Estimating breeding values for feed efficiency in dairy cattle by regression on expected feed intake

The efficiency with which a dairy cow utilises feed for the various physiological and metabolic processes can be evaluated by metrics that contrast realised feed intake with expected feed intake. In this study, we presented a new metric - regression on expected feed intake (ReFI). This metric is based on the idea of regressing DM intake (DMI) on expected DMI using a random regression model, where energy requirement formulations are applied for the calculation of expected DMI covariables. We compared this new metric with the metrics residual feed intake (RFI) and genetic residual feed intake (gRFI), by applying them on 18 581 feed efficiency records from 654 primiparous Nordic Red dairy cows. We estimated variance components for the three metrics and their respective genetic correlations with intake and production traits. In addition, we examined the phenotypes of superior cows. With ReFI, we estimated for feed efficiency a higher genetic variation (4.7%) and heritability (0.23) compared to applying RFI or gRFI. The ReFI metric was genetically uncorrelated with DMI and negatively correlated within energy-corrected milk (ECM), whereas the RFI metric was genetically positively correlated with DMI and metabolic BW. The gRFI metric was genetically positively correlated with DMI and uncorrelated with energy sink traits. Overall, the estimated SE were large. The ReFI metric resulted in a different ranking of cows compared to those based on RFI or gRFI and was superior in selecting the most efficient animals. When the selection was based on ReFI breeding values, then the 10% most efficient cows produced 12.3% more ECM per unit metabolisable energy intake, whereas the corresponding values were only 4.3 or 5.9% when using RFI or gRFI breeding values, respectively. Based on ReFI, superior cows had also higher milk production, whereas based on RFI or gRFI milk production either decreased or was unaffected, respectively. The superiority of the ReFI metric in selecting efficient cows was due to a better modelling of the expected feed intake. The ReFI metric simplified modelling of feed utilisation efficiency in dairy cattle and resulted in breeding values that are equal to percentages of feed saved.

Heat stress on breeding value prediction for milk yield and composition of a Brazilian Holstein cattle population

Due to the high milk production of Holstein cows, many countries have chosen to import semen to improve local dairy herds. This strategy would be more effective if this semen was used in the same environment conditions in which the bulls were selected. If the effect of genotype by environment (G × E) interaction is not considered, the estimated breeding values (EBVs) may vary, potentially reducing the selection response. We evaluate the impact of heat stress on selection for milk yield and composition of Holstein cows using random regression models. To verify the interference of heat stress in milk yield (MY) and composition traits (fat, protein, total saturated, and total unsaturated fatty acids content in milk), temperature-humidity index (THI) on test-day milk records was used. The threshold value to divide the environments using test-day information from Brazilian Holstein cows was 72 units of THI, i.e., < 72 represented no heat stress and > 72 represented heat stress. Legendre polynomials of second-order (Leg 2) model and two lactation points (33 and 122 DIM) were used to estimate heritabilities and EBVs for five important dairy traits. The heritabilities of milk components and fatty acids were low (0.09-0.29), regardless of lactation period and degree of heat stress, with the exception of protein content (0.30-0.35). Fat content was the only milk component that was reduced according to the degree of heat stress and lactation period. The EBVs tended to decrease in heat stress conditions, thus animals with high genetic potential demonstrated evidence of G × E interaction. However, acclimatization of dairy cows to heat stress in the farm production systems may have been responsible for the low differences among genetic parameters and EBVs with and without heat stress found in this study.

Genetic parameters of weekly egg production using random regression models in two strains of Japanese quails

The main objective of the present study was to detect the most appropriate random regression model for estimating the genetic parameters of weekly egg production. Two strains of Japanese quails including wild and white quails and pure and cross mating methods were considered. The egg collection started at the seventh week of age and lasted for 7 weeks. A random regression model using Legendre polynomial was used to analyze the weekly egg records. The model with Legendre polynomial of order 1 for the additive genetic and order 3 for permanent environmental effect was chosen as the appropriate model, based on Bayesian information criterion (BIC) and Akaike information criterion (AIC). The heritability estimates for weekly egg production were low (ranging from 0.06 to 0.09). Furthermore, the ratios of permanent environmental variance to the phenotypic variance were almost moderate, varying from 0.18 to 0.44. Genetic and phenotypic correlations between weekly egg records ranged from 0.65 to 0.93 and from 0.27 to 0.67, respectively. These results indicated that the selection based on the early egg numbers could effectively improve egg production in later periods. Furthermore, the efficiency of egg production can be enhanced with a combination of breeding programs and management strategies.

Combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle

A new method to estimate residual feed intake (RFI) was recently developed based on a multi-trait random regression model. This approach deals with the dynamic nature of the lactation, which is in contrast with classical linear approaches. However, an issue remains: pooling data across sites and years, which implies dealing with different (and sometimes unknown) diet energy contents. This will be needed for genomic evaluation. In this study, we tested whether merging two individual datasets into a larger one can lead to valuable results in comparison to analysing them on their own with the multi-trait random regression model. Three datasets were defined: the first one with 1 063 lactations, the second one with 205 lactations from a second farm and the third one combining the data of the two first datasets (1 268 lactations). The model was applied to the three datasets to estimate individual RFI as well as variance components and correlations between the four traits included in the model (fat and protein corrected milk production, BW, feed intake and body condition score), and a fixed month-year-farm effect was used to define the contemporary group. The variance components and correlations between animal effects of the four traits were very similar irrespective of the dataset used with correlations higher than 0.94 between the different datasets. The RFI estimates for animals from their single farm only were also very similar (r > 0.95) to the ones computed from the merged dataset (Dataset 3). This highlights that the contemporary group correction in the model adequately accounts for differences between the two feeding environments. The dynamic model can thus be used to produce RFI estimates from merged datasets, at least when animals are raised in similar systems. In addition, the 205 lactations from the second farm were also used to estimate the RFI with a linear approach. The RFI estimated by the two approaches were similar when the considered period was rather short (r = 0.85 for RFI for the first 84 days of lactation) but this correlation weakened as the period length grew (r = 0.77 for RFI for the first 168 days of lactation). This weakening in correlations between the two approaches when increasing the used time-period reflects that only the dynamic model permits the regression coefficients to evolve in line with the physiological changes through the lactation. The results of this study enlarge the possibilities of use for the dynamic RFI model.

Comparison of three methodologies for the genetic study of lactation persistency in Holstein cattle from Antioquia

Persistency is the rate of decrease after milk production peak, mathematical models such as Wood's can be used to estimate it for describing the lactation curve and its rate of descent; random regression models are also useful, as they describe the genetic lactation curve for each animal. The objective of this study was to compare Best Linear Unbiased Prediction (BLUP), marker-assisted BLUP (MBLUP) model and random regression model (RRM) to estimate genetic parameters and breeding values for the lactation persistency curve. 4,658 test day measurements were available for 733 individuals, from which lactation curves were described to calculate persistency, estimating genetic parameters and values for this trait through BLUP and MBLUP. A similar process was done for RRM, where persistency was estimated from the genetic lactation curve. The heritability obtained using RRM was 0.51, greater than that obtained by BLUP (0.29) and MBLUP (0.21). The reliability of the genetic value for persistency in bulls was greater when RRM was used, but there was no correlation between the genetic values of different models. The highest heritability for persistency and the more reliable genetic values for bulls were achieved under the RRM, it allows positioning this methodology as an important tool for genetic evaluation of persistency.

Estimation of additive genetic, dominance, and mate sire variances for fertility traits in Thai native (Pradu Hang Dam) chickens

The objective of this study was to compare the appropriate models used to estimate the value of genetic parameters in fertility traits: fertility (FER), hatchability of fertile eggs (HOF), and hatchability of eggs set (HOS) in Thai native (Pradu Hang Dam) chickens. Data were collected for each fertility trait from 3435 test-week records from 715 hens, 158 mate sires, and 972 pedigree animals. Three random regression models were analyzed: model 1 (M1: A + PE) was adjusted by using additive genetic and permanent environmental effects. Model 2 (M2: A + PE + D) was adjusted by using the dominance effect. Finally, model 3 (M3: A + MS + PE + D) was adjusted by using the mate sire effect. The results found the low heritability of FER (M1 to M3), HOF (M1 to M3), and HOS (M1 to M3) ranged from 0.031-0.040, 0.037-0.066, and 0.040-0.059, respectively. Adjustment for the dominance and mate sire effects in M3 reduced the upward bias in heritability and improved the accuracy of variance component estimates compared to M1 and M2. In conclusion, the genetic evaluation for FER, HOF, and HOS can include the dominance and MS effects to increase the accuracy of evaluation of breeding values and plan for mate selection in breeding programs.

Probing curvilinear-by-linear interactions when the predictors are randomly sampled

Curvilinear effects and curvilinear-by-linear interactions are hypothesized, tested, and probed in various fields of the social and behavioral sciences. When such effects are found, researchers traditionally have tested the simple slope, and more recently have utilized the Johnson-Neyman technique, often at the values of the sample statistics of the predictor(s). Both approaches implicitly assume that values on the predictors have been sampled according to a fixed sampling plan. More typically in social and behavioral research, however, values on the predictors can be more appropriately considered as randomly sampled from a multivariate population distribution. I show analytically and through simulation that for regression models containing a curvilinear-by-linear interaction, fixed and random regression models produce the same estimates but different standard errors of the simple slope at values of the sample statistics of the predictors. When values on the predictors are randomly sampled, treating them as fixed when testing the simple slope or generating conditional Johnson-Neyman confidence bands at values of the sample statistics of the predictors can lead to inflated Type I error rates and inaccurate coverage rates. Recommendations for researchers are provided, and directions for future research are discussed.

Sex-specific effects of parasites on telomere dynamics in a short-lived passerine-the blue tit

Parasitic infections potentially drive host's life-histories since they can have detrimental effects on host's fitness. Telomere dynamics is a candidate mechanism to underlie life-history trade-offs and as such may correlate with observed fitness reduction in infected animals. We examined the relationship of chronic infection with two genera of haemosporidians causing avian malaria and malaria-like disease with host's telomere length (TL) in a longitudinal study of free-ranging blue tits. The observed overall infection prevalence was 80% and increased with age, constituting a potentially serious selective pressure in our population. We found longer telomeres in individuals infected with a parasite causing lesser blood pathologies i.e. Haemoproteus compared to Plasmodium genus, but this only held true among males. Female TL was independent of the infection type. Our results indicate that parasitic infections could bring about other types of costs to females than to males with respect to TL. Additionally, we detected linear telomere loss with age, however a random regression analysis did not confirm significant heterogeneity in TL of first breeders and telomere shortening rates in further life.

Possibility of modifying the growth trajectory in Raeini Cashmere goat

The objective of this study was to investigate the possibility of modifying the growth trajectory in Raeini Cashmere goat breed. In total, 13,193 records on live body weight collected from 4788 Raeini Cashmere goats were used. According to Akanke's information criterion (AIC), the sing-trait random regression model included fourth-order Legendre polynomial for direct and maternal genetic effect; maternal and individual permanent environmental effect was the best model for estimating (co)variance components. The matrices of eigenvectors for (co)variances between random regression coefficients of direct additive genetic were used to calculate eigenfunctions, and different eigenvector indices were also constructed. The obtained results showed that the first eigenvalue explained 79.90% of total genetic variance. Therefore, changing the body weights applying the first eigenfunction will be obtained rapidly. Selection based on the first eigenvector will cause favorable positive genetic gains for all body weight considered from birth to 12 months of age. For modifying the growth trajectory in Raeini Cashmere goat, the selection should be based on the second eigenfunction. The second eigenvalue accounted for 14.41% of total genetic variance for body weights that is low in comparison with genetic variance explained by the first eigenvalue. The complex patterns of genetic change in growth trajectory observed under the third and fourth eigenfunction and low amount of genetic variance explained by the third and fourth eigenvalues.

Influence of milking frequency on genetic parameters associated with the milk production in the first and second lactations of Iranian Holstein dairy cows using random regression test day models

BACKGROUND

The milk yield can be affected by the frequency of milking per day, in dairy cows. Previous studies have shown that the milk yield is increased by 6-25 % per lactation when the milking frequency is increased from 2 to 3 times per day while the somatic cell count is decreased. To investigate the effect of milking frequency (3X vs. 4X) on milk yield and it's genetic parameters in the first and second lactations of the Iranian Holstein dairy cows, a total of 142,604 test day (TD) records of milk yield were measured on 20,762 cows.

RESULTS

Heritability estimates of milk yield were 0.25 and 0.19 for 3X milking frequency and 0.34 and 0.26 for 4X milking frequency throughout the first and second lactations, respectively. Repeatability estimates of milk yield were 0.70 and 0.71 for 3X milking frequency and 0.76 and 0.77 for 4X milking frequency, respectively. In comparison with 3X milking frequency, the milk yield of the first and second lactations was increased by 11.6 and 12.2 %, respectively when 4X was used (p < 0.01).

CONCLUSIONS

Results of this research demonstrated that increasing milking frequency led to an increase in heritability and repeatability of milk yield. The current investigation provided clear evidences for the benefits of using 4X milking frequency instead of 3X in Iranian Holstein dairy cows.