Affiliation:
1. College of Animal Science and Technology, Northwest A&F University, Yangling, China
2. School of Computer Science and Engineering, Yulin Normal University, Yulin, China
Abstract
Background:
A large number of studies have shown that susceptibility to diseases may be related to some
Single Nucleotide Polymorphisms (SNPs). Therefore, the location of SNPs associated with diseases in genes can help us
understand the genetic mechanism of disease, intervene in risk SNPs and prevent some genetic diseases/
Method:
Based on Graph Signal Processing (GSP) theory, a novel method is proposed to locate the risk SNPs in this
paper. The proposed method first builds the graph signal model of all SNP loci, and then realizes the location of abnormal
SNPs (risk SNPs) based on the joint analysis of vertex domain and frequency domain of graph
Results:
The experimental results on two real Genome-Wide Association Studies (GWAS) datasets, Agerelated
Macular Degeneration (AMD) and Genetic Disease A (GDA), show that our method not
only finds the risk SNPs found by several state-of-the-art methods, including RF, Chi-square Test
and LASSO regression, but also discovers three potential risk SNPs.
Conclusion:
Our method is suitable and effective for the identification of risk SNPs in GWAS.
Publisher
Bentham Science Publishers Ltd.
Subject
Computational Mathematics,Genetics,Molecular Biology,Biochemistry
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献