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

BMC Genomics

Research Article

Sabrina Micali¹  Ignazio Verde¹  Thierry Pascal²  Benedicte Quilot-Turion²  Maria José Aranzana³  Pere Arús³  Nelson Nazzicari⁴  Filippo Biscarini⁵  Alessandra Stella⁵  Laura Rossini⁶  Cassia da Silva Linge⁷  Daniele Bassi⁷  Patrick Lambert⁷  Igor Pacheco⁸  Marco Bink⁹ 

[1] Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) — Centro di Ricerca per la Frutticoltura (CREA-FRU), Via di Fioranello 52, Roma, Italy;GAFL, INRA, 84140, Montfavet, France;IRTA, Centre de Recerca en Agrigenòmica CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra (Cerdanyola del Vallés), Barcelona, Spain;PTP Science Park, Via Einstein - Loc. Cascina Codazza, Lodi, Italy;Council for Agricultural Research and Economics (CREA) Research Centre for Fodder Crops and Dairy Productions, Lodi, Italy;PTP Science Park, Via Einstein - Loc. Cascina Codazza, Lodi, Italy;IBBA-CNR, Via Edoardo Bassini, 15, 20133, Milan, Italy;PTP Science Park, Via Einstein - Loc. Cascina Codazza, Lodi, Italy;Università degli Studi di Milano - DiSAA, Via Celoria 2, Milano, Italy;Università degli Studi di Milano - DiSAA, Via Celoria 2, Milano, Italy;Università degli Studi di Milano - DiSAA, Via Celoria 2, Milano, Italy;Institute of Nutrition and Food Technology - INTA, Universidad de Chile, Av El Líbano 5524, Santiago, Chile;Wageningen UR Biometris, Wageningen, The Netherlands;Present Address: Hendrix Genetics Research, Technology & Services B.V., P.O. Box 114, 5830AC, Boxmeer NL, The Netherlands;

关键词: Prunus persica;    Genome-enabled predictions;    Fruit weight;    Sugar content;    Titratable acidity;    Genotype imputation;    Repeatability model;   

DOI  :  10.1186/s12864-017-3781-8

 received in 2017-01-27, accepted in 2017-05-10,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundHighly 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.ResultsA 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).ConclusionsThis 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.

【 授权许可】

CC BY   
© The Author(s) 2017

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