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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%...

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 Copying Errors

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their survival. Therefore, the copying errors are checked and repaired at three levels.

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...

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Related Experiment Video

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Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

A Statistical Method for Identifying Trait-Associated Copy Number Variants.

Jessie Jeng¹, Qian Wu, Hongzhe Li

¹Department of Statistics, North Carolina State University, Raleigh, N.C., USA.

|July 24, 2015

Summary

A new method, CNVtest, directly identifies trait-associated copy number variants (CNVs) without needing to identify sample-specific CNVs first. This approach improves accuracy and reliability in genetic association studies.

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Area of Science:

Genetics
Genomics
Bioinformatics

Background:

Copy number variants (CNVs) are DNA alterations associated with complex phenotypes and diseases.
Current methods for testing CNV association are limited and often depend on arbitrary thresholds.
A two-step approach is commonly used, identifying CNVs before testing for association.

Purpose of the Study:

To develop a novel method for directly identifying trait-associated CNVs.
To overcome limitations of existing two-step CNV association testing methods.
To improve the accuracy and reliability of CNV association studies.

Main Methods:

Developed CNVtest, a method for direct identification of trait-associated CNVs.
CNVtest bypasses the need for identifying sample-specific CNVs.
Validated the method using simulations and real-world data for population differentiation.

Main Results:

CNVtest asymptotically controls the type I error rate.
The method demonstrates high probability in identifying true trait-associated CNVs.
Successfully applied CNVtest to identify CNVs associated with population differentiation.

Conclusions:

CNVtest offers a more direct and robust approach to CNV association testing.
The method enhances the ability to discover genetic variants linked to phenotypes and population structure.
This advancement has implications for understanding genetic contributions to complex traits and diseases.