Matching microscopic and macroscopic responses in glasses

We first reproduce on the Janus and Janus II computers a milestone experiment that measures the spin-glass coherence length through the lowering of free-energy barriers induced by the Zeeman effect. Secondly we determine the scaling behavior that allows a quantitative analysis of a new experiment reported in the companion Letter [S. Guchhait and R. Orbach, Phys. Rev. Lett. 118, 157203 (2017)]. The value of the coherence length estimated through the analysis of microscopic correlation functions turns out to be quantitatively consistent with its measurement through macroscopic response functions. Further, non-linear susceptibilities, recently measured in glass-forming liquids, scale as powers of the same microscopic length.Comment: 6 pages, 4 figure

The peach volatilome modularity is reflected at the genetic and environmental response levels in a QTL mapping population

Background: The improvement of fruit aroma is currently one of the most sought-after objectives in peach breeding programs. To better characterize and assess the genetic potential for increasing aroma quality by breeding, a quantity trait locus (QTL) analysis approach was carried out in an F-1 population segregating largely for fruit traits. Results: Linkage maps were constructed using the IPSC peach 9 K Infinium (R) II array, rendering dense genetic maps, except in the case of certain chromosomes, probably due to identity-by-descent of those chromosomes in the parental genotypes. The variability in compounds associated with aroma was analyzed by a metabolomic approach based on GC-MS to profile 81 volatiles across the population from two locations. Quality-related traits were also studied to assess possible pleiotropic effects. Correlation-based analysis of the volatile dataset revealed that the peach volatilome is organized into modules formed by compounds from the same biosynthetic origin or which share similar chemical structures. QTL mapping showed clustering of volatile QTL included in the same volatile modules, indicating that some are subjected to joint genetic control. The monoterpene module is controlled by a unique locus at the top of LG4, a locus previously shown to affect the levels of two terpenoid compounds. At the bottom of LG4, a locus controlling several volatiles but also melting/non-melting and maturity-related traits was found, suggesting putative pleiotropic effects. In addition, two novel loci controlling lactones and esters in linkage groups 5 and 6 were discovered. Conclusions: The results presented here give light on the mode of inheritance of the peach volatilome confirming previously loci controlling the aroma of peach but also identifying novel ones.GS has financial support from INTA (Instituto Nacional de Tecnologia Agropecuaria, Argentina). HS-SPME-GC-MS analyses were performed at the Metabolomic lab facilities at the IBMCP (CSIC) in Spain. This project has been funded by the Ministry of Economy and Competitivity grant AGL2010-20595.Sánchez, G.; Martinez, J.; Romeu, J.; Garcia, J.; Monforte Gilabert, AJ.; Badenes, M.; Granell Richart, A. (2014). The peach volatilome modularity is reflected at the genetic and environmental response levels in a QTL mapping population. BMC Plant Biology. 14(137):1-16. https://doi.org/10.1186/1471-2229-14-137S11614137Klee, H. J., & Giovannoni, J. J. (2011). Genetics and Control of Tomato Fruit Ripening and Quality Attributes. Annual Review of Genetics, 45(1), 41-59. doi:10.1146/annurev-genet-110410-132507Sánchez, G., Besada, C., Badenes, M. L., Monforte, A. J., & Granell, A. (2012). A Non-Targeted Approach Unravels the Volatile Network in Peach Fruit. PLoS ONE, 7(6), e38992. doi:10.1371/journal.pone.0038992Eduardo, I., Chietera, G., Bassi, D., Rossini, L., & Vecchietti, A. (2010). Identification of key odor volatile compounds in the essential oil of nine peach accessions. Journal of the Science of Food and Agriculture, 90(7), 1146-1154. doi:10.1002/jsfa.3932Derail, C., Hofmann, T., & Schieberle, P. (1999). Differences in Key Odorants of Handmade Juice of Yellow-Flesh Peaches (Prunus persicaL.) Induced by the Workup Procedure. Journal of Agricultural and Food Chemistry, 47(11), 4742-4745. doi:10.1021/jf990459gGreger, V., & Schieberle, P. (2007). Characterization of the Key Aroma Compounds in Apricots (Prunus armeniaca) by Application of the Molecular Sensory Science Concept. Journal of Agricultural and Food Chemistry, 55(13), 5221-5228. doi:10.1021/jf0705015Zhang, B., Shen, J., Wei, W., Xi, W., Xu, C.