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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%...
Karyotyping01:17

Karyotyping

Describing the number and physical features of chromosomes can reveal abnormalities that underlie genetic diseases. This description is facilitated by special staining techniques that produce a particular banding pattern on each chromosome. State-of-the-art techniques make this approach even more powerful, enabling the detection of individual genes that cause disease.A Simple Chromosome Staining Technique Provides Valuable Scientific InsightSome genetic diseases can be detected by looking at...
Pedigree Analysis01:35

Pedigree Analysis

Overview
Pedigree Analysis01:35

Pedigree Analysis

Overview
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...

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Updated: Jun 10, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Reconstructing CNV genotypes using segregation analysis: combining pedigree information with CNV assay.

John M Henshall1, Vicki A Whan, Belinda J Norris

  • 1CSIRO Livestock Industries, FD McMaster Laboratory Chiswick, Armidale, 2350, NSW, Australia. John.Henshall@csiro.au

Genetics, Selection, Evolution : GSE
|August 13, 2010
PubMed
Summary

Segregation analysis accurately identifies copy number variant carriers in livestock by analyzing family data. This method significantly improves the precision of identifying non-carriers compared to individual analysis.

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Area of Science:

  • Genetics
  • Animal Breeding

Background:

  • Copy number variants (CNVs) influence genetic diversity, disease risk, and economically important traits in livestock.
  • Assaying CNVs can be challenging when allele combinations have non-additive effects, as standard assays sum copy numbers without distinguishing parental origins.
  • Segregation analysis offers a novel approach to inferring allele configurations from assay data in pedigreed populations.

Purpose of the Study:

  • To evaluate the efficacy of segregation analysis for estimating copy number alleles in livestock.
  • To determine the precision of identifying carriers and non-carriers of specific CNVs using this method.

Main Methods:

  • Simulated half-sib sheep populations were used, modeling copy number variation at the Agouti locus.
  • Segregation analysis was applied to assay data, incorporating a derived penetrance function.
  • The method's precision was assessed based on family size, assay strategy, and accuracy.

Main Results:

  • Segregation analysis, combined with relationship data, significantly enhanced the precision of estimating copy number alleles for fleece color compared to individual analysis.
  • The correct identification rate for non-carriers of an undesirable allele increased from 7% (individual analysis) to 80% (half-sib family analysis).

Conclusions:

  • Segregation analysis of related individuals substantially improves the precision of copy number allele estimation from quantitative assay data.
  • Existing segregation analysis software can be readily adapted for CNV data, requiring minimal modifications.