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Exploring gene-gene interaction in family-based data with an unsupervised machine learning method: EPISFA.

Xiao Xiang1, Siyue Wang1, Tianyi Liu2

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.

Genetic Epidemiology
|September 2, 2020
PubMed
Summary

New algorithms EPISFA and EPISFA-LD efficiently screen gene-gene interactions (G×G) in family studies. These unsupervised machine learning tools offer high power for complex disease genetics, identifying novel interactions.

Keywords:
family designsgene-gene interactionischemic strokesparse factor analysisunsupervised machine learning

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene-gene interactions (G×G) are crucial for understanding complex disease heritability.
  • Existing G×G tools often lack family-based design considerations and robustness against population stratification.
  • Family-based studies provide a reliable framework for investigating G×G and complex disease etiology.

Purpose of the Study:

  • To introduce novel unsupervised machine learning algorithms, EPISFA and EPISFA-LD, for screening G×G.
  • To evaluate the performance of EPISFA/EPISFA-LD against established family-based methods.
  • To apply EPISFA/EPISFA-LD to a real-world family-based dataset for identifying G×G in ischemic stroke.

Main Methods:

  • Development of epistasis sparse factor analysis (EPISFA) and EPISFA for linkage disequilibrium (EPISFA-LD) using unsupervised machine learning.
  • Extensive simulations to compare EPISFA/EPISFA-LD with family-based multifactor dimensionality reduction (FAM-MDR).
  • Application of EPISFA/EPISFA-LD to the Fangshan/family-based Ischemic Stroke Study in China.

Main Results:

  • EPISFA/EPISFA-LD demonstrated high power and computational efficiency in simulations compared to FAM-MDR.
  • The algorithms are suitable for high-dimensionality datasets and family-based designs.
  • Five G×G pairs were identified in the ischemic stroke study, with three validated and two novel findings.

Conclusions:

  • EPISFA/EPISFA-LD are powerful and efficient tools for detecting G×G in family-based studies.
  • These novel algorithms can uncover previously unidentified genetic interactions contributing to complex diseases.
  • The findings may provide new insights into the genetic basis of complex diseases like ischemic stroke.