scholarly journals Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat

2021 ◽  
Vol 12 ◽  
Author(s):  
Harsimardeep S. Gill ◽  
Jyotirmoy Halder ◽  
Jinfeng Zhang ◽  
Navreet K. Brar ◽  
Teerath S. Rai ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Agronomic Traits ◽  
Wheat Breeding ◽  
Successful Implementation ◽  
Limited Information ◽  
Multiple Traits ◽  
Breeding Lines

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

scholarly journals Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)

2020 ◽  
Vol 10 (3) ◽  
pp. 1113-1124 ◽  
Author(s):  
Madhav Bhatta ◽  
Lucia Gutierrez ◽  
Lorena Cammarota ◽  
Fernanda Cardozo ◽  
Silvia Germán ◽  
...  
Keyword(s):  
Prediction Model ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Prediction Models ◽  
Agronomic Traits ◽  
Predictive Ability ◽  
Malting Quality ◽  
Quality Traits ◽  
Multiple Traits ◽  
Correlated Traits

Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.

scholarly journals A New Strategy for Using Historical Imbalanced Yield Data to Conduct Genome-Wide Association Studies and Develop Genomic Prediction Models for Wheat Breeding

2021 ◽  
Author(s):  
Chenggen Chu ◽  
Shichen Wang ◽  
Jackie C. Rudd ◽  
Amir M.H. Ibrahim ◽  
Qingwu Xue ◽  
...  
Keyword(s):  
Grain Yield ◽  
Insect Resistance ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Hessian Fly ◽  
Wheat Breeding ◽  
Unbiased Estimation ◽  
Yield Data ◽  
Breeding Lines ◽  
Genomic Regions

Abstract Using imbalanced historical yield data to predict performance and select new lines is an arduous breeding task. An association mapping panel of 227 Texas elite (TXE) wheat breeding lines was used for GWAS and a training population to develop prediction models for grain yield selection. An imbalanced set of yield data collected from 102 environments (year-by-location) over ten years was used. Based on correlations among data from different environments within two adjacent years and broad-sense heritability estimated in each environment, yield data from 87 environments were selected and assigned to two correlation-based groups. The yield best linear unbiased estimation (BLUE) from each group, along with reaction to greenbug and Hessian fly in each line, were used for GWAS to reveal genomic regions associated with yield and insect resistance. A total of 74 genomic regions were associated with grain yield and two of them were commonly detected in both correlation-based groups. Greenbug resistance in TXE lines was mainly controlled by Gb3 on chromosome 7DL in addition to two novel regions on 3DL and 6DS, and Hessian fly resistance was conferred by the region on 1AS. Genomic prediction models developed in two correlation-based groups were validated using a set of 105 new advanced breeding lines and the model from correlation-based group G2 was more reliable for prediction. This research not only identified genomic regions associated with yield and insect resistance but also established the method of using historical imbalanced breeding data to develop a genomic prediction model for crop improvement.

scholarly journals Identification of QTL hotspots affecting agronomic traits and high-throughput vegetation indices in rainfed wheat

2021 ◽  
Author(s):  
Ruben Rufo ◽  
Andrea Lopez ◽  
Marta S. Lopes ◽  
Joaquim Bellvert ◽  
Jose Miguel Soriano
Keyword(s):  
Abiotic Stress ◽  
Grain Yield ◽  
Candidate Gene ◽  
High Throughput ◽  
Complex Traits ◽  
Vegetation Index ◽  
Genome Wide Association Study ◽  
Agronomic Traits ◽  
Wheat Breeding ◽  
A Genome

Understanding the genetic basis of agronomic traits is essential for wheat breeding programmes to develop new cultivars with enhanced grain yield under climate change conditions. The use of high-throughput phenotyping (HTP) technologies for the assessment of agronomic performance through drought-adaptive traits opens new possibilities in plant breeding. HTP together with a genome-wide association study (GWAS) mapping approach can become a useful method to dissect the genetic control of complex traits in wheat to enhance grain yield under drought stress. This study aimed to identify molecular markers associated with agronomic and remotely sensed vegetation index (VI)-related traits under rainfed conditions in bread wheat and to use an in silico candidate gene (CG) approach to search for upregulated CGs under abiotic stress. The plant material consisted of 170 landraces and 184 modern cultivars from the Mediterranean basin that were phenotyped for agronomic and VI traits derived from multispectral images over three and two years, respectively. GWAS identified 2579 marker-trait associations (MTAs). The QTL overview index statistic detected 11 QTL hotspots involving more than one trait in at least two years. A candidate gene analysis detected 12 CGs upregulated under abiotic stress in 6 QTL hotspots. The current study highlights the utility of VI to identify chromosome regions that contribute to yield and drought tolerance under rainfed Mediterranean conditions.

Differences in warming impacts on wheat productivity among varieties released in different eras in North China

2015 ◽  
Vol 153 (8) ◽  
pp. 1353-1364 ◽  
Author(s):  
C. Y. ZHENG ◽  
J. CHEN ◽  
Z. W. SONG ◽  
A. X. DENG ◽  
L. N. JIANG ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Biomass Production ◽  
Air Temperature ◽  
North China ◽  
Growth Period ◽  
Wheat Breeding ◽  
Wheat Growth ◽  
Positive Effects ◽  
Significant Difference

