Dataset Information

Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining.

ABSTRACT:

Background and objective

Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize.

Methods

On the basis of using the entropy estimation method with Gaussian kernel probability density estimator, we use the three-phase dependency analysis (TPDA) Bayesian network structure learning method to construct the network of maize gene and carotenoid components traits.

Results

In the case of using two discretization methods and setting different discretization values, we compare the learning effect and efficiency of 10 kinds of Bayesian network structure learning methods. The method is verified and analyzed on the maize dataset of global germplasm collection with 527 elite inbred lines.

Conclusions

The result confirmed the effectiveness of the TPDA method, which outperforms significantly another 9 kinds of Bayesian network learning methods. It is an efficient method of mining genes for maize carotenoid components traits. The parameters obtained by experiments will help carry out practical gene mining effectively in the future.

SUBMITTER: Liu J

PROVIDER: S-EPMC5554554 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining.

Liu Jianxiao J Tian Zonglin Z

BioMed research international 20170730

<h4>Background and objective</h4>Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize.<h4>Methods</h4>On the basis of using the entropy estimation method with Gaussian kernel probability density estimator, we use the three-phase dependency analysis (TPDA) Bayesian network structure learning method to construct the network of maize gene and carotenoid components traits.<h4>Results</h4>In the case of using two discretizatio ...[more]

PMID: 28828382

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Project description:Plant carotenoids have been implicated in preventing several age-related diseases, and they also provide vitamin A precursors; therefore, increasing the content of carotenoids in maize grains is of great interest. It is not well understood, however, how the carotenoid biosynthetic pathway is regulated. Fortunately, the maize germplasm exhibits a high degree of genetic diversity that can be exploited for this purpose. Here, the accumulation of carotenoids and the expression of genes from carotenoid metabolic and catabolic pathways were investigated in several maize landraces. The carotenoid content in grains varied from 10.03, in the white variety MC5, to 61.50 μg·g⁻¹, in the yellow-to-orange variety MC3, and the major carotenoids detected were lutein and zeaxanthin. PSY1 (phythoene synthase) expression showed a positive correlation with the total carotenoid content. Additionally, the PSY1 and HYD3 (ferredoxin-dependent di-iron monooxygenase) expression levels were positively correlated with β-cryptoxanthin and zeaxanthin, while CYP97C (cytochrome P450-type monooxygenase) expression did not correlate with any of the carotenoids. In contrast, ZmCCD1 (carotenoid dioxygenase) was more highly expressed at the beginning of grain development, as well as in the white variety, and its expression was inversely correlated with the accumulation of several carotenoids, suggesting that CCD1 is also an important enzyme to be considered when attempting to improve the carotenoid content in maize. The MC27 and MC1 varieties showed the highest HYD3/CYP97C ratios, suggesting that they are promising candidates for increasing the zeaxanthin content; in contrast, MC14 and MC7 showed low HYD3/CYP97C, suggesting that they may be useful in biofortification efforts aimed at promoting the accumulation of provitamin A. The results of this study demonstrate the use of maize germplasm to provide insight into the regulation of genes involved in the carotenoid pathway, which would thus better enable us to select promising varieties for biofortification efforts.

Dataset Information

Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining.

Background and objective

Methods

Results

Conclusions

Publications

Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining.

Similar Datasets

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