Biology subjects

Gho, C.

Publications and source records attributed to Gho, C..

8 recordsLinked to original sources

bioRxiv2026

Ectopic expression of ARGOS8 mediates silk elongation rate response to water deficit in maize (Zea mays L.)

Water deficit is ubiquitous in maize (Zea mays L.) cropping systems worldwide. Ethylene insensitivity in maize has been implicated in improving kernel set and yield under drought, and ARGOS genes modulate ethylene signal transduction by reducing ethylene sensitivity. Given the strong water sensitivity of silk elongation, ARGOS8 overexpression is expected to alter silk growth responses to drought. Experiments were conducted under controlled and field conditions to test the effect of ARGOS8 gene overexpression on silk growth under water deficit. Silk lengths and water use were continuously monitored, and silk elongation rate (SER) response to the fraction of transpirable soil water (FTSW) was evaluated. Silk emergence dynamics were measured in the field by daily counting the silks under contrasting water regimes. ARGOS8 transgenics maintained SER at lower FTSW than controls; however, responses varied among hybrids. Higher SER under water stress translated into a faster silk exertion rate in ARGOS8 transgenics than controls (45 vs. 25 silks d-1), leading to a greater number of exerted silks at three days post-silking (424 vs. 377, [~]90% vs. 84% of total silks, p < 0.01). Together, these results help clarify the mechanism underlying the ectopically expressed ARGOS8 effect on maize yield improvement under water stress. HighlightARGOS8 transgenic expression sustains silk elongation rates and increases silk emergence under water deficit, improving the reproductive performance of maize in water-limited environments.

plant biology↗

bioRxiv2022

Extending the breeder's equation to take aim at the Target Population of Environments

1)A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the reference population of genotypes and product performance in the target population of environments (TPE). To realise these breeding outcomes there must be a positive MET-TPE relationship that provides consistency between the trait variation expressed within the MET data sets that are used to train the genome-to-phenome (G2P) model for applications of genomic prediction and the realised trait and performance differences in the TPE for the genotypes that are the prediction targets. The strength of this MET-TPE relationship is usually assumed to be high, however it is rarely quantified. To date investigations of genomic prediction methods have not given adequate attention to quantifying the structure of the TPE and the MET-TPE relationship and its potential impact on training the G2P model for applications of genomic prediction to accelerate breeding outcomes for the on-farm TPE. We provide a perspective on the importance of the MET-TPE relationship as a key component for the design of genomic prediction methods to realize improved rates of genetic gain for the target yield, quality, stress tolerance and yield stability traits in the on-farm TPE.

genetics↗

bioRxiv2022

Transgene by Germplasm Interactions Can Impact Transgene Evaluation

Transgenes have been successfully commercialized for qualitatively inherited insect and herbicide resistance traits that show similar effects across genetic backgrounds. However, for quantitative traits like yield, genetic background may affect the measured transgene value. In this paper, we evaluated whether different genetic backgrounds impact the estimated value of a transgene for grain yield, ear height, and anthesis-silking interval for maize by developing isogenic pairs of lines with and without a transgene and testing them in hybrid combination with non-transgenic lines from a complementary heterotic group across eleven environments in the USA. Over all hybrid combinations, the transgene increased yield by 0.2 Mg ha-1. Across multiple non-transgenic lines of the opposing heterotic group, the transgene effect within a line pair ranged from an increase of 0.8 Mg ha-1 for the NSS4 and SS7 transgenic lines to a reduction of 0.3 Mg ha-1 for the NSS5 transgenic line when compared to their non-transgenic isoline. Transgenic hybrids were often taller than non-transgenic hybrids (P<0.05). Anthesis to silking interval was reduced by 4{square}C growing degree units overall, but no transgene x genotype interaction was detected among line pairs. Our results show the importance of testing transgene efficacy across a large sample of elite hybrid pairs to assess the genes value. By only testing in a specific hybrid background, as may be done for qualitative traits like insect resistance, transgenes could be erroneously advanced or eliminated.

