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Related Concept Videos

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,...
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%...
Point and Frameshift Mutations01:30

Point and Frameshift Mutations

Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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.

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Related Experiment Video

Updated: Jun 5, 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

Multiple testing corrections for imputed SNPs.

Xiaoyi Gao1

  • 1Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri 63108, USA. ray.x.gao@gmail.com

Genetic Epidemiology
|January 22, 2011
PubMed
Summary
This summary is machine-generated.

Accurate multiple testing corrections are crucial for genome-wide association studies (GWAS). The simpleM method effectively approximates permutation thresholds for estimated allelic dosages, offering a computationally efficient solution.

Related Experiment Videos

Last Updated: Jun 5, 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

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Multiple testing corrections are vital in genetic association studies, particularly genome-wide association studies (GWAS), due to the high number of single nucleotide polymorphisms (SNPs) tested.
  • Inappropriate handling of multiple comparisons can lead to false positives and inefficient follow-up studies.
  • Permutation tests, the gold standard for multiple testing adjustment, are computationally intensive for large GWAS datasets.

Purpose of the Study:

  • To evaluate the performance of recently developed multiple testing correction methods with estimated allelic dosages in GWAS.
  • To compare these methods against permutation thresholds derived from extensive simulations.
  • To identify computationally efficient and accurate methods for multiple testing correction in GWAS.

Main Methods:

  • Comparison of multiple testing correction algorithms using 2.5 million estimated allelic dosages.
  • Derivation of permutation significance levels from 10,000 simulated GWAS results under the null hypothesis.
  • Assessment of method accuracy and computational efficiency.

Main Results:

  • The simpleM method demonstrated good performance with estimated allelic dosages.
  • simpleM provided the closest approximation to the permutation threshold among the tested methods.
  • simpleM required the least amount of computation time, indicating high efficiency.

Conclusions:

  • The simpleM method is a reliable and computationally efficient approach for multiple testing correction in genome-wide association studies with estimated allelic dosages.
  • This finding can help improve the accuracy and efficiency of genetic association studies.
  • Further validation of rapid approximation algorithms with large-scale genetic data is warranted.