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Updated: Jun 17, 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

Test selection with application to detecting disease association with multiple SNPs.

Wei Pan¹, Fang Han, Xiaotong Shen

¹Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minn. 55455-0392, USA. weip@biostat.umn.edu

|December 10, 2009

Summary

This summary is machine-generated.

This study introduces a new method to select the best statistical test for finding associations between genetic variations and traits. The approach estimates test power to identify the most effective analysis for multiple single nucleotide polymorphisms (SNPs).

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Last Updated: Jun 17, 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

Area of Science:

Statistical Genetics
Bioinformatics
Genomic Association Studies

Background:

Testing for associations between phenotypes and multiple single nucleotide polymorphisms (SNPs) is crucial in genetic research.
Existing statistical methods, including single-locus and multi-locus analyses, lack a uniformly most powerful test for detecting associations with multiple SNPs.
The performance of different tests varies depending on the specific dataset and genetic architecture, making test selection challenging.

Purpose of the Study:

To propose a novel test selection procedure for identifying the most powerful statistical test for association between a phenotype and multiple SNPs.
To develop a methodology for estimating the power of a given test using a specific dataset.
To evaluate the performance of the proposed test selection procedure compared to existing methods.

Main Methods:

A new test selection procedure is proposed, evaluating five candidate tests: UminP, multivariate score test (and modifications), and a sum test.
The procedure utilizes a methodology for estimating the power of each candidate test on a given dataset.
A fast simulation-based method is employed to calculate p-values for the test selection procedure and for combining p-values.

Main Results:

The proposed test selection procedure consistently achieved power close to the most powerful test across various situations.
The procedure demonstrated superior or equivalent performance compared to the popular minimum p-value combination method.
The application of the procedure to selecting genotype codings for the sum test was also illustrated.

Conclusions:

The developed test selection procedure effectively identifies powerful statistical tests for multi-SNP association studies.
This approach offers a robust solution for optimizing association analysis in candidate gene or region studies.
The methodology provides a valuable tool for researchers seeking to maximize the power of their genetic association analyses.