-J., Ferguson, I., & Chen, K. (2010). Expression of Genes Associated with Aroma Formation Derived from the Fatty Acid Pathway during Peach Fruit Ripening. Journal of Agricultural and Food Chemistry, 58(10), 6157-6165. doi:10.1021/jf100172eAubert, C., Günata, Z., Ambid, C., & Baumes, R. (2003). Changes in Physicochemical Characteristics and Volatile Constituents of Yellow- and White-Fleshed Nectarines during Maturation and Artificial Ripening. Journal of Agricultural and Food Chemistry, 51(10), 3083-3091. doi:10.1021/jf026153iXI, W.-P., ZHANG, B., LIANG, L., SHEN, J.-Y., WEI, W.-W., XU, C.-J., … CHEN, K.-S. (2011). Postharvest temperature influences volatile lactone production via regulation of acyl-CoA oxidases in peach fruit. Plant, Cell & Environment, 35(3), 534-545. doi:10.1111/j.1365-3040.2011.02433.xBrandi, F., Bar, E., Mourgues, F., Horváth, G., Turcsi, E., Giuliano, G., … Rosati, C. (2011). Study of «Redhaven» peach and its white-fleshed mutant suggests a key role of CCD4 carotenoid dioxygenase in carotenoid and norisoprenoid volatile metabolism. BMC Plant Biology, 11(1), 24. doi:10.1186/1471-2229-11-24Sánchez, G., Venegas-Calerón, M., Salas, J. J., Monforte, A., Badenes, M. L., & Granell, A. (2013). An integrative «omics» approach identifies new candidate genes to impact aroma volatiles in peach fruit. BMC Genomics, 14(1), 343. doi:10.1186/1471-2164-14-343Verde, I., Abbott, A. G., Scalabrin, S., Jung, S., Shu, S., … Grimwood, J. (2013). The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution. Nature Genetics, 45(5), 487-494. doi:10.1038/ng.2586Verde, I., Bassil, N., Scalabrin, S., Gilmore, B., Lawley, C. T., Gasic, K., … Peace, C. (2012). Development and Evaluation of a 9K SNP Array for Peach by Internationally Coordinated SNP Detection and Validation in Breeding Germplasm. PLoS ONE, 7(4), e35668. doi:10.1371/journal.pone.0035668Zorrilla-Fontanesi, Y., Rambla, J.-L., Cabeza, A., Medina, J. J., Sánchez-Sevilla, J. F., Valpuesta, V., … Amaya, I. (2012). Genetic Analysis of Strawberry Fruit Aroma and Identification of O-Methyltransferase FaOMT as the Locus Controlling Natural Variation in Mesifurane Content. Plant Physiology, 159(2), 851-870. doi:10.1104/pp.111.188318Zanor, M. I., Rambla, J.-L., Chaïb, J., Steppa, A., Medina, A., Granell, A., … Causse, M. (2009). Metabolic characterization of loci affecting sensory attributes in tomato allows an assessment of the influence of the levels of primary metabolites and volatile organic contents. Journal of Experimental Botany, 60(7), 2139-2154. doi:10.1093/jxb/erp086Romeu, J. F., Monforte, A. J., Sánchez, G., Granell, A., García-Brunton, J., Badenes, M. L., & Ríos, G. (2014). Quantitative trait loci affecting reproductive phenology in peach. BMC Plant Biology, 14(1), 52. doi:10.1186/1471-2229-14-52Lander, E. S., Green, P., Abrahamson, J., Barlow, A., Daly, M. J., Lincoln, S. E., & Newburg, L. (1987). MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics, 1(2), 174-181. doi:10.1016/0888-7543(87)90010-3Voorrips, R. E. (2002). MapChart: Software for the Graphical Presentation of Linkage Maps and QTLs. Journal of Heredity, 93(1), 77-78. doi:10.1093/jhered/93.1.77Tikunov, Y., Lommen, A., de Vos, C. H. R., Verhoeven, H. A., Bino, R. J., Hall, R. D., & Bovy, A. G. (2005). A Novel Approach for Nontargeted Data Analysis for Metabolomics. Large-Scale Profiling of Tomato Fruit Volatiles. Plant Physiology, 139(3), 1125-1137. doi:10.1104/pp.105.068130Shannon, P. (2003). Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research, 13(11), 2498-2504. doi:10.1101/gr.1239303Yang, J., Hu, C., Hu, H., Yu, R., Xia, Z., Ye, X., & Zhu, J. (2008). QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics, 24(5), 721-723. doi:10.1093/bioinformatics/btm494Elshire, R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, E. S., & Mitchell, S. E. (2011). A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species. PLoS ONE, 6(5), e19379. doi:10.1371/journal.pone.0019379Quilot, B., Wu, B. H., Kervella, J., G�nard, M., Foulongne, M., & Moreau, K. (2004). QTL analysis of quality traits in an advanced backcross between Prunus persica cultivars and the wild relative species P. davidiana. Theoretical and Applied Genetics, 109(4), 884-897. doi:10.1007/s00122-004-1703-zDirlewanger, E., Quero-García, J., Le Dantec, L., Lambert, P., Ruiz, D., Dondini, L., … Arús, P. (2012). Comparison of the genetic determinism of two key phenological traits, flowering and maturity dates, in three Prunus species: peach, apricot and sweet cherry. Heredity, 109(5), 280-292. doi:10.1038/hdy.2012.38Dirlewanger, E., Graziano, E., Joobeur, T., Garriga-Caldere, F., Cosson, P., Howad, W., & Arus, P. (2004). Comparative mapping and marker-assisted selection in Rosaceae fruit crops. Proceedings of the National Academy of Sciences, 101(26), 9891-9896. doi:10.1073/pnas.030793710

