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¹School of Public Health, The University of Sydney, Sydney, NSW 2006, Australia. pkelly@health.usyd.edu.au

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Summary

This study introduces a new sequential method for analyzing genetic markers in clinical trials to find gene-drug interactions early. This approach improves decision-making and overcomes challenges in analyzing large genetic datasets.

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Area of Science:

Genomics
Biostatistics
Pharmacogenomics

Background:

Analyzing large genetic datasets in clinical trials is crucial for personalized medicine.
Current univariate testing methods are time-consuming and face multiple testing issues.
There is a need for efficient statistical methods to detect gene-drug interactions early.

Purpose of the Study:

To develop a sequential omnibus test for detecting gene-drug interactions across the genome.
To provide an early decision-making tool for clinical trial analysis.
To overcome the multiple testing problem inherent in analyzing numerous genetic markers.

Main Methods:

A fixed-sample omnibus test combining F-statistics for treatment-SNP interactions.
Utilizing permutations to calculate global p-values for correlated single nucleotide polymorphisms (SNPs).
Extending the omnibus test to a sequential framework with permutation-based stopping boundaries to control Type I error rates.

Main Results:

The sequential permutation method demonstrated higher power compared to alternative sequential methods like the inverse-normal method.
The proposed method is flexible, does not require assuming a mode of inheritance, and can adjust for confounding factors.
Computational feasibility was confirmed through an application to real clinical data with a large number of SNPs.

Conclusions:

The developed sequential permutation method offers a powerful and flexible approach for early detection of gene-drug interactions in large-scale genetic studies.
This method addresses statistical challenges in analyzing massive genetic data, enabling more informed and timely clinical trial decisions.
The approach is computationally efficient and adaptable to various genetic study designs.