|Title||QTL mapping and breeding value estimation through pedigree-based analysis of fruit size and weight in four diverse peach breeding programs|
|Author(s)||Fresnedo-Ramírez, Jonathan; Frett, Terrence J.; Sandefur, Paul J.; Bink, Marco C.A.M.; Weg, Eric van de|
|Source||Tree Genetics and Genomes 12 (2016)2. - ISSN 1614-2942|
WUR PB Biodiversiteit en Genetische Variatie
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Breeding germplasm diversity - Complex traits - Dominance - Prunus persica (L.) Batsch - “Orange Cling”|
The narrow genetic base of peach (Prunus persica L. Batsch) challenges efforts to accurately dissect the genetic architecture of complex traits. Standardized phenotypic assessment of pedigree-linked breeding germplasm and new molecular strategies and analytical approaches developed and conducted during the RosBREED project for enabling marker-assisted breeding (MAB) in Rosaceae crops has overcome several aspects of this challenge. The genetic underpinnings of fruit size (fruit equatorial diameter (FD)) and weight (fresh weight (FW)), two most important components of yield, were investigated using the pedigree-based analysis (PBA) approach under a Bayesian framework which has emerged as an alternative strategy to study the genetics of quantitative traits within diverse breeding germplasm across breeding programs. In this study, a complex pedigree with the common founder “Orange Cling” was identified and FD and FW data from 2011 and 2012 analyzed. A genetic model including genetic additive and dominance effects was considered, and its robustness was evaluated by using various prior and initial values in the Markov chain Monte Carlo procedure. Five QTLs were identified which accounted for up to 29 and 17 % of the phenotypic variation for FD and FW, respectively. Additionally, genomic breeding values were obtained for both traits, with accuracies >85 %. This approach serves as a model study for performing PBA across diverse pedigrees. By incorporating multiple breeding programs, the method and results presented support and highlight the ability of this strategy to identify genomic resources as targets for DNA marker development and subsequent MAB within each program.