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High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional

Gang Fang1, Majda Haznadar, Wen Wang

  • 1Department of Computer Science, University of Minnesota, Minneapolis, Minnesota, United States of America. gangfang@cs.umn.edu

Plos One
|April 27, 2012
PubMed
Summary

Discovering high-order single-nucleotide polymorphism (SNP) combinations is crucial for understanding complex diseases. This study introduces a data-mining approach to efficiently identify these SNP combinations, revealing functional gene interactions and offering insights into rare disease genetics.

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Area of Science:

  • Genetics
  • Computational Biology
  • Data Mining

Background:

  • Identifying combinations of single-nucleotide polymorphisms (SNPs) associated with phenotypes is challenging due to computational complexity and statistical power limitations.
  • Existing methods struggle with high-order SNP combinations, potentially missing important genetic interactions.

Purpose of the Study:

  • To develop an efficient and scalable method for discovering high-order SNP combinations.
  • To explore functional interactions within these combinations and their relationship to disease.
  • To provide novel insights into complex and rare diseases through the analysis of SNP combinations.

Main Methods:

  • Leveraged discriminative-pattern-mining algorithms for efficient searching of high-order SNP combinations in large-scale genome data.
  • Applied the method to synthetic and real-world datasets, including lung cancer and kidney transplant rejection data.
  • Analyzed mathematical and statistical properties of SNP combinations up to order eleven.

Main Results:

  • Demonstrated significantly improved efficiency and scalability compared to existing methods for SNP combination discovery.
  • Revealed a connection between the discriminative power of SNP combinations and the functional coherence of the involved genes.
  • Identified significant high-order SNP combinations in disease datasets, including those involving common variants in small population fractions.

Conclusions:

  • The discriminative-pattern-mining approach provides a powerful tool for systematically exploring functional interactions in high-order SNP combinations.
  • This methodology offers a novel perspective for investigating the genetic basis of complex and rare diseases.
  • The findings highlight the importance of considering combinations of common variants in understanding disease etiology.