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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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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.
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Related Experiment Video

Updated: Dec 31, 2025

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

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

Published on: June 21, 2018

SMaSH: Sample matching using SNPs in humans.

Maximillian Westphal¹, David Frankhouser^2,3, Carmine Sonzone⁴

¹Interdisciplinary Biophysics Graduate Program, The Ohio State University, 484 W. 12th Avenue, Columbus, 43210, OH, USA.

|January 1, 2020

Summary

This summary is machine-generated.

This study introduces SMASH, a Bayesian tool to identify sample swaps in omics data using thousands of single nucleotide polymorphisms (SNPs). SMASH ensures data integrity across diverse sequencing types, even with low-quality samples.

Keywords:

Identity matching Next generation sequencing data Sample swap

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Area of Science:

Genomics
Bioinformatics
Data Quality Assurance

Background:

Inadvertent sample swaps compromise data integrity in large-scale omics studies.
Identifying sample matches typically requires numerous single nucleotide polymorphisms (SNPs) due to variable omics data coverage.

Purpose of the Study:

To develop a robust method for identifying sample identity in next-generation sequencing (NGS) data.
To address the challenge of sample mix-ups in omics research.

Main Methods:

Selection of approximately six thousand SNPs across the human genome.
Development of a Bayesian framework for robust sample matching in sequencing datasets.

Main Results:

Validation of the approach across diverse datasets, including Exome, RNA-Seq, and MethylCap-Seq.
Demonstration that identity detection is reliable even with low-quality RNA-Seq data (20 million reads).
Assessment of how sample quality and read coverage impact identity detection accuracy.

Conclusions:

The SMASH tool accurately identifies sample mismatches in NGS data.
SMASH is effective across different sequencing modalities and for low-quality data.
Ensures reliable sample tracking in omics studies.