Biology subjects

Orford, S.

Publications and source records attributed to Orford, S..

7 recordsLinked to original sources

bioRxiv2026

Extending the seasons at both ends? Understanding the physiological and genetic context required for stay green mediated yield increase in wheat (Triticum aestivum)

Flowering time and monocarpic senescence are tightly environmentally and genetically controlled. Typically, early flowering and staygreen traits are associated with opposing life-history strategies; stress avoidance versus adaptation; with flowering time an overarching regulator of crop cycle length. We developed RIL populations segregating for Ppd-1 and NAM-1 variation, which are otherwise isogenic. Multi-year field experiments enabled exploration and uncoupling of the relationship between heading and staygreen traits. Heading date manipulation enabled introduction of staygreen traits to their target breeding environments, characterised by a hot-finish. Under moderate stress, we report a 2.9% and 1.9% increase in grain width (P<0.0001), and 5.8% and 3.7% increase in TGW (P<0.0001), plus significantly greater yield (P<0.1) for late heading staygreen RILs homozygous for NAM-A1, and NAM-D1 missense variants, respectively. Grain yield increases were proportionate to the delay in senescence, being greater for the NAM-A1 than the NAM-D1 variant. For RIL populations segregating for both traits, senescence variation was observed relative to heading-date. Regarding grain yield, the staygreen trait-associated increase in source size could not compensate for the Ppd-1a associated pleiotropic reduction in sink size, even under hypothesised continental target breeding environments, with trait competition identified. Therefore, to maximise the benefits associated with staygreen traits, especially in early-heading favouring environments required targeted manipulation of source-sink dynamics, and we propose multiple strategies. HighlightStaygreen traits were associated with extending grain fill duration, increasing grain width, TGW and grain yield. There appears an antagonist relationship between earlier heading and staygreen traits.

plant biology↗

bioRxiv2023

Harnessing Landrace Diversity Empowers Wheat Breeding for Climate Resilience

Breeding crops resilient to climate change is urgently needed to help ensure food security. A key challenge is to harness genetic diversity to optimise adaptation, yield, stress resilience and nutrition. We examined the genetic and phenotypic diversity of the A.E. Watkins landrace collection of bread wheat (Triticum aestivum), a major global cereal, through whole-genome re-sequencing (827 Watkins landraces and 208 modern cultivars) and in-depth field evaluation spanning a decade. We discovered that modern cultivars are derived from just two of the seven ancestral groups of wheat, leaving five groups as previously untapped sources for breeding. This provides access to landrace-specific functional variations using structured germplasm, genotyping and informatics resources. Employing complementary genetic populations and approaches, we identified thousands of high-resolution quantitative trait loci (QTL) and significant marker-trait associations for major traits, revealing many Watkins-unique loci that can confer superior traits in modern wheat. Furthermore, we identified and functionally verified causative genes for climate-change adaptation, nutritional enhancement and resistance to wheat blast. Finally, we assessed the phenotypic effects of 44,338 Watkins-unique haplotypes, introgressed from 143 prioritised QTL in the context of modern cultivars, bridging the gap between landrace diversity and current breeding. This study establishes a framework for systematically utilising genetic diversity in crop improvement to achieve sustainable food security.

plant biology↗

bioRxiv2020

Capturing and Selecting Senescence Variation in Wheat

Senescence is a highly quantitative trait, but in wheat the genetics underpinning senescence regulation remain relatively unknown. To select senescence variation, and ultimately identify novel genetic regulators, accurate characterisation of senescence phenotypes is essential. When investigating senescence, phenotyping efforts often focus on, or are limited to, visual assessment of the flag leaves. However, senescence is a whole plant process, involving remobilisation and translocation of resources into the developing grain. Furthermore, the temporal progression of senescence poses challenges regarding trait quantification and description, whereupon the different models and approaches applied result in varying definitions of apparently similar metrics. To gain a holistic understanding of senescence we phenotyped flag leaf and peduncle senescence progression, alongside grain maturation. Reviewing the literature, we identified techniques commonly applied in quantification of senescence variation and developed simple methods to calculate descriptive and discriminatory metrics. To capture senescence dynamism, we developed the idea of calculating thermal time to different flag leaf senescence scores, for which between year Spearmans rank correlations of r [≥] 0.59, P < 4.7 x 10-5(TT70), identify as an accurate phenotyping method. Following our experience of senescence trait genetic mapping, we recognised the need for singular metrics capable of discriminating senescence variation, identifying Thermal Time to Flag Leaf Senescence score of 70 (TT70) and Mean Peduncle senescence (MeanPed) scores as most informative. Moreover, grain maturity assessments confirmed a previous association between our staygreen traits and grain fill extension, illustrating trait functionality. Here we review different senescence phenotyping approaches and share our experiences of phenotyping two independent RIL populations segregating for staygreen traits. Together, we direct readers towards senescence phenotyping methods we found most effective, encouraging their use when investigating and discriminating senescence variation of differing genetic bases, and to aid trait selection and weighting in breeding and research programs alike.

plant biology↗

bioRxiv2020

Delaying or Delivering: Identification of novel NAM-1 alleles which delay senescence to extend grain fill duration of wheat

