A Genome-Wide SNP Locus Analysis Method Combining Random Forest and Relief‑F

Abstract

The invention discloses a genome-wide SNP (single nucleotide polymorphism) site analysis method based on combination of random forest and Relief-F. The method includes: primarily screening SNP sites with a generalized linear model; processing SNP interactive capability with Relief-F; preliminarily putting SNP sites, which are interactive, to the front of a queue; ranking the SNP sites at the rear of the queue with the random forest to recognize edge action of each single SNP site so as to obtain an SNP rank queue; removing the SNP sites at the tail of the queue; performing processing again with the Relief-F and the random forest; allowing iteration to obtain a ranking result of the SNP sites. The method has the advantages that the action of each single SNP site and interaction of the SNP sites are comprehensively considered, genome-wide SNP data can be processed so as to find those related to complex diseases, and the method is significant to the research on pathogenesis of the complex diseases, prediction on risk of diseases, development of biological drugs and the like.

Description

technical field [0001] The invention belongs to the technical field of data processing, conducts whole-genome association research based on whole-genome single nucleotide polymorphism (SNP, Single Nucleotide Polymorphisms) data, and identifies disease-related SNP sites, which can be used to explain the pathogenic mechanism of complex diseases, Disease risk prediction and biopharmaceutical development. Background technique [0002] Bioinformatics is an emerging discipline combining life science and computer science. It studies the collection, processing, storage, dissemination, analysis and interpretation of biological information, and reveals complex biological information through the comprehensive use of biology, computer science and information technology. Biological mysteries hidden in data. The basic principle of Genome-Wide Association Studies (GWAS, Genome-Wide Association Studies) is to select a certain statistical number of samples from the case group and the contro...

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Problems solved by technology

[0004] (1) Most of the current GWAS models only consider the association between a single SNP locus and complex diseases, ignoring the fact that SNP loci can affect complex diseases by interacting with other SNP loci or environmental factors

[0005] (2) It is difficult to identify rare SNP sites. The frequency of such SNP sites in the normal population is between 1% and 5%. It is difficult to directly identify them through GWAS when the number of samples is very small.

The Relief-F method uses the hypothesis interval maximization theory to sort features, which can be used to rank SNP sites. This method can identify the interaction of SNP sites well, but it is susceptible to noise data, and single site The identification effect is not ideal

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