Genetic Variance of Metabolomic Features and Their Relationship With Malting Quality Traits in Spring Barley

Guo, Xiangyu; Sarup, Pernille; Jensen, J.; Orabi, Jihad; Kristensen, Nanna Hellum; Mulder, Frans A. A.; Jahoor, A.; Jensen, Just

doi:10.3389/fpls.2020.575467

Cited by 7 publications

(14 citation statements)

References 43 publications

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“…The proportion of the variance of MFs shows the extent that MFs can be used to predict total variance in MQ traits. In our previous study, WCO, BG and WV were found to have significant phenotypic and genetic correlation to a large proportion of the MFs 13 , similarly to a large extend, their total variance could be explained by MFs. Potentially MQ traits of WCO, WV and BG can be predicted from the MFs because MFs explains almost all the variance in these MQ traits.…”

Section: Estimates Of Total Variance Of Malting Quality Traits Explained By Metabolomic Featuresmentioning

confidence: 68%

“…In our previous study, around 36% of MFs were found having significant heritability and among which many were found to be correlated with MQ traits in spring barley 13 . With this information, it is worthwhile to investigate the role of MFs involved in the prediction of phenotypes for MQ traits.…”

Section: Introductionmentioning

confidence: 82%

“…The preparation of NMR analysis is described in detail in Guo, et al 13 . MFs used in this study were 24,018 NMR intensities which obtained from one-dimensional (1D) 1 H NMR spectra.…”

Section: Metabolomic Features and Nmr Intensitiesmentioning

confidence: 99%

“…In this model, 𝝁 was a fixed parameter, m was a random parameter with 𝒎~𝑁(0, 𝑴𝜎 𝑚 2 ), and e was a random parameter with 𝒆~𝑁(0, 𝑰𝜎 𝑒 2 ). The reason for only 𝝁 taken as fixed parameter, instead of following the model in our previous study to consider more fixed parameters 13 , was because metabolomic information include environmental factors in addition to genomic information. M denoted the metabolomic similarity matrix built from MFs using the method as for building a genomic relationship matrix (G) computed using VanRaden method 1 20 .…”

Section: Metabolomic Best Linear Unbiased Prediction (Mblup)mentioning

confidence: 99%

“…NMR can produce signal intensities, which can be treated as an indicator of metabolites in a biological sample, were defined as metabolomic features (MFs) 12 . A total of 24,018 MFs from barley wort were investigated in a previous study where the genetic variation in the MFs was investigated using a univariate model and 8,604 MFs were found to be significantly heritable 13 . Partial least squares (PLS) was first developed for the modelling of information-scarce situations in social sciences by Wold 14 .…”

Section: Introductionmentioning

confidence: 99%

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Metabolomic Spectra For Phenotypic Prediction of Malting Quality In Spring Barley

Guo

Jahoor

Jensen

et al. 2021

Preprint

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The objectives were to investigate prediction of malting quality (MQ) phenotypes in different locations using information from metabolomic spectra, and compare the prediction ability using different models and different sizes of training population (TP). A total of 2,667 plots of 564 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each individual plot. Metabolomic features (MFs) used were 24,018 NMR intensities measured on each wort sample. Models involved in the statistical analyses were a metabolomic best linear unbiased prediction (MBLUP) model and a partial least squares regression (PLSR) model. Predictive ability within location and across locations were compared using cross-validation methods. The proportion of variance in MQ traits that could be explained by effects of MFs was above 0.9 for all traits. The prediction accuracy increased with increasing TP size but when the TP size reached 1,000, the rate of increase was negligible. The number of components considered in the PLSR models can affect the performance of PLSR models and 20 components were optimal. The accuracy of individual plots and line means using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation ranged from 0.517 to 0.817.In conclusion, it is possible to carry out metabolomic prediction of MQ traits using MFs, the prediction accuracy is high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.

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Section: Estimates Of Total Variance Of Malting Quality Traits Explained By Metabolomic Featuresmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 82%

“…The preparation of NMR analysis is described in detail in Guo, et al 13 . MFs used in this study were 24,018 NMR intensities which obtained from one-dimensional (1D) 1 H NMR spectra.…”

Section: Metabolomic Features and Nmr Intensitiesmentioning

confidence: 99%

Section: Metabolomic Best Linear Unbiased Prediction (Mblup)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Metabolomic Spectra For Phenotypic Prediction of Malting Quality In Spring Barley

Guo

Jahoor

Jensen

et al. 2021

Preprint

Self Cite

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Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landraces

Kim

Takahagi

Inoue

et al. 2023

Journal of Genetics and Genomics

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Metabolomic spectra for phenotypic prediction of malting quality in spring barley

Guo

Jahoor

Jensen

et al. 2022

Sci Rep

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We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.

show abstract

Genetic Variance of Metabolomic Features and Their Relationship With Malting Quality Traits in Spring Barley

Cited by 7 publications

References 43 publications

Metabolomic Spectra For Phenotypic Prediction of Malting Quality In Spring Barley

Metabolomic Spectra For Phenotypic Prediction of Malting Quality In Spring Barley

Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landraces

Metabolomic spectra for phenotypic prediction of malting quality in spring barley

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