Method for screening disease phenotype related mutation sites and application thereof

Abstract

The invention relates to the technical field of bioinformatics, in particular to a method for screening disease phenotype related mutation sites and application thereof. The method comprises the following steps: obtaining sequencing data of a plurality of disease samples and normal samples, and carrying out variation detection; performing association rule mining by taking the phenotype of the samples and the mutation type of the detected mutation site as a total project set to obtain a mutation site having a strong association relationship with the phenotype of the disease samples; and performing modeling analysis on the mutation sites obtained through association rule mining and screening to obtain mutation sites related to disease phenotypes. Alleles are converted into classification variables for association rule mining, and then modeling analysis is carried out on sites strongly associated with disease phenotypes, so that the total quantity of analyzed samples can be effectively reduced, and the influence of allele frequency on an analysis result is avoided; and screening and analysis of disease phenotype related sites can be completed only by obtaining mutation genotype information.

Description

technical field [0001] The invention relates to the technical field of bioinformatics, in particular to a method for screening mutation sites related to disease phenotypes and its application. Background technique [0002] A central goal of human genetics is to identify genetic risk factors for common complex diseases such as schizophrenia and type 2 diabetes and rare Mendelian disorders such as cystic fibrosis and sickle cell anemia. While understanding the complexities of human health and disease is one of the keys to current research, it is not the only focus of research in human genetics, and the field of pharmacology is an equally important one. The goal of pharmacogenomics is to identify DNA sequence variations associated with drug metabolism, efficacy, and side effects. For example, warfarin is a blood-thinning drug that helps prevent blood clots in patients. While using warfarin, it is necessary to strictly control the dosage of the drug formulated for each patient...

AI Technical Summary

Problems solved by technology

However, the Logistic regression model has certain limitations when using a small sample size to estimate more parameters (such as single nucleotide polymorphisms). For each additional SNP site, the required sample size will increase exponentially. Considering When it comes to genotype frequency, even if the sample size is large, the data distribution in high-dimensional space is still relatively sparse, and it is very likely that there will be no observation value for a certain genome combination, which is called "dimension trouble".

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