Multitrait, Random Regression, or Simple Repeatability Model in High‐Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield

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.

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

10.1101/772038 ◽

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.

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

Plant Direct ◽

10.1002/pld3.80 ◽

2018 ◽

Vol 2 (9) ◽

pp. e00080 ◽

Cited By ~ 17

Author(s):

Malachy Campbell ◽

Harkamal Walia ◽

Gota Morota

Keyword(s):

High Throughput ◽

Genomic Prediction ◽

Regression Models ◽

Random Regression ◽

High Throughput Phenotyping

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

PLoS ONE ◽

10.1371/journal.pone.0228118 ◽

2020 ◽

Vol 15 (2) ◽

pp. e0228118 ◽

Cited By ~ 5

Author(s):

Toshimi Baba ◽

Mehdi Momen ◽

Malachy T. Campbell ◽

Harkamal Walia ◽

Gota Morota

Keyword(s):

High Throughput ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Regression Models ◽

Random Regression ◽

Physiological Trait ◽

High Throughput Phenotyping

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Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Remote Sensing ◽

10.3390/rs12030574 ◽

2020 ◽

Vol 12 (3) ◽

pp. 574 ◽

Cited By ~ 2

Author(s):

Yuncai Hu ◽

Samuel Knapp ◽

Urs Schmidhalter

Keyword(s):

Grain Yield ◽

Plant Breeding ◽

High Throughput ◽

Complex Traits ◽

New Technologies ◽

Early Selection ◽

Spectral Indices ◽

Wheat Genotypes ◽

High Throughput Phenotyping ◽

Spectral Sensing

Enhancing plant breeding to ensure global food security requires new technologies. For wheat phenotyping, only limited seeds and resources are available in early selection cycles. This forces breeders to use small plots with single or multiple row plots in order to include the maximum number of genotypes/lines for their assessment. High-throughput phenotyping through remote sensing may meet the requirements for the phenotyping of thousands of genotypes grown in small plots in early selection cycles. Therefore, the aim of this study was to compare the performance of an unmanned aerial vehicle (UAV) for assessing the grain yield of wheat genotypes in different row numbers per plot in the early selection cycles with ground-based spectral sensing. A field experiment consisting of 32 wheat genotypes with four plot designs (1, 2, 3, and 12 rows per plot) was conducted. Near infrared (NIR)-based spectral indices showed significant correlations (p < 0.01) with the grain yield at flowering to grain filling, regardless of row numbers, indicating the potential of spectral indices as indirect selection traits for the wheat grain yield. Compared with terrestrial sensing, aerial-based sensing from UAV showed consistently higher levels of association with the grain yield, indicating that an increased precision may be obtained and is expected to increase the efficiency of high-throughput phenotyping in large-scale plant breeding programs. Our results suggest that high-throughput sensing from UAV may become a convenient and efficient tool for breeders to promote a more efficient selection of improved genotypes in early selection cycles. Such new information may support the calibration of genomic information by providing additional information on other complex traits, which can be ascertained by spectral sensing.

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High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling

Frontiers in Plant Science ◽

10.3389/fpls.2017.00421 ◽

2017 ◽

Vol 8 ◽

Cited By ~ 91

Author(s):

Kakeru Watanabe ◽

Wei Guo ◽

Keigo Arai ◽

Hideki Takanashi ◽

Hiromi Kajiya-Kanegae ◽

...

Keyword(s):

Plant Height ◽

Unmanned Aerial Vehicle ◽

High Throughput ◽

Genomic Prediction ◽

Prediction Modeling ◽

Sorghum Plant ◽

Aerial Vehicle ◽

High Throughput Phenotyping

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Identification of stay-green and early senescence phenotypes in high-yielding winter wheat, and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques

Functional Plant Biology ◽

10.1071/fp13221 ◽

2014 ◽

Vol 41 (3) ◽

pp. 227 ◽

Cited By ~ 35

Author(s):

Sebastian Kipp ◽

Bodo Mistele ◽

Urs Schmidhalter

Keyword(s):

Winter Wheat ◽

Grain Yield ◽

High Throughput ◽

Protein Concentration ◽

Partial Least Square ◽

Large Field ◽

Grain Protein ◽

Stay Green ◽

Grain Protein Concentration ◽

High Throughput Phenotyping

Yield and grain protein concentration (GPC) represent crucial factors in the global agricultural wheat (Triticum aestivum L.) production and are predominantly determined via carbon and nitrogen metabolism, respectively. The maintenance of green leaf area and the onset of senescence (Osen) are expected to be involved in both C and N accumulation and their translocation into grains. The aim of this study was to identify stay-green and early senescence phenotypes in a field experiment of 50 certified winter wheat cultivars and to investigate the relationships among Osen, yield and GPC. Colour measurements on flag leaves were conducted to determine Osen for 20 cultivars and partial least square regression models were used to calculate Osen for the remaining 30 cultivars based on passive spectral reflectance measurements as a high-throughput phenotyping technique for all varieties. Using this method, stay-green and early senescence phenotypes could be clearly differentiated. A significant negative relationship between Osen and grain yield (r2 = 0.81) was observed. By contrast, GPC showed a significant positive relationship to Osen (r2 = 0.48). In conclusion, the high-throughput character of our proposed phenotyping method should help improve the detection of such traits in large field trials as well as help us reach a better understanding of the consequences of the timing of senescence on yield.

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Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

10.1101/2020.04.21.054163 ◽

2020 ◽

Author(s):

Margaret R. Krause ◽

Suchismita Mondal ◽

José Crossa ◽

Ravi P. Singh ◽

Francisco Pinto ◽

...

Keyword(s):

Grain Yield ◽

High Throughput ◽

Vegetation Indices ◽

Early Generation ◽

Breeding Programs ◽

Breeding Lines ◽

High Throughput Phenotyping ◽

Seed Increase ◽

Yield Trials ◽

Selection Of

ABSTRACTBreeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

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The Importance of Dominance and Genotype-by-Environment Interactions on Grain Yield Variation in a Large-Scale Public Cooperative Maize Experiment

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkaa050 ◽

2021 ◽

Author(s):

Anna R Rogers ◽

Jeffrey C Dunne ◽

Cinta Romay ◽

Martin Bohn ◽

Edward S Buckler ◽

...

Keyword(s):

Grain Yield ◽

High Throughput ◽

Genomic Prediction ◽

Environmental Data ◽

Environment Interaction ◽

Hybrid Performance ◽

Interaction Patterns ◽

Genotype By Environment Interactions ◽

Genotype By Environment ◽

Main Effect

Abstract High-dimensional and high throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

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