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 Experiment Videos

Maximum-likelihood estimation of relatedness.

Brook G Milligan1

  • 1Institute of Cell, Animal and Population Biology, University of Edinburgh, Edinburgh EH9 3JT, Scotland. brook@nmsu.edu

Genetics
|March 29, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Modeling Probiotic-Associated Growth Dynamics in Juvenile Red Drum.

Integrative and comparative biology·2026
Same author

Why More Biologists Must Embrace Quantitative Modeling.

Integrative and comparative biology·2024
Same author

Alignment-free genome comparison enables accurate geographic sourcing of white oak DNA.

BMC genomics·2018
Same author

Evolutionary history of plant hosts and fungal symbionts predicts the strength of mycorrhizal mutualism.

Communications biology·2018
Same author

Erratum: Author Correction: Evolutionary history of plant hosts and fungal symbionts predicts the strength of mycorrhizal mutualism.

Communications biology·2018
Same author

Disentangling genetic structure for genetic monitoring of complex populations.

Evolutionary applications·2018

The maximum-likelihood estimator offers the lowest standard error for quantifying genetic relatedness using molecular markers. While it can be biased with limited data, this bias is often manageable, making it a top performer overall.

Area of Science:

  • Population genetics
  • Quantitative genetics
  • Molecular biology

Background:

  • Quantifying genetic relatedness is crucial in genetics and population biology.
  • Various estimators exist for molecular marker data, but the maximum-likelihood estimator's performance is under-characterized.
  • No comprehensive comparison exists between the maximum-likelihood estimator and other common relatedness estimators.

Purpose of the Study:

  • To statistically evaluate the performance of the traditional maximum-likelihood estimator for genetic relatedness.
  • To compare the maximum-likelihood estimator's performance against five other commonly used estimators.
  • To assess estimator performance under diverse, biologically relevant sampling conditions.

Main Methods:

  • Statistical performance quantification of relatedness estimators.

Related Experiment Videos

  • Evaluation across a range of genetic sampling conditions.
  • Comparison of standard error, bias, and root mean-square error metrics.

    • Main Results:

    • The maximum-likelihood estimator consistently shows lower standard error across most conditions.
    • Other estimators approach the maximum-likelihood estimator's performance only with substantial genetic data.
    • The maximum-likelihood estimator exhibits higher bias, particularly with limited data or boundary relationships, but this bias can be mitigated.
    • The maximum-likelihood estimator generally has the lowest root mean-square error, indicating small bias.

    Conclusions:

    • The maximum-likelihood estimator demonstrates superior overall performance in quantifying genetic relatedness.
    • While alternative estimators may offer biological interpretability, they do not consistently outperform the maximum-likelihood estimator.
    • Careful genetic sampling can minimize potential bias in the maximum-likelihood estimator, ensuring its broad applicability.