plant biology↗

bioRxiv2022

Methods for Evaluating Effects of Transgenes for Quantitative Traits

Transgenes that improve quantitative traits have traditionally been evaluated in one or a few genetic backgrounds across multiple environments. However, testing across multiple genetic backgrounds can be equally important to accurately quantify the value of a transgene for breeding objectives. Creating near-isogenic lines across a wide germplasm space is costly and time consuming, which renders it impractical during early stages of testing. In this experiment, we evaluate three approaches to sample the genetic space while concurrently testing across environments. We created both transgenic and non-transgenic doubled haploid lines, F2:3 lines, and bulk F3 families to determine if all methods resulted in similar estimation of transgene value and to identify the number of yield trial plots from each method necessary to obtain a stable estimate of the transgene value. With one exception, the three methods consistently estimated a similar effect of the transgene. We suggest that bulked F3 lines topcrossed to a tester inbred is the most effective method to estimate the value of a transgene across both genetic space and environments.

plant biology↗

bioRxiv2020

Two decades of creating drought tolerant maize and underpinning prediction technologies in the US corn-belt: Review and perspectives on the future of crop design

Over the last decade, society witnessed the largest expansion of agricultural land planted with drought tolerant (DT) maize (Zea mays L.) Dedicated efforts to drought breeding led to development of DT maize. Here we show that after two decades of sustained breeding efforts the rate of crop improvement under drought is in the range 1.0-1.6% yr-1, which is higher than rates (0.7% yr-1) reported prior to drought breeding. Prediction technologies that leverage biological understanding and statistical learning to improve upon the quantitative genetics framework will further accelerate genetic gain. A review of published and unpublished analyses conducted on data including 138 breeding populations and 93 environments between 2009 and 2019 demonstrated an average prediction skill (r) improvement around 0.2. These methods applied to pre-commercial stages showed accuracies higher that current statistical approaches (0.85 vs. 0.70). Improvement in hybrid and management choice can increase water productivity. Digital gap analyses are applicable at field scale suggesting the possibility of transition from evaluating hybrids to designing genotype x management (GxM) technologies for target cropping systems in drought prone areas. Due to the biocomplexity of drought, research and development efforts should be sustained to advance knowledge and iteratively improve models. HighlightCrop improvement rate in maize increased after implementation of drought breeding efforts. Harnessing crop, quantitative genetics and gap models will enable the transition from genetic evaluation to crop design.

plant biology↗

bioRxiv2020

Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

Plant breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeders equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeders equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimisation of selection in breeding programs.

genetics↗

bioRxiv2020

Reproductive resilience but not root architecture underpin yield improvement in maize

Plants capture soil resources to produce the grains required to feed a growing population. Because plants capture water and nutrients through roots, it was proposed that changes in root systems architecture (RSA) underpin the three-fold increase in maize grain yield over the last century1,2,3,4. Within this framework, improvements in reproductive resilience due to selection are caused by increased water capture1. Here we show that both root architecture and yield have changed with decades of maize breeding, but not the water capture. Consistent with Darwinian agriculture5 theory, improved reproductive resilience6,7 enabled farmers increase the number of plants per unit land8,9,10, capture soil resources, and produced more dry matter and grain. Throughout the last century, selection operated to adapt roots to crowding, enabling reallocation of C from large root systems to the growing ear and the small roots of plants cultivated in high plant populations in modern agriculture.

plant biology↗

bioRxiv2017

Leveraging biological insight and environmental variation to improve phenotypic prediction: Integrating crop growth models (CGM) with whole genome prediction (WGP)

A successful strategy for prediction of crop yield that accounts for the effects of genotype and environment will open up many opportunities for enhancing the productivity of agricultural systems. Crop growth models (CGMs) have a history of application for crop management decision support. Recently whole genome prediction (WGP) methodologies have been developed and applied in breeding to enable prediction of crop traits for new genotypes and thus increase the size of plant breeding programs without the need to expand expensive field testing. The presence of Genotype-by-Environment-by-Management (GxExM) interactions for yield presents a significant challenge for the development of prediction technologies for both product development by breeding and product placement within agricultural production systems. The integration of a CGM into the algorithm for whole genome prediction WGP, referred to as CGM-WGP, has opened up the potential for prediction of GxExM interactions for breeding and product placement applications. Here a combination of simulation and empirical studies are used to explain how the CGM-WGP methodology works and to demonstrate successful reduction to practice for applications to maize breeding and product placement recommendation in the US corn belt.

genetics↗