Tree Genetics & Genomes

, 14:93 | Cite as

Linkage and association mapping for the slow softening (SwS) trait in peach (P. persica L. Batsch) fruit

  • Angelo Ciacciulli
  • Marco Cirilli
  • Remo Chiozzotto
  • Giovanna Attanasio
  • Cassia Da Silva Linge
  • Igor Pacheco
  • Laura RossiniEmail author
  • Daniele BassiEmail author
Original Article
Part of the following topical collections:
  1. Complex Traits


Fruit texture is a crucial quality factor influencing consumer preference and shelf life. Peach (P. persica L. Batsch) is a highly perishable fruit subjected to a rapid softening after harvest. Improvement of peach shelf life is an important breeding objective, stimulating the characterization and exploitation of texture-related traits. Variants of melting (M) texture have captured an increasing interest, following the economic success of “Big Top” nectarine, one of the most cultivated varieties worldwide. “Big Top” fruit maintains a crispy texture for an extended period before the onset of the melting phase, prolonging its shelf life. Genetic determinants regulating this complex trait, defined as slow softening (SwS), are still unknown, mainly because of limitations in phenotyping methods. In this work, a mechanical approach for measuring SwS fruit texture was used to phenotype offspring derived from a cross between “Rebus028” (SwS texture) and “Max10” (M texture). Mechanical parameters were used in linkage mapping, allowing the identification of a major QTL on chromosome 8 (qSwS8.1). The presence of this QTL was validated by a genome-wide association study (GWAS) in a panel of accessions phenotyped for mechanical properties. Less significant signals were also detected by GWAS in other genomic regions, suggesting that additional loci may regulate the SwS trait, possibly depending on the genetic background. The inheritance pattern of the SwS trait and the presence of additional loci are crucial aspects to be addressed in future studies, along with a better characterization of other important textural attributes.


GWAS Texture QTL mapping Mechanical approach 



The authors wish to thank S. Foschi (CRPV, Cesena, Italy) and M. Lama (ASTRA, Faenza, Italy) for technical assistance in field and lab operations. Special thanks to ASUS for providing high-performing hardware.

Authors’ contributions

AC and MC: collected phenotypic data, performed genetic analysis, and wrote the manuscript; RC, GA, CS, and IP: helped to collect phenotypic and genotypic data; LR and DB: conceived the study and critically revised it.

Funding information

This work has been funded in the framework of the MAS.PES (Italian project for peach and apricot breeding) and the EU seventh Framework program FruitBreedomics project (FP7-KBBE-2010-265582).

Compliance with ethical standards

Conflict of interest

The authors declare that they do not have any conflict of interest.

Ethical review

This study does not involve any human or animal testing.

Supplementary material

11295_2018_1305_Fig6_ESM.png (83 kb)
Supplementary Figure 1

Squared Spearman’s rank correlation coefficients between mechanical parameters (E, elasticity; F, fracturability; TD, texture dynamic index; Kint, model intercept) and fruit quality indices and parameters estimated in M × R progeny: acidity, soluble solid content (SSC), skin blush, fresh weight (FW), IAD index and maturity date (MD). (PNG 83 kb)

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High resolution image (TIFF 4.11 mb)
11295_2018_1305_Fig7_ESM.png (18 kb)
Supplementary Figure 2

Location of the major QTL controlling low acid flesh trait (D locus) on linkage group 5 in M × R progeny. Marker names are listed on X-axes and LOD score for associations on Y-axes. (PNG 18 kb)

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High resolution image (BMP 542 kb)
11295_2018_1305_Fig8_ESM.png (54 kb)
Supplementary Figure 3

Manhattan plot (left panel) and Q-Q plots (right panel) of -log10p values estimated for binary (acid vs low-acid) coded fruit flesh taste in the panel of 92 accessions by FarmCPU algorithm adjusted for population structure (Q-matrix calculated for K = 3). Green horizontal line shows the Bonferroni-adjusted threshold (1.90e−06) for p = 0.01. (PNG 53 kb)

