Biology subjects

Aranzana, M. J.

Publications and source records attributed to Aranzana, M. J..

9 recordsLinked to original sources

bioRxiv2026

Crosstalk between Ovate Family Proteins, plant hormones, and microtubule dynamics regulating fruit shape

Fruit shape is a key horticultural trait shaped by conserved genetic pathways and hormonal interactions, yet the mechanisms underlying shape diversity in fleshy fruits remain incompletely understood. Studies in model species such as Arabidopsis thaliana, tomato, and rice have established Ovate Family Proteins (OFPs) as central regulators of organ morphology through their interactions with brassinosteroid (BR) and gibberellin (GA) pathways and cytoskeleton dynamics. Here, we combine phylogenetic, transcriptomic, and co-expression network analysis to investigate fruit shape regulation in peach and apple, two major Rosaceae crops. We show that flat and oblong phenotypes are associated with distinct OFP expression patterns and with coordinated changes in hormone-related modules, revealing conserved OFP-hormone-cytoskeleton regulatory circuits. Flat shapes were linked to the activation of flat-associated OFPs in the absence of brassinosteroid signalling, whereas oblong shapes were associated with the activation of elongation-related OFPs under brassinosteroid-responsive conditions. Our findings extend current models of fruit morphology by providing species-specific mechanistic insight into OFP-mediated regulation in Rosaceae, offering a refined framework for breeding fruit shape.

genomics↗

bioRxiv2025

WISER: an innovative and efficient method for correcting population structure in omics-based prediction and selection

This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and having the potential to improve association studies. Unlike existing approaches that correct for population structure, WISER provides a generalized framework applicable across diverse experimental setups, species, and omics datasets, including single nucleotide polymorphisms (SNPs), metabolomics, and near-infrared spectroscopy (NIRS) used as phenomic predictors. Central to WISER is the concept of whitening, a statistical transformation that removes correlations between variables and standardizes their variances. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby minimizing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISERs potential to advance omics analyses and related research fields by capturing stronger genetic signals.

genetics↗

bioRxiv2025

WISER: an innovative and efficient method for correcting population structure in omics-based selection and association studies

This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and association studies. Unlike existing approaches which correct for population structure, WISER offers a generalized framework that can be applied across diverse experimental setups, species, and omics datasets, such as single nucleotide polymorphisms (SNPs), near-infrared spectroscopy (NIRS), and metabolomics. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby removing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISERs potential to advance omics analyses and related research fields by capturing stronger genetic signals.

genetics↗

bioRxiv2024

Integrative multi-environmental genomic prediction in apple

Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions. Methodological advances allow effective integration of phenotypic, genomic (additive, non-additive), and large-scale environmental (enviromic) data into multi-environmental genomic prediction models. These models can also account for genotype-by-environment interaction, utilize alternative relationship matrices (kernels), or substitute statistical approaches with deep learning. However, the application of multi-environmental genomic prediction in apple remained limited, likely due to the challenge of building multi-environmental datasets and structurally complex models. Here, we applied efficient statistical and deep learning models for multi-environmental genomic prediction of eleven apple traits with contrasting genetic architectures by integrating genomic- and enviromic-based model components. Incorporating genotype-by-environment interaction effects into statistical models improved predictive ability by up to 0.08 for nine traits compared to the benchmark model. This outcome, based on Gaussian and Deep kernels, shows these alternatives can effectively substitute the standard G-BLUP. Including non-additive effects slightly improved predictive ability by up to 0.03 for two traits, but enviromic-based effects resulted in no improvement. The deep learning approach achieved the highest predictive ability for three traits with simpler genetic architectures, outperforming the benchmark by up to 0.10. Our results demonstrate that the tested statistical models capture genotype-by-environment interactions particularly well, and the deep learning models efficiently integrate data from diverse sources. This study will foster the adoption of multi-environmental genomic prediction to select apple cultivars adapted to diverse environmental conditions, providing an opportunity to address climate change impacts.

plant biology↗

bioRxiv2023

Genomic analysis of fruit size and shape traits in apple: unveiling candidate genes through GWAS analysis.

Genomic tools facilitate the efficient selection of improved genetic materials with within a breeding program. In this work, we focused on two apple fruit quality traits: shape and size. We utilized data from 11 fruit morphology parameters gathered across three years of harvest from 355 genotypes of the Apple REFPOP collection, which serves as a representative sample of the genetic variability present in European cultivated apples. The data was then employed for genome-wide association analysis (GWAS) using the FarmCPU and the BLINK models. The analysis identified 59 SNPs associated with fruit size and shape traits (35 with FarmCPU and 45 with BLINK) responsible for 71 QTNs. These QTNs were distributed across all chromosomes except for chromosome10 and 15. Thirty-four QTNs, identified by 27 SNPs, were related for size traits and thirty-seven QTNs, identified by 26 SNPs, were related to shape attributes. The definition of the haploblocks containing the most relevant SNPs served to propose candidate genes, among them the genes of the ovate family protein MdOFP17 and MdOFP4 which were in a 9.7kb haploblock on chromosome 11. RNA-seq data revealed low or null expression of these genes in the oblong cultivar Skovfoged" and higher expression in the flat Grandmere. In conclusion, this comprehensive GWAS analysis of the Apple REFPOP collection has revealed promising genetic markers and candidate genes associated with apple fruit shape and size attributes, providing valuable insights that could enhance the efficiency of future breeding programs.

