A major challenge of contemporary biology is to understand how naturally occurring genetic variation causes phenotypic variation in quantitative traits. Despite the biological plausibility that genetic variation affects nonlinear networks at multiple levels of biological organization, most efforts to explain the relationship between genetic and phenotypic variation concentrate on additive effects of individual loci.
Mapping gene–gene interactions (that is, epistasis) is challenging experimentally, statistically and computationally owing to the large number of interactions to be evaluated. This number is of the order of the square of the number of single-locus tests for pairwise interactions.
Epistatic interactions for quantitative traits result in a change of either the magnitude or the direction of allelic effects at one locus, depending on the genotype at the interacting locus. With epistasis, the additive effect (that is, the main effect) of a locus changes with the allele frequency of the interacting locus, such that estimates of effects at a single interacting locus will differ between populations with different allele frequencies.
Epistasis generates mostly additive variance for quantitative traits; therefore, the observation that most genetic variance for quantitative traits is additive is not inconsistent with an underlying epistatic genetic architecture. Experimental designs that are only possible in model organisms allow the exploration of the gene–gene interaction space, and the results of these analyses indicate that epistasis is pervasive.
Genetic interaction networks are derived by assessing quantitative trait phenotypes of wild-type, single-mutant and double-mutant genotypes. These networks show scale-free and small-world properties, such that the major features of network topology may be inferred by focusing on major hub genes and on interactions among the genes with which they interact. Combining genomics with mutant-interaction screens may aid the identification of network hubs.
Taking advantage of multifactorial perturbations in quantitative trait locus (QTL)-mapping populations is less laborious than constructing all pairwise combinations of mutant alleles, and the ability to construct chromosome substitution lines, introgression lines and near-isogenic lines in model organisms maximizes power to detect interactions. Epistasis is commonly observed, even between loci without significant main effects, but there are only a few cases in which the actual interacting variants have been identified.
Natural populations harbour hidden reservoirs of cryptic genetic variation that can be revealed by introducing mutations onto wild-derived backgrounds. When this approach is implemented in a QTL-mapping population, it is a powerful experimental design for identifying naturally occurring variants that either enhance or suppress the mutant phenotype.
Observations of cryptic genetic variation and less-than-additive epistatic interactions between QTLs suggest that natural populations have evolved suppressing epistatic interactions as homeostatic (that is, canalizing) mechanisms for quantitative traits. Pervasive epistasis has consequences for plant and animal breeding, evolutionary biology and human genetics.
In the future, assessment of the pleiotropic effects of genetic interactions on transcriptional, metabolic and protein–protein interaction networks will provide insights into the mechanistic basis of epistasis for organismal phenotypes.
The role of epistasis in the genetic architecture of quantitative traits is controversial, despite the biological plausibility that nonlinear molecular interactions underpin the genotype–phenotype map. This controversy arises because most genetic variation for quantitative traits is additive. However, additive variance is consistent with pervasive epistasis. In this Review, I discuss experimental designs to detect the contribution of epistasis to quantitative trait phenotypes in model organisms. These studies indicate that epistasis is common, and that additivity can be an emergent property of underlying genetic interaction networks. Epistasis causes hidden quantitative genetic variation in natural populations and could be responsible for the small additive effects, missing heritability and the lack of replication that are typically observed for human complex traits.
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Kauffman, S. A. The Origins of Order (Oxford Univ. Press,1993).
Phillips, P. C. Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems. Nature Rev. Genet. 9, 855–867 (2008). This is a comprehensive review that describes the importance of studying genetic interactions with respect to dissecting regulatory pathways, mapping the genetic basis of complex traits and understanding both the structure and the evolution of complex systems.
Waddington, C. H. Canalization of development and the inheritance of acquired characters. Nature 150, 563–565 (1942).
Waddington, C. H. The Strategy of Genes (George Allen and Unwin,1957).
Dobzhansky, T. Genetics and the Origin of Species (Columbia Univ. Press,1937).
