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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 Rossini
  • Daniele Bassi
Original Article
  • 92 Downloads
Part of the following topical collections:
  1. Complex Traits

Abstract

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.

Keywords

GWAS Texture QTL mapping Mechanical approach 

Notes

Acknowledgments

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)

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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)

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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)

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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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