Research Info

Title
Random regression models for genetic analysis of milk, fat, and protein yields in the Murciano-Granadina goats with the Legendre polynomials and B-spline functions
Type Article
Keywords
Animal model , Fat , Goat , Milk · Protein
Abstract
In the present study, test-day records of milk yield (TDMY), fat yield (TDFY), and protein yield (TDPY) of first-parity Murciana-Granadina goats, collected from 2017 to 2024 in the southern part of Kerman province of Iran, were used. The traits were analyzed by applying random regression models (RRMs) with the Legendre polynomials (LEG) and B-spline (BSP) functions. The RRM with order 3 for direct genetic and animal permanent environmental effects under the LEG function was the best for TDMY. The RRMs employing quadratic BSP functions with 5 and 4 knots were determined as the most suitable models for TDFY and TDPY, respectively. The heritability (h2) estimates of TDMY across the lactation period were low, varying from 0.15 ± 0.03 at days in milk (DIM) 5 to 0.03 ± 0.02 at DIM 275. For TDFY, the h2 estimates were medium to low and varied from 0.22 ± 0.09 at DIM 5 to 0.01 ± 0.01 at DIM 245. The h2 estimates of TDPY were low and ranged from 0.18 ± 0.07 at DIM 5 to 0.02 ± 0.01 at DIM 215. Genetic correlation estimates varied from 0.41 ± 0.18 (DIMs 5 and 203) to 0.99 ± 0.17 (DIMs 137 and 275) for TDMY, 0.41 ± 0.12 (DIMs 5 and 275) to 0.98 ± 0.16 (DIMs 203 and 275) for TDFY, and 0.18 ± 0.06 (DIMs 5 and 203) and also 0.18 ± 0.10 (DIMs 5 and 275) to 0.99 ± 0.17 (DIMs 137 and 203) for TDPY. The strongest genetic correlations were found between closely located DIMs, with these correlations decreasing as the interval between DIMs increased. In general, early DIMs showed higher genetic variations relative to the other stages of the lactation period with positive genetic correlations in late lactation. Therefore, there is an opportunity for early selection practices to improve the traits in the later stages of the lactation period.
Researchers Morteza Mokhtari (First researcher)
Masood Asadi Fozi (Second researcher)
Arsalan Brazandeh (Third researcher)
Ehsan Mohebbinejad (Fourth researcher)