In sweet cherry (Prunus avium L.), a large variability exists for various traits related to fruit quality. There is a need to discover the genetic architecture of these traits in order to enhance the efficiency of breeding strategies for consumer and producer demands. With this objective, a germplasm collection consisting of 116 sweet cherry accessions was evaluated for 23 agronomically fruit quality traits over two to six years, and characterized using a genotyping-by-sequencing (GBS) approach. The SNP coverage collected was used to conduct a genome-wide association study (GWAS) using two multi-locus models and three reference genomes. We identified numerous SNP-trait associations for the global fruit size (weight, width and thickness), fruit cracking, fruit firmness and stone size, and we pinpointed several candidate genes involved in phytohormones, calcium and cell wall metabolisms. Finally, we led a precise literature review focusing on the genetic architecture of fruit quality traits in sweet cherry to compare our results with potential colocalizations of marker-trait associations. This study brings new knowledge of the genetic control of important agronomic traits related to the fruit quality, and to the development of marker-assisted selection (MAS) strategies targeted towards the facilitation of breeding efforts.

Walnut trees are grown worldwide for their edible fruits, which have high nutritional value. To address climate change, researchers have studied walnut phenology to create cultivars adapted to warmer climates. The objective of this study is to propose a scale for phenological Persian walnut observations using the Biologische Bundesanstalt, Bundessortenamt, und CHemische Industrie (BBCH) codification and alignment with historical alphameric scales. Here, the principal growth stages (PGSs) of Persian walnut (Juglans regia L.) are described using stages from a previously available alphanumeric scale. This standardised phenological scale describes Persian walnut growth from the dormant vegetative state through reproductive budding and senescence. This phenological scale is expected to increase the efficiency of walnut phenological monitoring. Fifty-seven stages are used to describe the life cycle of Persian walnut in this BBCH scale. Of these 57 stages, 3 stages are dedicated to seed germination (PGS-0), 4 stages are dedicated to bud development (PGS-0), 7 stages are dedicated to leaf development (PGS-1), 4 stages are dedicated to stem elongation (PGS-3), 8 stages are dedicated to inflorescence emergence (PGS-5), 5 stages are dedicated to male flowering (PGS-6), 5 stages are dedicated to female flowering (PGS-6), 5 stages are dedicated to fruit development (PGS-7), 12 stages are dedicated to fruit ripening (PGS-8), and 4 stages are dedicated to leaf senescence (PGS-9).

Cherries are one of the economically important fruit crops in the Rosaceae family, Prunus genus. As the first fruits of the spring season in the northern hemisphere, their attractive appearance, intensely desirable tastes, high nutrients content and consumer-friendly size captivate consumers worldwide. In the past 30 years, although cherry geneticists and breeders have greatly progressed in understanding the genetic and molecular basis underlying fruit quality, adaptation to climate change, and biotic and abiotic stress resistance, the utilization of cherry genomic data in genetics and molecular breeding has remained limited to date. Here, we thoroughly investigated recent discoveries in constructing genetic linkage maps, identifying quantitative trait loci (QTLs), genome-wide association studies (GWAS), and validating functional genes of edible cherries based on available de novo genomes and genome resequencing data of edible cherries. We further comprehensively demonstrated the genetic architecture of the main agronomic traits of edible cherries by methodically integrating QTLs, GWAS loci, and functional genes into the identical reference genome with improved annotations. These collective endeavors will offer new perspectives on the availability of sequence data and the construction of an interspecific pangenome of edible cherries, ultimately guiding cherry breeding strategies and genetic improvement programs, and facilitating the exploration of similar traits and breeding innovations across Prunus species.

Walnut trees are grown worldwide for their edible fruits of high nutritional value. To address climate change, researchers have studied walnut phenology to create cultivars adapted to warmer climates. The standardisation of walnut phenological observations is needed and the Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale is the most accurate and internationally accepted for this purpose. Here, the principal growth stages (PGS) of Persian walnut (Juglans regia L.) are described using stages from a previously available alphanumerical scale. This standardised phenological scale describes Persian walnut growth from the dormant vegetative state through reproductive budding to senescence. This phenological scale is expected to increase the efficiency of walnut phenological monitoring. Fifty-seven stages were used to describe the life cycle of Persian walnut in this BBCH scale. Of these 57 stages, 3 stages are dedicated to seed germination (PGS-0), 4 stages to bud development (PGS-0), 7 stages to leaf development (PGS-1), 4 stages to stem elongation (PGS-3), 8 stages to inflorescence emergence (PGS-5), 5 stages to male flowering (PGS-6), 5 stages to female flowering (PGS-6), 5 stages to fruit development (PGS-7), 12 stages to fruit ripening (PGS-8) and 4 stages to leaf senescence (PGS-9).

Progesterone (P4) is an endogenous sex steroid hormone involved in the ovulatory cycle and pregnancy of animal species. In sheep and goats, P4 analogues are used to induce synchronized ovulations and oestrus behavior of the females. In humans, P4 from chemical synthesis is used to treat peri-menopausal disorders. However, such molecules are released into aquatic environment and can be a source of pollution, are prohibited in organic farms and go against the trend of “naturality” in animal production as well as in human health. A natural alternative may consist in the extraction and use of P4 in plants. Mammalian hormones were discovered in an increasing number of plant species, including walnut leaves that contain high levels of P4. We compared the content of P4 in leaves of 170 accessions of Juglans regia from the walnut germplasm collection of INRAE Prunus-Juglans Biological Resources Center previously genotyped using the Axiom™ J. regia 700 K SNP array. We conducted a genome-wide association study (GWAS) using multi-locus models. When collected in October, P4 content goes from 34,1 to 287,5 mg/kg dry weight of leaves. The two laciniate accessions have the largest P4 content. We identified seven significant marker-trait associations on chromosomes 1, 2, 3, 6, 7, 15 and 16, and a candidate gene involved in the metabolism of sterols, precursors of plant steroid hormones. Our results raise the huge variability of P4 content within J. regia and propose a candidate gene which may have a role in the control of this variability, opening the way to a potential use of walnut P4 by the pharmaceutical industry towards more natural source of chemical compounds.

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.