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Related Concept Videos

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...
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...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Complementation Tests00:49

Complementation Tests

A complementation test is a simple cross to identify whether the two mutations are located on the same gene or different genes. It was first performed by Edward Lewis in the 1940s while working on fruit flies. He developed the test to identify the location and arrangement of different mutations on chromosomes.
Organisms heterozygous for different mutations are crossed pairwise in all combinations. If present on different genes, the mutations can complement each other by providing the missing...

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Related Experiment Video

Updated: Jul 10, 2026

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

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Published on: June 21, 2018

Combining association tests across multiple genetic markers in case-control studies.

Huanyu Zhou1, Lee-Jen Wei, Xiping Xu

  • 1Program for Population Genetics, Harvard School of Public Health, Boston, MA 02115, USA.

Human Heredity
|October 18, 2007
PubMed
Summary

This study introduces a new robust testing approach (T(C)) for efficiently combining single marker association test statistics. The T(C) method, weighting lower p-values more, demonstrates superior power in identifying genetic associations for complex traits using tag Single Nucleotide Polymorphisms (SNPs).

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

  • Identifying genetic associations between complex traits and DNA variants often involves using tag Single Nucleotide Polymorphisms (SNPs) to infer information about untyped polymorphisms via linkage disequilibrium (LD).
  • Efficiently utilizing multiple SNP markers in association analysis is crucial for robust genetic studies.
  • Existing methods for combining association test statistics have limitations in power and efficiency.

Purpose of the Study:

  • To develop and present a robust testing approach (T(C)) for combining single marker association test statistics or p-values.
  • To evaluate the power of the proposed T(C) method in identifying common trait loci using tag SNPs.
  • To compare the performance of T(C) against established statistical tests in case-control settings.

Main Methods:

  • The proposed T(C) method combines single marker association test statistics or p-values, assigning greater weight to tests with lower p-values.
  • Power comparisons were conducted in case-control settings using tag SNPs within the same haplotype block as the trait loci.
  • The T(C) method was benchmarked against Bonferroni (T(B)), simple permutation (T(P)), Hoh et al. permutation (T(P-H)), deflated statistics (T(P-H_def)), chi(2) (T(CHI)), Hotelling T(2) (T(R)), and haplotype-based (T(H)) tests.

Main Results:

  • The T(C) method demonstrated superior power in identifying common trait loci compared to all competing methods across all examined scenarios.
  • The T(C) approach effectively leverages information from multiple tag SNPs for enhanced association detection.
  • Application of T(C) to a real dataset from an airway responsiveness study confirmed its practical utility.

Conclusions:

  • The T(C) method represents a preferred and robust approach for combining single marker association test statistics in genetic studies.
  • This method offers improved power for detecting genetic associations with complex traits using tag SNPs.
  • The T(C) approach provides a valuable tool for genetic association studies, enhancing the efficient analysis of SNP data.