Snpmasis:A Management And Analysis System Upon SNP Data For Disease Association Study

With the completion of the genome sequencing work, the emphasis of HGP (Human Genome Project, HGP) has been gradually transferred from "function" to "structure", life sciences has thus stepped into the post-genomic era. Scientists began to explore the relationship between disease and gene polymorphism with their sights on the most promising genetic markers-SNPs (Single Nucleotide Polymorphisms).In recent years, the rapid development of SNP genotyping technology increasingly highlights that the present situation of laboratory management and analysis of SNP data is not comprehensive, unsafe and lack of efficiency. Therefore, based on the research strategy of case-control study, we designed SNP related database systems and analysis framework and developed a lightweight SNP management and analysis platform (SNPMasis), which integrates data standardization, storage, classification, statistical analysis, and import and export as one datum center system.SNPMasis incorporates three kinds of data (sample data, SNP genotyping data, SNP data) and is compatible with raw genotyping data from ABI, Sequenom genotyping platform and standardized input files from others. According to the workflow of case-control study in epidemiology, it calculates basic statistical information of genotype data, and then makes significance analysis of allele and genotype data separately, finally, reliable results and scientific conclusions are obtained to explore the correlation between SNPs and diseases. Furthermore, the system supports users and project management. In strict accordance with the software development process, this dissertation firstly makes software requirement analysis, then, briefly introduced the design concept, system architecture, implementation, and finally validates the feasibility and practicality through software testing process of the correlation analysis experiment between six selected SNPs and gastric cancer.It promises that SNPMasis will greatly assist in exploring susceptibility for complex diseases, and shed light on more convenient and faster bioinformatics tools for statistical analysis and management of SNP data in future molecular biology, chemistry, medicine, and other laboratory disciplines.