scholarly journals Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping

2019 ◽  
Author(s):  
Toshimi Baba ◽  
Mehdi Momen ◽  
Malachy T. Campbell ◽  
Harkamal Walia ◽  
Gota Morota
Keyword(s):  
High Throughput ◽  
Genomic Prediction ◽  
Regression Models ◽  
Joint Modeling ◽  
Random Regression ◽  
Shoot Biomass ◽  
Time Points ◽  
Large Populations ◽  
High Throughput Phenotyping ◽  
Daily Water

AbstractRandom regression models (RRM) are used extensively for genomic inference and prediction of time-valued traits in animal breeding, but only recently have been used in plant systems. High-throughput phenotyping (HTP) platforms provide a powerful means to collect high-dimensional phenotypes throughout the growing season for large populations. However, to date, selection of an appropriate statistical genomic framework to integrate multiple temporal traits for genomic prediction in plants remains unexplored. Here, we demonstrate the utility of a multi-trait RRM (MT-RRM) for genomic prediction of daily water usage (WU) in rice (Oryza sativa) through joint modeling with shoot biomass (projected shoot area, PSA). Three hundred and fifty-seven accessions were phenotyped daily for WU and PSA over 20 days using a greenhouse-based HTP platform. MT-RRMs that modeled additive genetic and permanent environmental effects for both traits using quadratic Legendre polynomials were used to assess genomic correlations between traits and genomic prediction for WU. Predictive abilities of the MT-RRMs were assessed using two cross-validation (CV) scenarios. The first scenario was designed to predict genetic values for WU at all time points for a set of accessions with unobserved WU. The second scenario was designed to forecast future genetic values for WU for a panel of known accessions with records for WU at earlier time periods. In each scenario we evaluated two MT-RRMs in which PSA records were absent or available for time points in the testing population. Moderate to strong genomic correlations between WU and PSA were observed across the days of imaging (0.29-0.87). In both CV scenarios, MT-RRMs showed better predictive abilities compared to single-trait RRM, and prediction accuracies were greatly improved when PSA records were available for the testing population. In summary, these frameworks provide an effective approach to predict temporal physiological traits that are difficult or expensive to quantify in large populations.

scholarly journals Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping

2018 ◽  
Author(s):  
Malachy Campbell ◽  
Harkamal Walia ◽  
Gota Morota
Keyword(s):  
High Throughput ◽  
Legendre Polynomial ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Additive Genetic Variance ◽  
Second Order ◽  
Random Regression ◽  
Phenotypic Data ◽  
High Throughput Phenotyping ◽  
Time Point

AbstractThe accessibility of high-throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, have provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study is to demonstrate the utility of random regression (RR) models using Legendre polynomials for genomic prediction of shoot growth trajectories in rice (Oryza sativa). An estimate of shoot biomass, projected shoot area (PSA), was recored over a period of 20 days for a panel of 357 diverse rice accessions using an image-based greenhouse phenotyping platform. A RR that included a fixed second-order Legendre polynomial, a random second-order Legendre polynomial for the additive genetic effect, a first-order Legendre polynomial for the environmental effect, and heterogeneous residual variances was used to model PSA trajectories. The utility of the RR model over a single time point (TP) approach, where PSA is fit at each time point independently, is shown through four prediction scenarios. In the first scenario, the RR and TP approaches were used to predict PSA for a set of lines lacking phenotypic data. The RR approach showed a 11.6% increase in prediction accuracy over the TP approach. Much of this improvement could be attributed to the greater additive genetic variance captured by the RR approach. The remaining scenarios focused forecasting future phenotypes using a subset of early time points for known lines with phenotypic data, as well new lines lacking phenotypic data. In all cases, PSA could be predicted with high accuracy (r: 0.79 to 0.89 and 0.55 to 0.58 for known and unknown lines, respectively). This study provides the first application of RR models for genomic prediction of a longitudinal trait in rice, and demonstrates that RR models can be effectively used to improve the accuracy of genomic prediction for complex traits compared to a TP approach.

