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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%...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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...
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...
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.

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Related Experiment Video

Updated: May 17, 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

Genotype copy number variations using Gaussian mixture models: theory and algorithms.

Chang-Yun Lin1, Yungtai Lo, Kenny Q Ye

  • 1Department of Applied Mathematics and Institute of Statistics, National Chung Hsing University, Taiwan.

Statistical Applications in Genetics and Molecular Biology
|October 20, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for accurately estimating copy numbers by optimally weighting multiple probes. This approach improves disease association studies by reducing noise and enhancing signal from genomic data.

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

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Last Updated: May 17, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Copy number variations (CNVs) are crucial in disease association studies.
  • Microarray platforms for Genome-Wide Association Studies (GWAS) target CNVs, but probe performance varies significantly, with some yielding noisy data.

Purpose of the Study:

  • To develop an optimal method for combining multiple probe measurements to estimate individual copy numbers.
  • To improve the accuracy of CNV detection within the Gaussian Mixture Model (GMM) framework.

Main Methods:

  • Investigated combining multiple probe measurements using a Gaussian Mixture Model (GMM).
  • Developed an iterative algorithm to estimate GMM parameters and optimal probe weights for classification.
  • Demonstrated theoretical equivalence between weighted univariate GMM and multivariate GMM under specific conditions.

Main Results:

  • Optimal weights were derived for combining probe data, leading to improved copy number estimation.
  • The developed algorithm effectively estimated parameters and optimal weights.
  • The method showed superior performance on both simulated and real genomic data compared to equal weighting.

Conclusions:

  • The proposed weighted averaging method significantly enhances the accuracy of copy number estimation from microarray data.
  • This approach offers a clear advantage over traditional equal-weighted methods for CNV analysis in GWAS.
  • Improved CNV detection can lead to more robust disease association findings.