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Feature selection with interactions in logistic regression models using multivariate synergies for a GWAS

Easton Li Xu1,2, Xiaoning Qian3, Qilian Yu4

  • 1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, 48109, MI, USA.

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|March 29, 2018
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Summary

This study introduces a new measure for synergistic interactions between genetic markers, like Single Nucleotide Polymorphisms (SNPs), crucial for understanding disease and improving predictions. Theoretical analysis confirms its effectiveness in identifying genetic risk factors.

Keywords:
Feature selectionGenome-wide association studyGenotype-phenotype associationMutual informationSynergistic interaction

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Area of Science:

  • Bioinformatics
  • Genetics
  • Statistical Modeling

Background:

  • Genotype-phenotype association is a key challenge in bioinformatics.
  • Identifying marginal and epistatic effects of genetic markers (e.g., Single Nucleotide Polymorphisms/SNPs) is vital in Genome-Wide Association Studies (GWAS).
  • Synergistic interactions are critical for accurate phenotypic prediction and understanding biological systems, yet theoretical analysis of their measures is lacking.

Purpose of the Study:

  • To provide theoretical analysis on the power and limitations of existing synergistic interaction measures.
  • To propose an adjusted version of multivariate synergy as a novel measure for estimating interactive effects.
  • To demonstrate the effectiveness of the proposed measure using simulated and real-world GWAS data.

Main Methods:

  • Information-theoretic multivariate synergy analysis.
  • Development of an adjusted multivariate synergy measure.
  • Empirical validation using simulated datasets and a real-world Genome-Wide Association Study dataset.

Main Results:

  • Existing information-theoretic multivariate synergy measures depend on a limited subset of interaction parameters.
  • An adjusted multivariate synergy measure was proposed and demonstrated to be effective.
  • Rigorous theoretical and empirical evidence supports the utility of information-theoretic multivariate synergy for identifying genetic risk factors.

Conclusions:

  • The proposed adjusted multivariate synergy measure effectively estimates interactive genetic effects.
  • Theoretical analysis confirms the role of information-theoretic multivariate synergy in identifying genetic risk factors through synergistic interactions.
  • Sample complexity for detecting interactive effects was rigorously analyzed and validated on diverse datasets.