Method for filtering SNP (Single Nucleotide Polymorphism) unrelated to complex diseases from whole-genome

Abstract

A method for filtering SNP (Single Nucleotide Polymorphism) unrelated to complex diseases from a whole-genome is used for the pathogenetic mechanism research of complex diseases, the early diagnosis and biological medicine development. The method comprises the following steps: (1), pre-processing and initializing SNP data, and processing the SNP data into data only including 0, 1, 2, 3 as per the principle that the influence of the variation of a random gene among the alleles of homologous chromosomes on diseases can be in equal treatment; (2), defining the relevance measure, namely defining the relevance I (Y;X) between the SNP subset X and the diseases Y as mutual information MI (Y;X) between X and Y; (3), searching SNP groups of the candidate suspected pathogenesis in the SNP set by adopting an FGSA (factor based genetic search algorithm) method; (4), selecting the SNP group of which the occurrence frequency of frequentness exceeds a threshold value in the set of SNP groups of the candidate suspected pathogenesis according to the frequentness-relevance priority criterion; (5), outputting the SNP of which the frequentness is larger than the threshold value and ranking at the headmost. According to the invention, the method can reserve the SNP corresponding to the pathogenesis covered by other pathogenesis, so as to lay a foundation for the discovery of the follow-up pathogenesis.

Description

technical field [0001] The invention belongs to the technical field of data processing. Specifically, a method for filtering SNPs irrelevant to complex diseases from whole-genome single nucleotide polymorphism (Single Nucleotide Polymorphism, SNP) data is proposed, which can be used for pathogenicity of complex diseases Mechanism research, early diagnosis and biopharmaceutical development. Background technique [0002] Complex diseases are caused by multiple genetic factors and environmental factors, and their occurrence and development are affected by multiple genes in a complex network structure. Complex diseases are different from Mendelian genetic diseases. In most cases, there are often no major genes that are sufficient to cause disease, and the effect of a single gene on disease may be negligible or even non-existent, but these single gene combinations that may play a negligible role Together, their combined effects may be the cause of complex diseases. These charac...

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Problems solved by technology

[0005] 1. The order of SNPs that can be processed is very limited. For example, the amount of calculation for exhaustive enumeration is huge. Only two or two SNPs are exhausted to obtain their scores, so that only the second-order SNP interaction (that is, the interaction of two SNPs) can be retained, and the higher order SNP interactions

[0006] 2. The scale of SNPs that can be processed is very limited. This is because the filtering process requires complex calculations. For example, the data scale processed by the random forest method is only about 100 SNPs. Relatively speaking, AntEpiSeeker can handle more SNPs (such as 5,000 SNPs )

[0007] 3. It cannot handle the situation of multiple pathogenic causes when one causative cause is covered by other causative causes

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