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Secondary metabolite profiling deciphers the phenolic and volatile organic compound diversity within an interspecific Malus collection 认领 引用
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作者 Genny Zambiasi Marta Degasperi +8 位作者 Iuliia Khomenko Franco Biasioli Domenico Masuero Urska Vrhovsek Nicola Busatto Walter Guerra Michela Troggio Fabrizio Costa Brian Farneti 《Horticultural Plant Journal》 SCIE CAS CSCD 2026年第7期1554-1566,共13页
Secondary metabolites play fundamental roles in apple,influencing the interaction with pollinators and frugivores for seed dispersal,contributing to fruit quality and promoting human health through their antioxidant p... Secondary metabolites play fundamental roles in apple,influencing the interaction with pollinators and frugivores for seed dispersal,contributing to fruit quality and promoting human health through their antioxidant property.Domestication and breeding have significantly re-shaped the apple metabolism,altering both aromatic profiles and nutritional properties.This study assessed the secondary metabolite variation in a comprehensive Malus spp.collection comprising 163 accessions belonging to 44 species.The profiling of phenolic and volatile organic compounds(VOCs),performed with Ultra-Performance Liquid Chromatography(UPLC)and Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry(PTR-ToF-MS)instruments respectively,in both skin and pulp tissues,uncovered distinct metabolic patterns between wild and domesticated apples.This investigation underlined the higher concentration of these metabolites in the skin tissue and revealed a clear metabolic divergence between the two groups of Malus accessions.Wild Malus spp.accessions resulted particularly rich in specific polyphenols characterized by antioxidant activity,including catechin and procyanidins.Conversely,apples of Malus domestica accessions exhibited a more abundant VOC profile,particularly represented by esters associated with fruity aroma,enhancing sensory appeal.These findings provide a foundation for leveraging wild germplasm in breeding programs,and the identification of accessions with high polyphenolic concentration and desirable aromatic profiles offers valuable opportunities to improve the aromatic and nutraceutical properties of apple. 展开更多
关键词 Apple Diversity Secondary metabolites Polyphenols VOCs Domestication Quality
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Integrative multi-environmental genomic prediction in apple 认领 引用 被引量：1
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作者 Michaela Jung Carles Quesada-Traver +13 位作者 Morgane Roth Maria Jose Aranzana Hélène Muranty Marijn Rymenants Walter Guerra Elias Holzknecht Nicole Pradas Lidia Lozano Frédérique Didelot François Laurens Steven Yates Bruno Studer Giovanni A.L.Broggini Andrea Patocchi 《Horticulture Research》 SCIE CSCD 2025年第2期266-280,共15页
Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions.Methodological advances allow effective integration of phenotypic,genomic(additive,nonaddi... Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions.Methodological advances allow effective integration of phenotypic,genomic(additive,nonadditive),and large-scale environmental(enviromic)data into multi-environmental genomic prediction models.These models can also account for genotype-by-environment interaction,utilize alternative relationship matrices(kernels),or substitute statistical approaches with deep learning.However,the application of multi-environmental genomic prediction in apple remained limited,likely due to the challenge of building multi-environmental datasets and structurally complex models.Here,we applied efficient statistical and deep learning models for multi-environmental genomic prediction of eleven apple traits with contrasting genetic architectures by integrating genomic-and enviromic-based model components.Incorporating genotype-by-environment interaction effects into statistical models improved predictive ability by up to 0.08 for nine traits compared to the benchmark model.This outcome,based on Gaussian and Deep kernels,shows these alternatives can effectively substitute the standard genomic best linear unbiased predictor(G-BLUP).Including nonadditive and enviromic-based effects resulted in a predictive ability very similar to the benchmark model.The deep learning approach achieved the highest predictive ability for three traits with oligogenic genetic architectures,outperforming the benchmark by up to 0.10.Our results demonstrate that the tested statistical models capture genotype-by-environment interactions particularly well,and the deep learning models efficiently integrate data from diverse sources.This study will foster the adoption of multi-environmental genomic prediction to select apple cultivars adapted to diverse environmental conditions,providing an opportunity to address climate change impacts. 展开更多
关键词 selection genotypes suited genotype environment interaction deep learninghoweverthe alternative relationship matrices kernels genomic prediction multi environmental genomic prediction deep learning statistical approaches
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Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple 认领 引用 被引量：16
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作者 Morgane Roth Hélène Muranty +3 位作者 Mario Di Guardo Walter Guerra Andrea Patocchi Fabrizio Costa 《Horticulture Research》 SCIE 2020年第1期889-902,共14页
