Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects

Kaur, Balwinder; Sandhu, Karansher S.; Kamal, Roop; Kaur, Kawalpreet; Singh, Jagmohan; Röder, Marion S.; Muqaddasi, Quddoos H.

doi:10.3390/plants10101989

Cited by 58 publications

(37 citation statements)

References 232 publications

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“…The trained GS model predicts the genomic estimated breeding values of the selection/breeding population (Lorenz et al, 2011;Lorenz, 2013). Since the last decade, increasing the prediction accuracies for GS has been the main focus of research (Kaur et al, 2021). GS performance is affected by the relationship between testing and training set, trait heritability and architecture, population structure, population size, and the statistical model (Herter et al, 2019;Monteverde et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

et al. 2022

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Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait–based GS models. This study’s main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait– and multi-trait–based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait–based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.

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Section: Introductionmentioning

confidence: 99%

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

et al. 2022

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“…Nested association mapping (NAM) is a multi-parental design with higher allelic variation than biparental populations and stronger statistical power than association mapping populations, combining the advantages of each approach [16,17]. A universal parent is crossed to multiple genotypes, followed by inbreeding to make a combination of both full-sib and half-sib recombinant inbred lines (RILs) [18].…”

Section: Introductionmentioning

confidence: 99%

Genomic Selection and Genome-Wide Association Studies for Grain Protein Content Stability in a Nested Association Mapping Population of Wheat

Sandhu

Mihalyov²,

Lewien

et al. 2021

Agronomy

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Grain protein content (GPC) is controlled by complex genetic systems and their interactions and is an important quality determinant for hard spring wheat as it has a positive effect on bread and pasta quality. GPC is variable among genotypes and strongly influenced by the environment. Thus, understanding the genetic control of wheat GPC and identifying genotypes with improved stability is an important breeding goal. The objectives of this research were to identify genetic backgrounds with less variation for GPC across environments and identify quantitative trait loci (QTLs) controlling the stability of GPC. A spring wheat nested association mapping (NAM) population of 650 recombinant inbred lines (RIL) derived from 26 diverse founder parents crossed to one common parent, ‘Berkut’, was phenotyped over three years of field trials (2014–2016). Genomic selection models were developed and compared based on predictions of GPC and GPC stability. After observing variable genetic control of GPC within the NAM population, seven RIL families displaying reduced marker-by-environment interaction were selected based on a stability index derived from a Finlay–Wilkinson regression. A genome-wide association study identified eighteen significant QTLs for GPC stability with a Bonferroni-adjusted p-value < 0.05 using four different models and out of these eighteen QTLs eight were identified by two or more GWAS models simultaneously. This study also demonstrated that genome-wide prediction of GPC with ridge regression best linear unbiased estimates reached up to r = 0.69. Genomic selection can be used to apply selection pressure for GPC and improve genetic gain for GPC.

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“…Systematic stress responsive patterns in root and shoot tissues were observed at proteomic levels, whereas the leaf and root transcriptomes showed distinct responsive patterns under salinity and drought stress [ 28 ]. Such deviations may be because the genome is static and thus does not provide a snapshot of an organism’s metabolism at a particular developmental time point [ 52 ]. As a technique, proteomics has advantages over other omics techniques, such as genomics and transcriptomics as it deals with key players maintaining cellular homeostasis [ 49 , 53 ].…”

Section: Proteomics: Edge Over the Other Omics Techniquesmentioning

confidence: 99%

Wheat Proteomics for Abiotic Stress Tolerance and Root System Architecture: Current Status and Future Prospects

et al. 2022

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Wheat is an important staple cereal for global food security. However, climate change is hampering wheat production due to abiotic stresses, such as heat, salinity, and drought. Besides shoot architectural traits, improving root system architecture (RSA) traits have the potential to improve yields under normal and stressed environments. RSA growth and development and other stress responses involve the expression of proteins encoded by the trait controlling gene/genes. Hence, mining the key proteins associated with abiotic stress responses and RSA is important for improving sustainable yields in wheat. Proteomic studies in wheat started in the early 21st century using the two-dimensional (2-DE) gel technique and have extensively improved over time with advancements in mass spectrometry. The availability of the wheat reference genome has allowed the exploration of proteomics to identify differentially expressed or abundant proteins (DEPs or DAPs) for abiotic stress tolerance and RSA improvement. Proteomics contributed significantly to identifying key proteins imparting abiotic stress tolerance, primarily related to photosynthesis, protein synthesis, carbon metabolism, redox homeostasis, defense response, energy metabolism and signal transduction. However, the use of proteomics to improve RSA traits in wheat is in its infancy. Proteins related to cell wall biogenesis, carbohydrate metabolism, brassinosteroid biosynthesis, and transportation are involved in the growth and development of several RSA traits. This review covers advances in quantification techniques of proteomics, progress in identifying DEPs and/or DAPs for heat, salinity, and drought stresses, and RSA traits, and the limitations and future directions for harnessing proteomics in wheat improvement.

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Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects

Cited by 58 publications

References 232 publications

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

Genomic Selection and Genome-Wide Association Studies for Grain Protein Content Stability in a Nested Association Mapping Population of Wheat

Wheat Proteomics for Abiotic Stress Tolerance and Root System Architecture: Current Status and Future Prospects

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