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Related Concept Videos

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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Genetic Drift03:33

Genetic Drift

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.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...

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Updated: Jul 5, 2026

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
10:33

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Published on: June 17, 2019

Revealing cryptic spatial patterns in genetic variability by a new multivariate method.

T Jombart1, S Devillard, A-B Dufour

  • 1Laboratoire de Biométrie et Biologie Evolutive, UMR-CNRS 5558, Université de Lyon, Université Lyon 1, Villeurbanne Cedex, France. jombart@biomserv.univ-lyon1.fr

Heredity
|May 1, 2008
PubMed
Summary
This summary is machine-generated.

Spatial principal component analysis (sPCA) reveals spatial genetic patterns more effectively than traditional principal component analysis (PCA). This new method helps disentangle global and local genetic structures in populations.

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Last Updated: Jul 5, 2026

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
10:33

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Published on: June 17, 2019

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Genetics
  • Spatial Analysis
  • Ecology

Background:

  • Landscape information is increasingly vital in genetic studies.
  • Spatial variables are key components for understanding genetic patterns.
  • Existing statistical methods often lack spatial explicitness.

Purpose of the Study:

  • To introduce a novel spatially explicit multivariate method, spatial principal component analysis (sPCA).
  • To investigate the spatial patterns of genetic variability using allelic frequency data.
  • To provide tools for disentangling global and local genetic structures.

Main Methods:

  • Developed spatial principal component analysis (sPCA), a spatially explicit multivariate method.
  • Utilized allelic frequency data from individuals or populations.
  • Proposed two statistical tests to detect global and local spatial genetic patterns.
  • Implemented the methodology in the R package 'adegenet'.

Main Results:

  • sPCA effectively reveals spatial genetic patterns, outperforming traditional principal component analysis (PCA).
  • The method successfully disentangles global genetic structures (patches, clines) from local variations and noise.
  • sPCA scores summarize both genetic variability and spatial structure.

Conclusions:

  • sPCA is a powerful tool for analyzing spatial genetic patterns in populations.
  • The method is robust, not requiring Hardy-Weinberg or linkage equilibrium assumptions.
  • sPCA enhances our understanding of how space influences genetic variability.