Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies

Filippo Biscarini (1, 2) , Nelson Nazzicari (1, 3) , Marco Bink (4, 5) , Pere Arús (6) , Maria José Aranzana (6) , Ignazio Verde (7) , Sabrina Micali (7) , Thierry Pascal (8) , Bénédicte Quilot-Turion (8) , Patrick Lambert (9) , Cassia da Silva Linge (9) , Igor Pacheco (9, 10) , Daniele Bassi (9) , Alessandra Stella (1, 2) , Laura Rossini (9)

1 PTP Science Park
2 Istituto di Biologia e di Biotecnologia Agraria
3 Research Centre for Fodder Crops and Dairy Productions
4 Wageningen UR Biometris
5 Technology & Services
6 CSIC-IRTA-UAB-UB
7 Centro di Ricerca per la Frutticoltura (CREA-FRU)
8 GAFL - Génétique et Amélioration des Fruits et Légumes
9 DiSAA
10 INTA - Instituto de Nutricion y Tecnologia de los Alimentos [Santiago]

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.

Keywords: Peach (Prunus persica); Genome-enabled predictions; Fruit weight; Sugar content; Titratable acidity; Genotype imputation; Repeatability model; projections; genomic selection

Keywords: méthode de prévision; sélection génomique; qualité du fruit; caractère génotypique; rendement à la récolte; acidité titrable; verger de pêchers; teneur en sucres

Domains: Vegetal Biology

Filippo Biscarini, Nelson Nazzicari, Marco Bink, Pere Arús, Maria José Aranzana, et al.. Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies. BMC Genomics, 2017, 18 (1), ⟨10.1186/s12864-017-3781-8⟩. ⟨hal-01602738⟩