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Related Concept Videos

Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

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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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A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
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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).
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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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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.
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Expected Frequencies in Goodness-of-Fit Tests01:19

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
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Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
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Allele frequencies database.

Louise Y C Takeshita1, Andrew R Jones1, Faviel F Gonzalez-Galarza1

  • 1Institute of Integrative Biology, University of Liverpool, Liverpool, UK.

Transfusion Medicine and Hemotherapy : Offizielles Organ Der Deutschen Gesellschaft Fur Transfusionsmedizin Und Immunhamatologie
|December 25, 2014
PubMed
Summary
This summary is machine-generated.

This review introduces a database designed for managing immunogenetic gene and allele frequencies. It facilitates the collection, archiving, sorting, searching, and display of this crucial data.

Keywords:
Data management systemHLAImmunogeneticsKIR

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Area of Science:

  • Immunogenetics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate gene and allele frequency data is essential for immunogenetic research.
  • Existing methods for managing this data can be fragmented and inefficient.
  • A centralized, searchable database is needed to streamline data access.

Purpose of the Study:

  • To describe a novel database for immunogenetic gene and allele frequencies.
  • To provide a platform for efficient data collection, archiving, sorting, searching, and display.
  • To support and advance immunogenetic research through improved data accessibility.

Main Methods:

  • Development of a comprehensive database system.
  • Implementation of robust data collection and archiving protocols.
  • Integration of advanced sorting and searching functionalities.
  • Design of a user-friendly interface for data display.

Main Results:

  • A functional database for immunogenetic gene and allele frequencies has been established.
  • The database supports efficient data management throughout its lifecycle.
  • Users can readily access and visualize specific gene and allele frequency data.
  • The system facilitates the retrieval of information for diverse immunogenetic applications.

Conclusions:

  • The developed database offers a valuable resource for the immunogenetics community.
  • It enhances the accessibility and usability of gene and allele frequency data.
  • This tool has the potential to accelerate discoveries in immunogenetics and related fields.