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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Testing multiple gene interactions by the ordered combinatorial partitioning method in case-control studies.

Xing Hua1, Han Zhang, Hong Zhang

  • 1Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China.

Bioinformatics (Oxford, England)
|June 12, 2010
PubMed
Summary
This summary is machine-generated.

We introduce an optimal multifactor-dimensionality reduction (OMDR) method using ordered combinatorial partitioning (OCP) for efficient multi-locus interaction analysis. OMDR offers improved power and faster computation compared to traditional MDR, as demonstrated in breast cancer data analysis.

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Multifactor-dimensionality reduction (MDR) is a standard technique for analyzing interactions among multiple genetic loci.
  • Existing MDR methods partition genotypes based on a fixed risk ratio threshold.
  • Exhaustive search for optimal partitioning is computationally intensive.

Purpose of the Study:

  • To develop and validate an optimal multifactor-dimensionality reduction (OMDR) method.
  • To demonstrate the computational feasibility and improved performance of OMDR over traditional MDR.
  • To apply OMDR for analyzing gene-gene interactions in a breast cancer dataset.

Main Methods:

  • The proposed optimal MDR (OMDR) method utilizes an ordered combinatorial partitioning (OCP) strategy for exhaustive search over all possible genotype partitions.
  • A data-driven threshold is employed, differing from the fixed threshold in conventional MDR.
  • Generalized extreme value distribution (GEVD) theory is applied to determine optimal gene interaction order and assess statistical significance.

Main Results:

  • The OCP strategy exhibits linear computational complexity concerning the number of multi-locus genotypes, significantly outperforming the exponential complexity of naive exhaustive search.
  • Simulation studies indicate that OMDR possesses greater statistical power than MDR, with notable gains when OMDR partitioning differs from MDR.
  • Analysis of a breast cancer dataset using GEVD accelerated interaction order determination and reduced P-value calculation time by over 10-fold.

Conclusions:

  • OMDR, implemented via OCP, provides an efficient and powerful approach for multi-locus interaction analysis.
  • The method offers significant computational advantages and enhanced power compared to existing MDR techniques.
  • This approach facilitates more effective identification of complex genetic interactions in diseases like breast cancer.