New genetic resources in wheat breeding for increased grain protein content

2017 ◽  
Vol 7 (4) ◽  
pp. 477-487 ◽  
Author(s):  
O. P. Mitrofanova ◽  
A. G. Khakimova
Author(s):  
Karansher S. Sandhu ◽  
Shruti S. Patil ◽  
Michael O. Pumphrey ◽  
Arron H. Carter

AbstractPrediction of breeding values and phenotypes 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 in the context of GS. Spectral reflectance indices further provide information about various physiological parameters previously undetectable in plants. This research explores the potential of multi-trait (MT) machine and deep learning models for predicting grain yield and grain protein content in wheat using spectral information in GS models. This study compares the performance of four machine and deep learning-based uni-trait (UT) and MT models with traditional GBLUP and Bayesian models. The dataset consisted of 650 recombinant inbred lines from a spring wheat breeding program, grown for three years (2014-2016), and spectral data were collected at heading and grain filling stages. MT-GS models performed 0-28.5% and −0.04-15% superior to the UT-GS models for predicting grain yield and grain protein content. Random forest and multilayer perceptron were the best performing machine and deep learning models to predict both traits. These two models performed similarly under UT and MT-GS models. 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 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.Core IdeasPotential for combining high throughput phenotyping, machine and deep learning in breeding.Multi-trait models exploit information from secondary correlated traits efficiently.Spectral information improves genomic selection models.Deep learning can aid plant breeders owing to increased data generated in breeding programs


2018 ◽  
Vol 50 (4) ◽  
pp. 279-298 ◽  
Author(s):  
A.I. Rybalka ◽  
◽  
B.V. Morgun ◽  
S.S. Polyshchuk ◽  
◽  
...  

2020 ◽  
Vol 11 ◽  
Author(s):  
Diana Tomás ◽  
Luís Pinto Coelho ◽  
José Carlos Rodrigues ◽  
Wanda Viegas ◽  
Manuela Silva

Wheat is a dietary staple consumed worldwide strongly responsible for proteins and carbohydrate population intake. However, wheat production and quality will scarcely fulfill forward demands, which are compounded by high-temperature (HT) events as heatwaves, increasingly common in Portugal. Thus, landraces assume crucial importance as potential reservoirs of useful traits for wheat breeding and may be pre-adapted to extreme environmental conditions. This work evaluates four Portuguese landrace yield and grain composition through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, particularly protein content, and their responses to HT treatment mimicking a heatwave. Landraces showed distinct yield traits, especially plant height and first spike grain number, and a similar pattern in FTIR spectra, although revealing differences in grain components’ proportions. Comparison between spectra band intensity indicates that Ardito has the highest protein-related peaks, contrary to Magueija, which appears to be the landrace with higher lipid content. In plants submitted to 1 week of HT treatment 10 days after anthesis, the first spike grain size and weight were markedly reduced in all landraces. Additionally, it was observed that a general increase in grain protein content in the four landraces, being the increment observed in Ardito and Grécia, is statistically significant. The comparative assessment of control and HT average FTIR spectra denoted also the occurrence of alterations in grain polysaccharide composition. An integrated assessment of the evaluations performed revealed that Ardito and Magueija landraces presented diverse yield-related characteristics and distinct responses to cope with HT. In fact, the former landrace revealed considerable grain yield diminution along with an increase in grain protein proportion after HT, while the latter showed a significant increase in spikes and grain number, with grain quality detriment. These results reinforce the relevance of scrutinizing old genotype diversity seeking for useful characteristics, particularly considering HT impact on grain production and quality.


2012 ◽  
Vol 40 (4) ◽  
pp. 532-541 ◽  
Author(s):  
V. Mladenov ◽  
B. Banjac ◽  
A. Krishna ◽  
M. Milošević

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Saule Kenzhebayeva ◽  
Alfia Abekova ◽  
Saule Atabayeva ◽  
Gulzira Yernazarova ◽  
Nargul Omirbekova ◽  
...  

Deficiency of metals, primarily Fe and Zn, affects over half of the world’s population. Human diets dominated by cereal products cause micronutrient malnutrition, which is common in many developing countries where populations depend heavily on staple grain crops such as wheat, maize, and rice. Biofortification is one of the most effective approaches to alleviate malnutrition. Genetically stable mutant spring wheat lines (M7 generation) produced via 100 or 200 Gy gamma treatments to broaden genetic variation for grain nutrients were analyzed for nutritionally important minerals (Ca, Fe, and Zn), their bioavailability, and grain protein content (GPC). Variation was 172.3–883.0 mg/kg for Ca, 40.9–89.0 mg/kg for Fe, and 22.2–89.6 mg/kg for Zn. In mutant lines, among the investigated minerals, the highest increases in concentrations were observed in Fe, Zn, and Ca when compared to the parental cultivar Zhenis. Some mutant lines, mostly in the 100 Gy-derived germplasm, had more than two-fold higher Fe, Zn, and Ca concentrations, lower phytic acid concentration (1.4–2.1-fold), and 6.5–7% higher grain protein content compared to the parent. Variation was detected for the molar ratios of Ca:Phy, Phy:Fe, and Phy:Zn (1.27–10.41, 1.40–5.32, and 1.78–11.78, respectively). The results of this study show how genetic variation generated through radiation can be useful to achieve nutrient biofortification of crops to overcome human malnutrition.


Author(s):  
Isaiah O. Ochieng’ ◽  
Harun I. Gitari ◽  
Benson Mochoge ◽  
Esmaeil Rezaei-Chiyaneh ◽  
Joseph P. Gweyi-Onyango

2013 ◽  
Vol 13 (1) ◽  
pp. 35 ◽  
Author(s):  
Shengguan Cai ◽  
Gang Yu ◽  
Xianhong Chen ◽  
Yechang Huang ◽  
Xiaogang Jiang ◽  
...  

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