Muller, H. J. in The New Systematics (ed. Huxley, J. S.) 185–268 (Clarendon, 1940).
Carlborg, O. & Haley, C. S. Epistasis: too often neglected in complex trait studies? Nature Rev. Genet. 5, 618–625 (2004).
Cordell, H. J. Detecting gene–gene interactions that underlie human diseases. Nature Rev. Genet. 10, 392–404 (2009).
Cheverud, J. M. & Routman, E. J. Epistasis and its contribution to genetic variance components. Genetics 139, 1455–1461 (1995). This is the first paper to clearly articulate the difference between the effect of epistasis on individual genotypes — which is independent of allele frequency — and the contribution of epistasis to epistatic variance for a quantitative trait, which does depend on allele frequency.
Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics (Longman, 1996).
Lynch, M. & Walsh, J. B. Genetics and Analysis of Quantitative Traits (Sinauer Associates, 1998).
Fisher, R. A. The Genetical Theory of Natural Selection (Clarendon, 1930).
Wright, S. Evolution in Mendelian populations. Genetics 16, 97–159 (1931).
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).
Alvarez-Castro, J. M. & Carlborg, O. A unified model for functional and statistical epistasis and its application in quantitative trait loci analysis. Genetics 176, 1151–1167 (2007).
Meuwissen, T. H., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001).
Hayes, B. J., Lewin, H. A. & Goddard, M. E. The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends Genet. 29, 206–214 (2013).
Elena, S. F. & Lenski, R. E. Test of synergistic interactions among deleterious mutations in bacteria. Nature 390, 395–398 (1997). This is one of the first studies to show epistasis for new mutations that affect fitness, using the E. coli model system.
Clark, A. G. & Wang, L. Epistasis in measured genotypes: Drosophila P-element insertions. Genetics 147, 157–163 (1997).
Flint, J. & Mackay, T. F. C. Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res. 19, 723–733 (2009).
Mackay, T. F. C., Stone, E. A. & Ayroles, J. F. The genetics of quantitative traits: challenges and prospects. Nature Rev. Genet. 10, 565–577 (2009).
Magwire, M. M. et al. Quantitative and molecular genetic analyses of mutations increasing Drosophila life span. PLoS Genet. 6, e1001037 (2010).
Zwarts, L. et al. Complex genetic architecture of Drosophila aggressive behavior. Proc. Natl Acad. Sci. USA 108, 17070–17075 (2011).
Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).
Boone, C., Bussey, H. & Andrews, B. J. Exploring genetic interactions and networks with yeast. Nature Rev. Genet. 8, 437–449 (2007).
Tong, A. H. et al. Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004). This paper describes the first high-throughput analysis of synthetic lethal genetic interaction mapping in yeast, from which the first large-scale genetic interaction network was derived.
Collins, S. R., Schuldiner, M., Krogan, N. & Weissman, J. S. A strategy for extracting and analyzing large-scale quantitative epistatic interaction data. Genome Biol. 7, R63 (2006).
St Onge, R. P. et al. Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions. Nature Genet. 39, 199–206 (2007).
Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).
Szappanos, B. et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nature Genet. 43, 656–662 (2011).
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). This study describes the first large-scale mapping of genetic interaction networks in a metazoan, which was done by feeding hypomorphic C. elegans mutants with arrays of bacteria that expressed double-stranded RNAi molecules which target specific signalling pathways.
Byrne, A. B. et al. A global analysis of genetic interactions in Caenorhabditis elegans. J. Biol. 6, 8 (2007).
Horn, T. et al. Mapping of signaling networks through synthetic genetic interaction analysis by RNAi. Nature Methods 8, 341–346 (2011).
Van Driessche, N. et al. Epistasis analysis with global transcriptional phenotypes. Nature Genet. 37, 471–477 (2005).
