Biology subjects

Quesada-Traver, C.

Publications and source records attributed to Quesada-Traver, C..

3 recordsLinked to original sources

bioRxiv2026

From smartphone to gene: Automated image-based phenotyping captures flowering diversity of common buckwheat

Common buckwheat (Fagopyrum esculentum Moench) exhibits an indeterminate flowering habit, resulting in the simultaneous occurrence of flowering, seed development and seed ripening. This contributes to yield losses and complicates mechanized harvesting. Therefore, the development of varieties with a desirable flowering pattern is a breeding priority in buckwheat. However, phenotyping of buckwheat flowering under field conditions remains challenging because manual assessments are time consuming and expertise dependent. Here, we show that imaging devices such as smartphones or cameras can be used for phenotyping of flowering traits in a buckwheat collection encompassing 169 accessions. Flowering traits describing flowering time (n=10), flowering intensity (n=6) and abscission (n=6) were extracted from 37,078 images captured over five growing seasons using a Mask R-CNN segmentation model combined with statistical modelling of flowering dynamics. The 22 extracted traits were highly heritable (0.5 < h2 < 0.93) and correlated well (0.84 <r< 0.94) with manually assessed flowering stages while providing additional information on flowering intensity. Genome-wide association studies revealed 171 genomic regions associated with flowering traits, including marker-trait associations for agronomically important breeding targets. Candidate genes had functional annotations including "Auxin response factor" and "Ethylene-insensitive protein", gene families involved in hormone signalling and developmental regulation, controlling flowering in other species. These results demonstrate how digital imaging combined with automated feature extraction can be leveraged to gain insight on buckwheat flowering patterns in large scale field trials across different phenotyping platforms. This paves the way to breed for desired flowering patterns using molecular tools in a targeted manner.

plant biology↗

bioRxiv2024

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winners strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

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↗