A low‐cost greenhouse‐based high‐throughput phenotyping platform for genetic studies: A case study in maize under inoculation with plant growth‐promoting bacteria

Yassue, Rafael Massahiro; Galli, Giovanni; Borsato, Ronaldo; Cheng, Hao; Morota, Gota; Fritsche‐Neto, Roberto

doi:10.1002/ppj2.20043

Cited by 13 publications

(11 citation statements)

References 64 publications

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“…For genomic prediction, the higher predictive ability for PH was probably due to its higher heritability in comparison to SD and SDM (Yassue et al, 2021(Yassue et al, , 2022. Similar to the phenomic prediction, AutoML yielded relatively good predictions.…”

Section: Crop Sciencementioning

confidence: 81%

“…The manually measured phenotypes were plant height (PH), stalk diameter (SD), and shoot dry mass (SDM). Further information on the experimental design is available in Yassue et al (2021) and Yassue et al (2022).…”

Section: Crop Sciencementioning

confidence: 99%

“…Markers with pairwise linkage disequilibrium higher than 0.99 were removed using the SNPRelate R package (Zheng et al, 2012). Detailed descriptions of the genomic data are available in Yassue et al (2021Yassue et al ( , 2022 and also at https://doi.org/10.17632/5gvznd2b3n.3.…”

Section: Genomic Datamentioning

confidence: 99%

“…The hyperparameters were tuned in the inner training set, similar to the classification tasks. We did not consider the interaction effect between the genotype and management condition because of the lack of such an effect (Yassue et al, 2021(Yassue et al, , 2022. The predictive ability of the model was assessed using the Pearson correlation between the predictive values and BLUE of the genotypes.…”

Section: Accuracy = Tp + Tn Tp + Tn + Fn + Fpmentioning

confidence: 99%

“…Recent studies using the same population found that inoculation with PGPB can modify plant structure and increase shoot and root dry mass by 12.78 and 20.65%, respectively, under nitrogen stress (Yassue et al, 2021(Yassue et al, , 2022. This study evaluated PGPB responses at the level of hyperspectral reflectance data.…”

Section: Phenotyping Pgpb Responsementioning

confidence: 99%

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Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data

et al. 2022

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Recent technological advances in high-throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyperspectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize (Zea mays L.) lines with or without plant growth-promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1,021 nm and 13,826 single-nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth-related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyperspectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral-based phenotyping for management and phenomic prediction studies.

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Section: Crop Sciencementioning

confidence: 81%

Section: Crop Sciencementioning

confidence: 99%

Section: Genomic Datamentioning

confidence: 99%

Section: Accuracy = Tp + Tn Tp + Tn + Fn + Fpmentioning

confidence: 99%

Section: Phenotyping Pgpb Responsementioning

confidence: 99%

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Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data

et al. 2022

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Simulation‐based decision‐making and implementation of tools in hybrid crop breeding pipelines

Peixoto,

Coelho,

Leach

et al. 2023

Crop Science

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New technologies have been developed over the last few years aiming to support breeding pipeline optimization for long‐term genetic gains. However, the implementation of these new tools and their impact on any breeding program's budget are not well studied. Here, we compare multiple breeding pipeline strategies accounting for genomic selection and high‐throughput phenotyping by means of hybrid gain and cost effectiveness. We simulated a hybrid crop breeding program through coalescent theory. We compared two strategies for parental updates and four breeding pipelines: conventional breeding pipeline; conventional breeding pipeline with high‐throughput phenotyping; conventional breeding pipeline with genomic selection; conventional breeding pipeline with genomic selection and high‐throughput phenotyping. All analyses were implemented under three different levels of genotype‐by‐environment interaction (G×E) and two traits heritabilities (0.3 and 0.7). Overall, the results show that scenarios with early parental selection perform better than the others. In addition, the implementation of high‐throughput phenotyping (HTP) delivered the highest hybrid gain in the long‐term, whereas the implementation of genomic selection seems to be more cost‐effective. We suggest, considering breeding programs with complex trait inheritance and accounting for higher levels of G×E, to invest in breeding pipelines accounting for genomic selection as a strategy to create and maintain long‐term hybrid gain. Moreover, considering an unconstrained budget, the investment in both, genomic selection and HTP, represents the best strategy. Hence, these results provide strategies that may aid breeders in optimizing self‐pollination breeding programs.This article is protected by copyright. All rights reserved

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Genome‐wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth‐related traits in maize under plant growth‐promoting bacteria inoculation

et al. 2023

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Plant growth‐promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth‐related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome‐wide association analyses to examine maize growth‐related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386–1021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome‐wide association analysis as potential markers for growth‐related traits under PGPB inoculation. Eight SNPs were detected, which were commonly associated with manually measured and hyperspectral phenotypes. Different genomic regions were found for plant growth and hyperspectral phenotypes between with and without PGPB inoculation. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multiphenotype genome‐wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral‐based phenotyping for studying maize growth‐related traits in response to PGPB inoculation.

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A low‐cost greenhouse‐based high‐throughput phenotyping platform for genetic studies: A case study in maize under inoculation with plant growth‐promoting bacteria

Cited by 13 publications

References 64 publications

Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data

Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data

Simulation‐based decision‐making and implementation of tools in hybrid crop breeding pipelines

Genome‐wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth‐related traits in maize under plant growth‐promoting bacteria inoculation

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