Aylor, D. L. & Zeng, Z.-B. From classical genetics to quantitative genetics to systems biology: modeling epistasis. PLoS Genet. 4, e1000029 (2008).
Carter, G. W. et al. Prediction of phenotype and gene expression for combinations of mutations. Mol. Syst. Biol. 3, 96 (2007).
Bellen, H. J. et al. The Drosophila gene disruption project: progress using transposons with distinctive site specificities. Genetics 188, 731–743 (2011).
Dietzl, G. et al. A genome-wide transgenic RNAi library for conditional gene inactivation in Drosophila. Nature 448, 151–156 (2007).
Sabrautzki, S. et al. New mouse models for metabolic bone diseases generated by genome-wide ENU mutagenesis. Mamm. Genome 23, 416–430 (2012).
O'Rourke, E. J., Conery, A. L. & Moy, T. I. Whole-animal high-throughput screens: the C. elegans model. Methods Mol. Biol. 486, 57–75 (2009).
O'Malley, R. C., Alonso, J. M., Kim, C. J., Leisse, T. J. & Ecker, J. R. An adapter ligation-mediated PCR method for high-throughput mapping of T-DNA inserts in the Arabidopsis genome. Nature Protoc. 2, 2910–2917 (2007).
Anholt, R. R. H. et al. The genetic architecture of odor-guided behavior in Drosophila: epistasis and the transcriptome. Nature Genet. 35, 180–184 (2003). This is the first study to show that genes that are transcriptionally co-regulated in a mutant background themselves affect the same trait as the focal mutations, and that mutations in these genes epistatically interact with the focal mutation.
Deutschbauer, A. M. & Davis, R. W. Quantitative trait loci mapped to single-nucleotide resolution in yeast. Nature Genet. 37, 1333–1340 (2005).
Gerke, J., Lorenz, K. & Cohen, B. Genetic interactions between transcription factors cause natural variation in yeast. Science 323, 498–501 (2009).
Brem, R. B., Storey, J. D., Whittle, J. & Kruglyak, L. Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 436, 701–703 (2005).
Gaertner, B. E., Parmenter, M. D., Rockman, M. V., Kruglyak, L. & Phillips, P. C. More than the sum of its parts: a complex epistatic network underlies natural variation in thermal preference behavior in Caenorhabditis elegans. Genetics 192, 1533–1542 (2012).
Cheverud, J. M. et al. Genetic architecture of adiposity in the cross of LG/J and SM/J inbred mice. Mamm. Genome 12, 3–12 (2001).
Stylianou, I. M. et al. Quantitative trait locus analysis for obesity reveals multiple networks of interacting loci. Mamm. Genome 17, 22–36 (2006).
Jarvis, J. P. & Cheverud, J. M. Mapping the epistatic network underlying murine reproductive fatpad variation. Genetics 187, 597–610 (2011).
Leamy, L. J., Gordon, R. R. & Pomp, D. Sex-, diet-, and cancer-dependent epistatic effects on complex traits in mice. Front. Genet. 2, 71 (2011).
Peripato, A. C. et al. Epistasis affecting litter size in mice. J. Evol. Biol. 17, 593–602 (2004).
Hanlon, P. et al. Three-locus and four-locus QTL interactions influence mouse insulin-like growth factor-I. Physiol. Genom. 26, 46–54 (2006).
Carlborg, O., Jacobsson, L., Ahgren, P., Siegel, P. & Andersson, L. Epistasis and the release of genetic variation during long-term selection. Nature Genet. 38, 418–420 (2006). This study documents the importance of epistatic interactions that govern long-term response to artificial selection for growth rate in chickens.
Pettersson, M., Besnier, F., Siegel, P. B. & Carlborg, O. Replication and explorations of high-order epistasis using a large advanced intercross line pedigree. PLoS Genet. 7, e1002180 (2011).
