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

Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

76.0K
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.
76.0K
Pedigree Analysis01:35

Pedigree Analysis

88.8K
Overview
88.8K
Genetic Drift03:33

Genetic Drift

42.9K
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.
42.9K
Genetics of Speciation02:16

Genetics of Speciation

20.9K
Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
20.9K
Multiple Allele Traits01:49

Multiple Allele Traits

38.0K
The Concept of Multiple Allelism
38.0K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

61.8K
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).
61.8K

You might also read

Related Articles

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

Sort by
Same author

Genetic predisposition to serum 25 hydroxyvitamin D concentrations does not influence the risk of decreasing celiac disease in European ancestry: Evidence from meta-analysis and Mendelian randomization.

Medicine·2026
Same author

Genetically Predicted 25-Hydroxyvitamin D Levels on Hypothyroidism: A Two-Sample Mendelian Randomization.

BioMed research international·2026
Same author

Morphometric structure and age-related growth of body and udder traits in Khuzestani buffalo.

Tropical animal health and production·2026
Same author

Serum ferritin and delirium risk: an integrative genomic analysis of causal inference and multi-tissue regulatory signals.

Human genomics·2026
Same author

Formulation and characterization of agar-chia gum coated niosomes for controlled ferrous fumarate release.

Food chemistry·2026
Same author

Development of a food security assessment tool for prenatal care in pregnant women: a mixed-method study.

Reproductive health·2026

Related Experiment Video

Updated: Jan 16, 2026

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
06:18

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR

Published on: July 11, 2025

815

Pairwise Paternity Assignment With Forward-Backward Simulations: Refining CERVUS Using Trio-Based Likelihood and

Mahmoud Amiri Roudbar1, Seyedeh Fatemeh Mousavi2, Mahdi Akbarzadeh3

  • 1Department of Animal Science Safiabad-Dezful Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO) Dezful Iran.

Ecology and Evolution
|September 29, 2025
PubMed
Summary
This summary is machine-generated.

The new Pairwise algorithm improves parentage analysis by using trio-specific data and accounting for typing errors. This enhances accuracy in genetic relationship studies.

Keywords:
Pairwise assignment algorithmSTR markergenealogical relationshipspaternity analysis

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.6K
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

11.3K

Related Experiment Videos

Last Updated: Jan 16, 2026

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
06:18

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR

Published on: July 11, 2025

815
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.6K
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

11.3K

Area of Science:

  • Genetics
  • Bioinformatics
  • Population Genomics

Background:

  • Microsatellite markers are crucial for studying genealogical relationships.
  • Traditional paternity analysis methods rely on simplifying assumptions often violated in real-world data.
  • Existing methods can be inaccurate due to homogeneous genetic structure and consistent typing error assumptions.

Purpose of the Study:

  • To introduce an enhanced likelihood-based algorithm, "Pairwise", for more accurate paternity analysis.
  • To address limitations of traditional methods by incorporating trio-specific significance criteria.
  • To account for variable typing errors across genomic loci.

Main Methods:

  • Developed the "Pairwise" algorithm, an enhancement of the CERVUS method.
  • Utilized forward and backward simulations to calculate trio-specific significance.
  • Incorporated variable typing error rates across genomic loci into likelihood equations.

Main Results:

  • The Pairwise algorithm increased the power of paternity assignments.
  • Reduced the number of falsely assigned parents compared to traditional methods.
  • Accounting for variable typing errors significantly improved paternity assignment accuracy.

Conclusions:

  • The Pairwise algorithm offers a more robust framework for paternity analysis.
  • This advancement addresses key limitations of traditional genetic relationship inference.
  • Improved accuracy has significant implications for genealogical research and population genetics.