Research Info

Title
Modelling and genetic analysis of the latent variable of lactation performance in Chinese Holstein dairy cows
Type Article
Keywords
animal model, milk production and quality, repeated records, structural equation model
Abstract
The confirmatory factor analysis technique was used to quantify a latent variable for test-day lactation performance (TDLP) in the first parity of Chinese Holstein dairy cows by applying five measurable traits, including test-day milk yield (TDMY), test-day milk fat percentage (TDFP), test-day milk protein percentage (TDPP), test-day somatic cell score (TDSCS), and test-day milk urea nitrogen (TDMUN). The standardised factor loadings of TDMY, TDFP, TDPP, TDSCS, and TDMUN for describing TDLP were 0.46, -0.52, -0.70, -0.14, and -0.19, respectively. Genetic analysis was conducted using a multivariate repeatability model within a Bayesian framework. The posterior means for the heritability and repeatability estimates of TDLP were 0.26 ± 0.02 and 0.34 ± 0.02, respectively. In general, posterior means for heritability and repeatability estimates of the measurable traits were low to medium. The heritability estimates ranged from 0.05 for TDSCS to 0.28 for TDPP, and repeatability estimates ranged from 0.15 for TDMUN to 0.38 for TDMY. The latent variable of TDLP exhibited positive genetic (0.62) and phenotypic (0.40) correlations with TDMY, whereas its genetic and phenotypic correlations with other measurable traits were negative, ranging from -0.96 (TDLP-TDPP) to -0.11 (TDLP-TDSCS). The corresponding phenotypic correlations ranged from -0.85 (TDLP-TDPP) to -0.07 (TDLP-TDSCS). It may be concluded that breeding for higher TDLP might increase TDMY but could reduce milk composition traits. In general, the negative genetic and phenotypic correlations suggest a trade-off between milk quantity (yield) and quality (composition).
Researchers Hui Li (First researcher)
Morteza Mokhtari (Second researcher)
Jing Tian (Third researcher)
Guoquan Sun (Fourth researcher)
Ali Esmailizadeh (Fifth researcher)
Meng Zhao (Not in first six researchers)
Xiao Wang (Not in first six researchers)
Luda Jin (Not in first six researchers)
Lu Chen (Not in first six researchers)
Jixin Zhang (Not in first six researchers)
Rugang Tian (Not in first six researchers)