11295_2018_1305_MOESM3_ESM.bmp (1.6 mb)
High resolution image (BMP 1663 kb)
11295_2018_1305_Fig9_ESM.png (276 kb)
Supplementary Figure 4

Linkage disequilibrium pattern around the most associated marker SNP_IGA_880306 within qSwS8.1 locus on chromosome 8. Most associated SNPs and interval from QTL-mapping (non-parametric Kruskal-Wallis rank sum test, KW, and interval mapping, IM) were also shown. (PNG 276 kb)

11295_2018_1305_MOESM4_ESM.bmp (784 kb)
High resolution image (BMP 783 kb)
11295_2018_1305_Fig10_ESM.png (172 kb)
Supplementary Figure 5

Manhattan plot (left panel) and Q-Q plots (right panel) of -log10p values estimated for fracturability (F) parameter in a panel of 92 accessions using FarmCPU algorithm adjusted for population structure (Q-matrix calculated for K = 3). Blue horizontal line shows the Bonferroni-adjusted for p = 0.01; red horizontal line indicates a vector of minimum p value calculated from the 95% quantile of a permutation test with 100 random seed. (PNG 171 kb)

11295_2018_1305_MOESM5_ESM.tiff (7.5 mb)
High resolution image (TIFF 7698 kb)
11295_2018_1305_MOESM6_ESM.docx (21 kb)
Supplementary Table 1 Values of estimated mechanical parameters elasticity (E), fracturability (F), texture dynamic (TD) and model intercept (Kint) and sensory classification of fruit texture (M, melting and SwS, slow softening) in M × R progeny. (DOCX 21 kb)
11295_2018_1305_MOESM7_ESM.docx (24 kb)
Supplementary Table 2 Values of estimated mechanical parameters elasticity (E), fracturability (F), texture dynamic (TD), and model intercept (Kint) and sensory classification of fruit texture (M, melting and SwS, slow softening) in a panel of 92 accessions. (DOCX 24 kb)
11295_2018_1305_MOESM8_ESM.pdf (112 kb)
Supplementary File 1 Integrated linkage map of M × R progeny. (PDF 112 kb)


