TY - JOUR
T1 - Finding susceptible and protective interaction patterns in large-scale genetic association study
AU - Li, Yuan
AU - Zhao, Yuhai
AU - Wang, Guoren
AU - Zhu, Xiaofeng
AU - Zhang, Xiang
AU - Wang, Zhanghui
AU - Pang, Jun
PY - 2017/6/1
Y1 - 2017/6/1
N2 - Interaction detection in large-scale genetic association studies has attracted intensive research interest, since many diseases have complex traits. Various approaches have been developed for finding significant genetic interactions. In this article, we propose a novel framework SRMiner to detect interacting susceptible and protective genotype patterns. SRMiner can discover not only probable combination of single nucleotide polymorphisms (SNPs) causing diseases but also the corresponding SNPs suppressing their pathogenic functions, which provides a better prospective to uncover the underlying relevance between genetic variants and complex diseases. We have performed extensive experiments on several real Wellcome Trust Case Control Consortium (WTCCC) datasets. We use the pathway-based and the protein-protein interaction (PPI) network-based evaluation methods to verify the discovered patterns. The results show that SRMiner successfully identifies many disease-related genes verified by the existing work. Furthermore, SRMiner can also infer some uncomfirmed but highly possible disease-related genes.
AB - Interaction detection in large-scale genetic association studies has attracted intensive research interest, since many diseases have complex traits. Various approaches have been developed for finding significant genetic interactions. In this article, we propose a novel framework SRMiner to detect interacting susceptible and protective genotype patterns. SRMiner can discover not only probable combination of single nucleotide polymorphisms (SNPs) causing diseases but also the corresponding SNPs suppressing their pathogenic functions, which provides a better prospective to uncover the underlying relevance between genetic variants and complex diseases. We have performed extensive experiments on several real Wellcome Trust Case Control Consortium (WTCCC) datasets. We use the pathway-based and the protein-protein interaction (PPI) network-based evaluation methods to verify the discovered patterns. The results show that SRMiner successfully identifies many disease-related genes verified by the existing work. Furthermore, SRMiner can also infer some uncomfirmed but highly possible disease-related genes.
UR - http://www.scopus.com/inward/record.url?scp=84979995625&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84979995625&partnerID=8YFLogxK
U2 - 10.1007/s11704-016-5300-5
DO - 10.1007/s11704-016-5300-5
M3 - Article
AN - SCOPUS:84979995625
VL - 11
SP - 541
EP - 554
JO - Frontiers of Computer Science
JF - Frontiers of Computer Science
SN - 2095-2228
IS - 3
ER -