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Control of false discoveries in grouped hypothesis testing for eQTL data
Pratyaydipta Rudra1, Yi-Hui Zhou2, Andrew Nobel3
1Department of Statistics, Oklahoma State University, Stillwater, OK, USA. prudra@okstate.edu.
BMC Bioinformatics
|April 11, 2024
Summary
We developed Z-REG-FDR, a fast method to control the false discovery rate (FDR) for grouped hypotheses in expression quantitative trait locus (eQTL) analysis. This approach offers improved statistical power and is suitable for large-scale genomic studies.
Area of Science:
- Statistical Genomics
- Genetic Epidemiology
- Bioinformatics
Background:
- Expression quantitative trait locus (eQTL) analysis identifies genetic variants affecting gene expression.
- Gene-level eQTL testing is a grouped-hypothesis strategy crucial for biological understanding.
- Existing methods for controlling error rates in group testing may lack power or applicability to eQTL data.
Purpose of the Study:
- To develop a novel method for controlling the false discovery rate (FDR) in grouped hypothesis testing for eQTL analysis.
- To address the heterogeneity of effect sizes in eQTL data using a random effects component.
- To provide a computationally efficient alternative to existing FDR control methods.
Main Methods:
- Proposed the Random Effects model and testing procedure for Group-level FDR control (REG-FDR) in an empirical Bayesian framework.
- Introduced Z-REG-FDR, an approximation of REG-FDR utilizing only Z-statistics for computational efficiency.
- Evaluated method performance using simulated and real eQTL data.
Main Results:
- REG-FDR and Z-REG-FDR effectively control the FDR for grouped hypotheses in eQTL analysis.
- Z-REG-FDR demonstrates comparable statistical performance to REG-FDR.
- Z-REG-FDR offers significantly improved computational speed compared to REG-FDR.
Conclusions:
- Z-REG-FDR provides a favorable balance of statistical power and FDR control for eQTL analysis.
- The method's speed and ability to use summary statistics make it highly practical for large-scale statistical genomics.
- Z-REG-FDR is a valuable tool for grouped hypothesis testing in eQTL studies and related genomic research.
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