Quantitative trait loci affecting reproductive phenology in peach

Background: The reproductive phenology of perennial plants in temperate climates is largely conditioned by the duration of bud dormancy, and fruit developmental processes. Bud dormancy release and bud break depends on the perception of cumulative chilling and heat during the bud development. The objective of this work was to identify new quantitative trait loci (QTLs) associated to temperature requirements for bud dormancy release and flowering and to fruit harvest date, in a segregating population of peach. Results: We have identified QTLs for nine traits related to bud dormancy, flowering and fruit harvest in an intraspecific hybrid population of peach in two locations differing in chilling time accumulation. QTLs were located in a genetic linkage map of peach based on single nucleotide polymorphism (SNP) markers for eight linkage groups (LGs) of the peach genome sequence. QTLs for chilling requirements for dormancy release and blooming clustered in seven different genomic regions that partially coincided with loci identified in previous works. The most significant QTL for chilling requirements mapped to LG1, close to the evergrowing locus. QTLs for heat requirement related traits were distributed in nine genomic regions, four of them co-localizing with QTLs for chilling requirement trait. Two major loci in LG4 and LG6 determined fruit harvest time. Conclusions: We identified QTLs associated to nine traits related to the reproductive phenology in peach. A search of candidate genes for these QTLs rendered different genes related to flowering regulation, chromatin modification and hormone signalling. A better understanding of the genetic factors affecting crop phenology might help scientists and breeders to predict changes in genotype performance in a context of global climate change.We thank Matilde Gonzalez for technical assistance. This work was supported by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA)-FEDER (grant no. RTA2007-00060), and the Ministry of Science and Innovation of Spain (grant no. AGL2010-20595).Romeu, J.; Monforte Gilabert, AJ.; Sánchez, G.; Granell Richart, A.; Garcia-Brunton, J.; Badenes, M.; Rios Garcia, G. (2014). Quantitative trait loci affecting reproductive phenology in peach. BMC Plant Biology. 14(52):1-16. https://doi.org/10.1186/1471-2229-14-52S1161452Rohde, A., & Bhalerao, R. P. (2007). Plant dormancy in the perennial context. Trends in Plant Science, 12(5), 217-223. doi:10.1016/j.tplants.2007.03.012Coville, F. V. (1920). The Influence of Cold in Stimulating the Growth of Plants. Proceedings of the National Academy of Sciences, 6(7), 434-435. doi:10.1073/pnas.6.7.434Chuine, I. (2010). Why does phenology drive species distribution? 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New Phytologist, 189(1), 106-121. doi:10.1111/j.1469-8137.2010.03469.xFabbrini, F., Gaudet, M., Bastien, C., Zaina, G., Harfouche, A., Beritognolo, I., … Sabatti, M. (2012). Phenotypic plasticity, QTL mapping and genomic characterization of bud set in black poplar. BMC Plant Biology, 12(1), 47. doi:10.1186/1471-2229-12-47Celton, J.-M., Martinez, S., Jammes, M.-J., Bechti, A., Salvi, S., Legave, J.-M., & Costes, E. (2011). Deciphering the genetic determinism of bud phenology in apple progenies: a new insight into chilling and heat requirement effects on flowering dates and positional candidate genes. New Phytologist, 192(2), 378-392. doi:10.1111/j.1469-8137.2011.03823.xQuilot, B., Wu, B. H., Kervella, J., G�nard, M., Foulongne, M., & Moreau, K. (2004). QTL analysis of quality traits in an advanced backcross between Prunus persica cultivars and the wild relative species P. davidiana. Theoretical and Applied Genetics, 109(4), 884-897. doi:10.1007/s00122-004-1703-zDirlewanger, E., Quero-García, J., Le Dantec, L., Lambert, P., Ruiz, D., Dondini, L., … Arús, P. (2012). Comparison of the genetic determinism of two key phenological traits, flowering and maturity dates, in three Prunus species: peach, apricot and sweet cherry. Heredity, 109(5), 280-292. doi:10.1038/hdy.2012.38Olukolu, B. A., Trainin, T., Fan, S., Kole, C., Bielenberg, D. G., Reighard, G. L., … Holland, D. (2009). Genetic linkage mapping for molecular dissection of chilling