Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus C, Denmark.
Danish Pig Research Centre, Danish Agriculture and Food Council, 1609, Copenhagen V, Denmark.
Genet Sel Evol. 2023 Sep 5;55(1):61. doi: 10.1186/s12711-023-00835-w.
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.
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.
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.
代谢组学测量基因型和表型之间的中间阶段，因此可能对育种有用。我们的目标是研究整合基因组和代谢组信息时，用于麦芽质量（MQ）性状的遗传参数和预测育种值的准确性。总共包括三年两个地点的 562 个麦芽春大麦品系的 2430 个小区。每个小区的麦芽中都测量了 5 个 MQ 性状。代谢组学特征是对每个麦芽样品测量的 24018 个核磁共振强度。统计分析方法是基因组最佳线性无偏预测（GBLUP）和代谢组学-基因组最佳线性无偏预测（MGBLUP）。使用两种交叉验证策略（LOYO 和 LOLO）比较预测育种值的准确性，并使用线性回归（LR）方法研究从以下方面连续纳入验证群体（VP）中品系的代谢组数据、其次是代谢组数据和表型，来研究预测值准确性的提高。
对于所有性状，我们发现代谢组介导的遗传力很大。交叉验证结果表明，通常情况下，当在同一小区记录表型和代谢组数据时，MGBLUP 和 GBLUP 的预测准确性相似。LR 方法的结果表明，除一个性状外，对于所有性状，当在 VP 中的品系中包含代谢组数据时，MGBLUP 的准确性增加，当在 VP 中的品系中包含表型时，准确性进一步增加。然而，通常情况下，当在 VP 中的品系中同时包含代谢组数据和表型时，MGBLUP 的准确性提高与在 VP 中的品系中包含表型时 GBLUP 的准确性提高相似。因此，我们发现，当在 VP 中的品系中包含代谢组数据时，没有表型记录的品系的准确性大大提高，但当已经知道表型时，准确性提高不大。
MGBLUP 是一种将表型、基因组和代谢组数据结合起来预测 MQ 性状的有用方法。我们相信，我们的结果对大麦的实际育种以及潜在的许多其他物种具有重要意义。
