Epistasis locus mining method based on genetic tabu and Bayesian network

Abstract

The invention discloses an epistasis locus mining method based on a genetic tabu and Bayesian network, and the method comprises the following steps: 1, converting genotype data into Boolean data in binary representation; 2, using the logic and the operation to quickly calculate the mutual information between arbitrary SNP locus pairs and phenotype, extracting a top-N node pair, and constructing aninitial network map comprising the SNP loci; 3, generating new individuals based on an initial network individual through randomly adding edges, deleting edges and reversing the edges until the number of network individuals reaches the size of the population; 4, evolving a Bayesian network structure through three operations of the genetic algorithm and a scoring mechanism of the Bayesian network,finding an optimal solution of the network structure, and quickly and accurately obtaining the epistasis locus affecting the phenotypic traits. The method can help biological researchers to obtain epistatic gene loci affecting specific phenotypic traits, thereby assisting in gene function mining, and providing reference for genetic basis analysis of complex quantitative traits of different species.

Description

technical field [0001] The invention relates to the technical field of biological information, in particular to a method for mining epistasis sites based on genetic taboos and Bayesian networks. Background technique [0002] With the continuous improvement and improvement of people's living standards and medical environment, those diseases (such as infectious diseases, malnutrition, etc.) major diseases. Mendelian genetic disease is a single-gene disease, and its genetic process follows the law of Mendelian inheritance. At present, researchers have identified related genetic genes by positional cloning, and basically clarified their genetic methods. Complex diseases account for more than 80% of human diseases and have caused great harm to human health. Common chronic diseases such as asthma, cancer, diabetes, hypertension, Alzheimer's disease, rheumatoid arthritis, schizophrenia, heart disease, cardiovascular disease, obesity, and tumors are collectively referred to as com...

AI Technical Summary

Problems solved by technology

However, the current epistasis detection methods still have problems such as computational difficulties, high algorithm complexity, low efficiency, and high false positive rate, resulting in the inability to accurately and efficiently detect SNP sites and their combinations associated with diseases

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