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Dimensionality reduction approach for many-objective epistasis analysis.

Cheng-Hong Yang1,2, Ming-Feng Hou3, Li-Yeh Chuang4

  • 1Department of Information Management at the Tainan University of Technology, and at the Department of Electronic Engineering at National Kaohsiung of Science and Technology, Taiwan.

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Summary
This summary is machine-generated.

This study introduces Many-Objective MDR (MaODR) to improve the detection of gene interactions (SSIs) influencing complex diseases. The MaODR-CLN model significantly enhances accuracy in identifying these single-nucleotide polymorphism-single-nucleotide polymorphism interactions.

Keywords:
dimensionality reductionepistasis analysisgenome-wide association studiesmany-objective problemmultifactorial diseases

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Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) are crucial for understanding multifactorial disease risk, but their identification is complex.
  • Previous multiobjective approach-based multifactor dimensionality reduction (MOMDR) showed promise but lacked an optimal combination of objective functions for SSI detection.
  • Existing methods struggle with identifying SSIs, especially those with weak marginal effects, in binary trait analyses.

Purpose of the Study:

  • To extend MOMDR to a many-objective framework (MaODR) for improved SSI identification in case-control studies.
  • To develop an objective function selection approach to determine the optimal measure combination for MaODR.
  • To evaluate the performance of MaODR against MDR and MOMDR using various disease models.

Main Methods:

  • Developed Many-Objective MDR (MaODR) by integrating multiple disease probability measures from two-way contingency tables.
  • Implemented an objective function selection approach to identify the best combination of 10 well-known measures within MaODR.
  • Evaluated MaODR, MOMDR, and MDR using 6 disease models with and 40 without marginal effects, including a real-world dataset (Wellcome Trust Case Control Consortium).

Main Results:

  • The MaODR-based three-objective function model (MaODR-CLN), combining correct classification rate, likelihood ratio, and normalized mutual information, achieved higher detection success rates (6.47% over MOMDR, 17.23% over MDR).
  • MaODR-CLN successfully identified significant SSIs (P < 0.001) associated with coronary artery disease in the Wellcome Trust Case Control Consortium dataset.
  • The optimal measure combination in MaODR effectively detected SSIs with weak marginal effects, reducing spurious variables in scoring models.

Conclusions:

  • MaODR, particularly the MaODR-CLN model, represents a significant advancement in identifying complex genetic interactions (SSIs) for binary traits.
  • The objective function selection approach is effective in optimizing MaODR for robust SSI detection, even with weak marginal effects.
  • MaODR provides a powerful tool for reducing spurious variables and enhancing the accuracy of genetic association studies in complex diseases.