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Related Concept Videos

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...
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.In the early 20th century,...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

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Related Experiment Video

Updated: Jul 18, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Can a simple algebraic analysis predict markers-genome heterozygosity correlations?

José Miguel Aparicio1, Joaquín Ortego, Pedro J Cordero

  • 1Grupo de Investigación de la Biodiversidad Genética y Cultural, Instituto de Investigación en Recursos Cinegéticos, Ciudad Real, Spain.

The Journal of Heredity
|December 8, 2006
PubMed
Summary

Estimating genome-wide heterozygosity is feasible even with few markers in large genomes. Real population dynamics, unlike simple algebraic models, reveal stronger marker-heterozygosity correlations.

Related Experiment Videos

Last Updated: Jul 18, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Area of Science:

  • Genetics
  • Bioinformatics

Background:

  • Current algebraic models suggest limited feasibility for estimating genome-wide heterozygosity in large genomes using few markers.
  • These models posit a correlation (rho) dependent on the ratio of markers (r) to genome loci (n), approximated by rho ≈ √(r/n).

Purpose of the Study:

  • To investigate the accuracy of genome-wide heterozygosity estimation under realistic population dynamics.
  • To challenge the assumption that locus heterozygosity is solely population-dependent, proposing individual pedigree as a factor.

Main Methods:

  • Simulated random genomes for 100 individuals.
  • Simulated random mating across generations, including descendants, to model population dynamics.
  • Analyzed correlations between molecular markers and genome-wide heterozygosity.

Main Results:

  • Initial simulations aligned with the algebraic model (rho ≈ √(r/n)).
  • Simulations incorporating random mating and successive generations showed significantly higher correlations than predicted.
  • Genome-wide heterozygosity estimates showed slight improvement with increased loci.

Conclusions:

  • Individual pedigree influences locus heterozygosity, impacting genome-wide estimates.
  • Realistic population dynamics enhance the correlation between markers and genome-wide heterozygosity.
  • Genome-wide heterozygosity estimation is more feasible than previously suggested by simple algebraic models.