Random regression test‐day models to describe milk production and fatty acid traits in first lactation Walloon Holstein cows

Abstract

We investigated the use of different Legendre polynomial orders to estimate genetic parameters for milk production and fatty acid (FA) traits in the first lactation Walloon Holstein cows. The data set comprised 302,684 test-day records of milk yield, fat and protein contents, and FAs generated by mid-infrared (MIR) spectroscopy, C16:0 (palmitic acid), C18:1 cis-9 (oleic acid), LCFAs (long-chain FAs), SFAs (saturated FAs) and UFAs (unsaturated FAs) were studied. The models included random regression coefficients for herd-year of calving (h), additive genetic (a) and permanent environment (p) effects. The selection of the best random regression model (RRM) was based on the deviance information criterion (DIC), and genetic parameters were estimated via a Bayesian approach. For all analysed random effects, DIC values decreased as the order of the Legendre polynomials increased. Best-fit models had fifth-order (degree 4) for the p effect and ranged from second-to fifth-order (degree 1-4) for the a and h effects (LEGhap: LEG555 for milk yield and protein content; LEG335 for fat content and SFA; LEG545 for C16:0 and UFA; and LEG535 for C18:1 cis-9 and LCFA). Based on the best-fit models, an effect of overcorrection was observed in early lactation (5-35 days in milk [DIM]). On the contrary, third-order (LEG333; degree 2) models showed flat residual trajectories throughout lactation. In general, the estimates of genetic variance tended to increase over DIM, for all traits. Heritabilities for milk production traits ranged from 0.11 to 0.58. Milk FA heritabilities ranged from low-to-high magnitude (0.03-0.56). High Spearman correlations (>0.90 for all bulls and >0.97 for top 100) were found among breeding values for 155 and 305 DIM between the best RRM and LEG333 model. Therefore, third-order Legendre polynomials seem to be most parsimonious and sufficient to describe milk production and FA traits in Walloon Holstein cows.

Genetic parameters and genome-wide association for milk production traits and somatic cell score in different lactation stages of Shanghai Holstein population

“…Currently, there are many studies on the submodels in the random regression test day model. The results of these studies showed that the lactation curves of milk production traits obtained by different researchers were also quite different (El Faro et al, 2008;Zhou and Zhang, 2021;Paiva et al, 2022). Since 1994, with the application of Legendre polynomials in the random regression test day model, research on its order has continued.…”

Genomic prediction and genetic correlations estimated for milk production and fatty acid traits in Walloon Holstein cattle using random regression models

“…Fat yield (kg), protein yield (kg), fat content (%), protein content (%), C16:0 fatty acid (palmitic acid), C18:1 cis-9 fatty acid (oleic acid), longchain fatty acid (LCFA), saturated fatty acids (SFA), and group unsaturated fatty acids (UFA) were the traits predicted from the test-day milk samples evaluated in this study in addition to daily milk yield (kg). More details about the Walloon Holstein cow population and dataset quality control can be found in Paiva et al (2022). The final dataset is comprised of 302 684 testday records from 63 875 first-parity Walloon Holstein cows, raised in 856 herds.…”

“…The random regression test-day bi-trait model (RRM) was based on the optimal statistical model found in a previous study by Paiva et al (2022):…”

“…The genetic correlations were estimated through bi-trait analysis via the Gibbs sampling algorithm using GIBBS2F90 software (Misztal et al, 2002). A chain length of 250 000 iterations and convergence criteria were used similar to Paiva et al (2022).…”

“…Notwithstanding, Jorjong et al (2014) reported that C18:1 cis-9 shows potential as an early warning biomarker for metabolic status and subclinical ketosis in dairy cows. Studies have shown that the milk FA profile is under genetic control in dairy cattle (Bastin et al, 2011;Paiva et al, 2022). Thus, changes in FA composition can be made through genetic selection.…”