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A selection index with minimal genetic relatedness for multi-trait data via binary quadratic programming.

Osval A Montesinos-López1, Abelardo Montesinos-López2, Carlos M Hernández-Suárez3

  • 1Facultad de Telemática, Universidad de Colima, Colima, Colima, 28040, Mexico.

Plant Methods
|December 30, 2025
PubMed
Summary

Genomic selection (GS) optimizes plant breeding by maximizing genetic gain while minimizing relatedness. A new Quadratic Programming Multi-trait Selection Index (QPMSI) framework effectively balances selection response and genetic diversity.

Keywords:
Candidates individualsGenetic diversityLinear programmingMulti-trait index selectionPlant breedingQuadratic programing

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Area of Science:

  • Plant breeding and genetics
  • Quantitative genetics
  • Bioinformatics

Background:

  • Genomic selection (GS) is crucial for identifying superior individuals in plant breeding.
  • Optimizing multi-trait selection under genetic relatedness constraints is complex.
  • Maintaining genetic diversity is essential for sustainable breeding programs.

Purpose of the Study:

  • To develop a novel framework for multi-trait selection index construction.
  • To maximize genetic gain while minimizing average pairwise relatedness.
  • To identify superior plant breeding candidates while controlling coancestry.

Main Methods:

  • Proposed a binary Quadratic Programming framework for a multi-trait selection index (QPMSI).
  • Combined estimated breeding values (EBVs) across traits using economic weights.
  • Incorporated coancestry control via the genomic relationship matrix.

Main Results:

  • The QPMSI framework effectively balances selection response and genetic relatedness control.
  • QPMSI outperformed the Linear Programming Multi-trait Selection Index (LPMSI) using the MV metric.
  • QPMSI achieved at least 53.8% improvement in the gain-to-degree of relatedness ratio compared to LPMSI.

Conclusions:

  • The QPMSI offers a practical and computationally efficient tool for sustainable plant breeding.
  • This method enhances the identification of superior candidates for advancement.
  • The framework supports effective multi-trait selection strategies with controlled genetic diversity.