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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%...
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...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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Related Experiment Video

Updated: Jun 16, 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

Does replication groups scoring reduce false positive rate in SNP interaction discovery?

Marko Toplak1, Tomaz Curk, Janez Demsar

  • 1Faculty of Computer and Information Science, University of Ljubljana, TrZaska 25, SI-1000 Ljubljana, Slovenia.

BMC Genomics
|January 23, 2010
PubMed
Summary
This summary is machine-generated.

Replication groups do not reduce false positives in single nucleotide polymorphism (SNP) interaction analysis. Analyzing the entire dataset directly is more effective at minimizing false positives compared to using replication groups.

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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

Published on: June 21, 2018

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Last Updated: Jun 16, 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

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
  • Computational Biology
  • Systems Biology

Background:

  • Computational methods for inferring single nucleotide polymorphism (SNP) interactions are crucial for understanding non-Mendelian diseases.
  • Current studies face challenges with high-dimensional SNP data and limited sample sizes, leading to numerous false positives in interaction analysis.
  • A proposed method by Gayan et al. (2008) uses replication groups to mitigate false positives by analyzing data subsets.

Purpose of the Study:

  • To investigate whether the replication groups approach effectively reduces false positive rates in SNP-SNP interaction inference.
  • To compare the efficacy of replication groups scoring against standard interaction analysis techniques.

Main Methods:

  • Utilized simulated and experimental SNP datasets with imputed false interactions.
  • Compared SNP-SNP interaction inference using replication groups versus direct analysis of the entire dataset.
  • Evaluated the number of false positives reported by each method.

Main Results:

  • Direct analysis of the entire dataset consistently reported fewer false positives across all experimental conditions.
  • The replication groups approach did not demonstrate a reduction in false positive rates.

Conclusions:

  • The utility of replication groups for reducing false positive rates in SNP interaction analysis is not supported by this study.
  • The direct scoring approach, analyzing the entire dataset, is more effective and may perform better than replication groups.