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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
Summary
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.
- 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.
