Genome-Wide Association Study for Biomass Related Traits in a Panel of Sorghum bicolor and S. bicolor × S. halepense Populations

Habyarimana, Ephrem; Franceschi, Paolo De; Ercişli, Sezai; Baloch, Faheem Shehzad; Dall’Agata, Michela

doi:10.3389/fpls.2020.551305

Cited by 33 publications

(41 citation statements)

References 83 publications

(143 reference statements)

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“…High‐throughput phenotyping applications include spectral reflectance values obtained from plants to provide information about various physiological processes and have been used in wheat (Babar et al., 2006), rice ( Oryza sativa L.) (Zheng et al., 2018), maize ( Zea mays L.) (Aguate et al., 2017), barley (Barmeier et al., 2017), and sorghum [ Sorghum bicolor (L.) Moench] (Habyarimana et al., 2020). Different vegetation indices or spectral reflectance indices (SRIs) can be extracted by measuring reflection from plants.…”

Section: Introductionmentioning

confidence: 99%

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

et al. 2021

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Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine-and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Because of the tremendous increase in collected data in breeding programs and the slow rate of genetic gain increase, it is required to explore the potential of artificial intelligence in analyzing the data. The main objectives of this study include optimization of multitrait (MT) machine-and deep-learning models for predicting grain yield and grain protein content in wheat (Triticum aestivum L.) using spectral information. This study compares the performance of four machine-and deep-learning-based unitrait (UT) and MT models with traditional genomic best linear unbiased predictor (GBLUP) and Bayesian models. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat breeding program grown for three years (2014)(2015)(2016), and spectral data were collected at heading and grain filling stages. The MT-GS models performed 0-28.5 and −0.04 to 15% superior to the UT-GS models. Random forest and multilayer perceptron were the best performing machine-and deep-learning models to predict both traits. Four explored Bayesian models gave similar accuracies, which were less than machine-and deep-learning-based models and required increased computational time. Green normalized difference vegetation index (GNDVI) best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine-and deep-learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.

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Section: Introductionmentioning

confidence: 99%

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

et al. 2021

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“…Results from Ceron-Rojas et al (2015) based on both simulated and real data contributed to establish the efficiency of GSI over phenotypic selection indices. The efficiency of GSI models has been further supported from evidence in several crops including wheat, maize, rice, sorghum (see Habyarimana et al 2020).…”

Section: Selection Indices In Genomic Selectionmentioning

confidence: 91%

Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits

et al. 2021

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Key message Integrating genomics technologies and breeding methods to tweak core parameters of the breeder’s equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security. Abstract Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers’ fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder’s equation. A prerequisite to enhancing genetic variance (σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder′s equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.

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“…Total polyphenol content was measured with the Folin-Ciocalteu method, total antioxidant activity was assessed with DPPH (2,2-diphenyl-1-picrylhydrazyl) radical assay, and total flavonoid content was measured with AlCl3 method. The phenotypic characterization of sorghum lines was carried out according to international standard operating procedures following International Board for Plant Genetic Resources (IBPGR) and International Union for the Protection of New Varieties of Plants (UPOV) as described in previous works [2,3].…”

Section: Phenomicsmentioning

confidence: 99%

“…In tandem selection, only one character is selected in each cycle; in independent culling levels, all genotypes with a phenotypic value below the culling threshold for at least one characteristic are discarded; the selection index aims at improving several traits simultaneously in such a way as to make the biggest possible improvement in overall genetic merit [7]. In this work, we implemented the optimum selection Index of Smith [2,3,8], the performance of which was demonstrated in previous studies [7,9]. Our findings showed accuracy that was higher (acc = 0.52 -0.59) and comparable in genomic selection index, aboveground dry biomass yield and plant height, while it was lower (acc = 0.36) for the dry mass fraction of the fresh weight (Fig.…”

Section: Genomic Predictive and Selection Analyticsmentioning

confidence: 99%

Genomic Prediction and Selection in Support of Sorghum Value Chains

Habyarimana

Michailidou

2021

Big Data in Bioeconomy

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Genomic prediction and selection models (GS) were deployed as part of DataBio project infrastructure and solutions. The work addressed end-user requirements, i.e., the need for cost-effectiveness of the implemented technologies, simplified breeding schemes, and shortening the time to cultivar development by selecting for genetic merit. Our solutions applied genomic modelling in order to sustainably improve productivity and profits. GS models were implemented in sorghum crop for several breeding scenarios. We fitted the best linear unbiased predictions data using Bayesian ridge regression, genomic best linear unbiased predictions, Bayesian least absolute shrinkage and selection operator, and BayesB algorithms. The performance of the models was evaluated using Monte Carlo cross-validation with 70% and 30%, respectively, as training and validation sets. Our results show that genomic models perform comparably with traditional methods under single environments. Under multiple environments, predicting non-field evaluated lines benefits from borrowing information from lines that were evaluated in other environments. Accounting for environmental noise and other factors, also this model gave comparable accuracy with traditional methods, but higher compared to the single environment model. The GS accuracy was comparable in genomic selection index, aboveground dry biomass yield and plant height, while it was lower for the dry mass fraction of the fresh weight. The genomic selection model performances obtained in our pilots are high enough to sustain sorghum breeding for several traits including antioxidants production and allow important genetic gains per unit of time and cost.

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Genome-Wide Association Study for Biomass Related Traits in a Panel of Sorghum bicolor and S. bicolor × S. halepense Populations

Cited by 33 publications

References 83 publications

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits

Genomic Prediction and Selection in Support of Sorghum Value Chains

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