Biology subjects

York, L. M.

Publications and source records attributed to York, L. M..

4 recordsLinked to original sources

bioRxiv2018

Functional phenomics: An emerging field integrating high-throughput phenotyping, physiology, and bioinformatics

HighlightFunctional phenomics is an emerging field in plant biology that relies on high-throughput phenotyping, big data analytics, controlled manipulative experiments, and simulation modelling to increase understanding of plant physiology.\n\nAbstractThe emergence of functional phenomics signifies the rebirth of physiology as a 21st century science through the use of advanced sensing technologies and big data analytics. Functional phenomics highlights the importance of phenotyping beyond only identifying genetic regions because a significant knowledge gap remains in understanding which plant properties will influence ecosystem services beneficial to human welfare. Here, a general approach for the theory and practice of functional phenomics is outlined including exploring the phene concept as a unit of phenotype. The functional phenomics pipeline is proposed as a general method for conceptualizing, measuring, and validating utility of plant phenes. The functional phenomics pipeline begins with ideotype development. Second, a phenotyping platform is developed to maximize the throughput of phene measurements. A mapping population is screened measuring target phenes and indicators of plant performance such as yield and nutrient composition. Traditional forward genetics allows genetic mapping, while functional phenomics links phenes to plant performance. Based on these data, genotypes with contrasting phenotypes can be selected for smaller yet more intensive experiments to understand phene-environment interactions in depth. Simulation modeling can be used to understand the phenotypes and all stages of the pipeline feed back to ideotype and phenotyping platform development.

plant biology↗

bioRxiv2018

A novel multi-perspective imaging platform (M-PIP) for phenotyping soybean root crowns in the field increases throughput and separation ability of genotype root properties

BackgroundRoot crown phenotyping has linked root properties to shoot mass, nutrient uptake, and yield in the field, which increases the understanding of soil resource acquisition and presents opportunities for breeding. The original methods using manual measurements have been largely supplanted by image-based approaches. However, most image-based systems have been limited to one or two perspectives and rely on segmentation from grayscale images. An efficient high-throughput root crown phenotyping system is introduced that takes images from five perspectives simultaneously, constituting the Multi-Perspective Imaging Platform (M-PIP). A segmentation procedure using the Expectation-Maximization Gaussian Mixture Model (EM-GMM) algorithm was developed to distinguish plant root pixels from background pixels in color images and using hardware acceleration (CPU and GPU). Phenes were extracted using MatLab scripts. Placement of excavated root crowns for image acquisition was standardized and is ergonomic. The M-PIP was tested on 24 soybean [Glycine max (L.) Merr.] cultivars released between 1930 and 2005.\n\nResultsRelative to previous reports of imaging throughput, this system provides greater throughput with sustained rates of 1.66 root crowns min-1. The EM-GMM segmentation algorithm with hardware acceleration was able to segment images in 10 s, faster than previous methods, and the output images were consistently better connected with less loss of fine detail. Image-based phenes had similar heritabilities as manual measures with the greatest effect sizes observed for Maximum Radius and Fine Radius Frequency. Correlations were also noted, especially among the manual Complexity score and phenes such as number of roots and Total Root Length. Averaging phenes across perspectives generally increased heritability, and no single perspective consistently performed better than others. Angle-based phenes, Fineness Index, Maximum Width, Holes, Solidity and Width-to-Depth Ratio were the most sensitive to perspective with decreased correlations among perspectives.\n\nConclusionThe substantial heritabilities measured for many phenes suggest that they are potentially useful for breeding. Multiple perspectives together often produced the greatest heritabilities, and no single perspective consistently performed better than others. Thus, as illustrated here for soybean, multiple perspectives may be beneficial for root crown phenotyping systems. This system can contribute to breeding efforts that incorporate under-utilized root phenotypes to increase food security and sustainability.

physiology↗

bioRxiv2018

Wheat shovelomics II: Revealing relationships between root crown traits and crop growth

Optimization of root system architecture represents an important goal in wheat breeding. Adopting new field methods for root phenotyping is key to delivering this goal. A novel shovelomics method was applied for phenotyping root crown traits to characterize the Savannah x Rialto doubled-haploid (DH) population in two field experiments under irrigated and rain-fed conditions. Trait validation was carried out through soil coring on a subset of 14 DH lines and the two parents. We observed that drought reduced grain yield per plant by 21.0%. Under rain-fed conditions, nodal root angle and roots shoot-1 were positively associated with root length density (RLD) at 40-60 cm depth; RLD was also positively correlated with grain yield. Nodal root angle and roots shoot-1 were also positively associated with canopy stay green and grain yield under rain-fed conditions. We conclude that shovelomics is a valuable technique for quantifying genetic variation in nodal root traits in wheat, revealing nodal root angle and root number per shoot provide useful selection criteria in breeding programs aimed at improving drought tolerance in wheat.\n\nHighlightNodal root angle and number shoot-1 measured using shovelomics were positively associated with root density at depth and yield under drought in a Savanah x Rialto wheat DH population.

plant biology↗

bioRxiv2018

Wheat shovelomics I: A field phenotyping approach for characterising the structure and function of root systems in tillering species

Wheat represents a major crop, yet the current rate of yield improvement is insufficient to meet its projected global food demand. Breeding root systems more efficient for water and nitrogen capture represents a promising avenue for accelerating yield gains. Root crown phenotyping, or shovelomics, relies on excavation of the upper portions of root systems in the field and measuring root properties such as numbers, angles, densities and lengths. We report a new shovelomics method that images the whole wheat root crown, then partitions it into the main shoot and tillers for more intensive phenotyping. Root crowns were phenotyped using the new method from the Rialto x Savannah population consisting of both parents and 94 doubled-haploid lines. For the whole root crown, the main shoot, and tillers, root phenes including nodal root number, growth angle, length, and diameter were measured. Substantial variation and heritability were observed for all phenes. Principal component analysis revealed latent constructs that imply pleiotropic genetic control of several related root phenes. Correlational analysis revealed that nodal root number and growth angle correlate among the whole crown, main shoot, and tillers, indicating shared genetic control among those organs. We conclude that this phenomics approach will be useful for breeding ideotype root systems in tillering species.

plant biology↗