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Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Updated: May 23, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Similarity-based multimarker association tests for continuous traits.

Wan-Yu Lin1, Hemant K Tiwari, Guimin Gao

  • 1Department of Biostatistics, University of Alabama at Birmingham, USA.

Annals of Human Genetics
|April 14, 2012
PubMed
Summary
This summary is machine-generated.

We developed novel similarity-based tests (SIMc and SIMp) for continuous traits, outperforming existing methods in simulations. These genetic association tests efficiently analyze multiple markers to identify disease-related genes.

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

Published on: June 21, 2018

Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Simultaneous testing of multiple genetic markers captures linkage disequilibrium (LD) patterns, reducing the multiple-testing burden.
  • The principle that similar genotypes within a gene correlate with similar phenotypes forms the basis for association studies.

Purpose of the Study:

  • To introduce a general framework for analyzing continuous traits using similarity-based approaches.
  • To derive and evaluate two specific similarity-based tests, SIMc and SIMp, for genetic association studies.

Main Methods:

  • Development of a general similarity-based framework applicable to continuous traits.
  • Derivation of SIMc and SIMp tests as special cases of the general framework.
  • Comparative simulation study evaluating SIMc and SIMp against single-marker analysis, haplotype regression, and kernel machine regression.

Main Results:

  • The SIMc test demonstrated superior power when average R(2) (LD measure) exceeded 0.3 or causal allele frequency was high (0.3).
  • The SIMp test showed better performance when causal variants were present in common haplotypes (frequency >0.4).
  • Both tests were successfully applied to an adiposity dataset, demonstrating practical utility.

Conclusions:

  • The developed SIMc and SIMp tests offer powerful and efficient alternatives for genetic association studies with continuous traits.
  • These similarity-based methods are particularly effective under specific linkage disequilibrium and allele frequency conditions.
  • The framework and tests provide valuable tools for identifying genetic variants associated with complex phenotypes.