Web of Science: 14 citations, Scopus: 12 citations, Google Scholar: citations,
Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies
Biscarini, Filippo (Parco Tecnologico Padano)
Nazzicari, Nelson (Parco Tecnologico Padano)
Bink, Marco (Wageningen University and Research. Biometris)
Arús i Gorina, Pere (Centre de Recerca en Agrigenòmica)
Aranzana Civit, Mª José (Centre de Recerca en Agrigenòmica)
Verde, Ignazio (Centro di Ricerca per la Frutticoltura)
Micali, Sabrina (Centro di Ricerca per la Frutticoltura)
Pascal, Thierry (Institut national de la recherche agronomique (França). Génétique et Amélioration des Fruits et Légumes)
Quilot-Turion, Benedicte (Institut national de la recherche agronomique (França). Génétique et Amélioration des Fruits et Légumes)
Lambert, Patrick (Università degli Studi di Milano. Dipartimento di Scienze Agrarie e Ambientali)
Silva Linge, Cassia da (Università degli Studi di Milano. Dipartimento di Scienze Agrarie e Ambientali)
Pacheco, Igor (Università degli Studi di Milano. Dipartimento di Scienze Agrarie e Ambientali)
Bassi, Daniele (Università degli Studi di Milano. Dipartimento di Scienze Agrarie e Ambientali)
Stella, Alessandra (Parco Tecnologico Padano)
Rossini, Laura (Parco Tecnologico Padano)

Date: 2017
Abstract: Background: Highly polygenic traits such as fruit weight, sugar content and acidity strongly influence the agroeconomic value of peach varieties. Genomic Selection (GS) can accelerate peach yield and quality gain if predictions show higher levels of accuracy compared to phenotypic selection. The available IPSC 9K SNP array V1 allows standardized and highly reliable genotyping, preparing the ground for GS in peach. - Results: A repeatability model (multiple records per individual plant) for genome-enabled predictions in eleven European peach populations is presented. The analysis included 1147 individuals derived from both commercial and non-commercial peach or peach-related accessions. Considered traits were average fruit weight (FW), sugar content (SC) and titratable acidity (TA). Plants were genotyped with the 9K IPSC array, grown in three countries (France, Italy, Spain) and phenotyped for 3-5 years. An analysis of imputation accuracy of missing genotypic data was conducted using the software Beagle, showing that two of the eleven populations were highly sensitive to increasing levels of missing data. The regression model produced, for each trait and each population, estimates of heritability (FW:0. 35, SC:0. 48, TA:0. 53, on average) and repeatability (FW:0. 56, SC:0. 63, TA:0. 62, on average). Predictive ability was estimated in a five-fold cross validation scheme within population as the correlation of true and predicted phenotypes. Results differed by populations and traits, but predictive abilities were in general high (FW:0. 60, SC:0. 72, TA:0. 65, on average). - Conclusions: This study assessed the feasibility of Genomic Selection in peach for highly polygenic traits linked to yield and fruit quality. The accuracy of imputing missing genotypes was as high as 96%, and the genomic predictive ability was on average 0. 65, but could be as high as 0. 84 for fruit weight or 0. 83 for titratable acidity. The estimated repeatability may prove very useful in the management of the typical long cycles involved in peach productions. All together, these results are very promising for the application of genomic selection to peach breeding programmes.
Note: Número d'acord de subvenció EC/FP7/265582
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Language: Anglès
Document: article ; recerca ; publishedVersion
Subject: Peach (Prunus persica) ; Genome-enabled predictions ; Fruit weight ; Sugar content ; Titratable acidity ; Genotype imputation ; Repeatability model
Published in: BMC genomics, Vol. 18 (2017) , art. 432, ISSN 1471-2164

DOI: 10.1186/s12864-017-3781-8
PMID: 28583089


15 p, 1.3 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (scientific output) > Experimental sciences > CRAG (Centre for Research in Agricultural Genomics)
Articles > Research articles
Articles > Published articles

 Record created 2020-02-06, last modified 2020-08-02



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