Abstract
Like other crop species, barley, the fourth most important crop worldwide, suffers from the genetic bottleneck effect, where further improvements in performance through classical breeding methods become difficult. Therefore, indirect selection methods are of great interest. Here, genomic prediction (GP) based on 33,005 SNP markers and, alternatively, metabolic prediction (MP) based on 128 metabolites with sampling at two different time points in one year, were applied to predict multi-year agronomic traits in the nested association mapping (NAM) population HEB-25. We found prediction abilities of up to 0.93 for plant height with SNP markers and of up to 0.61 for flowering time with metabolites. Interestingly, prediction abilities in GP increased after reducing the number of incorporated SNP markers. The estimated effects of GP and MP were highly concordant, indicating MP as an interesting alternative to GP, being able to reflect a stable genotype-specific metabolite profile. In MP, sampling at an early developmental stage outperformed sampling at a later stage. The results confirm the value of GP for future breeding. With MP, an interesting alternative was also applied successfully. However, based on our results, usage of MP alone cannot be recommended in barley. Nevertheless, MP can assist in unravelling physiological pathways for the expression of agronomically important traits.
Highlights
Barley (Hordeum vulgare L.) is the fourth most important crop worldwide after wheat, maize and rice, with an acreage of 48.1 m hectares in 2017/18 [1]
genomic prediction (GP) overcomes the disadvantages of marker-assisted selection (MAS), which mainly relies on few selected quantitative trait loci (QTL), identified through linkage mapping and genome-wide association studies (GWAS)
Descriptive analysis of the phenotypic data showed a high variation between lines and between years, resulting in high coefficients of variation (S4 Table)
Summary
Barley (Hordeum vulgare L.) is the fourth most important crop worldwide after wheat, maize and rice, with an acreage of 48.1 m hectares in 2017/18 [1]. GP overcomes the disadvantages of MAS, which mainly relies on few selected quantitative trait loci (QTL), identified through linkage mapping and GWAS Those methods have achieved great success, in barley, for instance in the elucidation of genetic issues like disease resistance and flowering [9,10,11]. Other high-throughput methods such as liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance-mass spectrometry (NMR-MS) have been established for metabolite profiling of the experimental system [24] In this project we simultaneously characterize the multi-parental wild barley nested association mapping (NAM) population HEB-25 [11] with SNPs (50k SNP array [25]) and through metabolic profiling of 128 metabolites with sampling at two different developmental stages. We merge SNP, metabolite and phenotype data to alternatively predict phenotypes based on metabolites, SNPs or a combination of both and compare the prediction accuracies of the different methods
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