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Proteome phenotypes discriminate growing location and malting traits in field-grown barley

ABSTRACT: These uploaded files are related to the paper titles: Proteome phenotypes discriminate growing location and malting traits in field-grown barley This paper was accepted for publication in the Journal of Food and Agriculture Chemistry. Uploaded files including: -SWATH-MS data for three barley lines: 006,007,008 in three growing location Toodyay, Mingenew, Munglinup abbreviated as T, Mi and Mun. each sample has 4 replicates. -The FASTA file of the database that we used -Pooled Biological Quality Control sample used for the creation of ion library. -Malting trait data

INSTRUMENT(S): TripleTOF 6600

ORGANISM(S): Hordeum Vulgare (ncbitaxon:4513)

SUBMITTER: Mahya Bahmani

PROVIDER: MSV000090084 | MassIVE | Fri Aug 05 01:40:00 BST 2022

SECONDARY ACCESSION(S): PXD035827

REPOSITORIES: MassIVE

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Project description:Fusarium head blight (FHB) incited by Fusarium graminearum Schwabe is a devastating disease of barley and other cereal crops worldwide. Fusarium head blight is associated with trichothecene mycotoxins such as deoxynivalenol (DON), where contaminated grains are unfit for malting or animal feed industries. While genetically resistant cultivars offer the best economic and environmentally responsible means to mitigate disease, parent lines with adequate resistance are limited in barley. Resistancebreeding based upon quantitative genetic gains has been slow to date, due to intensive labour requirements of disease nurseries. The development of high throughput genome-wide molecular markers, allow application in genomic prediction models. A diverse genomic panel consisting of 400 two-row spring barley lines was assembled to focus on Canadian barley breeding programs. The panel was evaluated for FHB and DON content in three environments and over two years. Moreover, it was genotyped using an Illumina Infinium HTS iSelect custom beadchip array of single nucleotide polymorphic molecular markers (50K SNP), where over 23K molecular markers were polymorphic. Genomic prediction has been successfully demonstrated for reducing FHB and DON content in cereals using various statistically-based models of different underlying assumptions. Herein, we have studied an alternative method basedon machine learning and compare it with a statistical approach. Two encoding techniques were utilized (categorical or Hardy-Weinberg frequencies), followed by selecting essential genomic markers for phenotype prediction. Subsequently, we applied a transformer-based deep learning algorithm to predict FHB and DON. Apart from the transformer method, we also implemented a Residual Fully Connected Neural Network (RFCNN). Pearson correlation coefficients were calculated to compare true vs. predicted outputs. Under most model scenarios, the use of all markers vs. selected markers marginally improved prediction performance except for RFCNN method for FHB (27.6%). Hardy-Weinberg encoding generally improved correlation for FHB (6.9%) and DON (9.6%) for transformer. This study suggests the potential of the transformer based method for genomic prediction of complex traits such as FHB or DON, having performed better or equally compared with existing machine learning and statistical method. To genomic prediction in barley for Fusarium head blight and deoxynivalenol content using a custom Illumina Infinium array (BarleySNP50-JHI) (www.illumina.com). Sample types included leaves from 400 barley genotypes mostly of Canadian origin. This series includes 400 genotypes assayed on an Illumina infinium HTS platform 50K BeadChip.

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Proteome phenotypes discriminate growing location and malting traits in field-grown barley

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