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Related Concept Videos

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...
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...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
P-value01:10

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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.
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Updated: May 8, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

A powerful method for combining P-values in genomic studies.

Huann-Sheng Chen1, Ruth M Pfeiffer, Shunpu Zhang

  • 1Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.

Genetic Epidemiology
|August 21, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new permutation-based test for genetic association studies. The method accurately controls error rates and offers improved power for identifying significant genetic regions.

Keywords:
Simes testassociation studygene setpermutation test

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify potential genetic regions associated with traits.
  • Targeted follow-up studies require robust methods to test for associations within these regions.
  • Existing methods for combining single nucleotide polymorphism (SNP) P-values have limitations in controlling error rates.

Purpose of the Study:

  • To develop a permutation-based statistical test for genetic regions.
  • To address the challenge of correlated tests from SNPs within the same region.
  • To improve the control of the family-wise error rate (FWER) in regional association testing.

Main Methods:

  • A sequential testing procedure was adapted using a permutation-based approach.
  • The method accounts for correlations among SNP tests within a genetic region.
  • Performance was evaluated using simulated genetic data.

Main Results:

  • The permutation-based test demonstrated correct control of the type I error rate.
  • The proposed method showed higher or comparable statistical power to existing tests.
  • The test effectively identifies significant genetic associations while managing correlated SNP data.

Conclusions:

  • The developed permutation test provides a statistically sound method for regional genetic association analysis.
  • This approach enhances the reliability of identifying significant genetic regions post-GWAS.
  • The method offers a powerful and accurate tool for geneticists and bioinformaticians.