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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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-SNPs01:05

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,...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...
Genome-wide Association Studies-GWAS01:11

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...
Karyotyping01:17

Karyotyping

Overview
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform
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Copy number variation genotyping using family information.

Jen-Hwa Chu1, Angela Rogers, Iuliana Ionita-Laza

  • 1Channing Division of Network Medicine, Brigham and Women's Hospital, ,MA, USA. stjhc@channing.harvard.edu

BMC Bioinformatics
|May 10, 2013
PubMed
Summary

This study introduces a new statistical method for accurately calling copy number variations (CNVs) in families. Leveraging family data improves CNV detection accuracy and reduces errors in genetic association studies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Growing interest in copy number variations (CNVs) and their role in genetic diseases.
  • Challenges in CNV detection due to data quality issues in hybridization techniques.
  • Need for improved methods in CNV association studies.

Purpose of the Study:

  • To develop a statistical framework for accurate CNV calling using family-based array data.
  • To address data quality issues in CNV detection for genetic association studies.
  • To leverage family information for improved CNV assignment.

Main Methods:

  • Developed a statistical framework adapting Gaussian mixture models for intensity-based array data.
  • Implemented simultaneous CNV calling for all family members.
  • Utilized within-family data to reduce Mendelian inconsistencies while allowing de novo CNVs.

Main Results:

  • Significantly improved CNV calling accuracy in simulation and genome-wide association studies (asthma).
  • Reduced Mendelian inconsistency rates and false positive genotype calls.
  • Validated findings using quantitative polymerase chain reaction (qPCR) experiments.

Conclusions:

  • Family information enhances the quality of copy number variation calling.
  • The proposed method offers more powerful association testing for CNVs.
  • Improved CNV detection quality contributes to better understanding of genetic diseases.