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Single Nucleotide Polymorphisms-SNPs

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,...

Genome-wide Association Studies-GWAS

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 SNPs

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%...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

A SNP profiling panel for sample tracking in whole-exome sequencing studies.

Reuben J Pengelly¹, Jane Gibson¹, Gaia Andreoletti¹

¹Human Genetics and Genomic Medicine, Faculty of Medicine, University of Southampton, Duthie Building (MP 808), Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK.

Genome Medicine

|September 28, 2013

Summary

A new panel of single nucleotide polymorphisms (SNPs) ensures data integrity in whole-exome sequencing. This method validates sample origins, crucial for multi-facility genetic studies.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Whole-exome sequencing (WES) is a cost-effective method for analyzing protein-coding regions, which harbor many disease-causing variants.
Ensuring data provenance is critical in complex genomic studies involving multiple processing steps and sample transfers between facilities.
Maintaining the integrity of genetic samples throughout the workflow is essential for reliable research outcomes.

Purpose of the Study:

To develop and validate a panel of single nucleotide polymorphisms (SNPs) for robust data provenance in whole-exome sequencing studies.
To establish a reliable method for assigning intrinsic identifiers to genetic samples, safeguarding against sample mix-ups or contamination.
To enhance the trustworthiness of WES data by verifying sample origin and processing history.

Main Methods:

Identification of single nucleotide polymorphisms (SNPs) consistently captured by common exome enrichment kits.
Filtering identified SNPs to exclude those with potential confounding properties that could affect discriminatory power.
Development of an optimized SNP panel for intrinsic sample identification.

Main Results:

A panel of validated single nucleotide polymorphisms (SNPs) was identified for effective data provenance.
The optimized SNP panel demonstrates high discriminatory power for assigning unique identifiers to genetic samples.
The method is compatible with commonly used exome enrichment kits.

Conclusions:

The developed SNP panel offers a simple yet powerful tool for validating data provenance in whole-exome sequencing.
This approach significantly enhances the reliability and traceability of genetic samples throughout complex laboratory workflows.
Implementing this SNP panel can improve the overall quality control and data integrity in genomic research.