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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...
Genome Copying Errors02:46

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Related Experiment Video

Updated: May 9, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Correction for multiple testing in a gene region.

Audrey E Hendricks1, Josée Dupuis2, Mark W Logue3

  • 1Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.

European Journal of Human Genetics : EJHG
|July 11, 2013
PubMed
Summary
This summary is machine-generated.

Two efficient methods, the effective number of independent single-nucleotide polymorphisms (SNPs) and extreme tail theory, effectively control for multiple testing in gene regions. These approaches offer alternatives to computationally intensive methods in genetic association studies.

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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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Last Updated: May 9, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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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
  • Statistical Genetics
  • Bioinformatics

Background:

  • Multiple testing corrections are crucial for gene region analysis in candidate gene studies and Genome-Wide Association Studies (GWAS).
  • Traditional methods like Bonferroni correction and permutation testing present limitations such as being overly conservative or computationally intensive.
  • Alternative approaches involve calculating the effective number of independent single-nucleotide polymorphisms (SNPs) or employing theoretical approximations.

Purpose of the Study:

  • To compare the performance of a theoretical approximation based on extreme tail theory against four methods for calculating the effective number of independent SNPs.
  • To evaluate the type-I error rates of these multiple testing correction methods in gene regions.
  • To identify efficient and accurate methods for controlling multiple testing in genetic studies.

Main Methods:

  • Simulated 10 gene regions using 1000 Genomes data.
  • Employed single SNP association tests to evaluate type-I error rates.
  • Compared extreme tail theory with four distinct methods for calculating the effective number of independent SNPs.

Main Results:

  • The effective number of independent SNPs method by Gao et al. and extreme tail theory demonstrated type-I error rates at or near the chosen significance level.
  • Type-I error rates for other effective number of independent SNPs methods showed variability depending on gene region characteristics.
  • Gao et al.'s method and extreme tail theory proved to be efficient alternatives to computationally demanding methods.

Conclusions:

  • The effective number of independent SNPs (Gao et al.) and extreme tail theory are reliable methods for controlling multiple testing within gene regions.
  • These methods offer a computationally efficient alternative to Bonferroni correction and permutation testing.
  • The choice of method may depend on specific gene region characteristics, but Gao et al. and extreme tail theory show broad applicability.