Biology subjects

Dicenta, F.

Publications and source records attributed to Dicenta, F..

4 recordsLinked to original sources

bioRxiv2026

From Phenomics to Genomics: Macro-GWAS of Almond Morphology and Quality

In plant breeding and genetics, recent advances in high-throughput phenotyping are beginning to meet the growing demand for large-scale, high-quality phenotypic data that emerged after the development of next-generation sequencing technologies. Recent developments in phenomics have been incorporated into almond breeding programs, facilitating the large-scale acquisition of quantitative phenotypes and the dissection of the genetic architecture underlying morphological and quality-related traits. The implementation of a high-throughput phenotyping platform integrating RGB and hyperspectral imaging with genotyping using the 60K almond SNP array enabled the large-scale characterization of almond populations and the identification of 567 robust marker-trait associations across 66 traits. These analyses revealed two major genomic hotspots on chromosomes 2 and 5 associated with morphological and quality-related traits. These regions harbored biologically relevant candidate genes, including genes associated with OVATE family proteins, brassinosteroid signaling, protein ubiquitination, and acyl-CoA metabolism, as well as other regulators of organ growth, cell proliferation, hormone signaling, and seed development. Furthermore, a novel candidate gene encoding a COMT-like O-methyltransferase involved in lignin biosynthesis was identified and proposed to contribute to shell hardness, a major genetically controlled trait in almond. Together, these findings demonstrate the potential of integrating high-throughput phenomics and genomics to dissect complex traits, identify candidate genes, and accelerate genomics-informed breeding in almond.

plant biology↗

bioRxiv2025

Dissecting the genetic architecture of flowering and maturity time in almond (Prunus dulcis): heritability estimates and breeding value predictions from historical data

Almond (Prunus dulcis) is a major nut crop with high genetic complexity due to its heterozygosity and self-incompatibility. In this study, genetic parameters for flowering and maturity time--two key complex traits in almond breeding--were estimated using classical methods (midparent-offspring regression and between/within family variance components) as well as a Bayesian linear mixed model. A comprehensive dataset from the CEBAS-CSIC Almond Breeding Program (CC-ABP), comprising over 17,500 individuals and more than 30 years of historical phenotypic records, was used to generate the first complete pedigree and evaluate trait inheritance. Narrow-sense and broad-sense heritability estimates were obtained, showing substantial variation across traits and methods, with the highest values derived from classical approaches. Bayesian mixed models, implemented via the MCMCglmm R package, allowed for the estimation of breeding values (EBVs), repeatability, and variance components under unbalanced data conditions. Repeatability estimates ranged from 0.15 to 0.56. EBVs were calculated for all individuals, including those without phenotypic records, enabling the construction of trait-specific rankings for early or late flowering and maturity. Reliability values were used to refine these rankings, improving the accuracy of parental selection. Genetic trends based on EBVs revealed changes in breeding objectives over time and highlighted the limited genetic progress achieved through phenotypic selection alone. The integration of the complete pedigree, EBVs, and trait rankings offers a robust framework to optimize crossing strategies. This work lays the groundwork for incorporating genomic selection into future almond breeding efforts, improving selection efficiency for traits of agronomic importance.

plant biology↗

bioRxiv2025

Open RGB Imaging Workflow for Morphological and Morphometric Analysis of Fruits using AI: A Case Study on Almonds.

High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of AI brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology and heritable morphometric traits using AI, which can be applied to fruits and other plant organs. This workflow has been implemented in almond (Prunus dulcis), a species where efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals have been phenotyped, making this the largest morphological study conducted in almond. As result, new heritable morphometric traits of interest have been identified. These findings pave the way for more efficient breeding strategies, ultimately facilitating the development of improved cultivars with desirable traits.

plant biology↗

bioRxiv2020

PEDIGREE ANALYSIS OF 222 ALMOND GENOTYPES REVEALS TWO WORLD MAINSTREAM BREEDING LINES BASED ON ONLY THREE DIFFERENT CULTIVARS

Loss of genetic variability is a steadily increasing challenge in tree breeding programs due to the repeated use of a reduced number of founder genotypes. High-quality pedigree data of 222 almond [Prunus dulcis (Miller) D.A. Webb, syn. P. amygdalus (L) Batsch] cultivars and breeding selections were used to study global genetic variability in modern breeding programs from Argentina, Australia, France, Greece, Israel, Italy, Russia, Spain and the USA. Inbreeding coefficients, pairwise relatedness and genetic contribution were calculated for these genotypes. The results reveal two mainstream breeding lines based on three cultivars from two different geographical regions: Tuono- Cristomorto (local landraces from Puglia, Italy) and Nonpareil (chance seedling selected in California, USA, from French original stock). Direct descendants from Tuono or Cristomorto number 75 (sharing 30 descendants), while Nonpareil has 72 direct descendants. The mean inbreeding coefficient of the analyzed genotypes was 0.036, with 13 genotypes presenting a high inbreeding coefficient, over 0.250. Breeding programs from the USA, France and Spain showed inbreeding coefficients of 0.067, 0.050 and 0.034, respectively. According to their genetic contribution, modern cultivars from Israel, France, the USA, Spain and Australia, trace back to six, five, four, four and two main founding genotypes respectively. Among the group of 65 genotypes carrying the Sf allele for self-compatibility, the mean relatedness coefficient was 0.133, with Tuono as the main founding genotype (23.75% of total genetic contribution). Increasing as well as preserving current genetic variability is required in almond breeding programs worldwide to assure genetic gain and continuing breeding progress. Breeding objectives, apart from high and efficient productivity, should include disease resistance and adaptation to climate change. Ultimately, any new commercial almond cultivar has to be economically viable and breeders play a critical role in achieving this goal.

genetics↗