Genetic evaluation of calf and heifer survival in Iranian Holstein cattle using linear and threshold models

Abstract

Calf and heifer survival are important traits in dairy cattle affecting profitability. This study was carried out to estimate genetic parameters of survival traits in female calves at different age periods, until nearly the first calving. Records of 49,583 female calves born during 1998 and 2009 were considered in five age periods as days 1-30, 31-180, 181-365, 366-760 and full period (day 1-760). Genetic components were estimated based on linear and threshold sire models and linear animal models. The models included both fixed effects (month of birth, dam's parity number, calving ease and twin/single) and random effects (herd-year, genetic effect of sire or animal and residual). Rates of death were 2.21, 3.37, 1.97, 4.14 and 12.4% for the above periods, respectively. Heritability estimates were very low ranging from 0.48 to 3.04, 0.62 to 3.51 and 0.50 to 4.24% for linear sire model, animal model and threshold sire model, respectively. Rank correlations between random effects of sires obtained with linear and threshold sire models and with linear animal and sire models were 0.82-0.95 and 0.61-0.83, respectively. The estimated genetic correlations between the five different periods were moderate and only significant for 31-180 and 181-365 (r(g) = 0.59), 31-180 and 366-760 (r(g) = 0.52), and 181-365 and 366-760 (r(g) = 0.42). The low genetic correlations in current study would suggest that survival at different periods may be affected by the same genes with different expression or by different genes. Even though the additive genetic variations of survival traits were small, it might be possible to improve these traits by traditional or genomic selection.

Genetic parameters for dairy calf and replacement heifer wellness traits and their association with cow longevity and health indicators in Holstein cattle

“…Low to moderate genetic correlations were observed among Sur1, Sur2, and Sur3, which agrees with the literature (Forutan et al, 2015;Pathak et al, 2018). There are differences on the replacement survival trait definitions across studies, and, as expected, higher genetic correlations are observed between survival indica-tors based on similar or adjacent life stages (Harbers et al, 2002;Hansen et al, 2003;Fuerst-Waltl and Sørensen, 2010;Pathak et al, 2018).…”

“…Generally, replacement survivability has been defined as binary traits corresponding to the survival during various life stages from birth to first calving. In Iranian (Forutan et al, 2015), UK and US (Pritchard et al, 2013), Danish (Fuerst-Waltl and Sørensen, 2010), and Dutch (Harbers et al, 2002) Holstein cattle; Austrian (Fuerst-Waltl and Fuerst, 2012) and US (Erf et al, 1990) Brown Swiss; Danish Jersey (Norberg et al, 2013); and Spanish local beef cattle (Asturian Valley breed; Goyache et al, 2003) populations, the heritability of replacement survival ranged from 0.01 to 0.14. In US Holstein (Gonzalez-Peña et al, 2019) and Jersey (Gonzalez-Peña et al, 2020) cattle, genomic evaluation on replacement survivability defined as a binary trait between 2 and 365 d of age has been performed with a reliability of 47.3 to 50.5%.…”

Preweaning Calf Survival of a Nellore Beef Cattle Population

“…However, the heritability obtained in ABT model (Table 4) can be considered overstated and incorrect when compared to the values estimated through the other analytical models. In the studies of Van Tassel et al (1998), Cole et al (2007), and Forutan, Mahyari, and Sargolzaei (2015) which also involved threshold models, the frequency of each categorical class was higher than in the present study (4.6% of mortality versus 95.4% of survival). Thus, the overestimation of the variance components by using the ABT model can be due to the low frequency of some SW phenotypes.…”

“…Variance components and genetic parameters for categorical traits can be estimated in a one-trait or two-trait analyse under the assumption of linear (Forutan, Mahyari, & Sargolzaei, 2015) or threshold distribution of phenotypes (Faria et al, 2010;Forutan, Mahyari, & Sargolzaei, 2015), by using sire or animal models (Forutan, Mahyari, & Sargolzaei, 2015); and by adopting restricted maximum likelihood (REML, Queiroz et al, 2011), Bayesian (Santana Jr. et al, 2013) or generalized linear mixed model approaches (GLMM, Forutan, Mahyari, & Sargolzaei, 2015) as estimation methods.…”

Categorical Visual Score Traits of a Nellore Beef Cattle Population

“…The methodologies vary according to the number of evaluated traits, the nature of the phenotypic data distribution, the genetic structure adopted and the estimation method of the variance components. For example, by the assumption of linear (Gordo et al, 2012;Forutan, Mahyari, & Sargolzaei, 2015) or threshold distribution of phenotypes (Faria et al, 2009;Boligon et al, 2012;Forutan, Mahyari, & Sargolzaei, 2015), by using sire or animal models (Forutan, Mahyari, & Sargolzaei, 2015); and by adopting restricted maximum likelihood (Bouquet et al, 2010;Queiroz et al, 2011), Bayesian (Boligon et al, 2012;Santana Jr. et al, 2013) or generalized linear mixed model approaches (Forutan, Mahyari, & Sargolzaei, 2015) as estimation methods. These methodologies can be applied in a combined way.…”