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LEAP: biomarker inference through learning and evaluating association patterns.

Xia Jiang1, Richard E Neapolitan

  • 1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

Genetic Epidemiology
|February 14, 2015
PubMed
Summary
This summary is machine-generated.

LEAP, a new system, effectively identifies interacting single nucleotide polymorphisms (SNPs) from Genome Wide Association Studies (GWAS) data. It also assesses the probability of these SNP associations, improving disease genetic risk discovery.

Keywords:
Alzheimer's diseaseBayesian networkGWASLOADSNPbiomarkerbreast cancerepistasishigh-dimensionalinteraction

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome Wide Association Studies (GWAS) provide high-dimensional single nucleotide polymorphism (SNP) datasets crucial for understanding complex disease genetics.
  • Epistatic interactions, where combinations of genes influence disease, represent a significant but often undiscovered component of genetic risk.
  • Existing research on discovering interacting SNPs from GWAS data has focused on evaluating candidate interactions and developing search algorithms, with less attention paid to assessing the probability of these associations.

Purpose of the Study:

  • To develop a comprehensive system for identifying interacting SNPs from high-dimensional GWAS datasets.
  • To introduce a novel heuristic search algorithm for learning interacting SNPs.
  • To implement a Bayesian network-based algorithm for calculating the probability of SNP associations.

Main Methods:

  • Developed LEAP, a system incorporating a heuristic search algorithm for learning interacting SNPs.
  • Integrated a Bayesian network-based algorithm within LEAP to compute the probability of SNP associations.
  • Evaluated LEAP's performance on 100 simulated 1,000-SNP datasets containing 15 interacting SNPs and on real Alzheimer's disease and breast cancer GWAS datasets.

Main Results:

  • LEAP outperformed seven other methods in learning interacting SNPs from simulated datasets.
  • The system successfully identified only SNPs involved in interactions as probable.
  • Analysis of real GWAS data yielded interesting and novel findings for Alzheimer's disease, with limited results for breast cancer.

Conclusions:

  • LEAP is a valuable tool for extracting candidate interacting SNPs from high-dimensional datasets.
  • The system's ability to determine the probability of SNP associations enhances the discovery of genetic risk factors.
  • The findings support the utility of LEAP in genetic research, particularly for complex diseases.