Kroymann, J. & Mitchell-Olds, T. Epistasis and balanced polymorphism influencing complex trait variation. Nature 435, 95–98 (2005). This paper reports the surprising observation that a small chromosome interval that has no effect on growth rate of A. thaliana contains two epistatically interacting QTLs that affect growth.
Wentzel, A. M. et al. Linking metabolic QTLs with network and cis-eQTLs controlling biosynthetic pathways. PLoS Genet. 3, 1687–1701 (2007).
Rowe, H. C., Hansen, B. G., Halkier, B. A. & Kliebenstein, D. J. Biochemical networks and epistasis shape the Arabidopsis thaliana metabolome. Plant Cell 20, 1199–1216 (2008).
Doebley, J., Stec, A. & Gustus, C. teosinte branched1 and the origin of maize: evidence for epistasis and the evolution of dominance. Genetics 141, 333–346 (1995).
Stam, L. F. & Laurie, C. C. Molecular dissection of a major gene effect on a quantitative trait: the level of alcohol dehydrogenase expression in Drosophila melanogaster. Genetics 144, 1559–1564 (1996). This classic paper uses P-element transformation to create all possible combinations of three segments of the Adh gene and showed that each of the three segments affects Adh activity, and that there is, surprisingly, epistasis between two different regions within the gene.
Shao, H. et al. Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc. Natl Acad. Sci. USA 105, 19910–19914 (2008).
Gale, G. D. et al. A genome-wide panel of congenic mice reveals widespread epistasis of behavior quantitative trait loci. Mol. Psychiatry 14, 631–645 (2009).
Spiezio, S. H., Takada, T., Shiroishi, T. & Nadeau, J. H. Genetic divergence and the genetic architecture of complex traits in chromosome substitution strains of mice. BMC Genet. 13, 38 (2012).
Edwards, A. C. & Mackay, T. F. C. Quantitative trait loci for aggressive behavior in Drosophila melanogaster. Genetics 182, 889–897 (2009).
Eshed, Y. & Zamir, D. Less-than-additive epistatic interactions of quantitative trait loci in tomato. Genetics 143, 1807–1817 (1996).
Causse, M., Chaïb, J., Lecomte, L., Buret, M. & Hospital, F. Both additivity and epistasis control the genetic variation for fruit quality traits in tomato. Theor. Appl. Genet. 115, 429–442 (2007).
Greene, C. S., Penrod, N. M., Williams, S. M. & Moore, J. H. Failure to replicate a genetic association may provide important clues about genetic architecture. PLoS ONE 4, e5639 (2009).
Mackay, T. F. C. et al. The Drosophila melanogaster Genetic Reference Panel. Nature 482, 173–178 (2012).
Huang, W. et al. Epistasis dominates the genetic architecture of Drosophila quantitative traits. Proc. Natl Acad. Sci. USA 109, 15553–15559 (2012).
Ehrenreich, I. M. et al. Dissection of genetically complex traits with extremely large pools of yeast segregants. Nature 464, 1039–1042 (2010).
Rendel, J. M. Canalization of the scute phenotype of Drosophila. Evolution 13, 425–439 (1959).
Gibson, G. & van Helden, S. Is function of the Drosophila homeotic gene Ultrabithorax canalized? Genetics 147, 1155–1168 (1997).
Gibson, G., Wemple, M. & van Helden, S. Potential variance affecting homeotic Ultrabithorax and Antennapedia phenotypes in Drosophila melanogaster. Genetics 151, 1081–1091 (1999).
Polaczyk, P. J., Gasperini, R. & Gibson, G. Naturally occurring genetic variation affects Drosophila photoreceptor determination. Dev. Genes Evol. 207, 462–470 (1998).
Dworkin, I. et al. Genomic consequences of background effects on scalloped mutant expressivity in the wing of Drosophila melanogaster. Genetics 181, 1065–1076 (2009).
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. 13, 1888–1893 (2003).
Rutherford, S. L. & Lindquist, S. Hsp90 as a capacitor for morphological evolution. Nature 396, 336–342 (1998).
