Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mutation, Gene Flow, and Genetic Drift

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).

Gene Flow

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.

Hardy-Weinberg Principle

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.

Gene Conversion

Gene Conversion

Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...

Gene Conversion

Gene Conversion

Genetics of Speciation

Genetics of Speciation

Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.

You might also read

Related Articles

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

Sort by

Same author

Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.

G3 (Bethesda, Md.)·2024

Same author

AUTALASSO: an automatic adaptive LASSO for genome-wide prediction.

BMC bioinformatics·2019

Same journal

MOREshiny: a user-friendly application for the inference of phenotype-specific multi-omic regulatory networks.

Bioinformatics advances·2026

Same journal

spammR: an R package designed for analysis and integration of spatial multi-omic measurements.

Bioinformatics advances·2026

Same journal

Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.

Bioinformatics advances·2026

Same journal

vClassifier: a toolkit for high-resolution phylogenetic classification of prokaryotic viruses.

Bioinformatics advances·2026

Same journal

GWAIS-Web: a free and secure web service for ultra-fast and large-scale genome-wide association interaction studies.

Bioinformatics advances·2026

Same journal

Folding the unfoldable 2: using AlphaFold and ESMFold to explore spurious proteins.

Bioinformatics advances·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 11, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Genomic optimum contribution selection and mate allocation using JuMP.

Patrik Waldmann¹

¹Research Unit of Mathematical Sciences, University of Oulu, Oulu, FIN-90014, Finland.

Bioinformatics Advances

|November 18, 2025

Summary

This summary is machine-generated.

Genomic selection balances genetic gain and diversity using optimum contribution selection (OCS) and mate allocation (MA). The new GOCSMA method efficiently integrates these for sustainable breeding programs.

More Related Videos

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

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

Published on: June 21, 2018

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

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

Published on: July 11, 2025

Related Experiment Videos

Last Updated: Jan 11, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

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

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

Published on: June 21, 2018

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

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

Published on: July 11, 2025

Area of Science:

Animal breeding and genetics
Quantitative genetics
Bioinformatics

Background:

Artificial selection enhances desired traits but reduces genetic diversity.
Modern breeding programs require balancing genetic gain with variation for sustainability.
Optimum Contribution Selection (OCS) uses pedigree data to maximize gain while limiting inbreeding.

Purpose of the Study:

To develop and implement an efficient genomic OCS and mate allocation (GOCSMA) method.
To integrate genomic relatedness with OCS and mate allocation for improved breeding strategies.
To address computational challenges in large-scale breeding programs.

Main Methods:

Developed a two-stage GOCSMA method using JuMP/Julia.
Formulated OCS as a linear program with quadratic constraints, solved via COSMO.
Expressed MA as a mixed integer program, solved using SCIP's branch-cut-and-price algorithm.

Main Results:

GOCSMA efficiently balanced genetic gain and coancestry, outperforming traditional top selection.
The MA stage demonstrated rapid convergence (<0.01 seconds).
Integer mating constraints yielded lower coancestry and higher genetic gain than binary constraints.

Conclusions:

GOCSMA offers an efficient deterministic mathematical optimization framework for integrated genomic OCS and MA.
The method provides a robust solution for balancing genetic gain and diversity in large-scale breeding programs.
Advanced solvers within JuMP enable efficient optimization for complex breeding scenarios.