Biology subjects

Baraibar, S.

Publications and source records attributed to Baraibar, S..

3 recordsLinked to original sources

bioRxiv2026

Leveraging synthetic environmental covariates to explore crop genotype by environment interactions

Environments are unpredictable. Genotype by environment interactions (GEI) further complicate any assessment of genotype performance or stability across diverse environments posing a challenge for plant breeders. Several approaches exist to address this challenge. However existing approaches present a trade-off between scale and interpretability often acting in a 'black box' manner. Here we extend the Finlay-Wilkinson regression on synthetic environmental covariates of Piepho and Blancon (2023) to dissect GEI at the level of individual developmental windows, resolving which growth stages drive GEI and how genotype stability varies across them. Yield data for 961 genotypes from a barley breeding program, tested across 12 environments, is used as a case study to illustrate how this approach can provide valuable insight into the GEI present. Exploring the dynamic impact of environmental variables across growth stages revealed that environmental factors during early grain fill contributed the most to yield GEI variance. Stability was found to be dynamic across development windows, presenting growth-stage-specific yield stability as a new potential breeding target. This approach yields new, growth-stage-resolved measures of yield stability and a new means of dissecting GEI in complex traits, while also opening a route to environmental clustering based on stage-specific GEI patterns.

genetics↗

bioRxiv2026

Identifying water stress response haplotypes in barley using latent environmental covariates

PurposeGenotype-by-environment (G x E) interactions represent a major obstacle to increasing genetic gain in crop breeding, with the underlying physiological drivers often remaining obscured within conventional statistical models. This case study presents a novel framework that transforms the latent factors from Factor Analytic (FA) multi-environment trial (MET) models into heritable quantitative traits, enabling the genetic dissection of adaptive response patterns. MethodsA Factor Analytical Linear Mixed Model (FA-LMM) was fit to plot-level yield data for 1,036 barley genotypes across eight Australian trials. ResultsCorrelation of the factor loadings with APSIM-simulated environmental covariates demonstrated that the second latent factor FA2 was strongly correlated with the Water Stress Index (r = -0.83) during the critical flowering period, establishing water availability as the main biological axis of crossover Gx E. Genotypic scores for the derived traits, Overall Performance (OP) and Water Stress Response (WSR), were subjected to high-resolution haplotype-based mapping using local Genomic Estimated Breeding Values (GEBV). ConclusionThis analysis successfully identified major genomic regions that accounted for a substantial proportion of the additive genetic variance. Gene Ontology enrichment of candidate genes within the top haploblocks implicated fundamental pathways related to energy homeostasis, root development, and stress response, with notable candidates including FTsH11, BPS1, and TDP1. The distribution of favourable Haplotypes of Interest (HOI) in elite cultivars suggested a historical signature of inadvertent selection for these adaptive mechanisms. This framework provides an explicit bridge between statistical modelling and functional genomics, offering breeders actionable genetic targets for accelerated development of climate-resilient cereals.

plant biology↗

bioRxiv2024

Phenotyping the hidden half: Combining UAV phenotyping and machine learning to predict barley root traits in the field

Improving crop root systems for enhanced adaptation and productivity remains challenging due to limitations in scalable non-destructive phenotyping approaches, inconsistent translation of root phenotypes from controlled environment to the field, and a lack of understanding of the genetic controls. This study serves as a proof of concept, evaluating a panel of Australian barley breeding lines and cultivars (Hordeum vulgare L) in two field experiments. Integrated ground-based root and shoot phenotyping was performed at key growth stages. UAV-captured vegetation indices (VIs) were explored for their potential to predict root distribution and above-ground biomass. Machine learning models, trained on a subset of 20 diverse lines, with the most accurate model applied to predict traits across a broader panel of 395 lines. Unlike previous studies focusing on above-ground traits or indirect proxies, this research directly predicts root traits in field conditions using VIs, machine learning and root phenotyping. Root trait predictions for the broader panel enabled genomic analysis using a haplotype-based approach, identifying key genetic drivers, including EGT1 and EGT2 which regulate root gravitropism. This approach offers the potential to advance root research across various crops and integrate root traits into breeding programs, fostering the development of varieties adapted to future environments. HighlightIntegrating UAV phenotyping and machine learning can be used to predict RSA traits non-destructively and offers a new approach to support root research and crop improvement.

physiology↗