Autoregressive and random regression test‐day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

Abstract

Autoregressive (AR) and random regression (RR) models were fitted to test‐day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.

Autoregressive model for genetic evaluation of longitudinal reproductive traits in Brazilian Holstein cattle

“…According to those authors, the autoregressive model represents a more parsimonious alternative due the lower number of parameters to be estimated, justifying the superiority towards model‐fitting criterion in relation to the random regression model. Silva, Costa, et al (2020) also reported better results of model‐fitting criteria for the AR model when evaluating reproductive traits in Portuguese Holstein cattle. The AIC and MSE estimates obtained in their analyses for the AR model were lower than those from the REP model.…”

Random regression models using B‐splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloes

“…The confidence intervals were obtained using the R package “boot” (Canty & Ripley, 2021) with 10,000 bootstrap samples. This strategy has been previously used (Silva et al, 2019) and seems to be more appropriate for model comparisons.…”

“…The most commonly used method for genetic evaluation of milk‐related traits based on TD is random regression models (RRM, Oliveira et al, 2019; Silva et al, 2019), which have been used for national dairy cattle genetic evaluations in several countries (Interbull, 2021). The choice of the covariance function used for modelling the additive genetic and permanent environmental effects is an important step for the implementation of RRM in genetic evaluations.…”

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

“…However, the heritability of a phenotype in GWAS is too low, resulting in the reduced possibility of detecting the actual association between single nucleotide polymorphisms (SNPs) and traits or non-detection ( Shao et al, 2021 ). Recently, there has been considerable interest in using the random regression model (RRM) to model individual test-day records for the genetic evaluation of milk traits ( Khanzadeh et al, 2013 ; Silva et al, 2020 ; Soumri et al, 2020 ).…”

“…Thus far, many researchers have studied the Holstein population in different countries and provinces, including the north of China ( Ferreri et al, 2011 ; Jiang et al, 2012 ; Liu et al, 2020 ; Silva et al, 2020 ). A previous study of the Shanghai Holstein population used the genotyping by genome reducing and sequencing (GGRS) of 1,092 cattle and revealed some SNPs associated with MY, FP, PP, and SCS ( Chen Z. et al, 2018 ), but the study had a small sample size and only conducted association analysis of part of milk production traits using GGRS data.…”