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On the substructure controls in rare variant analysis: Principal components or variance components?

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Population substructure (PS) significantly impacts rare variant tests. Variance component (VC)-based methods using all genetic variants offer a robust strategy for correcting confounding effects in association studies.

Keywords:
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Area of Science:

  • Population Genetics
  • Statistical Genetics
  • Genomic Association Studies

Background:

  • Population substructure (PS) poses a complex challenge in rare variant association tests.
  • Similarity-based collapsing tests, such as SKAT, are potentially more susceptible to PS confounding than burden-based tests.
  • Understanding and correcting for PS is crucial for accurate genetic association findings.

Purpose of the Study:

  • To evaluate the performance of SKAT when combined with principal component (PC) or variance component (VC) based PS correction methods.
  • To investigate the impact of different variant types (common, less frequent, rare, all) on PS confounding control.
  • To assess correction strategies across various confounding scenarios, including stratified, admixed, and spatially distributed populations.

Main Methods:

  • Simulated genetic data representing diverse population substructure scenarios.
  • Application of SKAT with PC-based and VC-based methods for PS correction.
  • Evaluation of performance based on confounding control and statistical power using different variant sets.

Main Results:

  • PC-based methods effectively control confounding in most scenarios, except admixture, with performance dependent on PC number and variant types.
  • Using all variants (common + less frequent + rare) for PC construction often requires fewer PCs and yields higher power.
  • VC-based methods demonstrate robust confounding control across all scenarios, including admixture, with 'all variants' providing consistent performance.
  • While VC based on all variants is consistent, its power may be lower than methods using specific variant subsets.

Conclusions:

  • The optimal PS correction method and variant set depend on the specific confounding mechanism.
  • VC-based methods utilizing all genetic variants represent a reliable and robust strategy for SKAT analyses across diverse population structures.
  • This approach offers a consistent way to mitigate confounding effects in rare variant association studies.