scholarly journals Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

2019 ◽  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Pacific Northwest ◽  
Genomic Prediction ◽  
Prediction Models

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 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 An Assessment of the Factors Influencing the Prediction Accuracy of Genomic Prediction Models Across Multiple Environments

2021 ◽  
Vol 12 ◽  
Author(s):  
Sarah Widener ◽  
George Graef ◽  
Alexander E. Lipka ◽  
Diego Jarquin
Keyword(s):  
Grain Yield ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Extreme Environments ◽  
Extreme Environment ◽  
Future Research ◽  
Nested Association Mapping ◽  
Estimated Breeding Values ◽  
The Impact ◽  
Gp Model

The effects of climate change create formidable challenges for breeders striving to produce sufficient food quantities in rapidly changing environments. It is therefore critical to investigate the ability of multi-environment genomic prediction (GP) models to predict genomic estimated breeding values (GEBVs) in extreme environments. Exploration of the impact of training set composition on the accuracy of such GEBVs is also essential. Accordingly, we examined the influence of the number of training environments and the use of environmental covariates (ECs) in GS models on four subsets of n = 500 lines of the soybean nested association mapping (SoyNAM) panel grown in nine environments in the US-North Central Region. The ensuing analyses provided insights into the influence of both of these factors for predicting grain yield in the most and the least extreme of these environments. We found that only a subset of the available environments was needed to obtain the highest observed prediction accuracies. The inclusion of ECs in the GP model did not substantially increase prediction accuracies relative to competing models, and instead more often resulted in negative prediction accuracies. Combined with the overall low prediction accuracies for grain yield in the most extreme environment, our findings highlight weaknesses in current GP approaches for prediction in extreme environments, and point to specific areas on which to focus future research efforts.

The Effect of Imazamox Application Timing and Rate on Imazamox Resistant Wheat Cultivars in the Pacific Northwest

2007 ◽  
Vol 21 (4) ◽  
pp. 895-899 ◽  
Author(s):  
Arron H. Carter ◽  
Jennifer Hansen ◽  
Thomas Koehler ◽  
Donald C. Thill ◽  
Robert S. Zemetra
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Plant Height ◽  
Pacific Northwest ◽  
Wheat Cultivars ◽  
Breeding Lines ◽  
Application Timing ◽  
Crop Injury ◽  
The Pacific ◽  
End Use

Grass weeds are a major problem in winter wheat fields in the Pacific Northwest (PNW). Control of these weeds is now enhanced with the use of imazamox resistant winter wheat cultivars, which have been rapidly adopted by wheat growers. However, the effect of spray rate and timing on crop injury and agronomic traits of wheat cultivars with different genetic backgrounds has not been adequately evaluated. Thus, experiments were conducted near Moscow and Genesee, ID in the 2003–2004 and 2004–2005 growing seasons to evaluate the effect of imazamox on four resistant cultivars and seven resistant breeding lines. Wheat plants were treated at the 3- to 5-leaf stage and the 3- to 7-tiller stage with 45 and 90 g ai/ha of imazamox. Visible crop injury was evaluated from 14 to 35 d after treatment (DAT). Heading date, plant height, grain yield and test weight, and end-use grain quality also were measured. The cultivar by treatment interaction was significant at 21 DAT, caused by a differential response of wheat lines to imazamox treatment. This interaction also was significant for plant height and grain yield. Although cultivars and breeding lines responded differently to imazamox treatment, two lines consistently showed the least levels (3 to 8%) of crop injury, with no reductions in plant height or grain yield following imazamox application. Orthogonal contrasts of visible crop injury at 21 DAT showed that the 2× imazamox rate caused more crop injury (12%) than the 1× rate (7%). The 2× rate of imazamox reduced plant height 1%, grain yield 8%, test weight 1%, and percent flour yield 1%. All other traits were not affected by application of imazamox. Application timing only minimally affected crop injury, and had no effect on agronomic or end-use quality traits.

scholarly journals Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction

2021 ◽  
Vol 12 ◽  
Author(s):  
Damiano Puglisi ◽  
Stefano Delbono ◽  
Andrea Visioni ◽  
Hakan Ozkan ◽  
İbrahim Kara ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Genetic Material ◽  
Predictive Ability ◽  
Gaussian Kernel ◽  
Environment Interaction ◽  
Magic Population ◽  
Genetic Bases ◽  
Empirical Analyses

Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.

Seeding Arrangement on Winter Wheat (Triticum aestivum) Grain Yield and Interaction with Italian Ryegrass (Lolium multiflorum)

1992 ◽  
Vol 6 (4) ◽  
pp. 820-823 ◽  
Author(s):  
Arnold P. Appleby ◽  
Bill D. Brewster
Keyword(s):  
Triticum Aestivum ◽  
Winter Wheat ◽  
Grain Yield ◽  
Pacific Northwest ◽  
Lolium Multiflorum ◽  
Italian Ryegrass ◽  
Wheat Grain ◽  
Seeding Rate ◽  
Grid Pattern ◽  
Grain Yields

Studies were conducted to determine whether cross-seeding of winter wheat, while maintaining an equal seeding rate per hectare, would increase wheat grain yields and help the wheat compete more effectively against Italian ryegrass than conventional seeding. Wheat was seeded at rates of 50, 100, and 150 kg ha-1 in conventional parallel rows or cross seeded in a grid pattern. Ryegrass was seeded at densities of 0, 20, 100, and 200 plants per m-2. Cross-seeded wheat yielded less grain than single-seeded wheat in 1990 in the absence of ryegrass; there were no differences in 1991. Cross seeding did not reduce competition from Italian ryegrass. Ryegrass was highly competitive against wheat, especially in 1990. Even 20 plants per m-2 reduced grain yield by an average of 38%. Wheat seeding rate had essentially no effect on grain yield in 1990, but the higher seeding rates reduced yields in 1991 because of extensive lodging. In 1991, wheat grain yields were higher in the cross-seeded plots than the single-seeded plots at the two high wheat seeding rates and highest ryegrass densities. Cross seeding does not appear promising as an aid to controlling ryegrass in Pacific Northwest wheat.

scholarly journals Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
C. Saint Pierre ◽  
J. Burgueño ◽  
J. Crossa ◽  
G. Fuentes Dávila ◽  
P. Figueroa López ◽  
...  
Keyword(s):  
Grain Yield ◽  
Bread Wheat ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Spring Bread Wheat ◽  
Ecological Zones ◽  
Agro Ecological Zones
Sign in / Sign up

Export Citation Format

Share Document