  1. Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664CrossRefPubMedPubMedCentralGoogle Scholar
  2. Amyotte B, Bowen AJ, Banks T, Rajcan I, Somers DJ (2017) Mapping the sensory perception of apple using descriptive sensory evaluation in a genome wide association study. PLoS One 12(2):e0171710CrossRefPubMedPubMedCentralGoogle Scholar
  3. Aurand R, Faurobert M, Page D, Maingonnat JF, Brunel B, Causse M, Bertin N (2012) Anatomical and biochemical trait network underlying genetic variations in tomato fruit texture. Euphytica 187:99–116CrossRefGoogle Scholar
  4. Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263–265CrossRefPubMedGoogle Scholar
  5. Bassi D, Mignani I, Rizzo M (1998) Calcium and pectin influence peach flesh texture. Acta Hortic 465:433–438CrossRefGoogle Scholar
  6. Ben Sadok I, Tiecher A, Galvez-Lopez D, Lahaye M, Lasserre-Zuber P, Bruneau M, Hanteville S, Robic R, Cournol R, Laurens F (2015) Apple fruit texture QTLs: year and cold storage effects on sensory and instrumental traits. Tree Genet Genomes, 11Google Scholar
  7. Biscarini F, Nazzicari N, Bink M, Arús P, Aranzana MJ, Verde I, Micali S, Pascal T, Quilot-Turion B, Lambert P, Da Silva Linge C, Pacheco I, Bassi D, Stella A and Rossini L (2017) Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies. BMC Genomics, 18(432).Google Scholar
  8. Blake MA (1937) Progress in peach breeding. Proc Am Soc Hort Sci 35:49–53Google Scholar
  9. Blake MA (1940) Some results of crosses of early ripening varieties of peaches. Proc Am Soc Hort Sci 37:232–241Google Scholar
  10. Boudehri K, Bendahmane A, Cardinet G, Troadec C, Moing A, Dirlewanger E (2009) Phenotypic and fine genetic characterization of the D locus controlling fruit acidity in peach. BMC Plant Biol 9:59CrossRefPubMedPubMedCentralGoogle Scholar
  11. Brennan JG (1984) Texture perception and measurement. In: Piggot JR (ed) sensory analysis of foods. Elsevier Applied Science, LondonGoogle Scholar
  12. British Standards Institution (1975) British Standards Glossary of Rheological Terms. British Standards, 5168Google Scholar
  13. Callahan AM, Scorza R, Bassett C, Nickerson M, Abeles FB (2004) Deletions in an endopolygalacturonase gene cluster correlate with non-melting flesh texture in peach. Funct Plant Biol 31:159–168CrossRefGoogle Scholar
  14. Chaïb J, Devaux MF, Grotte MG, Robini K, Causse M, Lahaye M, Marty I (2007) Physiological relationships among physical, sensory, and morphological attributes of texture in tomato fruits. J Exp Bot 58:1915–1925CrossRefPubMedGoogle Scholar
  15. Chen L, Opara UL (2013) Approaches to analysis and modeling texture in fresh and processed foods - a review. J Food Eng 119(3):497–507CrossRefGoogle Scholar
  16. Ciacciulli A, Chiozzotto R, Attanasio G, Cirilli M, Bassi D (2018) Identification of a melting type variant among peach (P. persica L. Batsch) fruit textures by a digital penetrometer. J Texture Stud 49:370–377CrossRefGoogle Scholar
  17. Cirilli M, Rossini L, Geuna F, Palmisano F, Minafra A, Castrignanò T, Gattolin S, Ciacciulli A, Babini AR, Liverani A, Bassi D (2017) Genetic dissection of Sharka disease tolerance in peach (P. persica L. Batsch). BMC Plant Biol 17:192CrossRefPubMedPubMedCentralGoogle Scholar
  18. Cirilli M, Giovannini D, Ciacciulli A, Chiozzotto R, Gattolin G, Rossini L et al (2018) Integrative genomics approaches validate PpYUC11-like as candidate gene for the stony hard trait in peach (P. persica L. Batsch). BMC Plant Biol 18:88CrossRefPubMedPubMedCentralGoogle Scholar
  19. Contador L, Díaz M, Millanao M, Hernández E, Shinya P, Sáenz C, et al. (2016) A proposal for determining the flesh softening of peach and nectarine in postharvest through simplified targeted modeling. Sci Hortic (Amsterdam). Elsevier B.V.; 209: 47–52Google Scholar