SUMMARYTen leading varieties of winter wheat released during 1950–2009 in North China were tested in a free-air temperature increase (FATI) facility. The FATI facility mimicked the local air temperature pattern well, with an increase of 1·1 °C in the daily mean temperature. For all the tested varieties, warming caused a significant reduction in the total length of wheat growth period by 5 days and especially in the pre-anthesis period, where it was reduced by 9 days. However, warming increased wheat biomass production and grain yield by 8·4 and 11·4%, respectively, on an average of all the tested varieties. There was no significant difference in the warming-led reduction in the entire growth period among the tested varieties. Interestingly, the warming-led increments in biomass production and grain yield increased along with the variety release year. Significantly higher warming-led increases in post-anthesis biomass production and 1000-grain weight were found in the new varieties compared to the old ones. Meanwhile, a significant improvement in plant productivity was noted due to wheat breeding during the past six decades, while no significant difference in the length of entire growth period was found among the varieties released in different eras. The results demonstrate that historical wheat breeding might have enhanced winter wheat productivity and adaptability through exploiting the positive effects rather than mitigating the negative impacts of warming on wheat growth in North China.

scholarly journals GWAS revealed effect of genotype × environment interactions for grain yield of Nebraska winter wheat

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shamseldeen Eltaher ◽  
P. Stephen Baenziger ◽  
Vikas Belamkar ◽  
Hamdy A. Emara ◽  
Ahmed A. Nower ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Winter Wheat ◽  
Grain Yield ◽  
Genome Wide Association Study ◽  
Phenotypic Selection ◽  
Wheat Breeding ◽  
Wheat Genotypes ◽  
Genome Wide ◽  
Genomic Regions ◽  
Significant Linkage

Abstract Background Improving grain yield in cereals especially in wheat is a main objective for plant breeders. One of the main constrains for improving this trait is the G × E interaction (GEI) which affects the performance of wheat genotypes in different environments. Selecting high yielding genotypes that can be used for a target set of environments is needed. Phenotypic selection can be misleading due to the environmental conditions. Incorporating information from phenotypic and genomic analyses can be useful in selecting the higher yielding genotypes for a group of environments. Results A set of 270 F3:6 wheat genotypes in the Nebraska winter wheat breeding program was tested for grain yield in nine environments. High genetic variation for grain yield was found among the genotypes. G × E interaction was also highly significant. The highest yielding genotype differed in each environment. The correlation for grain yield among the nine environments was low (0 to 0.43). Genome-wide association study revealed 70 marker traits association (MTAs) associated with increased grain yield. The analysis of linkage disequilibrium revealed 16 genomic regions with a highly significant linkage disequilibrium (LD). The candidate parents’ genotypes for improving grain yield in a group of environments were selected based on three criteria; number of alleles associated with increased grain yield in each selected genotype, genetic distance among the selected genotypes, and number of different alleles between each two selected parents. Conclusion Although G × E interaction was present, the advances in DNA technology provided very useful tools and analyzes. Such features helped to genetically select the highest yielding genotypes that can be used to cross grain production in a group of environments.

scholarly journals Reaction of winter wheat genotypes to infection by Mycosphaerella graminicola(Fuckel) Schroeter

2013 ◽  
Vol 55 (1) ◽  
pp. 233-246
Author(s):  
Ewa Mirzwa-Mróz ◽  
Czesław Zamorski
Keyword(s):  
Winter Wheat ◽  
Broad Spectrum ◽  
Mycosphaerella Graminicola ◽  
Wheat Breeding ◽  
Septoria Tritici ◽  
Wheat Cultivars ◽  
Breeding Lines ◽  
Wheat Genotypes ◽  
Winter Wheat Cultivars

The response of Polish winter wheat genotypes to <i>M.graminicola</i> (preliminary experiments and cultivar collections) was observed in different regions of Poland. Observations were carried out in 1995-1999. The winter wheat genotypes showed a broad spectrum of reaction to this pathogen. Between 1997 and 1999 the highest degree of infection on winter wheat breeding lines was noted in Kończewice. During this time no genotypes free from infection were observed (preliminary breeding experiments). Cultivars with no symptoms of <i>Septoria tritici</i> blotch (Leszczyńska Wczesna and Żelazna) were found among old genotypes in Słupia Wielka only in earlier experiments (1995-1996). In the years 1997-1999 the winter wheat cultivars were classified into groups on the basis of their response to the pathogen. The degree of infection for the majority cultivars was quite high.

scholarly journals Breeding progress for pathogen resistance is a second major driver for yield increase in German winter wheat at contrasting N levels

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Holger Zetzsche ◽  
Wolfgang Friedt ◽  
Frank Ordon
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Fungal Pathogens ◽  
Large Scale ◽  
Pathogen Resistance ◽  
Wheat Breeding ◽  
Yield Potential ◽  
Head Blight ◽  
Wheat Production ◽  
Over Time

AbstractBreeding has substantially increased the genetic yield potential, but fungal pathogens are still major constraints for wheat production. Therefore, breeding success for resistance and its impact on yield were analyzed on a large panel of winter wheat cultivars, representing breeding progress in Germany during the last decades, in large scale field trials under different fungicide and nitrogen treatments. Results revealed a highly significant effect of genotype (G) and year (Y) on resistances and G × Y interactions were significant for all pathogens tested, i.e. leaf rust, strip rust, powdery mildew and Fusarium head blight. N-fertilization significantly increased the susceptibility to biotrophic and hemibiotrophic pathogens. Resistance was significantly improved over time but at different rates for the pathogens. Although the average progress of resistance against each pathogen was higher at the elevated N level in absolute terms, it was very similar at both N levels on a relative basis. Grain yield was increased significantly over time under all treatments but was considerably higher without fungicides particularly at high N-input. Our results strongly indicate that wheat breeding resulted in a substantial increase of grain yield along with a constant improvement of resistance to fungal pathogens, thereby contributing to an environment-friendly and sustainable wheat production.

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