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Related Concept Videos

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.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
McNemar's Test01:23

McNemar's Test

McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...

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Related Experiment Video

Updated: Jul 2, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

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

Published on: November 3, 2010

A new association test to test multiple-marker association.

Xuexia Wang1, Shuanglin Zhang, Qiuying Sha

  • 1Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan 49931, USA.

Genetic Epidemiology
|August 23, 2008
PubMed
Summary
This summary is machine-generated.

A new likelihood ratio test enhances genetic association studies by simultaneously comparing means and variance-covariance matrices of genotypic scores. This method shows improved power for detecting complex genetic associations, especially with interaction effects.

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Last Updated: Jul 2, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Published on: November 3, 2010

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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:

  • The increasing availability of single nucleotide polymorphisms (SNPs) fuels interest in genetic associations involving multiple linked loci.
  • Existing methods like Hotelling's T(2) and LD contrast (LDC) tests analyze genotypic scores by comparing means or variance-covariance matrices, respectively.

Purpose of the Study:

  • To propose a novel likelihood ratio test for genetic association studies.
  • This test simultaneously compares both the means and the variance-covariance matrices of genotypic scores between cases and controls.

Main Methods:

  • Development of a likelihood ratio test for comparing means and variance-covariance matrices of genotypic scores.
  • Simulation studies were conducted to evaluate the type I error rate and statistical power of the proposed test.
  • Comparison of the proposed test's power against Hotelling's T(2) and LDC tests.

Main Results:

  • The proposed test demonstrates comparable or slightly lower power than Hotelling's T(2) when marginal effects of disease loci are strong.
  • The new method exhibits greater power than Hotelling's T(2) and LDC tests when interaction effects are present with weak or no marginal effects.
  • Simulation results confirm the type I error rate of the proposed test.

Conclusions:

  • The proposed likelihood ratio test offers a powerful approach for detecting genetic associations, particularly in scenarios involving gene-gene interactions.
  • This method provides a unified framework for assessing both mean and variance differences in genotypic scores, enhancing the detection of complex genetic architectures.