Genomic selection of forage agronomic traits in winter wheat

Maulana, Frank; Kim, Ki-Seung; Anderson, Joshua D.; Sorrells, Mark E.; Butler, Twain J.; Baenziger, P. S.; Byrne, Patrick F.; Ma, Xuefeng

doi:10.1002/csc2.20304

Cited by 6 publications

(6 citation statements)

References 65 publications

(111 reference statements)

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“…We used rrBLUP as a baseline model (ST-CV1) for comparison with different multivariate approaches. The PA for agronomic traits using ST-CV1 was comparable with other studies using the same model (Pérez-Rodríguez et al, 2012 ; Charmet et al, 2014 ; He et al, 2016 ; Maulana et al, 2021 ). For instance, the PA for YLD was between 0.13 and 0.43 for 2018–19 and 0.27 and 0.5 for 2019–20.…”

Section: Discussionsupporting

confidence: 84%

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

et al. 2021

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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.

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Section: Discussionsupporting

confidence: 84%

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

et al. 2021

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“…The gradual increase in accuracies for FHB related traits along with the increased TP size agrees with many previous research, including FHB resistance in wheat [ 30 , 58 ] and barley [ 28 ], agronomics traits in winter wheat [ 68 ] and maize [ 69 ]. The size of the training set has a big impact on the accuracy of GS, and a TP of sufficient size is required for good prediction [ 33 , 40 , 48 ].…”

Section: Discussionsupporting

confidence: 90%

Evaluation of Genomic Prediction for Fusarium Head Blight Resistance with a Multi-Parental Population

Zhang

Boyle

Brûlé‐Babel

et al. 2021

Biology

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Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat. In this study, we performed a comprehensive evaluation of factors that affect GS accuracies with a multi-parental population designed for FHB resistance. We found larger sample sizes could get better accuracies. Training population designed by CDmean based optimization algorithms significantly increased accuracies than random sampling approach, while mean of predictor error variance (PEVmean) had the poorest performance. Different genomic selection models performed similarly for accuracies. Including prior known large effect quantitative trait loci (QTL) as fixed effect into the GS model considerably improved the predictability. Multi-traits models had almost no effects, while the multi-environment model outperformed the single environment model for prediction across different environments. By comparing within and across family prediction, better accuracies were obtained with the training population more closely related to the testing population. However, achieving good accuracies for GS prediction across populations is still a challenging issue for GS application.

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“…Genomic selection builds a model using phenotypic and genotypic data from a set of breeding lines called training population (TP). The model is then used to estimate the genetic values called genomic estimated breeding value (GEBV) of a set of tested lines called validation population (VP) that only have genotypic data [ 1 , 4 , 10 , 11 ]. Genomic selection decreases the breeding cycle by selecting the progeny in the early stages or before being tested in field experiments based on GEBV.…”

Section: Introductionmentioning

confidence: 99%

Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat

Shahi¹,

Guo

Pradhan³

et al. 2022

BMC Genomics

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Background Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2. Results In this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model. Conclusions The study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops.

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Genomic selection of forage agronomic traits in winter wheat

Abstract: This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Cited by 6 publications

References 65 publications

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

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

Evaluation of Genomic Prediction for Fusarium Head Blight Resistance with a Multi-Parental Population

Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat

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