requirement and budbreak in apricot (Prunus armeniacaL.). Genome, 52(10), 819-828. doi:10.1139/g09-050Fan, S., Bielenberg, D. G., Zhebentyayeva, T. N., Reighard, G. L., Okie, W. R., Holland, D., & Abbott, A. G. (2009). Mapping quantitative trait loci associated with chilling requirement, heat requirement and bloom date in peach (Prunus persica). New Phytologist, 185(4), 917-930. doi:10.1111/j.1469-8137.2009.03119.xJiménez, S., Li, Z., Reighard, G. L., & Bielenberg, D. G. (2010). Identification of genes associated with growth cessation and bud dormancy entrance using a dormancy-incapable tree mutant. BMC Plant Biology, 10(1), 25. doi:10.1186/1471-2229-10-25Verde, I., Bassil, N., Scalabrin, S., Gilmore, B., Lawley, C. T., Gasic, K., … Peace, C. (2012). Development and Evaluation of a 9K SNP Array for Peach by Internationally Coordinated SNP Detection and Validation in Breeding Germplasm. PLoS ONE, 7(4), e35668. doi:10.1371/journal.pone.0035668Leida, C., Terol, J., Marti, G., Agusti, M., Llacer, G., Badenes, M. L., & Rios, G. (2010). Identification of genes associated with bud dormancy release in Prunus persica by suppression subtractive hybridization. Tree Physiology, 30(5), 655-666. doi:10.1093/treephys/tpq008Leida, C., Conesa, A., Llácer, G., Badenes, M. L., & Ríos, G. (2011). Histone modifications and expression of DAM6 gene in peach are modulated during bud dormancy release in a cultivar-dependent manner. New Phytologist, 193(1), 67-80. doi:10.1111/j.1469-8137.2011.03863.xHolec, S., & Berger, F. (2011). Polycomb Group Complexes Mediate Developmental Transitions in Plants. Plant Physiology, 158(1), 35-43. doi:10.1104/pp.111.186445Pandey, R. (2002). Analysis of histone acetyltransferase and histone deacetylase families of Arabidopsis thaliana suggests functional diversification of chromatin modification among multicellular eukaryotes. Nucleic Acids Research, 30(23), 5036-5055. doi:10.1093/nar/gkf660Verde, I., Abbott, A. G., Scalabrin, S., Jung, S., Shu, S., … Grimwood, J. (2013). The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution. Nature Genetics, 45(5), 487-494. doi:10.1038/ng.2586Jiménez, S., Lawton-Rauh, A. L., Reighard, G. L., Abbott, A. G., & Bielenberg, D. G. (2009). Phylogenetic analysis and molecular evolution of the dormancy associated MADS-box genes from peach. BMC Plant Biology, 9(1), 81. doi:10.1186/1471-2229-9-81Li, Z., Reighard, G. L., Abbott, A. G., & Bielenberg, D. G. (2009). Dormancy-associated MADS genes from the EVG locus of peach [Prunus persica (L.) Batsch] have distinct seasonal and photoperiodic expression patterns. Journal of Experimental Botany, 60(12), 3521-3530. doi:10.1093/jxb/erp195Jiménez, S., Reighard, G. L., & Bielenberg, D. G. (2010). Gene expression of DAM5 and DAM6 is suppressed by chilling temperatures and inversely correlated with bud break rate. Plant Molecular Biology, 73(1-2), 157-167. doi:10.1007/s11103-010-9608-5Yamane, H., Ooka, T., Jotatsu, H., Hosaka, Y., Sasaki, R., & Tao, R. (2011). Expressional regulation of PpDAM5 and PpDAM6, peach (Prunus persica) dormancy-associated MADS-box genes, by low temperature and dormancy-breaking reagent treatment. Journal of Experimental Botany, 62(10), 3481-3488. doi:10.1093/jxb/err028Leida, C., Conejero, A., Arbona, V., Gómez-Cadenas, A., Llácer, G., Badenes, M. L., & Ríos, G. (2012). Chilling-Dependent Release of Seed and Bud Dormancy in Peach Associates to Common Changes in Gene Expression. PLoS ONE, 7(5), e35777. doi:10.1371/journal.pone.0035777Horvath, D. P., Sung, S., Kim, D., Chao, W., & Anderson, J. (2010). Characterization, expression and function of DORMANCY ASSOCIATED MADS-BOX genes from leafy spurge. Plant Molecular Biology, 73(1-2), 169-179. doi:10.1007/s11103-009-9596-5Sasaki, R., Yamane, H., Ooka, T., Jotatsu, H., Kitamura, Y., Akagi, T., & Tao, R. (2011). Functional and Expressional Analyses of PmDAM Genes Associated with Endodormancy in Japanese Apricot. Plant Physiology, 157(1), 485-497. doi:10.1104/pp.111.181982Hemming, M. N., & Trevaskis, B. (2011). Make hay when the sun shines: The role of MADS-box genes in temperature-dependant seasonal flowering responses. 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The three dimensional Ising spin glass in an external magnetic field: the role of the silent majority

