Genetic correlations between the cumulative pseudo-survival rate, milk yield, and somatic cell score during lactation in Holstein cattle in Japan using a random regression model

Abstract

Trends in genetic correlations between longevity, milk yield, and somatic cell score (SCS) during lactation in cows are difficult to trace. In this study, changes in the genetic correlations between milk yield, SCS, and cumulative pseudo-survival rate (PSR) during lactation were examined, and the effect of milk yield and SCS information on the reliability of estimated breeding value (EBV) of PSR were determined. Test day milk yield, SCS, and PSR records were obtained for Holstein cows in Japan from 2004 to 2013. A random subset of the data was used for the analysis (825 herds, 205,383 cows). This data set was randomly divided into 5 subsets (162-168 herds, 83,389-95,854 cows), and genetic parameters were estimated in each subset independently. Data were analyzed using multiple-trait random regression animal models including either the residual effect for the whole lactation period (H0), the residual effects for 5 lactation stages (H5), or both of these residual effects (HD). Milk yield heritability increased until 310 to 351 d in milk (DIM) and SCS heritability increased until 330 to 344 DIM. Heritability estimates for PSR increased with DIM from 0.00 to 0.05. The genetic correlation between milk yield and SCS increased negatively to under -0.60 at 455 DIM. The genetic correlation between milk yield and PSR increased until 342 to 355 DIM (0.53-0.57). The genetic correlation between the SCS and PSR was -0.82 to -0.83 at around 180 DIM, and decreased to -0.65 to -0.71 at 455 DIM. The reliability of EBV of PSR for sires with 30 or more recorded daughters was 0.17 to 0.45 when the effects of correlated traits were ignored. The maximum reliability of EBV was observed at 257 (H0) or 322 (HD) DIM. When the correlations of PSR with milk yield and SCS were considered, the reliabilities of PSR estimates increased to 0.31-0.76. The genetic parameter estimates of H5 were the same as those for HD. The rank correlation coefficients of the EBV of PSR between H0 and H5 or HD were greater than 0.9. Additionally, the reliabilities of EBV of PSR of H0 were similar to those for H5 and HD. Therefore, the genetic parameter estimates in H0 were not substantially different from those in H5 and HD. When milk yield and SCS, which were genetically correlated with PSR, were used, the reliability of PSR increased. Estimates of the genetic correlations between PSR and milk yield and between PSR and SCS are useful for management and breeding decisions to extend the herd life of cows.

Genetic Parameters Estimation of Milking Traits in Polish Holstein-Friesians Based on Automatic Milking System Data

“…The curve showing the daily heritabilities of milk yield during lactation has the shape of an inverted parabola, which coincides with the results of other authors [8,15,32]. However, many studies reported that the parameters of milk yield were higher in the initial and final stages of lactation than in mid-lactation [9,20,24].…”

“…Genetic parameters can be estimated from varying amounts of information concerning milk yield traits of evaluated animals and their relatives-in the simplest case, based on lactation milk yield or milk yield standardized to 305 lactation days [1][2][3]. The current standard for estimating the genetic parameters in most countries is to use test-day (TD) yields recorded regularly as part of cow milk recording schemes [4][5][6][7][8]. Test-day models (TDM) allow for modeling daily milk yield based on changing environmental conditions on different days and stages of lactation [9].…”

“…Many different proposals for lactation curve modeling have been presented, including the functions, among other of Ali and Schaffer [10], Wilmink [11], and, since 1994, Legendre orthogonal polynomials [12], which produce varied results in terms of lactation curve modeling quality [13]. Functions of the lactation curve can be included in the calculations as fixed regression models or random regression models (RRM) [6,8,9,14,15]. The first model assumes that the effects of animal and fixed environmental factors are identical on each day of lactation, while the latter assumes they are different [16].…”

Invited review: Academic and applied approach to evaluating longevity in dairy cows

“…However, current genetics trends allow for improvement of milk production through a more flattened persistent lactation curve, parallel to improved health parameters. Estimates of genetic correlation between milk yield and longevity, however, have varied (Tsuruta et al, 2005;Tokuhisa et al, 2014;Sasaki et al, 2017), indicating that selection for increased milk yield has indirectly affected longevity by reducing resilience against factors associated with an increased risk of culling (McConnel et al, 2008).…”

Estimation of the economic value of herd‐life length based on simulated changes in survival rate

“…Therefore, the economic value of survival rate was larger in Hokkaido than in the other regions. The longevity traits for genetic estimation have been shown to be a relative risk, according to survival analysis (Sasaki et al, 2012;Terawaki & Ducrocq, 2009), and a pseudo-survival rate, according to random regression model analysis (Sasaki, Aihara, Nishiura, & Takeda, 2017;Sasaki, Aihara, Nishiura, Takeda, & Satoh, 2015). In these cases, the economic weight of longevity corresponded to the economic value per percentage survival rate.…”