Objective Estimate genetic parameters, the rate of inbreeding, and the effect of inbreeding on growth and egg production traits of a Thai native chicken breed Lueng Hang Kao Kabinburi housed under intensive management under a tropical climate. Methods Genetic parameters were estimated for weight measured at four weekly intervals from body weight at day 1 (BW1D) to body weight at 24 weeks (BW24) of age, as well as weight at first egg, age at first egg (AFE), egg weight at first egg, and total number of eggs (EN) produced during the first 17 weeks of lay using restricted maximum likelihood. Inbreeding depression was estimated using a linear regression of individual phenotype on inbreeding coefficient. Results Direct additive genetic effect was significant for all traits. Maternal genetic effect and permanent environmental hen effects were significant for all early growth traits, expect for BW24. For BW24, maternal genetic effect was also significant. Permanent environmental hen effect was significant for AFE. Direct heritabilities ranged from 0.10 to 0.47 for growth traits and ranged from 0.15 to 0.16 for egg production traits. Early growth traits had high genetic correlations between them. The EN was lowly negatively correlated with other traits. The average rate of inbreeding for the population was 0.09% per year. Overall, the inbreeding had no effect on body weight traits, except for BW1D. An increase in inbreeding coefficient by 1% reduced BWID by 0.09 g (0.29% of the mean). Conclusion Improvement in body weight gain can be achieved by selecting for early growth traits. Selection for higher body weight traits is expected to increase the weight of first egg. Due to low but unfavorable correlations with body weight traits, selection on EN needs to be combined with other traits via multi-trait index selection to improve body weight and EN simultaneously.

The advantages of using a univariate threshold animal model (TAM) over the conventional linear animal model (AM) in the development of a genetic evaluation system for feet and leg traits of Angus cattle were explored. The traits were scored on a scale of 1–9 with scores 5 and 6 being the most desirable. The genetic parameters and estimated breeding values for front feet angle (FA), rear feet angle (RA), front feet claw set (FC), rear feet claw set (RC), rear leg hind view (RH) and rear leg side view (RS) were compared from AM and TAM. In order to predict breeding values to identify the animals with intermediate optimum, the scores were categorised to form three groups to differentiate the desirable group (5–6) from the other two groups with less desirable feet and leg appearances (1–4 and 7–9). The AM and TAM were used to estimate genetic parameters for the grouped data as well as the original score data. A TAM using the group data was used to predict the probability and breeding value for the desirable intermediate group. For the original score data, estimated heritabilities on the underlying scale, using TAM, were 0.50, 0.46, 0.35, 0.44, 0.32 and 0.22 for FA, FC, RA, RC, RH and RS, respectively, and were 0.01–0.18 higher than the heritabilities estimated using AM. Genetic correlation between the six traits using a bivariate TAM with all scores ranged from 0.02 to 0.50 with front and rear angles had the highest genetic correlation at 0.50. For all six traits, proportion in the intermediate desirable group was higher than the other two groups combined. The low annual genetic change observed for all six traits over the 10 years of data recording reflected the lack of directional selection to improve the traits in Angus cattle. For genetic evaluation of feet and leg traits with an intermediate optimum, TAM is a preferred method for estimating genetic parameters and predicting breeding values for the desirable category. The TAM has now been implemented for regular estimated breeding value analysis of feet and leg traits of Angus cattle.

Genetic parameters were estimated for 5 economically important egg production traits using records collected over 9 years in chickens reared under tropical conditions in Thailand. The data were from two purebred lines and two hybrid lines of layer parent stocks. The two purebred lines were Rhode Island Red (RIR) and White Plymouth Rock (WPR) and the hybrid lines were formed by crossing a commercial brown egg laying strain to Rhode Island Red (RC) and White Plymouth Rock (WC), respectively. Five egg production traits were analysed, including age at first egg (AFE), body weight at first egg (BWT), egg weight at first egg (EWFE), number of eggs from the first 17 weeks of lay (EN) and average egg weight over the 17th week of lay (EW). Fixed effects of year and hatch within year were significant for all 5 traits and were included in the model. Maternal genetic and permanent environmental effects of the dam were not significant, except for EN and EW in RIR and BWT and EW in WPR. Estimated heritability of AFE, BWT, EWFE, EN and EW were 0.45, 0.50, 0.29, 0.19 and 0.43 in RIR; 0.44, 0.38, 0.33, 0.20 and 0.38 in WPR; 0.37, 0.41, 0.38, 0.18 and 0.36 in RC; and 0.46, 0.53, 0.36, 0.38 and 0.45 in WC lines, respectively. The EN was negatively correlated with other traits, except for BWT in RC and AFE and BWT in WC. It was concluded that selection for increased EN will reduce other egg production traits in purebred and hybrid chicken and therefore EN needs to be combined with other egg production traits in a multi-trait selection index to improve all traits optimally according to a defined breeding objective.

The temperament of cattle is believed to affect the profitability of the herd through impacting production costs, meat quality, reproduction, maternal behaviour and the welfare of the animals and their handlers. As part of the national beef cattle genetic evaluation in Australia by BREEDPLAN, 50 935 Angus and 50 930 Limousin calves were scored by seedstock producers for temperament using docility score. Docility score is a subjective score of the animal’s response to being restrained and isolated within a crush, at weaning, and is scored on a scale from 1 to 5 with 1 representing the quiet and 5 the extremely nervous or anxious calves. Genetic parameters for docility score were estimated using a threshold animal model with four thresholds (five categories) from a Bayesian analysis carried out using Gibbs sampling in THRGIBBS1F90 with post-Gibbs analysis in POSTGIBBSF90. The heritability of docility score on the observed scale was 0.21 and 0.39 in Angus and Limousin, respectively. Since the release of the docility breeding value to the Australian Limousin population there has been a favourable trend within the national herd towards more docile cattle. Weak but favourable genetic correlations between docility score and the production traits indicates that docility score is largely independent of these traits and that selection to improve temperament can occur without having an adverse effect on growth, fat, muscle and reproduction.

Abstract. The magnitude of genotype · environment interactions (G · E) were estimated for growth, real time ultrasound scanned carcass and reproductive traits in Angus cattle. Traits measured in the states of Victoria and Queensland were assumed as different traits and the genetic correlations between them were estimated. Estimated heritabilities across states were similar for all traits. However, additive genetic variances of fat depth at the P8 (rump) site for bulls (BP8), intramuscular fat percent at the 12/13th rib for bulls (BIMF) and heifers (HIMF) were significantly different between states. Estimated genetic correlations across states for direct genetic effects were high for growth traits and ranged from 0.89 to 1.00. For the maternal genetic effects the correlations across states ranged from 0.66 to 0.87. The across state correlations for scanned traits were also high. The exception was for BIMF (0.65), where measurement procedures were observed to influence the result. The genetic correlation between the states increased to 0.94 when the records of bulls with low IMF value were removed. For reproductive traits, the estimated genetic correlations ranged from 0.97 to 1.00. These results indicated little evidence of G · E for growth, ultrasound scanned carcass and reproductive traits of Angus cattle from Victoria and Queensland. Combining performance data across states in a national genetic evaluation is appropriate and it is expected that the progeny of Angus cattle would rank similarly across states.

Genetic parameters for test-day milk yield, lactation persistency, and age at first calving (as a fertility trait) were estimated for the first 4 lactations in multiplebreed dairy cows in low-, medium-, and high-production systems in Kenya. Data included 223,285 test-day milk yield records from 11,450 cows calving from 1990 to 2015 in 148 herds. A multivariate random regression model was used to estimate variance and covariance components. The fixed effects in the model included herd, year, and test month, and age as a covariate. The lactation profile over days in milk (DIM) was fitted as a cubic smoothing spline. Random effects included herd, year, and test month interaction effects, genetic group effects, and additive genetic and permanent environmental effects modeled with a cubic Legendre polynomial function. The residual variance was heterogeneous with 11 classes. Consequently, the variance components were varied over the lactation and with the production system. The estimated heritability for milk yield was lower in the low-production system (0.04-0.48) than in the medium-(0.22-0.59) and high-production (0.21-0 60) systems. The genetic correlations estimated between different DIM within lactations decreased as the time interval increased, becoming negative between the ends of the lactations in the low-and medium-production systems. Low (0.05) to medium (0.60) genetic correlations were estimated among first lactation test-day milk yields across the 3 production systems. Genetic correlations between the first lactation test-day milk yield and age at first calving ranged from 0.27 to 0.49, 0 to 0.81, and −0.08 to 0.27 in the low-, medium-, and high-production systems, respectively. Medium to high heritabilities (0.17-0.44) were estimated for persistency, with moderate to high (0.30-0.87) genetic correlations between 305-d milk yield and persistency. This indicates that genetic improvement in persistency would lead to increased milk yield. The low to medium genetic correlations between test-day milk yield between production systems indicate that sires may be re-ranked between production systems. Therefore, we conclude that sires should be selected based on a genetic evaluation within the target production system.

Data on Angus (ANG), Charolais (CHA), Hereford (HER), Limousin (LIM) and Simmental (SIM) cattle were used to estimate genetic parameters for calving difficulty (CD), birthweight (BWT) and gestation length (GL) using threshold-linear models and to examine the effect of inclusion of random effect of sire × herd interaction (SxH) in the models. For models without SxH, estimated heritabilities for direct genetic effect of CD were 0.24 (±0.02), 0.22 (±0.04), 0.31 (±0.02), 0.22 (±0.04) and 0.17 (±0.01) for ANG, CHA, HER, LIM and SIM, respectively, whereas maternal heritabilities ranged from 0.13 to 0.20. Estimated heritabilities for direct genetic effect of BWT were 0.38 (±0.01), 0.37 (±0.03), 0.46 (±0.01), 0.35 (±0.02) and 0.36 (±0.01) for ANG, CHR, HER, LIM and SIM, respectively, whereas maternal heritabilities ranged from 0.08 to 0.11. Estimated heritabilities for direct genetic effect of GL were 0.59 (±0.02), 0.42 (±0.04), 0.50 (±0.03), 0.45 (±0.04) and 0.42 (±0.03) for ANG, CHR, HER, LIM and SIM, respectively, whereas maternal heritabilities ranged from 0.03 to 0.09. Genetic correlations between direct genetic effects of CD with BWT were highly positive and with GL were moderately positive for all five breeds. Estimated genetic correlations between direct genetic effects and maternal genetic effects (rdm) ranged across the five breeds from –0.40 (±0.05) to –0.16 (±0.02), –0.41 (±0.03) to –0.27 (±0.08) and –0.47 (±0.10) to –0.06 (±0.12) for BWT, GL and CD, respectively. Fitting SxH interaction as additional random effect significantly increased the log-likelihood for analyses of BWT, GL and CD of all breeds, except for GL of CHA. The estimated heritabilities were less than or equal to the estimates obtained with models omitting SxH. The rdm increased (i.e. became less negative) for BWT, GL and CD of all five breeds. However, the increase for GL was not substantially high in comparison to the increase observed for BWT and CD. Genetic parameters obtained for BWT, GL and CD, by fitting SxH as an additional random effect, are more appropriate to use in the genetic evaluation of calving ease in BREEDPLAN.

Dairy records from the Dairy Recording Service of Kenya were classified into low, medium and high production systems based on mean 305‐day milk yield using the K‐means clustering method. Milk and fertility records were then analysed to develop genetic evaluation systems accounting for genotype‐by‐environment interaction between the production systems. Data comprised 26,638 lactation yield, 3,505 fat yield, 9,235 age at first calving and 17,870 calving interval records from 12,631 cows which were descendants of 2,554 sires and 8,433 dams. An animal model was used to estimate variance components, genetic correlations and breeding values for the production systems. Variance components increased with production means, apart from genetic group variances, which decreased from the low to the high production system. Moderate heritabilities were estimated for milk traits (0.21–0.27) and fat traits (0.11–0.38). Low heritabilities were estimated for lactation length (0.04–0.10) and calving interval (0.03–0.06). Moderate heritabilities (0.25–0.26) were estimated for age at first calving, except under the high production system (0.05). Within production systems, lactation milk yield, 305‐day milk yield and lactation length had high positive genetic correlations (0.52–0.96), while lactation milk yield and lactation length with age at first calving had negative genetic correlations. Milk yield and calving interval were positively correlated except under the low production system. The genetic correlations for lactation milk yield and 305‐day milk yield between low and medium (0.48 ± 0.20 and 0.46 ± 0.21) and low and high production systems’ (0.74 ± 0.15 and 0.62 ± 0.17) were significantly lower than one. Milk yield in the low production system is, therefore, a genetically different trait. The low genetic correlations between the three production systems for most milk production and fertility traits suggested that sires should be selected based on progeny performance in the targeted production system.