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Fruit size QTL identification and the prediction of parental QTL genotypes and breeding values in multiple pedigreed populations of sweet cherry
Authors:Umesh R Rosyara  Marco C A M Bink  Eric van de Weg  Guorong Zhang  Dechun Wang  Audrey Sebolt  Elisabeth Dirlewanger  Jose Quero-Garcia  Mirko Schuster  Amy F Iezzoni
Institution:1. Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
2. Department of Horticulture, Michigan State University, East Lansing, MI, 48824, USA
3. Biometris, Wageningen University and Research Centre, PO Box 100, 6700 AC, Wageningen, The Netherlands
4. Plant Breeding, Wageningen-UR Plant Breeding, PO Box 16, 6700 AA, Wageningen, The Netherlands
5. Agricultural Research Center-Hays, Kansas State University, 1232 240th Avenue, Hays, KS, 67601, USA
6. INRA, UMR 1332 de Biologie du Fruit et Pathologie, 33140, Villenave d’Ornon, France
7. Université Bordeaux, UMR 1332 de Biologie du Fruit et Pathologie, 33140, Villenave d’Ornon, France
8. Julius Kühn-Institut, Federal Centre for Cultivated Plants, Pillnitzer Platz 3a, 01326, Dresden, Germany
Abstract:Large fruit size is a critical trait for any new sweet cherry (Prunus avium L.) cultivar, as it is directly related to grower profitability. Therefore, determining the genetic control of fruit size in relevant breeding germplasm is a high priority. The objectives of this study were (1) to determine the number and positions of quantitative trait loci (QTL) for sweet cherry fruit size utilizing data simultaneously from multiple families and their pedigreed ancestors, and (2) to estimate fruit size QTL genotype probabilities and genomic breeding values for the plant materials. The sweet cherry material used was a five-generation pedigree consisting of 23 founders and parents and 424 progeny individuals from four full-sib families, which were phenotyped for fruit size and genotyped with 78 RosCOS single nucleotide polymorphism and 86 simple sequence repeat markers. These data were analyzed by a Bayesian approach implemented in FlexQTL? software. Six QTL were identified: three on linkage group (G) 2 with one each on groups 1, 3, and 6. Of these QTL, the second G2 QTL and the G6 QTL were previously discovered while other QTL were novel. The predicted QTL genotypes show that some QTL were segregating in all families while other QTL were segregating in a subset of the families. The progeny varied for breeding value, with some progeny having higher breeding values than their parents. The results illustrate the use of multiple pedigree-linked families for integrated QTL mapping in an outbred crop to discover novel QTL and predict QTL genotypes and breeding values.
Keywords:
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