Gibson, G. & Dworkin, I. Uncovering cryptic genetic variation. Nature Rev. Genet. 5, 681–690 (2004). This review documents the presence of cryptic genetic variation for complex traits and argues that such variation is important in understanding the genetic bases of common diseases in humans, artificial selection response in livestock and crops, and evolutionary responses to new mutations.
Spencer, C. C., Howell, C. E., Wright, A. R. & Promislow, D. E. Testing an 'aging gene' in long-lived Drosophila strains: increased longevity depends on sex and genetic background. Aging Cell 2, 123–130 (2003).
Yamamoto, A., Anholt, R. R. H. & Mackay, T. F. C. Epistatic interactions attenuate mutations affecting startle behaviour in Drosophila melanogaster. Genet. Res. 91, 373–382 (2009).
Swarup, S. et al. Extensive epistasis for olfactory behavior, sleep and waking activity in Drosophila melanogaster. Genet. Res. 94, 9–20 (2012).
Cheng, Y. et al. Mapping genetic loci that interact with myostatin to affect growth traits. Heredity 107, 565–573 (2011).
Chaikam, V. et al. Use of Mutant-Assisted Gene Identification and Characterization (MAGIC) to identify novel genetic loci that modify the maize hypersensitive response. Theor. Appl. Genet. 123, 985–997 (2011).
Sangster, T. A. et al. HSP90-buffered genetic variation is common in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 105, 2969–2974 (2008).
Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).
Valdar, W. et al. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nature Genet. 38, 879–887 (2006).
Buckler, E. S. et al. The genetic architecture of maize flowering time. Science 325, 714–718 (2009).
Barton, N. H. & Turelli, M. Evolutionary quantitative genetics: how little do we know? Annu. Rev. Genet. 23, 337–370 (1989).
Barton, N. H. & Keightley, P. D. Understanding quantitative genetic variation. Nature Rev. Genet. 3, 11–21 (2002).
Johnson, T. & Barton, N. Theoretical models of selection and mutation on quantitative traits. Phil. Trans. R. Soc. B 360, 1411–1425 (2005).
Houle, D., Morikawa, B. & Lynch, M. Comparing mutational variabilities. Genetics 143, 1467–1483 (1996).
Zhang, X.-S. & Hill, W. G. Genetic variability under mutation selection balance. Trends Ecol. Evol. 20, 468–470 (2005).
Carson, H. L. & Templeton, A. R. Genetic revolutions in relation to speciation phenomena: the founding of new populations. Annu. Rev. Ecol. Syst. 15, 97–131 (1984).
Goodnight, C. J. On the effect of founder events on epistatic genetic variance. Evolution 41, 80–91 (1987).
Tachida, H. & Cockerham, C. C. A building block model for quantitative genetics. Genetics 121, 839–844 (1989).
Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nature Genet. 42, 565–569 (2010).
Makowsky, R. et al. Beyond missing heritability: prediction of complex traits. PLoS Genet. 7, e1002051 (2011).
Benyamin, B. et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol. Psychiatry http://dx.doi.org/10.1038/mp.2012.184 (2013).
Hu, Z. et al. Genomic value prediction for quantitative traits under the epistatic model. BMC Genet. 12, 15 (2011).
Long, N., Gianola, D., Rosa, G. J. & Weigel, K. A. Marker-assisted prediction of non-additive genetic values. Genetica 139, 843–854 (2011).
Bulmer, M. G. The Mathematical Theory of Quantitative Genetics (Clarendon,1985).
Anholt, R. R. H. & Mackay, T. F. C. Principles of Behavioral Genetics (Elsevier, 2010).
Mackay, T. F. C. et al. Genetics and genomics of Drosophila mating behavior. Proc. Natl Acad. Sci. USA 102 (Suppl. 1), 6622–6629 (2005).
Ayroles, J. F. et al. Systems genetics of complex traits in Drosophila melanogaster. Nature Genet. 41, 299–307 (2009).
