Darlene Dos Santos Daltro, Elisandra Lurdes Kern, Jaime Araújo Cobuci, Renata Negri, Sabrina Kluska, Pamela Itajara Otto, Marco Antonio Machado, João Cláudio do Carmo Panetto, Marta Fonseca Martins, Edivaldo Ferreira Júnior, Fabrício Pilonetto, Marcos Vinicius Gualberto Barbosa da Silva
This study aimed to estimate (co)variance components, heritability, and genetic associations among productive-life indicators related to longevity, productive traits, and fertility traits in Girolando cattle and to explore associated genomic regions.
This study aimed to estimate (co)variance components, heritability, and genetic associations among productive-life indicators related to longevity, productive traits, and fertility traits in Girolando cattle and to explore associated genomic regions. Four productive-life indicators were evaluated: total milk production across all lactations (L1), number of lactations initiated (L2), total days in milk across all lactations (L3), and number of complete lactations (L4). Productive traits comprised 305-d milk yield (MY305) and lactation length (LL), whereas fertility traits comprised age at first calving (AFC), calving interval (CI), and days open (DO). Variance components were estimated under linear animal models using the single-step genomic relationship matrix, using AIREMLF90 from the BLUPF90 suite. Heritability estimates ranged from 0.06 to 0.31 for productive-life indicators, 0.06 to 0.14 for productive traits, and 0.03 to 0.06 for fertility traits. Genetic correlations of productive-life indicators ranged from 0.02 to 0.31 with productive traits and from - 0.03 to 0.13 with fertility traits, indicating stronger associations with productive traits. An exploratory single-step genome-wide association study identified candidate regions for L1 and L3, whereas no SNP met the adopted significance criteria for L4. Because culling and death dates were unavailable, L1, L3, and L4 represent proxies of productive life rather than direct measures of functional longevity; consequently, related inferences remain preliminary. Overall, environmental and management effects substantially influence these indicators. Selection for improved productive life may therefore benefit from indexes combining productive-life indicators with routinely recorded productive and fertility traits.