Optimization of high-throughput marker systems for genomic prediction in alfalfa family bulks

• Autores: Sipowicz, Pablo; Andrade, Mario Henrique Murad Leite; Fernandes Filho, Claudio Carlos; Benevenuto, Juliana; Muñoz, Patricio; Ferrão, L. Felipe V.; Resende Jr., Marcio F. R.; Messina, C.; Ríos, Esteban
• Año de publicación: 2025
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: The Plant Genome 18 (1) : e20526. (March 2025)
• Materia: Medicago sativa; Genetic Markers; Genomics; Phenotypes; Genotyping; Marcadores Genéticos; Genómica; Fenotipos; Genotipado; Alfalfa; Lucerne
• Institución: Instituto Nacional de Tecnología Agropecuaria

Alfalfa (Medicago sativa L.) is a perennial forage legume esteemed for its exceptional quality and dry matter yield (DMY); however, alfalfa has historically exhibited low genetic gain for DMY. Advances in genotyping platforms paved the way for a cost-effective application of genomic prediction in alfalfa family bulks. In this context, the optimization of marker density holds potential to reallocate resources within genomic prediction pipelines. This study aimed to (i) test two genotyping platforms for population structure discrimination and predictive ability (PA) of genomic prediction models (G-BLUP) for DMY, and (ii) explore optimal levels of marker density to predict DMY in family bulks. For this, 160 nondormant alfalfa families were phenotyped for DMY across 11 harvests and genotyped via targeted sequencing using Capture-seq with 17K probes and the DArTag 3K panel. Both platforms discriminated similarly against the population structure and resulted in comparable PA for DMY. For genotyping optimization, different levels of marker density were randomly extracted from each platform. In both cases, a plateau was achieved around 500 markers, yielding similar PA as the full set of markers. For phenotyping optimization, models with 500 markers built with data from five harvests resulted in similar PA compared to the full set of 11 harvests and full set of markers. Altogether, genotyping and phenotyping efforts were optimized in terms of number of markers and harvests. Capture-seq and DArTag yielded similar results and have the flexibility to adjust their panels to meet breeders’ needs in terms of marker density.
EEA Manfredi
Fil: Sipowicz, Pablo. University of Florida. Plant Breeding Graduate Program; Estados Unidos
Fil: Sipowicz, Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina.
Fil: Andrade, Mario Henrique Murad Leite. University of Maine. School of Food and Agriculture; Estados Unidos
Fil: Fernandes Filho, Claudio Carlos. Centro de Tecnologia Canavieira (CTC); Brasil
Fil: Benevenuto, Juliana. University of Florida. Horticultural Sciences Department; Estados Unidos
Fil: Muñoz, Patricio. University of Florida. Horticultural Sciences Department; Estados Unidos
Fil: Ferrão, L. Felipe V. University of Florida. Horticultural Sciences Department; Estados Unidos
Fil: Resende Jr., Marcio F. R. University of Florida. Horticultural Sciences Department; Estados Unidos
Fil: Messina, C. University of Florida. Horticultural Sciences Department; Estados Unidos
Fil: Ríos, Esteban. University of Florida. Agronomy Department; Estados Unidos