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

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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.

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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.