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Walking in the PPI network to identify the risky SNP of osteoporosis with decision tree algorithm

While much progress has been made on the genetic analysis of osteoporosis in the past 20 years, there are a lot of genes and SNPs that are associated with osteoporosis through GWAS. In this paper, we aim to identify the risky SNPs associated with osteoporosis by algorithms based on the known osteoporosis GWAS-associated SNPs. The whole framework of our prediction method includes two steps: Firstly, we identify whether the associated genes of the suspected risky SNPs is osteoporosis GWAS-associated genes by the method of random walk algorithm on the PPI network of osteoporosis GWAS-associated genes. Then, we classify the positive result SNPs based on their features of position and function through ID3 decision tree algorithm. We verify the accuracy of the prediction framework with the data set of GWAS-associated SNPs, and the result shows that the method is feasible. It provides a more convenient way to identify the risky SNPs of osteoporosis associated.