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

Overview

Journal Biomed Res Int

Publisher Wiley

Specialties Biomedical Engineering
Biotechnology
General Medicine

Date 2017 Aug 23

PMID 28828382

Authors

Jianxiao Liu

Zonglin Tian

Affiliations

Soon will be listed here.

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.

References

Wong J, Lambert R, Wurtzel E, Rocheford T . QTL and candidate genes phytoene synthase and zeta-carotene desaturase associated with the accumulation of carotenoids in maize. Theor Appl Genet. 2003; 108(2):349-59. DOI: 10.1007/s00122-003-1436-4. View

Scutari M, Howell P, Balding D, Mackay I . Multiple quantitative trait analysis using bayesian networks. Genetics. 2014; 198(1):129-37. PMC: 4174925. DOI: 10.1534/genetics.114.165704. View

Liu H, Wang F, Xiao Y, Tian Z, Wen W, Zhang X . MODEM: multi-omics data envelopment and mining in maize. Database (Oxford). 2016; 2016. PMC: 4976297. DOI: 10.1093/database/baw117. View

Jennen D, van Leeuwen D, Hendrickx D, Gottschalk R, van Delft J, Kleinjans J . Bayesian Network Inference Enables Unbiased Phenotypic Anchoring of Transcriptomic Responses to Cigarette Smoke in Humans. Chem Res Toxicol. 2015; 28(10):1936-48. DOI: 10.1021/acs.chemrestox.5b00145. View

Sonis S, Antin J, Tedaldi M, Alterovitz G . SNP-based Bayesian networks can predict oral mucositis risk in autologous stem cell transplant recipients. Oral Dis. 2013; 19(7):721-7. DOI: 10.1111/odi.12146. View

Zhang X, Zhao J, Hao J, Zhao X, Chen L . Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks. Nucleic Acids Res. 2014; 43(5):e31. PMC: 4357691. DOI: 10.1093/nar/gku1315. View

OReilly P, Hoggart C, Pomyen Y, Calboli F, Elliott P, Jarvelin M . MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS One. 2012; 7(5):e34861. PMC: 3342314. DOI: 10.1371/journal.pone.0034861. View

Jiang X, Barmada M, Cooper G, Becich M . A bayesian method for evaluating and discovering disease loci associations. PLoS One. 2011; 6(8):e22075. PMC: 3154195. DOI: 10.1371/journal.pone.0022075. View

Gehlenborg N, ODonoghue S, Baliga N, Goesmann A, Hibbs M, Kitano H . Visualization of omics data for systems biology. Nat Methods. 2010; 7(3 Suppl):S56-68. DOI: 10.1038/nmeth.1436. View

10.

da Silva Messias R, Galli V, Dos Anjos E Silva S, Rombaldi C . Carotenoid biosynthetic and catabolic pathways: gene expression and carotenoid content in grains of maize landraces. Nutrients. 2014; 6(2):546-63. PMC: 3942716. DOI: 10.3390/nu6020546. View

11.

Yan J, Kandianis C, Harjes C, Bai L, Kim E, Yang X . Rare genetic variation at Zea mays crtRB1 increases beta-carotene in maize grain. Nat Genet. 2010; 42(4):322-7. DOI: 10.1038/ng.551. View

12.

Bushel P, Wolfinger R, Gibson G . Simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations reveals phenotypic prototypes. BMC Syst Biol. 2007; 1:15. PMC: 1839893. DOI: 10.1186/1752-0509-1-15. View

13.

Owens B, Lipka A, Magallanes-Lundback M, Tiede T, Diepenbrock C, Kandianis C . A foundation for provitamin A biofortification of maize: genome-wide association and genomic prediction models of carotenoid levels. Genetics. 2014; 198(4):1699-716. PMC: 4256781. DOI: 10.1534/genetics.114.169979. View

14.

Harjes C, Rocheford T, Bai L, Brutnell T, Kandianis C, Sowinski S . Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science. 2008; 319(5861):330-3. PMC: 2933658. DOI: 10.1126/science.1150255. View

15.

Fu J, Cheng Y, Linghu J, Yang X, Kang L, Zhang Z . RNA sequencing reveals the complex regulatory network in the maize kernel. Nat Commun. 2013; 4:2832. DOI: 10.1038/ncomms3832. View

16.

Han B, Chen X . bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies. BMC Genomics. 2011; 12 Suppl 2:S9. PMC: 3194240. DOI: 10.1186/1471-2164-12-S2-S9. View

17.

Duarte C, Klimentidis Y, Harris J, Cardel M, Fernandez J . A Hybrid Bayesian Network/Structural Equation (BN/SEM) Modeling Approach for Detecting Physiological Networks for Obesity-related Genetic Variants. Proceedings (IEEE Int Conf Bioinformatics Biomed). 2012; :696-702. PMC: 3272699. DOI: 10.1109/BIBMW.2011.6112455. View

18.

Moore J, Gilbert J, Tsai C, Chiang F, Holden T, Barney N . A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol. 2006; 241(2):252-61. DOI: 10.1016/j.jtbi.2005.11.036. View

19.

McGeachie M, Chang H, Weiss S . CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data. PLoS Comput Biol. 2014; 10(6):e1003676. PMC: 4055564. DOI: 10.1371/journal.pcbi.1003676. View

20.

Chander S, Guo Y, Yang X, Zhang J, Lu X, Yan J . Using molecular markers to identify two major loci controlling carotenoid contents in maize grain. Theor Appl Genet. 2007; 116(2):223-33. DOI: 10.1007/s00122-007-0661-7. View