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

Genomic prediction for yield and malting traits in barley using metabolomic and near-infrared spectra

KEY MESSAGE

Genetic variation for malting quality as well as metabolomic and near-infrared features was identified. However, metabolomic and near-infrared features as additional omics-information did not improve accuracy of predicted breeding values. Significant attention has recently been given to the potential benefits of metabolomics and near-infrared spectroscopy technologies for enhancing genetic evaluation in breeding programs. In this article, we used a commercial barley breeding population phenotyped for grain yield, grain protein content, and five malting quality traits: extract yield, wort viscosity, wort color, filtering speed, and β-glucan, and aimed to: (i) investigate genetic variation and heritability of metabolomic intensities and near-infrared wavelengths originating from leaf tissue and malted grain, respectively; (ii) investigate variance components and heritabilities for genomic models including metabolomics (GOBLUP-MI) or near-infrared wavelengths (GOBLUP-NIR); and (iii) evaluate the developed models for prediction of breeding values for traits of interest. In total, 639 barley lines were genotyped using an iSelect9K-Illumina barley chip and recorded with 30,468 metabolomic intensities and 141 near-infrared wavelengths. First, we found that a significant proportion of metabolomic intensities and near-infrared wavelengths had medium to high additive genetic variances and heritabilities. Second, we observed that both GOBLUP-MI and GOBLUP-NIR, increased the proportion of estimated genetic variance for grain yield, protein, malt extract, and β-glucan compared to a genomic model (GBLUP). Finally, we assessed these models to predict accurate breeding values in fivefold and leave-one-breeding-cycle-out cross-validations, and we generally observed a similar accuracy between GBLUP and GOBLUP-MI, and a worse accuracy for GOBLUP-NIR. Despite this trend, GOBLUP-MI and GOBLUP-NIR enhanced predictive ability compared to GBLUP by 4.6 and 2.4% for grain protein in leave-one-breeding-cycle-out and grain yield in fivefold cross-validations, respectively, but differences were not significant (P-value > 0.01).

Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley

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.

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

Barley (Hordeum vulgare ssp. vulgare) was one of the first crops to be domesticated and is adapted to a wide range of environments. Worldwide barley germplasm collections possess valuable allelic variations that could further improve barley productivity. Although barley genomics has offered a global picture of allelic variation among varieties and its association with various agronomic traits, polymorphisms from East Asian varieties remain scarce. In this study, we analyzed exome polymorphisms in a panel of 274 barley varieties collected worldwide, including 137 varieties from East Asian countries and Ethiopia. We revealed the underlying population structure and conducted genome-wide association studies for ten agronomic traits. Moreover, we examined genome-wide associations for traits related to grain size such as awn length and glume length. Our results demonstrate the value of diverse barley germplasm panels containing Eastern varieties, highlighting their distinct genomic signatures relative to Western subpopulations.

Metabolomic spectra for phenotypic prediction of malting quality in spring barley

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.

Elucidation of gene action and combining ability for productive tillering in spring barley

The purpose of the present study is to identify breeding and genetic peculiarities for productive tillering in spring barley genotypes of different origin, purposes of usage and botanical affiliation, as well as to identify effective genetic sources to further improving of the trait. There were created two complete (6 × 6) diallel crossing schemes. Into the Scheme I elite Ukrainian (MIP Tytul and Avhur) and Western European (Datcha, Quench, Gladys, and Beatrix) malting spring barley varieties were involved. Scheme II included awnless covered barley varieties Kozyr and Vitrazh bred at the Plant Production Institute named after V. Y. Yuriev of NAAS of Ukraine, naked barley varieties Condor and CDC Rattan from Canada, as well as awned feed barley variety MIP Myroslav created at MIW and malting barley variety Sebastian from Denmark. For more reliable and informative characterization of barley varieties and their progeny for productive tillering in terms of inheritance, parameters of genetic variation and general combining ability (GCA) statistical analyses of experimental data from different (2019 and 2020) growing seasons were conducted. Accordingly to the indicator of phenotypic dominance all possible modes of inheritance were detected, except for negative dominance in the Scheme I in 2020. The degree of phenotypic dominance significantly varied depending on both varieties involved in crossing schemes and conditions of the years of trials. There was overdominance in loci in both schemes in both years. The other parameters of genetic variation showed significant differences in gene action for productive tillering between crossing Schemes. In Scheme I in both years the dominance was mainly unidirectional and due to dominant effects. In the Scheme II in both years there was multidirectional dominance. In Scheme I compliance with the additive-dominant system was revealed in 2019, but in 2020 there was a strong epistasis. In Scheme II in both years non-allelic interaction was identified. In general, the mode of gene action showed a very complex gene action for productive tillering in barley and a significant role of non-genetic factors in phenotypic manifestation of the trait. Despite this, the level of heritability in the narrow sense in both Schemes pointed to the possibility of the successful selection of individuals with genetically determined increased productive tillering in the splitting generations. In Scheme I the final selection for productive tillering will be more effective in later generations, when dominant alleles become homozygous. In Scheme II it is theoretically possible to select plants with high productive tillering on both recessive and dominant basis. In both schemes the non-allelic interaction should be taken into consideration. Spring barley varieties Beatrix, Datcha, MIP Myroslav and Kozyr can be used as effective genetic sources for involvement in crossings aimed at improving the productive tillering. The results of present study contribute to further development of studies devoted to evaluation of gene action for yield-related traits in spring barley, as well as identification of new genetic sources for plant improvement.

Genetic evaluation including intermediate omics features

In animal and plant breeding and genetics, there has been an increasing interest in intermediate omics traits, such as metabolomics and transcriptomics, which mediate the effect of genetics on the phenotype of interest. For inclusion of such intermediate traits into a genetic evaluation system, there is a need for a statistical model that integrates phenotypes, genotypes, pedigree, and omics traits, and a need for associated computational methods that provide estimated breeding values. In this paper, a joint model for phenotypes and omics data is presented, and a formula for the breeding values on individuals is derived. For complete omics data, three equivalent methods for best linear unbiased prediction of breeding values are presented. In all three cases, this requires solving two mixed model equation systems. Estimation of parameters using restricted maximum likelihood is also presented. For incomplete omics data, extensions of two of these methods are presented, where in both cases, the extension consists of extending an omics-related similarity matrix to incorporate individuals without omics data. The methods are illustrated using a simulated data set.