Jordan, K. W., Carbone, M. A., Yamamoto, A., Morgan, T. J. & Mackay, T. F. C. Quantitative genomics of locomotor behavior in Drosophila melanogaster. Genome Biol. 8, R172 (2007).
Edwards, A. C., Rollmann, S. M., Morgan, T. J. & Mackay, T. F. C. Quantitative genomics of aggressive behavior in Drosophila melanogaster. PLoS Genet. 2, e154 (2006).
Morozova, T. V., Anholt, R. R. & Mackay, T. F. C. Phenotypic and transcriptional response to selection for alcohol sensitivity in Drosophila melanogaster. Genome Biol. 8, R231 (2007).
Leips, J. & Mackay, T. F. C. Quantitative trait loci for lifespan in Drosophila melanogaster: interactions with genetic background and larval density. Genetics 155, 1773–1788 (2000).
The author thanks S. Zhou for helping with figures 2 and 5b, and R. Anholt for comments on the manuscript. Work in the Mackay laboratory is supported by the US National Institutes of Health grants R01 GM45146, R01 GM076083, R01 GM59469 and R01 AA016560.
The author declares no competing financial interests.
- Main effect
The effect of a variable averaged over all other variables; also known as marginal effect.
The phenomenon whereby the mean value of a quantitative trait in the F1 progeny of two inbred lines exceeds, in the direction of increased fitness, either the mean value of the parental lines (that is, mid-parent heterosis) or the mean value of the best parent (that is, high parent heterosis); also known as hybrid vigour.
- Missing heritability
The phenomenon whereby the fraction of total phenotypic variance that is explained by all individually significant loci in human genome-wide association analyses for common diseases and quantitative traits is typically much less than the heritability that is estimated from relationships among relatives.
- Di-hybrid cross
A cross between parental lines that are fixed for alternative alleles at two unlinked loci (for example, A1A1B2B2 × A2A2B1B1, where A and B denote the loci and the subscripts represent the alleles) in which nine genotypes segregate in the F2 generation.
- Dominance effects
Differences between the genotypic values of the heterozygous genotypes and the average genotypic values of the homozygous genotypes at loci that affect quantitative traits.
- Standing variation
Allelic variation that is currently segregating within a population, as opposed to alleles that appear as the result of new mutation events.
The substitution of a genomic region from one strain with that of another, typically by repeated backcrosses.
- Diallel cross
A class of experimental designs that are used to estimate both additive and non-additive variance components for a quantitative trait from all possible crosses among a population of inbred lines. Full diallel designs include reciprocal crosses, whereas half-diallel designs do not; parental lines can be included or excluded in either case.
- Synthetic enhancement
A type of epistatic interaction whereby the phenotype of a double mutant is more severe than that predicted from the additive effects of the single mutants.
- Multiple testing penalty
The downward adjustment of the significance threshold for individual statistical tests that is required when multiple hypothesis tests are carried out on a single data set; for n independent tests, the Bonferroni-adjusted 5% significance threshold is 0.05/n.
- Minor allele frequency
The frequency of the less common allele at a bi-allelic locus.
- Founder-effect speciation models
A class of models for the evolution of reproductive isolation that is based on changes in selection pressures and on allele frequencies of epistatically interacting loci, which result from the establishment of a new population in a new environment from a small number of individuals.
- Dobzhansky–Muller incompatibilities
Substitutions that occur during divergence of two lineages; these substitutions are neutral in the respective genetic backgrounds of the two lineages but cause a reduction in fertility and/or viability in hybrids between the two lineages.
- Genomic prediction methods
Models that are derived from a discovery sample which consists of individuals with measured phenotypes and genome-wide marker data; these models are used to predict individual phenotypes in an independent sample from the same population using only genome-wide marker data.
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Mackay, T. Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nat Rev Genet 15, 22–33 (2014). https://doi.org/10.1038/nrg3627
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