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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...
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%...
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...
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 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.
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

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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

On multiple-testing correction in genome-wide association studies.

Valentina Moskvina1, Karl Michael Schmidt

  • 1Department of Psychological Medicine, School of Medicine, Cardiff University, Heath Park, Cardiff, UK. MoskvinaV1@Cardif f.ac.uk

Genetic Epidemiology
|April 22, 2008
PubMed
Summary
This summary is machine-generated.

Large genome-wide association studies face false positives. This study introduces a faster, more accurate multiple testing correction method using pairwise marker correlations for reliable genomic analysis.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify genetic variants linked to diseases.
  • A major challenge in GWAS is managing a high rate of false positives due to multiple testing.
  • Current methods for correcting multiple testing may overestimate results, especially with correlated markers.

Purpose of the Study:

  • To develop a more accurate and efficient method for correcting multiple testing in GWAS.
  • To address the issue of overestimated results caused by ignoring marker correlations.
  • To provide a computationally feasible correction applicable to genome-wide data.

Main Methods:

  • Estimating the type I error probability for association tests with correlated markers.
  • Developing a novel multiple testing correction based on pairwise marker correlations.
  • Comparing the new method's accuracy and speed against existing approaches.

Main Results:

  • The proposed correction method provides more realistic estimates of the effective number of tests compared to existing methods.
  • The new method accounts for the nonlinear dependence of the type I error on individual test significance levels.
  • The calculation is significantly faster, enabling genome-wide application and early study design use.

Conclusions:

  • Existing multiple testing corrections in GWAS can lead to overestimated results due to unaddressed marker correlations.
  • The novel pairwise correlation-based correction offers improved accuracy and computational efficiency.
  • This method enhances the reliability of genetic association findings and can be integrated early in study design.