We perform equilibrium parallel-tempering simulations of the 3D Ising Edwards-Anderson spin glass in a field. A traditional analysis shows no signs of a phase transition. Yet, we encounter dramatic fluctuations in the behaviour of the model: Averages over all the data only describe the behaviour of a small fraction of it. Therefore we develop a new approach to study the equilibrium behaviour of the system, by classifying the measurements as a function of a conditioning variate. We propose a finite-size scaling analysis based on the probability distribution function of the conditioning variate, which may accelerate the convergence to the thermodynamic limit. In this way, we find a non-trivial spectrum of behaviours, where a part of the measurements behaves as the average, while the majority of them shows signs of scale invariance. As a result, we can estimate the temperature interval where the phase transition in a field ought to lie, if it exists. Although this would-be critical regime is unreachable with present resources, the numerical challenge is finally well posed.Comment: 42 pages, 19 figures. Minor changes and added figure (results unchanged

Critical parameters of the three-dimensional Ising spin glass

We report a high-precision finite-size scaling study of the critical behavior of the three-dimensional Ising Edwards-Anderson model (the Ising spin glass). We have thermalized lattices up to L=40 using the Janus dedicated computer. Our analysis takes into account leading-order corrections to scaling. We obtain Tc = 1.1019(29) for the critical temperature, \nu = 2.562(42) for the thermal exponent, \eta = -0.3900(36) for the anomalous dimension and \omega = 1.12(10) for the exponent of the leading corrections to scaling. Standard (hyper)scaling relations yield \alpha = -5.69(13), \beta = 0.782(10) and \gamma = 6.13(11). We also compute several universal quantities at Tc.Comment: 9 pages, 5 figure

