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

被引:43
作者
Biscarini, Filippo [1 ]
Nazzicari, Nelson [1 ,2 ]
Bink, Marco [3 ,10 ]
Arus, Pere [4 ]
Jose Aranzana, Maria [4 ]
Verde, Ignazio [5 ]
Micali, Sabrina [5 ]
Pascal, Thierry [6 ]
Quilot-Turion, Benedicte [6 ]
Lambert, Patrick [7 ]
Linge, Cassia da Silva [7 ]
Pacheco, Igor [7 ,9 ]
Bassi, Daniele [7 ]
Stella, Alessandra [1 ,8 ]
Rossini, Laura [1 ,7 ]
机构
[1] PTP Sci Pk, Via Einstein Loc Cascina Codazza, Lodi, Italy
[2] Council Agr Res & Econ CREA, Res Ctr Fodder Crops & Dairy Prod, Lodi, Italy
[3] Wageningen UR Biometris, Wageningen, Netherlands
[4] CSIC IRTA UAB UB, Ctr Recerca Agrigen, IRTA, Campus UAB, Barcelona, Spain
[5] Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Frutticoltura CREA FRU, Via Fioranello 52, Rome, Italy
[6] INRA, GAFL, F-84140 Montfavet, France
[7] Univ Milano DiSAA, Via Celoria 2, Milan, Italy
[8] IBBA CNR, Via Edoardo Bassini 15, I-20133 Milan, Italy
[9] Univ Chile, Inst Nutr & Food Technol INTA, Av El Libano 5524, Santiago, Chile
[10] Hendrix Genet Res Technol & Serv BV, POB 114, NL-5830 AC Boxmeer Nl, Netherlands
关键词
Peach (Prunus persica); Genome-enabled predictions; Fruit weight; Sugar content; Titratable acidity; Genotype imputation; Repeatability model; POPULATION-STRUCTURE; GENETIC DIVERSITY; COMPLEX TRAITS; SELECTION; ACCURACY; ASSOCIATION; DOMESTICATION; GENOTYPES; MODELS; IMPACT;
D O I
10.1186/s12864-017-3781-8
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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.
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页数:15
相关论文
共 71 条
[1]   Peach:: The model genome for Rosaceae [J].
Abbott, A ;
Georgi, L ;
Yvergniaux, D ;
Inigo, M ;
Sosinski, B ;
Wang, Y ;
Blenda, A ;
Reighard, G .
PROCEEDINGS OF THE INTERNATIONAL SYMPOSIUM ON TROPICAL AND SUBTROPICAL FRUITS, VOLS 1 AND 2, 2002, (575) :145-155
[2]   Genome-wide view of genetic diversity reveals paths of selection and cultivar differentiation in peach domestication [J].
Akagi, Takashi ;
Hanada, Toshio ;
Yaegaki, Hideaki ;
Gradziel, Thomas M. ;
Tao, Ryutaro .
DNA RESEARCH, 2016, 23 (03) :271-282
[3]   Accuracy of genomic selection for alfalfa biomass yield in different reference populations [J].
Annicchiarico, Paolo ;
Nazzicari, Nelson ;
Li, Xuehui ;
Wei, Yanling ;
Pecetti, Luciano ;
Brummer, E. Charles .
BMC GENOMICS, 2015, 16
[4]   The genome-assisted barnyard [J].
不详 .
NATURE BIOTECHNOLOGY, 2009, 27 (06) :487-487
[5]   Population Structure and Cryptic Relatedness in Genetic Association Studies [J].
Astle, William ;
Balding, David J. .
STATISTICAL SCIENCE, 2009, 24 (04) :451-471
[6]   Multiple QTL mapping in related plant populations via a pedigree-analysis approach [J].
Bink, MCAM ;
Uimari, P ;
Sillanpää, MJ ;
Janss, LLG ;
Jansen, RC .
THEORETICAL AND APPLIED GENETICS, 2002, 104 (05) :751-762
[7]   Across-Line SNP Association Study for Direct and Associative Effects on Feather Damage in Laying Hens [J].
Biscarini, F. ;
Bovenhuis, H. ;
van der Poel, J. ;
Rodenburg, T. B. ;
Jungerius, A. P. ;
van Arendonk, J. A. M. .
BEHAVIOR GENETICS, 2010, 40 (05) :715-727
[8]   Across-line SNP association study of innate and adaptive immune response in laying hens [J].
Biscarini, F. ;
Bovenhuis, H. ;
van Arendonk, J. A. M. ;
Parmentier, H. K. ;
Jungerius, A. P. ;
van der Poel, J. J. .
ANIMAL GENETICS, 2010, 41 (01) :26-38
[9]   Genome-Wide Association Study for Traits Related to Plant and Grain Morphology, and Root Architecture in Temperate Rice Accessions [J].
Biscarini, Filippo ;
Cozzi, Paolo ;
Casella, Laura ;
Riccardi, Paolo ;
Vattari, Alessandra ;
Orasen, Gabriele ;
Perrini, Rosaria ;
Tacconi, Gianni ;
Tondelli, Alessandro ;
Biselli, Chiara ;
Cattivelli, Luigi ;
Spindel, Jennifer ;
McCouch, Susan ;
Abbruscato, Pamela ;
Vale, Giampiero ;
Piffanelli, Pietro ;
Greco, Raffaella .
PLOS ONE, 2016, 11 (05)
[10]   Genome-enabled predictions for binomial traits in sugar beet populations [J].
Biscarini, Filippo ;
Stevanato, Piergiorgio ;
Broccanello, Chiara ;
Stella, Alessandra ;
Saccomani, Massimo .
BMC GENETICS, 2014, 15