scholarly journals High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production

2021 ◽  
Vol 12 ◽  
Author(s):  
Fabiana Freitas Moreira ◽  
Hinayah Rojas de Oliveira ◽  
Miguel Angel Lopez ◽  
Bilal Jamal Abughali ◽  
Guilherme Gomes ◽  
...  
Keyword(s):  
High Throughput ◽  
Regression Models ◽  
Genetic Architecture ◽  
Crop Improvement ◽  
Random Regression ◽  
Genome Wide Association Studies ◽  
Breeding Values ◽  
High Throughput Phenotyping ◽  
General Response ◽  
Over Time

Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.

scholarly journals Haplotype analysis of data from UAV imagery of rice MAGIC population for the trait dissection of biomass and plant architecture

2020 ◽  
Author(s):  
Daisuke Ogawa ◽  
Toshihiro Sakamoto ◽  
Hiroshi Tsunematsu ◽  
Noriko Kanno ◽  
Yasunori Nonoue ◽  
...  
Keyword(s):  
High Resolution ◽  
High Throughput ◽  
Early Stage ◽  
Association Studies ◽  
Image Data ◽  
Shoot Biomass ◽  
Genome Wide Association Studies ◽  
Vegetation Fraction ◽  
High Throughput Phenotyping ◽  
Uav Images

Abstract Unmanned aerial vehicles (UAVs) are popular tools for high-throughput phenotyping of crops in the field. However, their use for evaluation of individual lines is limited in crop breeding because research on what the UAV image data represent is still developing. Here, we investigated the connection between shoot biomass of rice plants and the vegetation fraction (VF) estimated from high-resolution orthomosaic images taken by a UAV 10 m above a field during the vegetative stage. Haplotype-based genome-wide association studies of multi-parental advanced generation inter-cross (MAGIC) lines revealed four QTL for VF. VF was correlated with shoot biomass, but the haplotype effect on VF was better correlated with that on shoot biomass at these QTL. Further genetic characterization revealed the relationships between these QTL and plant spreading habit, final shoot biomass and panicle weight. Thus, genetic analysis using high-throughput phenotyping data derived from low-altitude, high-resolution UAV images during early stage of rice in the field provides insight into plant growth, architecture, final biomass and yield.

scholarly journals Using High-Throughput Phenotyping to Explore Growth Responses to Mycorrhizal Fungi and Zinc in Three Plant Species

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
S. J. Watts-Williams ◽  
N. Jewell ◽  
C. Brien ◽  
B. Berger ◽  
T. Garnett ◽  
...  
Keyword(s):  
Plant Species ◽  
High Throughput ◽  
Mycorrhizal Fungi ◽  
Repeated Measures ◽  
Plant Biomass ◽  
Shoot Biomass ◽  
Growth Responses ◽  
High Throughput Phenotyping ◽  
Soil Zn ◽  
Amf Inoculation

There are many reported benefits to plants of arbuscular mycorrhizal fungi (AMF), including positive plant biomass responses; however, AMF can also induce biomass depressions in plants, and this response receives little attention in the literature. High-throughput phenotyping (HTP) technology permits repeated measures of an individual plant’s aboveground biomass. We examined the effect on AMF inoculation on the shoot biomass of three contrasting plant species: a vegetable crop (tomato), a cereal crop (barley), and a pasture legume (Medicago). We also considered the interaction of mycorrhizal growth responses with plant-available soil zinc (Zn) and phosphorus (P) concentrations. The appearance of a depression in shoot biomass due to inoculation with AMF occurred at different times for each plant species; depressions appeared earliest in tomato, then Medicago, and then barley. The usually positive-responding Medicago plants were not responsive at the high level of soil available P used. Mycorrhizal growth responsiveness in all three species was also highly interactive with soil Zn supply; tomato growth responded negatively to AMF inoculation in all soil Zn treatments except the toxic soil Zn treatment, where it responded positively. Our results illustrate how context-dependent mycorrhizal growth responses are and the value of HTP approaches to exploring the complexity of mycorrhizal responses.