Texture is a complex trait and a major component of fruit quality in apple.While the major effect of MdPG1,a gene controlling firmness,has already been exploited in elite cultivars,the genetic basis of crispness remai... Texture is a complex trait and a major component of fruit quality in apple.While the major effect of MdPG1,a gene controlling firmness,has already been exploited in elite cultivars,the genetic basis of crispness remains poorly understood.To further improve fruit texture,harnessing loci with minor effects via genomic selection is therefore necessary.In this study,we measured acoustic and mechanical features in 537 genotypes to dissect the firmness and crispness components of fruit texture.Predictions of across-year phenotypic values for these components were calculated using a model calibrated with 8,294 SNP markers.The best prediction accuracies following cross-validations within the training set of 259 genotypes were obtained for the acoustic linear distance(0.64).Predictions for biparental families using the entire training set varied from low to high accuracy,depending on the family considered.While adding siblings or half-siblings into the training set did not clearly improve predictions,we performed an optimization of the training set size and composition for each validation set.This allowed us to increase prediction accuracies by 0.17 on average,with a maximal accuracy of 0.81 when predicting firmness in the‘Gala’בPink Lady’family.Our results therefore identified key genetic parameters to consider when deploying genomic selection for texture in apple.In particular,we advise to rely on a large training population,with high phenotypic variability from which a‘tailored training population’can be extracted using a priori information on genetic relatedness,in order to predict a specific target population. 展开更多
关键词 prediction optimization fir
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Genetic architecture and genomic predictive ability of apple quantitative traits across environments 认领 引用 被引量：8
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作者 Michaela Jung Beat Keller +15 位作者 Morgane Roth Maria José Aranzana Annemarie Auwerkerken Walter Guerra Mehdi Al-Rifaï Mariusz Lewandowski Nadia Sanin Marijn Rymenants Frédérique Didelot Christian Dujak Carolina Font i Forcada Andrea Knauf François Laurens Bruno Studer Hélène Muranty Andrea Patocchi 《Horticulture Research》 SCIE CSCD 2022年第1期1595-1616,共22页
Implementation of genomic tools is desirable to increase the efficiency of apple breeding.Recently,the multi-environment apple reference population(apple REFPOP)proved useful for rediscovering loci,estimating genomic ... Implementation of genomic tools is desirable to increase the efficiency of apple breeding.Recently,the multi-environment apple reference population(apple REFPOP)proved useful for rediscovering loci,estimating genomic predictive ability,and studying genotype by environment interactions(G×E).So far,only two phenological traits were investigated using the apple REFPOP,although the population may be valuable when dissecting genetic architecture and reporting predictive abilities for additional key traits in apple breeding.Here we show contrasting genetic architecture and genomic predictive abilities for 30 quantitative traits across up to six European locations using the apple REFPOP.A total of 59 stable and 277 location-specific associations were found using GWAS,69.2%of which are novel when compared with 41 reviewed publications.Average genomic predictive abilities of 0.18-0.88 were estimated using main-effect univariate,main-effect multivariate,multi-environment univariate,and multi-environment multivariate models.The G×E accounted for up to 24% of the phenotypic variability.This most comprehensive genomic study in apple in terms of traitenvironment combinations provided knowledge of trait biology and predictionmodels that can be readily applied for marker-assisted or genomic selection,thus facilitating increased breeding efficiency. 展开更多
关键词 studying genotype environment interactions g e rediscovering lociestimating genomic predictive abilityand reference population apple genomic predictive ability genomic tools genetic architecture reporting predictive abilities apple
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The apple REFPOP—a reference population for genomics-assisted breeding in apple 认领 引用 被引量：12
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作者 Michaela Jung Morgane Roth +19 位作者 Maria JoséAranzana Annemarie Auwerkerken Marco Bink Caroline Denancé Christian Dujak Charles-Eric Durel Carolina Font i Forcada Celia M.Cantin Walter Guerra Nicholas P.Howard Beat Keller Mariusz Lewandowski Matthew Ordidge Marijn Rymenants Nadia Sanin Bruno Studer Edward Zurawicz François Laurens Andrea Patocchi Hélène Muranty 《Horticulture Research》 SCIE 2020年第1期364-379,共16页
Breeding of apple is a long-term and costly process due to the time and space requirements for screening selection candidates.Genomics-assisted breeding utilizes genomic and phenotypic information to increase the sele... Breeding of apple is a long-term and costly process due to the time and space requirements for screening selection candidates.Genomics-assisted breeding utilizes genomic and phenotypic information to increase the selection efficiency in breeding programs,and measurements of phenotypes in different environments can facilitate the application of the approach under various climatic conditions.Here we present an apple reference population:the apple REFPOP,a large collection formed of 534 genotypes planted in six European countries,as a unique tool to accelerate apple breeding.The population consisted of 269 accessions and 265 progeny from 27 parental combinations,representing the diversity in cultivated apple and current European breeding material,respectively.A high-density genome-wide dataset of 303,239 SNPs was produced as a combined output of two SNP arrays of different densities using marker imputation with an imputation accuracy of 0.95.Based on the genotypic data,linkage disequilibrium was low and population structure was weak.Two well-studied phenological traits of horticultural importance were measured.We found marker–trait associations in several previously identified genomic regions and maximum predictive abilities of 0.57 and 0.75 for floral emergence and harvest date,respectively.With decreasing SNP density,the detection of significant marker–trait associations varied depending on trait architecture.Regardless of the trait,10,000 SNPs sufficed to maximize genomic prediction ability.We confirm the suitability of the apple REFPOP design for genomics-assisted breeding,especially for breeding programs using related germplasm,and emphasize the advantages of a coordinated and multinational effort for customizing apple breeding methods in the genomics era. 展开更多
关键词 breeding planted utilize
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