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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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Comparing Copy Number Variations and SNPs02:26

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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%...
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Genome-wide Association Studies-GWAS01:11

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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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Infinium Assay for Large-scale SNP Genotyping Applications
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A SNP panel and online tool for checking genotype concordance through comparing QR codes.

Yonghong Du1, Joshua S Martin2, John McGee3

  • 1School of Statistics, Beijing Normal University, Beijing, China.

Plos One
|September 20, 2017
PubMed
Summary

A new method uses 80 fingerprinting Single Nucleotide Polymorphisms (SNPs) to uniquely identify personal genomes and detect sample mix-ups. A web tool (QRC) generates QR codes from genotypes for easy concordance checking, enhancing genetic data accuracy.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Precision Medicine

Background:

  • High-throughput genotyping and sequencing generate vast amounts of genetic data.
  • Sample mix-ups are a significant concern, with reported rates between 0.1% and 1%, impacting research and clinical applications.
  • The increasing scale of genomic data necessitates robust methods for sample identification and tracking.

Purpose of the Study:

  • To develop a method for uniquely identifying personal genomes using a minimal set of Single Nucleotide Polymorphisms (SNPs).
  • To create a user-friendly web tool for checking genetic data concordance and detecting sample mix-ups.
  • To reduce the complexity and number of markers required for genetic data labeling and tracking.

Main Methods:

  • Identified a set of informative SNPs utilizing allele frequencies from the 1000 Genomes Project and ExAC Consortium.
  • Incorporated SNPs for ABO blood type and sex prediction into the SNP panel.
  • Developed a web interface (QRC) to extract, encode genotypes as QR codes, and compare them for concordance analysis.

Main Results:

  • A panel of 80 fingerprinting SNPs was established for unique personal genome identification.
  • The QRC web tool efficiently processes raw genetic data, generates QR codes, and reports concordance.
  • The method significantly reduces the complexity and number of markers for genetic data labeling.

Conclusions:

  • The developed SNP panel and QRC web tool offer an accessible and efficient solution for identifying personal genomes and ensuring genetic data accuracy.
  • This approach is crucial for maintaining data integrity in the era of precision medicine and large-scale sequencing projects.
  • The method provides a valuable resource for researchers and the public to verify the accuracy of complex genetic datasets.