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A proteomic analysis of grain yield-related traits in wheat.

ABSTRACT: Grain yield, which is mainly contributed by tillering capacity as well as kernel number and weight, is the most important trait to plant breeders and agronomists. Label-free quantitative proteomics was used to analyse yield-contributing organs in wheat. These were leaf sample, tiller initiation, spike initiation, ovary and three successive kernel development stages at 5, 10 and 15 days after anthesis (DAA). We identified 3182 proteins across all samples. The largest number was obtained for spike initiation (1673), while the smallest was kernel sample at 15 DAA (709). Of the 3182 proteins, 296 of them were common to all seven organs. Organ-specific proteins ranged from 148 in ovary to 561 in spike initiation. When relative protein abundances were compared to that of leaf sample, 347 and 519 proteins were identified as differentially abundant in tiller initiation and spike initiation, respectively. When compared with ovary, 81, 35 and 96 proteins were identified as differentially abundant in kernels sampled at 5, 10 and 15 DAA, respectively. Our study indicated that two Argonaute proteins were solely expressed in spike initiation. Of the four expansin proteins detected, three of them were mainly expressed during the first 10 days of kernel development after anthesis. We also detected cell wall invertases and sucrose and starch synthases mainly during the kernel development period. The manipulation of these proteins could lead to increases in tillers, kernels per spike or final grain weight, and is worth exploring in future studies.

SUBMITTER: Daba SD

PROVIDER: S-EPMC7586745 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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A proteomic analysis of grain yield-related traits in wheat.

Daba Sintayehu D SD Liu Xiaoqin X Aryal Uma U Mohammadi Mohsen M

AoB PLANTS 20200824 5

Grain yield, which is mainly contributed by tillering capacity as well as kernel number and weight, is the most important trait to plant breeders and agronomists. Label-free quantitative proteomics was used to analyse yield-contributing organs in wheat. These were leaf sample, tiller initiation, spike initiation, ovary and three successive kernel development stages at 5, 10 and 15 days after anthesis (DAA). We identified 3182 proteins across all samples. The largest number was obtained for spike ...[more]

PMID: 33133478

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Project description:Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.

Dataset Information

A proteomic analysis of grain yield-related traits in wheat.

Publications

A proteomic analysis of grain yield-related traits in wheat.

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