Evaluating Genomic Selection in beef cattle: Insights from computer simulations using real SNP data
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https://doi.org/10.57201/ieuna2424208Palabras clave:
precisión predictiva, selección de marcadores, cruzamientoResumen
La Selección Genómica (SG) es un método que emplea datos genómicos para estimar valores de cría y clasificar a los candidatos para la selección. A pesar de las numerosas ventajas, su aplicación en programas de mejoramiento de ganado vacuno permanece en etapas incipientes en muchos sistemas ganaderos desarrollados en ambientes tropicales y subtropicales, como los de Paraguay. Las simulaciones computacionales son herramientas poderosas, que mejoran nuestra comprensión de las aplicaciones de la SG en diferentes escenarios y son invaluables como paso inicial antes de implementar esta técnica en programas "reales" de mejoramiento genético. En este estudio, se emplearon datos reales de polimorfismos de nucleótido único (SNPs) de las razas Indicus y Taurus para simular tres esquemas de cruzamiento: cruces F1, absorbente y cruzamientos rotacionales. Se seleccionaron fenotipos para rasgos relacionados con la fuerza de corte, el crecimiento y la tolerancia. Se comparó la precisión predictiva de tres chips de SNP de 50k que diferían en las metodologías de selección: selección aleatoria, selección basada en diferencias mínimas de frecuencia alélica entre razas y selección basada en diferencias mínimas de frecuencia alélica entre razas con un umbral de 0.09 en Taurus. Los hallazgos indican que el cruce rotacional demuestra una precisión predictiva óptima (0.38), mientras que la selección de marcadores basada en diferencias de frecuencia alélica entre razas (0.18 y 0.17, respectivamente) no beneficia significativamente a las predicciones.
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Derechos de autor 2024 Lino César Ramírez Ayala, Jordi Leno-Colorado, Laura M. Zingaretti, Elies Ramón Gurrea, Yuliaxis Ramayo-Caldas, Miguel Pérez-Enciso
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
