Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley
Abstract Background Metabolomics measures an intermediate stage between genotype and phenotype, and may therefore be useful for breeding. Our objectives were to investigate genetic parameters and accuracies of predicted breeding values for malting quality (MQ) traits when integrating both genomic an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | deu |
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BMC
2023-09-01
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| Series: | Genetics Selection Evolution |
| Online Access: | https://doi.org/10.1186/s12711-023-00835-w |
| _version_ | 1827725082853638144 |
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| author | Xiangyu Guo Pernille Sarup Ahmed Jahoor Just Jensen Ole F. Christensen |
| author_facet | Xiangyu Guo Pernille Sarup Ahmed Jahoor Just Jensen Ole F. Christensen |
| author_sort | Xiangyu Guo |
| collection | DOAJ |
| description | Abstract Background Metabolomics measures an intermediate stage between genotype and phenotype, and may therefore be useful for breeding. Our objectives were to investigate genetic parameters and accuracies of predicted breeding values for malting quality (MQ) traits when integrating both genomic and metabolomic information. In total, 2430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 nuclear magnetic resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO), and the increase in accuracy from the successive inclusion of first, metabolomic data on the lines in the validation population (VP), and second, both metabolomic data and phenotypes on the lines in the VP, was investigated using the linear regression (LR) method. Results For all traits, we saw that the metabolome-mediated heritability was substantial. Cross-validation results showed that, in general, prediction accuracies from MGBLUP and GBLUP were similar when phenotypes and metabolomic data were recorded on the same plots. Results from the LR method showed that for all traits, except one, accuracy of MGBLUP increased when including metabolomic data on the lines of the VP, and further increased when including also phenotypes. However, in general the increase in accuracy of MGBLUP when including both metabolomic data and phenotypes on lines of the VP was similar to the increase in accuracy of GBLUP when including phenotypes on the lines of the VP. Therefore, we found that, when metabolomic data were included on the lines of the VP, accuracies substantially increased for lines without phenotypic records, but they did not increase much when phenotypes were already known. Conclusions MGBLUP is a useful approach to combine phenotypic, genomic and metabolomic data for predicting breeding values for MQ traits. We believe that our results have significant implications for practical breeding of barley and potentially many other species. |
| first_indexed | 2024-03-10T22:24:28Z |
| format | Article |
| id | doaj.art-b2e721286b884d63a6f0ab49a9215dda |
| institution | Directory Open Access Journal |
| issn | 1297-9686 |
| language | deu |
| last_indexed | 2024-03-10T22:24:28Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Genetics Selection Evolution |
| spelling | doaj.art-b2e721286b884d63a6f0ab49a9215dda2023-11-19T12:09:46ZdeuBMCGenetics Selection Evolution1297-96862023-09-0155111310.1186/s12711-023-00835-wMetabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barleyXiangyu Guo0Pernille Sarup1Ahmed Jahoor2Just Jensen3Ole F. Christensen4Center for Quantitative Genetics and Genomics, Aarhus UniversityNordic Seed A/SNordic Seed A/SCenter for Quantitative Genetics and Genomics, Aarhus UniversityCenter for Quantitative Genetics and Genomics, Aarhus UniversityAbstract Background Metabolomics measures an intermediate stage between genotype and phenotype, and may therefore be useful for breeding. Our objectives were to investigate genetic parameters and accuracies of predicted breeding values for malting quality (MQ) traits when integrating both genomic and metabolomic information. In total, 2430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 nuclear magnetic resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO), and the increase in accuracy from the successive inclusion of first, metabolomic data on the lines in the validation population (VP), and second, both metabolomic data and phenotypes on the lines in the VP, was investigated using the linear regression (LR) method. Results For all traits, we saw that the metabolome-mediated heritability was substantial. Cross-validation results showed that, in general, prediction accuracies from MGBLUP and GBLUP were similar when phenotypes and metabolomic data were recorded on the same plots. Results from the LR method showed that for all traits, except one, accuracy of MGBLUP increased when including metabolomic data on the lines of the VP, and further increased when including also phenotypes. However, in general the increase in accuracy of MGBLUP when including both metabolomic data and phenotypes on lines of the VP was similar to the increase in accuracy of GBLUP when including phenotypes on the lines of the VP. Therefore, we found that, when metabolomic data were included on the lines of the VP, accuracies substantially increased for lines without phenotypic records, but they did not increase much when phenotypes were already known. Conclusions MGBLUP is a useful approach to combine phenotypic, genomic and metabolomic data for predicting breeding values for MQ traits. We believe that our results have significant implications for practical breeding of barley and potentially many other species.https://doi.org/10.1186/s12711-023-00835-w |
| spellingShingle | Xiangyu Guo Pernille Sarup Ahmed Jahoor Just Jensen Ole F. Christensen Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley Genetics Selection Evolution |
| title | Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley |
| title_full | Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley |
| title_fullStr | Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley |
| title_full_unstemmed | Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley |
| title_short | Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley |
| title_sort | metabolomic genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley |
| url | https://doi.org/10.1186/s12711-023-00835-w |
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