Application Of Pvalue In SNP Association Study

Genome-wide association analysis is a powerful method to study the connection between complex diseases and genes. Single nucleotide polymorphism(SNP) mainly refers to the change of the DNA sequence caused by the mutation of a single nucleotide, and it may lead to show different characteristics of the same species, because the diversity of the genome. Now the majority of the articles are to calculate the pvalue of SNP, which is referring to correlation between the SNP and disease, and we can find the SNP that cause people to complex disease by pvalue. This paper studies if pvalue is appropriate for the SNPs which is associated with a disease.The work of this paper is to study the accuracy of pvalue in the interaction of multiple disease models. In the data simulation stage, we choose the epi SIM software to simulate SNP data, because it not only can modify the parameters of simulation, but also can embed many disease models in SNP data. To ensure that our experimental results will not be affected by the parameters of simulation, we generate many datum of the same disease model by changing the parameters of simulation. In order to make sure that the results of experimental can not be affected by the algorithm, we will make two softwares to compute pvalue: permutation&pareto is based on the combination of mathematical statistics and probability distribution, as a fast calculating algorithm. Chi-square test is an algorithm based on probability with high accuracy in calculating the pvalue.We compare the pvalue of single disease model data to multiple disease data. The accuracy of the pvalue calculated from multiple disease models data has been decreased, and it has been unable to use pvalue in the multi disease model data as a screening for susceptibility loci.