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Enhanced Permutation Tests via Multiple Pruning.

Sangseob Leem1, Iksoo Huh2, Taesung Park1

  • 1Department of Statistics, Seoul National University, Seoul, South Korea.

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|July 17, 2020
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Summary

This study introduces ENhanced Permutation tests via multiple Pruning (ENPP) to efficiently analyze big multi-omics data. ENPP significantly reduces computation time by pruning non-significant features during permutation tests.

Keywords:
GWASbig multi-omics datamultiple hypothesis testingpermutation testpruning

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Area of Science:

  • Bioinformatics and Computational Biology
  • Statistical Genetics
  • Genomics and Multi-omics Data Analysis

Background:

  • Multi-omics data presents challenges due to high dimensionality and distinct feature characteristics.
  • Standard statistical methods may fail with multi-omics data, necessitating distribution-free approaches like permutation tests.
  • Traditional permutation tests are computationally intensive for large feature sets, requiring stringent error control.

Purpose of the Study:

  • To develop an efficient computational strategy for analyzing big multi-omics data.
  • To address the computational infeasibility of standard permutation tests with a large number of features.
  • To introduce a novel method, ENhanced Permutation tests via multiple Pruning (ENPP), for faster and more accurate multi-omics analysis.

Main Methods:

  • Proposed ENhanced Permutation tests via multiple Pruning (ENPP) strategy.
  • ENPP prunes non-significant features in each permutation round based on a predetermined threshold.
  • Applied ENPP to a large-scale real-world dataset (KARE) with 327,872 SNPs and a non-normally distributed phenotype.

Main Results:

  • ENPP demonstrated significant computational efficiency, removing approximately 98% of features by the 100th permutation round.
  • The method utilized only 7.4% of the computation time compared to the unpruned approach.
  • Successful application to the KARE dataset identified associations between SNPs and fasting plasma glucose levels.

Conclusions:

  • ENPP offers a computationally feasible and effective solution for analyzing large-scale multi-omics data.
  • The pruning strategy drastically reduces computation time while maintaining analytical integrity.
  • The approach is validated for its feasibility and advantages in real-world genetic association studies.