  20. Costa F, Cappellin L, Longhi S, Guerra W, Magnago P, Porro D, et al. (2011) Assessment of apple (Malus×domestica Borkh.) fruit texture by a combined acoustic-mechanical profiling strategy. Postharvest Biol Technol. Elsevier B.V.; 2011;61: 21-28Google Scholar
  21. DeLong JM, Prange RK, Harrison PA, McRae KB (2000) Comparison of a new firmness penetrometer with three standard instruments. Postharvest Biol Technol 19:201–209CrossRefGoogle Scholar
  22. Dettori MT, Quarta R, Verde I (2001) A peach linkage map integrating RFLPs, SSRs, RAPDs, and morphological markers. Genome 44:783–790CrossRefPubMedGoogle Scholar
  23. Dirlewanger E, Cosson P, Boudehri K, Renaud C, Capdeville G, Tauzin Y, Laigret F, Moing A (2006) Development of a second-generation genetic linkage map for peach [Prunus persica (L.) Batsch] and characterization of morphological traits affecting flower and fruit. Tree Genetics & Genomics 3:1–13CrossRefGoogle Scholar
  24. Ghiani A, Onelli E, Aina R, Cocucci M, Citterio S (2011a) A comparative study of melting and non-melting flesh peach cultivars reveals that during fruit ripening endo-polygalacturonase (endo-PG) is mainly involved in pericarp textural changes, not in firmness reduction. J Exp Bot 62:4043–4054CrossRefPubMedGoogle Scholar
  25. Ghiani A, Negrini N, Morgutti S, Baldin F, Nocito FF, Spinardi A et al (2011b) Melting of “Big Top” nectarine fruit: some physiological, biochemical, and molecular aspects. J Am Soc Hortic Sci 136:61–68Google Scholar
  26. Giné-Bordonaba J, Cantín CM, Echeverría G, Ubach D, Larrigaudiére C. (2016) The effect of chilling injury-inducing storage conditions on quality and consumer acceptance of different Prunus persica cultivars. Postharvest Biol Technol. Elsevier B.V.; 115Google Scholar
  27. Giovannini D, Liverani A (2014) Il breeding del pesco, un percorso secolare ricco di nuove tipologie di frutti. Frutticultura 8:7–14 (Italian)Google Scholar
  28. Gu C, Wang L, Wang W, Zhou H, Ma B, Zheng H, Fang T, Ogutu C, Vimolmangkang S, Han Y (2016) Copy number variation of a gene cluster encoding endopolygalacturonase mediates flesh texture and stone adhesion in peach. J Exp Bot 67:1993–2005CrossRefPubMedPubMedCentralGoogle Scholar
  29. Harker FR, Redgwell RJ, Hallett IC, Murray SH, Carter G (2010) Texture of fresh fruit. In: Janick J (ed) Horticultural reviews. John Wiley & Sons, Inc., Oxford, UK, pp 121–224CrossRefGoogle Scholar
  30. Hernández Mora JR, Micheletti D, Bink M, Van de Weg E, Cantín C, Nazzicari N et al (2017) Integrated QTL detection for key breeding traits in multiple peach progenies. BMC Genomics 18:404CrossRefPubMedPubMedCentralGoogle Scholar
  31. Iglesias I & Echeverria G (2009) Differential effect of cultivar and harvest date on nectarine colour, quality and consumer acceptance. Sci Hort 120: 41–50Google Scholar
  32. Infante R (2012) Harvest maturity indicators in the stone fruit industry. Stewart Postharvest RevGoogle Scholar
  33. Kader AA (2002). Postharvest biology and technology: an overview. In A.A. Kader (Ed.) postharvest technology of horticultural crops. University of California, Division of Agriculture and Natural Resources, Special publ. 3311, pp. 39–47Google Scholar
  34. Layne D, Bassi D (2008) The peach: botany, production and uses. CABI, WallingfordCrossRefGoogle Scholar
  35. Lester D, Sherman WB, Atwell BJ (1996) Endopolygalacturonase and the melting flesh (M) locus in peach. J Amer Soc Hort Sci 121:231–235Google Scholar
  36. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397–2399CrossRefGoogle Scholar
  37. Liu X, Huang M, Fan B, Buckler ES, Zhang Z (2016) FarmCPU manual. PLoS Genet 12:e1005767CrossRefPubMedPubMedCentralGoogle Scholar
  38. Magness JR, Taylor GF (1925) An improved type of pressure tester for the determination of fruit maturity. US Dept Agr Circ 350:8Google Scholar
