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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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Updated: Nov 21, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

SeeCiTe: a method to assess CNV calls from SNP arrays using trio data.

Ksenia Lavrichenko^1,2, Øyvind Helgeland^2,3, Pål R Njølstad^2,4

¹Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.

Bioinformatics (Oxford, England)

|January 18, 2021

Summary

SeeCiTe is a new quality control tool for copy number variant (CNV) detection using child-parent trio data. It improves accuracy and reduces false positives in large biobank studies by analyzing single nucleotide polymorphism (SNP) array data.

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Area of Science:

Genetics
Bioinformatics
Genomic Analysis

Background:

Single nucleotide polymorphism (SNP) genotyping arrays are widely used for copy number variant (CNV) detection in large cohorts.
Current CNV calling tools often produce high false positive rates on biobank-scale SNP array data.
There is a need for improved quality control methods, especially those leveraging trio data for enhanced accuracy.

Purpose of the Study:

To develop a novel quality control tool, SeeCiTe (Seeing CNVs in Trios), for CNV calls derived from SNP genotyping arrays.
To enhance the specificity and sensitivity of CNV detection in large population studies.
To provide systematic quality assessment and visualization of CNV calls using trio data.

Main Methods:

SeeCiTe postprocesses CNV calls from existing tools using child-parent trio data.
The tool classifies CNV calls into quality categories.
It generates visualizations of signal intensities for putative CNV calls in offspring.

Main Results:

SeeCiTe was applied to the Norwegian Mother, Father and Child Cohort Study (MoBa).
The tool demonstrated improved specificity and sensitivity compared to standard empiric filtering methods.
It systematically identifies potential artifacts and visualizes probe-level CNV data in trios and singletons.

Conclusions:

SeeCiTe offers a novel approach to CNV quality control in large-scale biobank studies.
The tool effectively utilizes trio data to refine CNV calls and reduce false positives.
SeeCiTe provides a streamlined method for analyzing probe-level CNV data, suitable for biobank scale research.