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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%...
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...
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,...
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
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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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Identifying Copy Number Variants under Selection in Geographically Structured Populations Based on F-statistics.

Hae-Hiang Song1, Hae-Jin Hu, In-Hae Seok

  • 1Division of Biostatistics, Department of Medical Lifescience, The Catholic University of Korea, College of Medicine, Seoul 137-040, Korea.

Genomics & Informatics
|October 30, 2012
PubMed
Summary

This study reviews methods for identifying copy number variants (CNVs) under natural selection. A Bayesian approach using F(ST) estimates reveals CNV loci potentially driving human diversity and disease.

Keywords:
Bayes theoremDNA copy number variationsWright's FSTpopulation structureselection

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Area of Science:

  • Human genetics
  • Population genetics
  • Evolutionary biology

Background:

  • Large-scale copy number variants (CNVs) are crucial for understanding human population differences.
  • Natural selection may have influenced the distribution of CNVs across human populations.
  • Identifying specific instances of natural selection acting on CNVs is an emerging area of research.

Purpose of the Study:

  • To review and clarify the application of statistical inference methods for identifying natural selection on CNVs.
  • To demonstrate how traditional F(ST) measures can be enhanced by advanced Bayesian techniques for CNV analysis.
  • To highlight the potential of CNV analysis in understanding human genetic diversity and disease.

Main Methods:

  • Review of advances in statistical inference for population genetics.
  • Application of multinomial-Dirichlet likelihood methods for F(ST) estimation.
  • Utilizing a hierarchical Bayesian method with Markov Chain Monte Carlo (MCMC) for locus-specific F(ST) estimation.
  • Analysis of publicly available CNV data.

Main Results:

  • The review clarifies the applicability of advanced statistical methods to CNV data.
  • A Bayesian approach successfully estimates locus-specific F(ST) values.
  • Identification of specific CNV loci exhibiting signals of natural selection.
  • Potential elucidation of genetic underpinnings for human disease and diversity.

Conclusions:

  • Advanced Bayesian methods are applicable and effective for analyzing natural selection on CNVs.
  • The identified CNV loci provide insights into human evolutionary adaptations.
  • This approach can advance our understanding of the genetic basis of human variation and disease susceptibility.