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Índice de selección con mínima relación genética para datos multicaracterística mediante programación cuadrática

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

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

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|December 30, 2025
PubMed
Resumen

La selección genómica (SG) optimiza el mejoramiento vegetal maximizando la ganancia genética y minimizando la relación. Un nuevo marco de Índice de Selección Multicaracterística de Programación Cuadrática (QPMSI) equilibra eficazmente la respuesta de selección y la diversidad genética.

Palabras clave:
individuos candidatosdiversidad genéticaprogramación linealselección de índice multicaracterísticamejoramiento vegetalprogramación cuadrática

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Área de la Ciencia:

  • Mejoramiento y genética vegetal; Genética cuantitativa; Bioinformática

Sus antecedentes:

  • La selección genómica (SG) es crucial para identificar individuos superiores en el mejoramiento vegetal.
  • Optimizar la selección multicaracterística bajo restricciones de relación genética es complejo.
  • Mantener la diversidad genética es esencial para programas de mejoramiento sostenibles.

Objetivo del estudio:

  • Desarrollar un marco novedoso para la construcción de índices de selección multicaracterística.
  • Maximizar la ganancia genética minimizando la relación promedio entre pares.
  • Identificar candidatos superiores de mejoramiento vegetal controlando la coancestría.

Principales métodos:

  • Se propuso un marco de Programación Cuadrática binaria para un índice de selección multicaracterística (QPMSI).
  • Se combinaron los valores estimados de cría (EBV) entre rasgos utilizando pesos económicos.
  • Se incorporó el control de la coancestría a través de la matriz de relación genómica.

Principales resultados:

  • El marco QPMSI equilibra eficazmente la respuesta de selección y el control de la relación genética.
  • QPMSI superó al Índice de Selección Multicaracterística de Programación Lineal (LPMSI) utilizando la métrica MV.
  • QPMSI logró al menos un 53,8% de mejora en la relación entre ganancia y grado de relación en comparación con LPMSI.

Conclusiones:

  • El QPMSI ofrece una herramienta práctica y computacionalmente eficiente para el mejoramiento vegetal sostenible.
  • Este método mejora la identificación de candidatos superiores para el avance.
  • El marco apoya estrategias efectivas de selección multicaracterística con diversidad genética controlada.