genomics↗

bioRxiv2023

GenoDrawing: An autoencoder framework for image prediction from SNP markers

Advancements in genome sequencing have facilitated whole genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks, particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, a neural network model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrated proficiency in its task while using a small dataset of shape-related SNPs, and multiple experiments were conducted to evaluate the impact of SNP selection and shape relation. Results indicated that the correct relationship of SNPs with visual traits had a significant impact on the generated images, consistent with biological interpretation. While using significant SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs. The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping, particularly beneficial for small to medium breeding companies to predict economically significant heritable traits. Although GenoDrawing has limitations, it sets the groundwork for future research in image prediction from genomic markers. Future studies should focus on using stronger models for image reproduction, SNP information extraction, and improved dataset balance in terms of shape for more precise outcomes.

plant biology↗

bioRxiv2022

An LTR retrotransposon in the promoter of a PsMYB10.2 gene associated with the regulation of fruit flesh color in Japanese plum

Japanese plums exhibit wide diversity of fruit coloration. The red to black hues are caused by the accumulation of anthocyanins, while their absence results in yellow, orange or green fruits. In Prunus, MYB10 genes are determinants for anthocyanin accumulation. In peach, QTLs for red plant organ traits map in an LG3 region with three MYB10 copies (PpMYB10.1, PpMYB10.2 and PpMYB10.3). In Japanese plum the gene copy number in this region differs with respect to peach, with at least three copies of PsMYB10.1. Polymorphisms in one of these copies correlate with fruit skin color. The objective of this study was to determine a possible role of LG3-PsMYB10 genes in the natural variability of the flesh color trait and to develop a molecular marker for marker-assisted selection (MAS). We explored LG3-PsMYB10 variability, including the analysis of long-range sequences obtained in previous studies through CRISPR-Cas9 enrichment sequencing. We found that the PsMYB10.2 gene was only expressed in red flesh fruits. Its role in promoting anthocyanin biosynthesis was validated by transient overexpression in Japanese plum fruits. The analysis of long-range sequences identified an LTR retrotransposon in the promoter of the expressed PsMYB10.2 gene that explained the trait in 93.1% of the 145 individuals analyzed. We hypothesize that the LTR retrotransposon may promote the PsMYB10.2 expression and activate the anthocyanin biosynthesis pathway. We provide a molecular marker for the red flesh trait which, together with that for skin color, will serve for the early selection of fruit color in breeding programs.

molecular biology↗

bioRxiv2022

An efficient CRISPR-Cas9 enrichment sequencing strategy for characterizing complex and highly duplicated genomic regions. A case study in the Prunus salicina LG3-MYB10 genes cluster.

Genome complexity is largely linked to diversification and crop innovation. Examples of regions with duplicated genes with relevant roles in agricultural traits are found in many crops. In both duplicated and non-duplicated genes, much of the variability in agronomic traits is caused by large as well as small and middle scale structural variants (SVs), which highlights the relevance of the identification and characterization of complex variability between genomes for plant breeding. Here we improve and demonstrate the use of CRISPR-Cas9 enrichment combined with long-read sequencing technology to resolve the MYB10 region in the linkage group 3 (LG3) of Japanese plum (Prunus salicina), which has a length from 90 kb to 271 kb according to the P. salicina genomes available. We demonstrate the high complexity of this region, with homology levels between Japanese plum varieties comparable to those between Prunus species. We cleaved MYB10 genes in five plum varieties using the Cas9 enzyme guided by a pool of crRNAs. The barcoded fragments were then pooled and sequenced in a single MinION Oxford Nanopore Technologies (ONT) run, yielding 194 Mb of sequence. The enrichment was confirmed by aligning the long reads to the plum reference genomes, with a mean read on-target value of 4.5% and a depth per sample of 11.9x. From the alignment, 3,261 SNPs and 287 SVs were called and phased. A de novo assembly was constructed for each variety, which also allowed detection, at the haplotype level, of the variability in this region. CRISPR-Cas9 enrichment is a versatile and powerful tool for long-read targeted sequencing even on highly duplicated and/or polymorphic genomic regions, being especially useful when a reference genome is not available. Potential uses of this methodology as well as its limitations are further discussed.

molecular biology↗

bioRxiv2021

Genetic architecture and genomic prediction accuracy of apple quantitative traits across environments

Implementation of genomic tools is desirable to increase the efficiency of apple breeding. The apple reference population (apple REFPOP) proved useful for rediscovering loci, estimating genomic prediction accuracy, and studying genotype by environment interactions (GxE). Here we show contrasting genetic architecture and genomic prediction accuracies for 30 quantitative traits across up to six European locations using the apple REFPOP. A total of 59 stable and 277 location-specific associations were found using GWAS, 69.2% of which are novel when compared with 41 reviewed publications. Average genomic prediction accuracies of 0.18-0.88 were estimated using single-environment univariate, single-environment multivariate, multi-environment univariate, and multi-environment multivariate models. The GxE accounted for up to 24% of the phenotypic variability. This most comprehensive genomic study in apple in terms of trait-environment combinations provided knowledge of trait biology and prediction models that can be readily applied for marker-assisted or genomic selection, thus facilitating increased breeding efficiency.

genomics↗