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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...

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Related Experiment Video

Updated: Jul 19, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

SNiPer-HD: improved genotype calling accuracy by an expectation-maximization algorithm for high-density SNP arrays.

Jianping Hua1, David W Craig, Marcel Brun

  • 1Computational Biology Division Phoenix, 445 N 5th Street, Phoenix, AZ, USA.

Bioinformatics (Oxford, England)
|October 26, 2006
PubMed
Summary

We developed SNiPer-High Density (SNiPer-HD), a novel genotype calling program for accurate single nucleotide polymorphism (SNP) genotyping. This tool improves complex disease association studies by reducing false positives from genotyping errors.

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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: Jul 19, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

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
  • Bioinformatics

Background:

  • High-density genotyping enables genome-wide association studies for complex diseases.
  • Genotyping algorithm errors can inflate false positive associations between genetic markers and phenotypes.

Purpose of the Study:

  • To develop a novel, highly accurate genotype calling program for high-density single nucleotide polymorphism (SNP) data.
  • To introduce a quality control metric for filtering poorly performing SNPs.

Main Methods:

  • Developed SNiPer-High Density (SNiPer-HD) using an expectation-maximization (EM) algorithm.
  • Trained the EM algorithm parameters on a sample set.
  • Implemented a quality control metric for SNP filtering based on genotype class separation.

Main Results:

  • SNiPer-HD achieves highly accurate genotype calling across hundreds of thousands of SNPs.
  • The developed quality control metric effectively filters poor-behaving SNPs.
  • SNiPer-HD demonstrates superior performance compared to standard dynamic modeling algorithms and is complementary to other methods like BRLMM.

Conclusions:

  • Combining SNiPer-HD with other algorithms can yield highly accurate genotype calls, minimizing false positives.
  • Accurate SNP genotyping reduces false positive and false negative signals in association studies.
  • This facilitates the rapid identification of disease susceptibility loci for complex traits.