2025
Journal articles
Persian walnut (Juglans regia L.) is a widespread cultivated nut tree species in temperate regions. Advances in genomic tools, such as the high-density Axiom J. regia 700K single nucleotide polymorphism (SNP) genotyping array, enable the exploration of genomic prediction (GP) for this crop. This study is the first to evaluate GP accuracy and several influencing factors in walnut for traits related to phenology and nut quality. A core-collection of 170 accessions was phenotyped for 25 traits over 1 or 2 years. Highly heritable traits, such as budbreak date and female flowering date, were predicted with high accuracy (∼0.75) using ridge regression best linear unbiased prediction (rrBLUP). Three key factors influencing GP performance were examined: marker density, prediction model, and training set size. Selecting the top 1% of 364,275 SNPs based on their variance (∼3600 SNPs) was sufficient to achieve accurate predictions. Bayesian models slightly improved prediction accuracy for some traits when using this reduced SNP set, but rrBLUP provided robust results, balancing accuracy, simplicity, and computational efficiency. Training population size also influenced accuracy, with a subset comprising 50% of the population still yielding reliable predictions. Optimization of training set was assessed using coefficient of determination mean, prediction error variance mean, and mean relatedness (MeanRel) parameters, with MeanRel performing best for shell traits. However, incorporating SNPs identified in genome-wide association study into the prediction models did not enhance accuracy. In summary, this study demonstrates the feasibility and potential of GPs for walnut breeding programs using a core-collection, offering valuable insights for optimizing GP approaches in this crop.
Using the same reference to guide read acceptance or rejection with NAS, we successfully and accurately reconstructed the 15 regions in two newly assembled ssp. melo genomes and in a third ssp. agrestis cultivar. We obtained fourfold enrichment regardless of the tested samples, but with some variations according to the enriched regions. The accuracy of our assembly was further confirmed by PCR in the agrestis cultivar. We discussed parameters that could influence the enrichment and accuracy of NAS generated assemblies.
Conclusions Overall, we demonstrated that NAS is a simple and efficient approach for exploring complex genomic regions, such as clusters of Nucleotide-binding site leucine-rich repeat (NLR) resistance genes. These regions are characterized by containing a high number of copy number variations, presence-absence polymorphisms and repetitive elements. These features make accurate assembly challenging but are crucial to study due to their central role in plant immunity and disease resistance. This approach facilitates resistance gene characterization in a large number of individuals, as required when breeding new cultivars suitable for the agroecological transition.
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