Thermodynamic glass transition in a spin glass without time-reversal symmetry

Spin glasses are a longstanding model for the sluggish dynamics that appears at the glass transition. However, spin glasses differ from structural glasses for a crucial feature: they enjoy a time reversal symmetry. This symmetry can be broken by applying an external magnetic field, but embarrassingly little is known about the critical behaviour of a spin glass in a field. In this context, the space dimension is crucial. Simulations are easier to interpret in a large number of dimensions, but one must work below the upper critical dimension (i.e., in d<6) in order for results to have relevance for experiments. Here we show conclusive evidence for the presence of a phase transition in a four-dimensional spin glass in a field. Two ingredients were crucial for this achievement: massive numerical simulations were carried out on the Janus special-purpose computer, and a new and powerful finite-size scaling method.Comment: 10 pages, 6 figure

Critical Behavior of Three-Dimensional Disordered Potts Models with Many States

We study the 3D Disordered Potts Model with p=5 and p=6. Our numerical simulations (that severely slow down for increasing p) detect a very clear spin glass phase transition. We evaluate the critical exponents and the critical value of the temperature, and we use known results at lower $p$ values to discuss how they evolve for increasing p. We do not find any sign of the presence of a transition to a ferromagnetic regime.Comment: 9 pages and 9 Postscript figures. Final version published in J. Stat. Mec

Nature of the spin-glass phase at experimental length scales

We present a massive equilibrium simulation of the three-dimensional Ising spin glass at low temperatures. The Janus special-purpose computer has allowed us to equilibrate, using parallel tempering, L=32 lattices down to T=0.64 Tc. We demonstrate the relevance of equilibrium finite-size simulations to understand experimental non-equilibrium spin glasses in the thermodynamical limit by establishing a time-length dictionary. We conclude that non-equilibrium experiments performed on a time scale of one hour can be matched with equilibrium results on L=110 lattices. A detailed investigation of the probability distribution functions of the spin and link overlap, as well as of their correlation functions, shows that Replica Symmetry Breaking is the appropriate theoretical framework for the physically relevant length scales. Besides, we improve over existing methodologies to ensure equilibration in parallel tempering simulations.Comment: 48 pages, 19 postscript figures, 9 tables. Version accepted for publication in the Journal of Statistical Mechanic

A cryptic variation in a member of the Ovate Family Proteins is underlying the melon fruit shape QTL fsqs8.1

Melon cultivars have a wide range of fruit morphologies. Quantitative trait loci (QTL) have been identifed underlying such diversity. This research focuses on the fruit shape QTL fsqs8.1, previously detected in a cross between the accession PI 124112 (CALC, producing elongated fruit) and the cultivar ‘Piel de Sapo’ (PS, producing oval fruit). The CALC fsqs8.1 allele induced round fruit shape, being responsible for the transgressive segregation for this trait observed in that population. In fact, the introgression line CALC8-1, carrying the fsqs8.1 locus from CALC into the PS genetic background, produced perfect round fruit. Following a map-based cloning approach, we found that the gene underlying fsqs8.1 is a member of the Ovate Family Proteins (OFP), CmOFP13, likely a homologue of AtOFP1 and SlOFP20 from Arabidopsis thaliana and tomato, respectively. The induction of the round shape was due to the higher expression of the CALC allele at the early ovary development stage. The fsqs8.1 locus showed an important structural variation, being CmOFP13 surrounded by two deletions in the CALC genome. The deletions are present at very low frequency in melon germplasm. Deletions and single nucleotide polymorphisms in the fsqs8.1 locus could not be not associated with variation in fruit shape among diferent melon accessions, what indicates that other genetic factors should be involved to induce the CALC fsqs8.1 allele efects. Therefore, fsqs8.1 is an example of a cryptic variation that alters gene expression, likely due to structural variation, resulting in phenotypic changes in melon fruit morphology.info:eu-repo/semantics/publishedVersio