A Major QTL is associated with berry grape texture characteristics

Abstract

Berry texture and berry skin mechanical properties are traits with high agronomic relevance because they are related to quality parameters and marketing requirements of wine, table, and raisin grapes. Searching for QTLs linked to berry texture, an F1 population of 152 individuals and their parents were used in this study. These F1 plants were obtained crossing Raboso Veronese, a seeded black wine grape cultivar, and Sultanina, a seedless white grape variety, especially used for raisins. Density flotation was applied for berry sorting improving the management of many and highly variable genotypes, irrespective of the quantification of specific molecule classes. Berries were evaluated for technological ripeness parameters and mechanical properties. Texture parameters were taken as raw data and as data normalised on berry dimensions, i.e., berry diameter or surface or volume. SSR molecular markers were used to produce a genetic map and a major QTL for berry texture was found on chromosome 18 with traits related to berry firmness showing a phenotypical explained variance higher than 60 %, and traits related to berry resilience, springiness and cohesiveness showing a variance higher than 50 %. Surprisingly, this QTL showed to be associated with SSR markers linked to VviAGL11, the main gene linked to seedlessness. VviAGL11 expression and co-expression profiling during grape ripening was evaluated using available information; this data suggested a role for this gene on the texture of a ripe berry.List of Abbreviations:ABW, average berry weightBR, berry resilienceBR_diam, berry resilience normalised on berry diameterBR_sur, berry resilience normalised on berry surfaceBR_vol, berry resilience normalised on berry volumeBS_ratio, berry springinessBS_ratio_diam, berry springiness normalised on berry diameterBS_ratio_sur, berry springiness normalised on berry surfaceBS_ratio_vol, berry springiness normalised on berry volumeBCo, berry cohesivenessBCo_diam, berry cohesiveness normalised on berry diameterBCo_sur, berry cohesiveness normalised on berry surfaceBCo_vol, berry cohesiveness normalised on berry volumeBH, berry hardnessBH_diam, berry hardness normalised on berry diameterBH_sur, berry hardness normalised on berry surfaceBH_vol, berry hardness normalised on berry volumeBG, berry gumminessBG_diam, berry gumminess normalised on berry diameterBG_sur, berry gumminess normalised on berry surfaceBG_vol, berry gumminess normalised on berry volumeBCh_ratio, berry chewinessBCh_ratio_diam, berry chewiness normalised on berry diameterBCh_ratio_sur, berry chewiness normalised on berry surfaceBCh_ratio_vol, berry chewiness normalised on berry volumeFsk, berry skin break forceWsk, berry skin break energyEsk, berry skin resistance to the axial deformationSpsk, berry skin thickness

Towards Sensor-Based Phenotyping of Physical Barriers of Grapes to Improve Resilience to Botrytis Bunch Rot

“…To answer the question about the involvement of wax- and cuticle-related genes on the phenotypic expression of berry impedance and berry texture, further fine mapping of detected regions are necessary to detect candidate genes. However, QTL analysis for berry firmness (TA AREA ) is mostly performed on table grape populations where QTLs and candidate genes like expansin are identified on LG 8 ( Wang et al, 2020 ), LG 18 ( Carreño et al, 2015 ; Ni et al, 2020 ; Crespan et al, 2021 ), and further on LG1, 4, 5, 9, 10, and LG13 ( Carreño et al, 2015 ). Recently, Crespan et al (2021) identified VviAGL11 on LG18, one main gene that regulates seedlessness in table grape that seems to be involved in the phenotypic behavior of berry texture.…”

“…LG 18 (Carreño et al, 2015;Ni et al, 2020;Crespan et al, 2021), and further on LG1, 4, 5, 9, 10, and LG13 (Carreño et al, 2015). Recently, Crespan et al (2021) identified VviAGL11 on LG18, one main gene that regulates seedlessness in table grape that seems to be involved in the phenotypic behavior of berry texture. QTLs for berry firmness on LG 2 have not been described yet.…”

Use of Artificial Neural Networks and NIR Spectroscopy for Non-Destructive Grape Texture Prediction

“…A model built without any information about berry weight or size led to models with low prediction ability (data not shown). Due to the known strong influence of berry size on the TPA parameters, the instrumental outcome of the texture profile analysis is often normalized with the berry diameter or the berry volume [43]. Therefore, we used the equatorial diameter, which can be easily measured with a caliper as an additional factor.…”

“…In previous works, the importance of densimetric sorting of the berries before TPA testing was shown, since density greatly affects the texture properties of berries. The density class influences the berry hardness [43]; therefore, without any densimetric sorting, higher variability was expected. This is reflected in the prediction model's accuracy and precision.…”

“…Several authors have suggested that the influence of berry size on the force developed is of great importance. Indeed, the instrumental outcome of the TPA is often normalized using the berry diameter or the berry volume [43]. Therefore, we added the equatorial diameter values as an additional factor.…”