Dataset Information

A rare SNP mutation in Brachytic2 moderately reduces plant height and increases yield potential in maize.

ABSTRACT: Plant height has long been an important agronomic trait in maize breeding. Many plant height QTLs have been reported, but few of these have been cloned. In this study, a major plant height QTL, qph1, was mapped to a 1.6kb interval in Brachytic2 (Br2) coding sequence on maize chromosome 1. A naturally occurring rare SNP in qph1, which resulted in an amino acid substitution, was validated as the causative mutation. QPH1 protein is located in the plasma membrane and polar auxin transport is impaired in the short near-isogenic line RIL88(qph1). Allelism testing showed that the SNP variant in qph1 reduces longitudinal cell number and decreases plant height by 20% in RIL88(qph1) compared to RIL88(QPH1), and is milder than known br2 mutant alleles. The effect of qph1 on plant height is significant and has no or a slight influence on yield in four F2 backgrounds and in six pairs of single-cross hybrids. Moreover, qph1 could reduce plant height when heterozygous, allowing it to be easily employed in maize breeding. Thus, a less-severe allele of a known dwarf mutant explains part of the quantitative variation for plant height and has great potential in maize improvement.

SUBMITTER: Xing A

PROVIDER: S-EPMC4473982 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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A rare SNP mutation in Brachytic2 moderately reduces plant height and increases yield potential in maize.

Xing Anqi A Gao Yufeng Y Ye Lingfeng L Zhang Weiping W Cai Lichun L Ching Ada A Llaca Victor V Johnson Blaine B Liu Lin L Yang Xiaohong X Kang Dingming D Yan Jianbing J Li Jiansheng J

Journal of experimental botany 20150428 13

Plant height has long been an important agronomic trait in maize breeding. Many plant height QTLs have been reported, but few of these have been cloned. In this study, a major plant height QTL, qph1, was mapped to a 1.6kb interval in Brachytic2 (Br2) coding sequence on maize chromosome 1. A naturally occurring rare SNP in qph1, which resulted in an amino acid substitution, was validated as the causative mutation. QPH1 protein is located in the plasma membrane and polar auxin transport is impaire ...[more]

PMID: 25922491

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Project description:Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an "enviromic assembly approach," which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.

Dataset Information

A rare SNP mutation in Brachytic2 moderately reduces plant height and increases yield potential in maize.

Publications

A rare SNP mutation in Brachytic2 moderately reduces plant height and increases yield potential in maize.

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