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Using Importance Sampling to Estimate p-values in All-Subset Meta-Analysis, with Applications to Single-Cell eQTL

Samuel Anyaso-Samuel1, Thong Luong1, Fei Qin1

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, U.S.A.

Arxiv
|July 2, 2026
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A new importance-sampling (IS) algorithm accurately estimates p-values from the ASSET method, crucial for detecting pleiotropic genetic effects. This method remains reliable even when normality assumptions are violated, unlike ASSET

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Pooling genome-wide association studies (GWAS) enhances power for detecting pleiotropic genetic effects.
  • The ASSET method efficiently searches study subsets for association signals, improving interpretability.
  • ASSET's analytic p-value approximation is validated primarily for larger p-values and limited scenarios.

Purpose of the Study:

  • To systematically assess ASSET's p-value approximation accuracy in the extreme tail.
  • To develop a computationally efficient method for accurate ASSET p-value estimation, especially for small p-values.
  • To evaluate ASSET's performance under normality violations.

Main Methods:

  • Developed a computationally efficient importance-sampling (IS) algorithm for ASSET p-value estimation.

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  • Compared IS algorithm accuracy against naive Monte Carlo simulations and ASSET's analytic approximation.
  • Applied the IS method to analyze single-cell eQTL mapping data from human populations.

    • Main Results:

    • The IS algorithm provides accurate ASSET p-value estimates for independent and overlapping studies, significantly outperforming naive Monte Carlo.
    • ASSET's analytic approximation is highly accurate across most p-value ranges when normality holds.
    • When normality is violated, ASSET p-values can be inaccurate, while the IS approach maintains accuracy.

    Conclusions:

    • The IS algorithm offers a computationally efficient and accurate solution for ASSET p-value estimation, particularly in extreme tails and under normality violations.
    • This method enhances the reliability of detecting pleiotropic genetic effects using pooled GWAS data.
    • The findings are relevant for applications like single-cell eQTL mapping and genetic variant association studies.