Biomaterial Database

An Approach of Epistasis Detection Using Integer Linear Programming Optimizing Bayesian Network. 2022

Xuan Yang, and Chen Yang, and Jimeng Lei, and Jianxiao Liu

Proposing a more effective and accurate epistatic loci detection method in large-scale genomic data has important research significance for improving crop quality, disease treatment, etc. Due to the characteristics of high accuracy and processing non-linear relationship, Bayesian network (BN) has been widely used in constructing the network of SNPs and phenotype traits and thus to mine epistatic loci. However, the shortcoming of BN is that it is easy to fall into local optimum and unable to process large-scale of SNPs. In this work, we transform the problem of learning Bayesian network into the optimization of integer linear programming (ILP). We use the algorithms of branch-and-bound and cutting planes to get the global optimal Bayesian network (ILPBN), and thus to get epistatic loci influencing specific phenotype traits. In order to handle large-scale of SNP loci and further to improve efficiency, we use the method of optimizing Markov blanket to reduce the number of candidate parent nodes for each node. In addition, we use α-BIC that is suitable for processing the epistatis mining to calculate the BN score. We use four properties of BN decomposable scoring functions to further reduce the number of candidate parent sets for each node. Experiment results show that ILPBN can not only process 2-locus and 3-locus epistasis mining, but also realize multi-locus epistasis detection. Finally, we compare ILPBN with several popular epistasis mining algorithms by using simulated and real Age-related macular disease (AMD) dataset. Experiment results show that ILPBN has better epistasis detection accuracy, F1-score and false positive rate in premise of ensuring the efficiency compared with other methods. Availability: Codes and dataset are available at: http://122.205.95.139/ILPBN/.

UI	MeSH Term	Description	Entries
D011382	Programming, Linear	A technique of operations research for solving certain kinds of problems involving many variables where a best value or set of best values is to be found. It is most likely to be feasible when the quantity to be optimized, sometimes called the objective function, can be stated as a mathematical expression in terms of the various activities within the system, and when this expression is simply proportional to the measure of the activities, i.e., is linear, and when all the restrictions are also linear. It is different from computer programming, although problems using linear programming techniques may be programmed on a computer.	Linear Programming
D004843	Epistasis, Genetic	A form of gene interaction whereby the expression of one gene interferes with or masks the expression of a different gene or genes. Genes whose expression interferes with or masks the effects of other genes are said to be epistatic to the effected genes. Genes whose expression is affected (blocked or masked) are hypostatic to the interfering genes.	Deviation, Epistatic,Epistatic Deviation,Genes, Epistatic,Genes, Hypostatic,Epistases, Genetic,Gene-Gene Interaction, Epistatic,Gene-Gene Interactions, Epistatic,Genetic Epistases,Genetic Epistasis,Interaction Deviation,Non-Allelic Gene Interactions,Epistatic Gene,Epistatic Gene-Gene Interaction,Epistatic Gene-Gene Interactions,Epistatic Genes,Gene Gene Interaction, Epistatic,Gene Gene Interactions, Epistatic,Gene Interaction, Non-Allelic,Gene Interactions, Non-Allelic,Gene, Epistatic,Gene, Hypostatic,Hypostatic Gene,Hypostatic Genes,Interaction, Epistatic Gene-Gene,Interaction, Non-Allelic Gene,Interactions, Epistatic Gene-Gene,Interactions, Non-Allelic Gene,Non Allelic Gene Interactions,Non-Allelic Gene Interaction
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D001499	Bayes Theorem	A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.	Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D055106	Genome-Wide Association Study	An analysis comparing the allele frequencies of all available (or a whole GENOME representative set of) polymorphic markers to identify gene candidates or quantitative trait loci associated with a specific organism trait or specific disease or condition.	Genome Wide Association Analysis,Genome Wide Association Study,GWA Study,Genome Wide Association Scan,Genome Wide Association Studies,Whole Genome Association Analysis,Whole Genome Association Study,Association Studies, Genome-Wide,Association Study, Genome-Wide,GWA Studies,Genome-Wide Association Studies,Studies, GWA,Studies, Genome-Wide Association,Study, GWA,Study, Genome-Wide Association
D020641	Polymorphism, Single Nucleotide	A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.	SNPs,Single Nucleotide Polymorphism,Nucleotide Polymorphism, Single,Nucleotide Polymorphisms, Single,Polymorphisms, Single Nucleotide,Single Nucleotide Polymorphisms