Finding susceptible and protective interaction patterns in large-scale genetic association study

Yuan Li, Yuhai Zhao, Guoren Wang, Xiaofeng Zhu, Xiang Zhang, Zhanghui Wang, Jun Pang

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)541-554
Number of pages14
JournalFrontiers of Computer Science
Volume11
Issue number3
DOIs
Publication statusPublished - Jun 1 2017

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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