×E for Lifespan in Drosophila: Mitochondrial, Nuclear, and Dietary Interactions that Modify Longevity. Larsson N-G, editor. PLoS Genet. 2014;10: e1004354. doi: 10.1371/journal.pgen.1004354 24832080

26. Freda PJ, Ali ZM, Heter N, Ragland GJ, Morgan TJ. Stage-specific genotype-by-environment interactions for cold and heat hardiness in Drosophila melanogaster. Heredity. 2019;123: 479–491. doi: 10.1038/s41437-019-0236-9 31164731

27. Bandyopadhyay S, Mehta M, Kuo D, Sung M-K, Chuang R, Jaehnig EJ, et al. Rewiring of Genetic Networks in Response to DNA Damage. Science. 2010;330: 1385–1389. doi: 10.1126/science.1195618 21127252

28. Kuzmin E, VanderSluis B, Wang W, Tan G, Deshpande R, Chen Y, et al. Systematic analysis of complex genetic interactions. Science. 2018;360: eaao1729. doi: 10.1126/science.aao1729 29674565

29. Leung GP, Aristizabal MJ, Krogan NJ, Kobor MS. Conditional Genetic Interactions of RTT107, SLX4, and HRQ1 Reveal Dynamic Networks upon DNA Damage in S. cerevisiae. G3 Genes|Genomes|Genetics. 2014;4: 1059–1069. doi: 10.1534/g3.114.011205 24700328

30. Bloom JS, Kotenko I, Sadhu MJ, Treusch S, Albert FW, Kruglyak L. Genetic interactions contribute less than additive effects to quantitative trait variation in yeast. Nat Commun. 2015;6: 8712. doi: 10.1038/ncomms9712 26537231

31. Forsberg SKG, Bloom JS, Sadhu MJ, Kruglyak L, Carlborg Ö. Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast. Nat Genet. Nature Research; 2017;49: 497–503. doi: 10.1038/ng.3800 28250458

32. Gibson G. Decanalization and the origin of complex disease. Nat Rev Genet. 2009;10: 134–140. doi: 10.1038/nrg2502 19119265

33. Paaby AB, Rockman M V. Cryptic genetic variation: evolution’s hidden substrate. Nat Rev Genet. Nature Research; 2014;15: 247–258. doi: 10.1038/nrg3688 24614309

34. Hayden EJ, Ferrada E, Wagner A. Cryptic genetic variation promotes rapid evolutionary adaptation in an RNA enzyme. Nature. 2011;474: 92–95. doi: 10.1038/nature10083 21637259

35. Queitsch C, Sangster TA, Lindquist S. Hsp90 as a capacitor of phenotypic variation. Nature. 2002;417: 618–624. doi: 10.1038/nature749 12050657

36. Rutherford SL, Lindquist S. Hsp90 as a capacitor for morphological evolution. Nature. 1998;396: 336–342. doi: 10.1038/24550 9845070

37. Dworkin I, Palsson A, Birdsall K, Gibson G. Evidence that Egfr contributes to cryptic genetic variation for photoreceptor determination in natural populations of Drosophila melanogaster. Curr Biol. 2003;13: 1888–93. doi: 10.1016/j.cub.2003.10.001 14588245

38. Miyajima I, Nakafuku M, Nakayama N, Brenner C, Miyajima A, Kaibuchi K, et al. GPA1, a haploid-specific essential gene, encodes a yeast homolog of mammalian G protein which may be involved in mating factor signal transduction. Cell. 1987;50: 1011–1019. doi: 10.1016/0092-8674(87)90167-x 3113739

39. Bourgarel D, Nguyen C-C, Bolotin-Fukuhara M. HAP4, the glucose-repressed regulated subunit of the HAP transcriptional complex involved in the fermentation-respiration shift, has a functional homologue in the respiratory yeast Kluyveromyces lactis. Mol Microbiol. 1999;31: 1205–1215. doi: 10.1046/j.1365-2958.1999.01263.x 10096087

40. Zhang L, Hach A. Molecular mechanism of heme signaling in yeast: the transcriptional activator Hap1 serves as the key mediator. Cell Mol Life Sci. 1999;56: 415–426. doi: 10.1007/s000180050442 11212295

41. Creusot F, Verdière J, Gaisne M, Slonimski PP. CYP1 (HAP1) regulator of oxygen-dependent gene expression in yeast. J Mol Biol. 1988;204: 263–276. doi: 10.1016/0022-2836(88)90574-8 2851658

42. Sharma S, Langhendries J-L, Watzinger P, Kötter P, Entian K-D, Lafontaine DLJ. Yeast Kre33 and human NAT10 are conserved 18S rRNA cytosine acetyltransferases that modify tRNAs assisted by the adaptor Tan1/THUMPD1. Nucleic Acids Res. Oxford University Press; 2015;43: 2242–58. doi: 10.1093/nar/gkv075 25653167

43. Wickner RB. MKT1, a nonessential Saccharomyces cerevisiae gene with a temperature-dependent effect on replication of M2 double-stranded RNA. J Bacteriol. 1987;169: 4941–4945. doi: 10.1128/jb.169.11.4941-4945.1987 2822656

44. Tanaka K, Lin BK, Wood DR, Tamanoi F. IRA2, an upstream negative regulator of RAS in yeast, is a RAS GTPase-activating protein. Proc Natl Acad Sci. 1991;88: 468–472. doi: 10.1073/pnas.88.2.468 1988946

45. Albert FW, Bloom JS, Siegel J, Day L, Kruglyak L. Genetics of trans-regulatory variation in gene expression. Elife. 2018;7: e35471. doi: 10.7554/eLife.35471 30014850

46. Smith EN, Kruglyak L. Gene–Environment Interaction in Yeast Gene Expression. Mackay T, editor. PLoS Biol. 2008;6: e83. doi: 10.1371/journal.pbio.0060083 18416601

47. Lewis JA, Broman AT, Will J, Gasch AP. Genetic architecture of ethanol-responsive transcriptome variation in Saccharomyces cerevisiae strains. Genetics. Genetics Society of America; 2014;198: 369–82. doi: 10.1534/genetics.114.167429 24970865

48. Boyle EA, Li YI, Pritchard JK. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell. 2017. pp. 1177–1186. doi: 10.1016/j.cell.2017.05.038 28622505

49. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461: 747–753. doi: 10.1038/nature08494 19812666

50. Zan Y, Sheng Z, Lillie M, Rönnegård L, Honaker CF, Siegel PB, et al. Artificial Selection Response due to Polygenic Adaptation from a Multilocus, Multiallelic Genetic Architecture. Mol Biol Evol. R Found. Stat. Comput, Vienna; 2017;2: 7–10. doi: 10.1093/molbev/msx194 28957504

51. Sheng Z, Pettersson ME, Honaker CF, Siegel PB, Carlborg Ö. Standing genetic variation as a major contributor to adaptation in the Virginia chicken lines selection experiment. Genome Biol. 2015;16: 219. doi: 10.1186/s13059-015-0785-z 26438066

52. Zeileis A, Hothorn T. Diagnostic checking in regression relationships. R News. 2002;2: 7–10.

53. R Core Team. R Core Team, 2015 R: A Language and Environment for Statistical Computing. R Found. Stat. Comput. Vienna Austria URL: https://www.R-project.org/. R Foundation for Statistical Computing Vienna Austria. 2015. ISBN 3-900051-07-0

54. YU G, CHEN Y, GUO Y. Design of integrated system for heterogeneous network query terminal. J Comput Appl. 2009;29: 2191–2193. doi: 10.3724/SP.J.1087.2009.02191

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