Senescence is a complex quantitative trait under genetic and environmental control, involving the remobilisation of resources from vegetative tissue into grain. Delayed senescence, or staygreen traits, are associated with conferring stress tolerance, with extended photosynthetic activity hypothesised to sustain grain filling. The genetics of senescence regulation are largely unknown, with senescence variation often correlated with phenological traits. Here, we confirm staygreen phenotypes of two Triticum aestivum cv. Paragon EMS mutants previously identified during a forward genetic screen and selected for their agronomic performance, similar phenology and differential senescence phenotypes. Through grain filling experiments, we confirm a positive relationship between onset of senescence and grain fill duration, reporting an associated [~]14 % increase in final dry grain weight for one mutant, P < 0.05. Recombinant Inbred Line (RIL) populations segregating for senescence were developed for trait mapping purposes, and phenotyped over multiple years under field conditions. Staygreen traits were mapped using exome-capture enabled bulk segregant analysis (BSA), whereupon senescence was quantified, and metrics compared to qualify senescence traits and aid RIL selection. Using BSA we mapped our two staygreen traits to two independent, dominant, loci of 4.8 and 16.7 Mb in size encompassing 56 and 142 genes. Combining single marker association analysis with variant effect prediction, we identified SNPs encoding self-validating mutations located in NAM-1 homoeologues and propose these as gene candidates. SummaryApplying an exome-capture enabled Bulk Segregant Analysis, we identified novel NAM-1 alleles underpinning two staygreen traits associated with enhancing grain fill duration and grain weight

plant biology↗

bioRxiv2019

CONSTRUTION OF A GENETIC MAP OF RILs DERIVED FROM WHEAT (T. AESTIVUM L.) VARIETIES PAMYATII AZIEVA X PARAGON USING HIGH-THROUGHPUT SNP GENOTYPING PLATFORM KASP - COMPETATIVE ALLELE SPECIFIC PCR

The main purposes of the study were i) to develop a first mapping population for bread wheat grown in Kazakhstan, ii) to construct its genetic map for further identification of genes associated with important agronomic traits.\n\nTo the best of our knowledge this is the first segregating population and genetic map developed for Kazakh bread wheat. The work is an example of how plant breeding programs in Kazakhstan have started successfully deploying next generation plant breeding methods.\n\nThe KASP (Compatative Allele Specific PCR) technology of LGC Group and SNP DNA-markers have been exploited to genotype and build a genetic map of the segregating population. The total length of the map was 1376 cM. A total 157 out of initial 178 SNP markers used formed 26 linkage groups leaving 1 duplicated and 20 unassigned markers. The threshold distance between markers was set [≤] 30 cM. Therefore, two linkage groups were obtained for chromosomes such as 2A, 2B, 2D, 3A, 5A, 6B and 7A. Despite one duplicated and 20 unassigned markers, the 157 KASP SNP markers that were mapped spanned A, B and D genomes of wheat. Kosambi Mapping function was employed to calculate recombination units between makers. RILs were developed through SSD method up to F4 generation. Almost 97% of identified alleles were useful in evaluating the populations genetic diversity; the remaining 3% showed no outcome. As a result, 77 DNA markers were mapped for A, 74 for B and 27 for D genomes. The mapping population will be genotyped using high marker density array planform such as Illumina iSelect to obtain a genetic map with a relatively high coverage. Then, the population and high-resolution genetic map will be used to identify genes influencing wheat adaptation in Kazakhstan.

genomics↗

bioRxiv2017

Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat

BackgroundPlants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch processing of image datasets, multiple trait analysis, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.\n\nResultsHere, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different platforms. To facilitate diverse scientific user communities, we provide three versions of the software, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and an interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying growth phenotypes over time, we are able to identify diverse plant growth patterns based on a variety of key growth-related phenotypes under varied experimental conditions.\n\nAs Leaf-GP has been evaluated with noisy image series acquired by different imaging devices and still produced reliable biologically relevant outputs, we believe that our automated analysis workflow and customised computer vision based feature extraction algorithms can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, our software can not only contribute to biological research, but also exhibit how to utilise existing open numeric and scientific libraries (including Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, efficiently and effectively.\n\nConclusionsLeaf-GP is a comprehensive software application that provides three approaches to quantify multiple growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and high-performance computing clusters (HPC), with open Python-based scientific libraries preinstalled. We share our modulated source code and executables (.exe for Windows; .app for Mac) together with this paper to serve the plant research community. The software, source code and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

plant biology↗

bioRxiv2017

CropQuant: An automated and scalable field phenotyping platform for crop monitoring and trait measurements to facilitate breeding and digital agriculture

Automated phenotyping technologies are capable of providing continuous and precise measurements of traits that are key to todays crop research, breeding and agronomic practices. In additional to monitoring developmental changes, high-frequency and high-precision phenotypic analysis can enable both accurate delineation of the genotype-to-phenotype pathway and the identification of genetic variation influencing environmental adaptation and yield potential. Here, we present an automated and scalable field phenotyping platform called CropQuant, designed for easy and cost-effective deployment in different environments. To manage infield experiments and crop-climate data collection, we have also developed a web-based control system called CropMonitor to provide a unified graphical user interface (GUI) to enable realtime interactions between users and their experiments. Furthermore, we established a high-throughput trait analysis pipeline for phenotypic analyses so that lightweight machine-learning modelling can be executed on CropQuant workstations to study the dynamic interactions between genotypes (G), phenotypes (P), and environmental factors (E). We have used these technologies since 2015 and reported results generated in 2015 and 2016 field experiments, including developmental profiles of five wheat genotypes, performance-related traits analyses, and new biological insights emerged from the application of the CropQuant platform.

plant biology↗