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Related Experiment Video

Updated: Jun 25, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Robust tests for single-marker analysis in case-control genetic association studies.

Qizhai Li¹, Gang Zheng, Xueying Liang

¹Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

Annals of Human Genetics

|February 12, 2009

Summary

Selecting the right genetic association test is key for case-control studies. The MAX test demonstrates the most robust performance across various disease risk models compared to traditional methods.

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

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Last Updated: Jun 25, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Case-control genetic association studies are crucial for identifying disease-related genetic markers.
The performance of single-marker association tests can vary significantly depending on the underlying genetic architecture of the disease.
Robust statistical methods are needed to ensure reliable results across diverse disease risk models.

Purpose of the Study:

To derive the power calculation formula for the MAX (Marker Association Explorer) test.
To conduct a comprehensive power comparison of the MAX test against the Cochran-Armitage trend test and the Pearson chi2 test.
To evaluate the robustness of MAX across different genetic risk models.

Main Methods:

Derivation of the power calculation formula for the MAX test.
Simulation studies comparing the power of MAX, 1-df Cochran-Armitage trend test, and 2-df Pearson chi2 test.
Evaluation under both single-marker and two-marker (haplotype) disease risk models.

Main Results:

The power calculation formula for MAX was successfully derived.
Each of the three tested methods exhibited optimal performance under specific conditions ('sweet spots').
The MAX test demonstrated superior and more consistent robustness across a wider range of tested genetic risk models compared to the other two tests.

Conclusions:

The MAX test offers a robust approach for single-marker association analysis in genetic studies.
MAX provides a reliable alternative to traditional tests, particularly when the specific disease risk model is unknown.
The derived power formula facilitates better study design and interpretation of results when using the MAX test.