Integrated QTL detection for key breeding traits in multiple peach progenies

BMC Genomics

Research Article

José Antonio Campoy¹  Elisabeth Dirlewanger¹  Sabrina Micali²  Ignazio Verde²  Maria Teresa Dettori²  Patrick Lambert³  Thierry Pascal³  Bénédicte Quilot-Turion³  Marco Bink⁴  François Laurens⁵  Maria José Aranzana⁶  Pere Arús⁶  José R. Hernández Mora⁶  Celia Cantín⁷  Andrea Caprera⁸  Nelson Nazzicari⁹  Laura Rossini^1,10  Eric Van de Weg^1,11  Diego Micheletti^1,12  Michela Troggio^1,12  Elisa Banchi^1,12  Daniele Bassi^1,13 

[1] BFP, INRA, 33140, Villenave d’Ornon, France;Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria (CREA) - Centro di Ricerca per la Frutticoltura, 00134, Roma, Italy;GAFL, INRA, 84140, Montfavet, France;Hendrix Genetics Research, Technology & Services B.V., P.O. Box 114, 5830AC, Boxmeer, The Netherlands;IRHS, INRA, SFR 4207 QuaSaV, 49071, Beaucouze, France;IRTA, Centre de Recerca en Agrigenòmica CSIC-IRTA-UAB-UB; Campus UAB, Bellaterra (Cerdanyola del Vallès), 08193, Barcelona, Spain;IRTA, FruitCentreParc Cientific i Tecnològic Agroalimentari de Lleida (PCiTAL), Lleida, Spain;PTP Science Park, Via Einstein, 26900, Lodi, Italy;PTP Science Park, Via Einstein, 26900, Lodi, Italy;Council for Agricultural Research and Economics (CREA) Research Centre for Fodder Crops and Dairy Productions, Lodi, Italy;PTP Science Park, Via Einstein, 26900, Lodi, Italy;Università degli Studi di Milano, DiSAA, Via Celoria 2, 20133, Milan, Italy;Plant Breeding, Wageningen University and Research Droevendaalsesteeg 1, P.O. Box 386, 6700AJ, Wageningen, The Netherlands;Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via Mach 1, 38010, San Michele all’Adige, TN, Italy;Università degli Studi di Milano, DiSAA, Via Celoria 2, 20133, Milan, Italy;

关键词: Peach QTL;    Pedigre-based Analysis;    PBA;    FlexQTL;    Peach breeding;   

DOI  :  10.1186/s12864-017-3783-6

 received in 2017-01-25, accepted in 2017-05-10,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundPeach (Prunus persica (L.) Batsch) is a major temperate fruit crop with an intense breeding activity. Breeding is facilitated by knowledge of the inheritance of the key traits that are often of a quantitative nature. QTLs have traditionally been studied using the phenotype of a single progeny (usually a full-sib progeny) and the correlation with a set of markers covering its genome. This approach has allowed the identification of various genes and QTLs but is limited by the small numbers of individuals used and by the narrow transect of the variability analyzed. In this article we propose the use of a multi-progeny mapping strategy that used pedigree information and Bayesian approaches that supports a more precise and complete survey of the available genetic variability.ResultsSeven key agronomic characters (data from 1 to 3 years) were analyzed in 18 progenies from crosses between occidental commercial genotypes and various exotic lines including accessions of other Prunus species. A total of 1467 plants from these progenies were genotyped with a 9 k SNP array. Forty-seven QTLs were identified, 22 coinciding with major genes and QTLs that have been consistently found in the same populations when studied individually and 25 were new. A substantial part of the QTLs observed (47%) would not have been detected in crosses between only commercial materials, showing the high value of exotic lines as a source of novel alleles for the commercial gene pool. Our strategy also provided estimations on the narrow sense heritability of each character, and the estimation of the QTL genotypes of each parent for the different QTLs and their breeding value.ConclusionsThe integrated strategy used provides a broader and more accurate picture of the variability available for peach breeding with the identification of many new QTLs, information on the sources of the alleles of interest and the breeding values of the potential donors of such valuable alleles. These results are first-hand information for breeders and a step forward towards the implementation of DNA-informed strategies to facilitate selection of new cultivars with improved productivity and quality.

【 授权许可】

CC BY   
© The Author(s). 2017

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