Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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.
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).
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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.
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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Previously undocumented regional variability in crab-eating macaque skull sexual dimorphism and its implications for biological and morphometric studies.

Anatomical record (Hoboken, N.J. : 2007)·2026
Same author

Developmental instability, body mass, and reproduction predict immunological response in short-tailed bats.

Current zoology·2025
Same author

A physico-mechanical model of postnatal craniofacial growth in human.

iScience·2024
Same author

Exploring motion using geometric morphometrics in microscopic aquatic invertebrates: 'modes' and movement patterns during feeding in a bdelloid rotifer model species.

Movement ecology·2024
Same author

Functional adaptation of the infant craniofacial system to mechanical loadings arising from masticatory forces.

Proceedings. Biological sciences·2024
Same author

Normal human craniofacial growth and development from 0 to 4 years.

Scientific reports·2023

Related Experiment Video

Updated: May 15, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Type I error rates for testing genetic drift with phenotypic covariance matrices: a simulation study.

Miguel Prôa1, Paul O'Higgins, Leandro R Monteiro

  • 1Centre for Anatomical and Human Sciences, The Hull York Medical School, The University of York, Heslington, York, YO10 5DD, United Kingdom. miguel.proa@hyms.ac.uk

Evolution; International Journal of Organic Evolution
|January 8, 2013
PubMed
Summary

This study validates using the pooled phenotypic variance-covariance matrix (W) instead of the genetic variance-covariance matrix (G) for evolutionary divergence tests. Acceptable error rates are achieved under specific conditions of matrix similarity and heritability.

More Related Videos

Measuring Microbial Mutation Rates with the Fluctuation Assay
07:44

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

Related Experiment Videos

Last Updated: May 15, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Measuring Microbial Mutation Rates with the Fluctuation Assay
07:44

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

Area of Science:

  • Evolutionary biology
  • Quantitative genetics
  • Population genetics

Background:

  • Studies of evolutionary divergence rely on the additive genetic variance-covariance matrix (G).
  • Estimating G requires large samples and complex designs.
  • Multivariate tests often substitute G with the pooled phenotypic within-group variance-covariance matrix (W), despite concerns about proportionality.

Purpose of the Study:

  • To examine the impact of replacing average G with W on type I error rates in neutral evolution tests.
  • To evaluate the Ackermann and Cheverud (AC) test under genetic drift when using W.

Main Methods:

  • A simulation approach was used to generate random observations under genetic drift.
  • The study analyzed type I error rates of the AC test under different conditions.

Main Results:

  • Type I error rates are acceptable when using W instead of G if matrix correlation (>0.6), average heritability (>0.7), and shared principal components exist.
  • For less similar matrices, acceptable error rates occur when the ratio of divergence generations to effective population size (t/N(e)) is <0.01.
  • A simulation approach for estimating expected slopes for the AC test when G is unknown is discussed.

Conclusions:

  • The use of W instead of G in evolutionary divergence tests is validated under specific conditions.
  • The study provides guidelines for when W can be reliably substituted for G.
  • Methods for handling unknown G in real-world data analyses are proposed.