Dataset Information

Proteome Phenotypes Discriminate the Growing Location and Malting Traits in Field-Grown Barley.

ABSTRACT: Barley is one of the key cereal grains for malting and brewing industries. However, climate variability and unprecedented weather events can impact barley yield and end-product quality. The genetic background and environmental conditions are key factors in defining the barley proteome content and malting characteristics. Here, we measure the barley proteome and malting characteristics of three barley lines grown in Western Australia, differing in genetic background and growing location, by applying liquid chromatography-mass spectrometry (LC-MS). Using data-dependent acquisition LC-MS, 1571 proteins were detected with high confidence. Quantitative data acquired using sequential window acquisition of all theoretical (SWATH) MS on barley samples resulted in quantitation of 920 proteins. Multivariate analyses revealed that the barley lines' genetics and their growing locations are strongly correlated between proteins and desired traits such as the malt yield. Linking meteorological data with proteomic measurements revealed how high-temperature stress in northern regions affects seed temperature tolerance during malting, resulting in a higher malt yield. Our results show the impact of environmental conditions on the barley proteome and malt characteristics; these findings have the potential to expedite breeding programs and malt quality prediction.

SUBMITTER: Bahmani M

PROVIDER: S-EPMC9449971 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Proteome Phenotypes Discriminate the Growing Location and Malting Traits in Field-Grown Barley.

Bahmani Mahya M Juhász Angéla A Broadbent James J Bose Utpal U Nye-Wood Mitchell G MG Edwards Ian B IB Colgrave Michelle L ML

Journal of agricultural and food chemistry 20220818 34

Barley is one of the key cereal grains for malting and brewing industries. However, climate variability and unprecedented weather events can impact barley yield and end-product quality. The genetic background and environmental conditions are key factors in defining the barley proteome content and malting characteristics. Here, we measure the barley proteome and malting characteristics of three barley lines grown in Western Australia, differing in genetic background and growing location, by apply ...[more]

PMID: 35981222

Similar Datasets

Project description:Key messageGenetic 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).

Project description:BackgroundMetabolomics 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.ResultsFor 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.ConclusionsMGBLUP 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.

Dataset Information

Proteome Phenotypes Discriminate the Growing Location and Malting Traits in Field-Grown Barley.

Publications

Proteome Phenotypes Discriminate the Growing Location and Malting Traits in Field-Grown Barley.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets