Modelling and genetic analysis of the latent variable of lactation performance in Chinese Holstein dairy cows

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).