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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Empirical BayesFalse discovery rateGrouped hypothesis testingeQTL

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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.