scholarly journals Using High-Throughput Phenotyping to Explore Growth Responses to Mycorrhizal Fungi and Zinc in Three Plant Species

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
S. J. Watts-Williams ◽  
N. Jewell ◽  
C. Brien ◽  
B. Berger ◽  
T. Garnett ◽  
...  
Keyword(s):  
Plant Species ◽  
High Throughput ◽  
Mycorrhizal Fungi ◽  
Repeated Measures ◽  
Plant Biomass ◽  
Shoot Biomass ◽  
Growth Responses ◽  
High Throughput Phenotyping ◽  
Soil Zn ◽  
Amf Inoculation

There are many reported benefits to plants of arbuscular mycorrhizal fungi (AMF), including positive plant biomass responses; however, AMF can also induce biomass depressions in plants, and this response receives little attention in the literature. High-throughput phenotyping (HTP) technology permits repeated measures of an individual plant’s aboveground biomass. We examined the effect on AMF inoculation on the shoot biomass of three contrasting plant species: a vegetable crop (tomato), a cereal crop (barley), and a pasture legume (Medicago). We also considered the interaction of mycorrhizal growth responses with plant-available soil zinc (Zn) and phosphorus (P) concentrations. The appearance of a depression in shoot biomass due to inoculation with AMF occurred at different times for each plant species; depressions appeared earliest in tomato, then Medicago, and then barley. The usually positive-responding Medicago plants were not responsive at the high level of soil available P used. Mycorrhizal growth responsiveness in all three species was also highly interactive with soil Zn supply; tomato growth responded negatively to AMF inoculation in all soil Zn treatments except the toxic soil Zn treatment, where it responded positively. Our results illustrate how context-dependent mycorrhizal growth responses are and the value of HTP approaches to exploring the complexity of mycorrhizal responses.

scholarly journals High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254908
Author(s):  
Sameer Joshi ◽  
Emily Thoday-Kennedy ◽  
Hans D. Daetwyler ◽  
Matthew Hayden ◽  
German Spangenberg ◽  
...  
Keyword(s):  
Drought Stress ◽  
Drought Tolerance ◽  
High Throughput ◽  
Crop Yields ◽  
Geometric Mean ◽  
Shoot Biomass ◽  
Screening Methods ◽  
Tolerance Index ◽  
Genotypic Differences ◽  
High Throughput Phenotyping

Drought is one of the most severe and unpredictable abiotic stresses, occurring at any growth stage and affecting crop yields worldwide. Therefore, it is essential to develop drought tolerant varieties to ensure sustainable crop production in an ever-changing climate. High-throughput digital phenotyping technologies in tandem with robust screening methods enable precise and faster selection of genotypes for breeding. To investigate the use of digital imaging to reliably phenotype for drought tolerance, a genetically diverse safflower population was screened under different drought stresses at Agriculture Victoria’s high-throughput, automated phenotyping platform, Plant Phenomics Victoria, Horsham. In the first experiment, four treatments, control (90% field capacity; FC), 40% FC at initial branching, 40% FC at flowering and 50% FC at initial branching and flowering, were applied to assess the performance of four safflower genotypes. Based on these results, drought stress using 50% FC at initial branching and flowering stages was chosen to further screen 200 diverse safflower genotypes. Measured plant traits and dry biomass showed high correlations with derived digital traits including estimated shoot biomass, convex hull area, caliper length and minimum area rectangle, indicating the viability of using digital traits as proxy measures for plant growth. Estimated shoot biomass showed close association having moderately high correlation with drought indices yield index, stress tolerance index, geometric mean productivity, and mean productivity. Diverse genotypes were classified into four clusters of drought tolerance based on their performance (seed yield and digitally estimated shoot biomass) under stress. Overall, results show that rapid and precise image-based, high-throughput phenotyping in controlled environments can be used to effectively differentiate response to drought stress in a large numbers of safflower genotypes.

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