  39. Micheletti D, Dettori MT, Micali S, Aramini V, Pacheco I, Da Silva LC et al (2015) Whole-genome analysis of diversity and SNP-major gene association in peach germplasm. PLoS One 10:1–19CrossRefGoogle Scholar
  40. Mignani I, Ortugno C, and Bassi D (2006) Biochemical parameters for the evaluation of different peach flesh types. In Acta Horticulturae, (International Society for Horticultural Science (ISHS), Leuven, Belgium), pp. 441-448Google Scholar
  41. Okie WR, Bacon T, Bassi D (2008) Fresh market cultivar development. In: The peach. Botany, production, uses. Ed. Layne DR and Bassi DGoogle Scholar
  42. Pan L, Zeng W, Niu L, Lu Z, Liu H, Cui G, Zhu Y, Chu J, Li W, Fang W, Cai Z, Li G, Wang Z (2015) PpYUC11, a strong candidate gene for the stony hard phenotype in peach (Prunus persica L. Batsch), participates in IAA biosynthesis during fruit ripening. J Exp Bot 66:7031–7044CrossRefPubMedPubMedCentralGoogle Scholar
  43. Peace CP, Norelli JL (2009) Genomics approaches to crop improvement in Rosaceae. In: Folta KM, Gardiner SE (eds) Genetics and genomics of Rosaceae. Springer, Berlin, pp 19–53CrossRefGoogle Scholar
  44. Peace CP, Crisosto CH, Gradziel TM (2005) Endopolygalacturonase: a candidate gene for freestone and melting flesh in peach. Mol Breed 16:21–31CrossRefGoogle Scholar
  45. Rao MA (2014) Rheology of fluid, semisolid, and solid foods. Springer US, Boston, MACrossRefGoogle Scholar
  46. Reig G, Alegre S, Cantín CM, Gatius F, Puy J, Iglesias I (2017) Tree ripening and postharvest firmness loss of eleven commercial nectarine cultivars under Mediterranean conditions. Sci Hortic (Amsterdam). Elsevier B.V.; 219: 335–343Google Scholar
  47. Sandefur P, Clark JR, Peace C (2013) Peach texture. Hortic Rev:241–302Google Scholar
  48. Serra O, Giné-Bordonaba J, Eduardo I, Bonany J, Echeverria G, Larrigaudière C et al (2017) Genetic analysis of the slow-melting flesh character in peach. Tree Genet Genomes:13Google Scholar
  49. Tatsuki M, Haji T, Yamaguchi M (2006) The involvement of 1-aminocyclopropane-1-carboxylic acid synthase isogene, Pp-ACS1, in peach fruit softening. J Exp Bot 57(6):1281-1289 Tatsuki M, Nakajima N, Fujii H, Shimada T, Nakano M, Hayashi K, Hayama H, Yoshioka H, Nakamura Y (2013) Increased levels of IAA are required for system 2 ethylene synthesis causing fruit softening in peach (Prunus persica L. Batsch). J Exp Bot 64(4): 1049–1059Google Scholar
  50. Van Ooijen JW (2006) JoinMap 4, Software for the calculation of genetic linkage maps in experimental populations. Kyazma BV: Wageningen, NetherlandsGoogle Scholar
  51. Van Ooijen JW (2009) MapQTL 6, software for the mapping of quantitative trait loci in experimental populations of dipoid species. Kyazma BV: Wageningen, NetherlandsGoogle Scholar
  52. Verde I, Bassil N, Scalabrin S, Gilmore B, Lawley CT, Gasic K, Micheletti D, Rosyara UR, Cattonaro F, Vendramin E, Main D, Aramini V, Blas AL, Mockler TC, Bryant DW, Wilhelm L, Troggio M, Sosinski B, Aranzana MJ, Arús P, Iezzoni A, Morgante M, 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:e35668CrossRefPubMedPubMedCentralGoogle Scholar
  53. Verde I, Jenkins J, Dondini L, Micali S, Pagliarani G, Vendramin E, et al. (2017) The peach v2.0 release: high-resolution linkage mapping and deep resequencing improve chromosome-scale assembly and contiguity. BMC Genomics;18Google Scholar
  54. Yoshida M (1976) Genetical studies on the fruit quality of peach varieties, 3: texture and keeping quality. Bull Fruit Tree Res Station Ser A HiratsukaGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Agricultural and Environmental Sciences (DISAA)University of MilanMilanItaly
  2. 2.Universidad de ChileInstitute of Nutrition and Food Technology - INTASantiagoChile

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