Dataset Information

Functional mutation allele mining of plant architecture and yield-related agronomic traits and characterization of their effects in wheat.

ABSTRACT: BACKGROUND:Wheat mutant resources with phenotypic variation have been developed in recent years. These mutants might carry favorable mutation alleles, which have the potential to be utilized in the breeding process. Plant architecture and yield-related features are important agronomic traits for wheat breeders and mining favorable alleles of these traits will improve wheat characteristics. RESULTS:Here we used 190 wheat phenotypic mutants as material and by analyzing their SNP variation and phenotypic data, mutation alleles for plant architecture and yield-related traits were identified, and the genetic effects of these alleles were evaluated. In total, 32 mutation alleles, including three pleiotropic alleles, significantly associated with agronomic traits were identified from the 190 wheat mutant lines. The SNPs were distributed on 12 chromosomes and were associated with plant height (PH), tiller number, flag leaf angle (FLA), thousand grain weight (TGW), and other yield-related traits. Further phenotypic analysis of multiple lines carrying the same mutant allele was performed to determine the effect of the allele on the traits of interest. PH-associated SNPs on chromosomes 2BL, 3BS, 3DL, and 5DL might show additive effects, reducing PH by 10.0?cm to 31.3?cm compared with wild type, which means that these alleles may be favorable for wheat improvement. Only unfavorable mutation alleles that reduced TGW and tiller number were identified. A region on chromosome 5DL with mutation alleles for PH and TGW contained several long ncRNAs, and their sequences shared more than 90% identity with cytokinin oxidase/dehydrogenase genes. Some of the mutation alleles we mined were colocalized with previously reported QTLs or genes while others were novel; these novel alleles could also result in phenotypic variation. CONCLUSION:Our results demonstrate that favorable mutation alleles are present in mutant resources, and the region between 409.5 to 419.8?Mb on chromosome 5DL affects wheat plant height and thousand grain weight.

SUBMITTER: Guo H

PROVIDER: S-EPMC6937682 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Functional mutation allele mining of plant architecture and yield-related agronomic traits and characterization of their effects in wheat.

Guo Huijun H Xiong Hongchun H Xie Yongdun Y Zhao Linshu L Gu Jiayu J Zhao Shirong S Ding Yuping Y Liu Luxiang L

BMC genetics 20191230 1

<h4>Background</h4>Wheat mutant resources with phenotypic variation have been developed in recent years. These mutants might carry favorable mutation alleles, which have the potential to be utilized in the breeding process. Plant architecture and yield-related features are important agronomic traits for wheat breeders and mining favorable alleles of these traits will improve wheat characteristics.<h4>Results</h4>Here we used 190 wheat phenotypic mutants as material and by analyzing their SNP var ...[more]

PMID: 31888475

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Project description:BackgroundGenomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections.ResultsGenomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64-70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between - 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was "superior" to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone.ConclusionsOur results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.

Dataset Information

Functional mutation allele mining of plant architecture and yield-related agronomic traits and characterization of their effects in wheat.

Publications

Functional mutation allele mining of plant architecture and yield-related agronomic traits and characterization of their effects in wheat.

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