Genotype by environment interaction for yield and somatic cell score with alternative environmental definitions

Genetic parameters for test-day milk yield, lactation persistency, and fertility in low-, medium-, and high-production systems in Kenya

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 multiple-breed 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.

Modelling and estimation of genotype by environment interactions for production traits in French dairy cattle

Background

Genotype by environment interactions are currently ignored in national genetic evaluations of dairy cattle. However, this is often questioned, especially when environment or herd management is wide-ranging. The aim of this study was to assess genotype by environment interactions for production traits (milk, protein, fat yields and fat and protein contents) in French dairy cattle using an original approach to characterize the environments.

Methods

Genetic parameters of production traits were estimated for three breeds (Holstein, Normande and Montbéliarde) using multiple-trait and reaction norm models. Variables derived from Herd Test Day profiles obtained after a test day model evaluation were used to define herd environment.

Results

Multiple-trait and reaction norm models gave similar results. Genetic correlations were very close to unity for all traits, except between some extreme environments. However, a relatively wide range of heritabilities by trait and breed was found across environments. This was more the case for milk, protein and fat yields than for protein and fat contents.

Conclusions

No real reranking of animals was observed across environments. However, a significant scale effect exists: the more intensive the herd management for milk yield, the larger the heritability.

Genetic relationship of conformation traits with average somatic cell score at 150 and 305 days in milk in Holstein cows of Iran

Genetic, environmental, and phenotypic correlations among average somatic cell score (SCS) at different stages of lactation and conformation traits were estimated. Data consisted of the lactational average of SCS at 150 (SCS(150)) and 305 (SCS(305)) d in milk and 19 conformation traits recorded on 57,154 primiparous Holstein cows, that calved from 1996 to 2009 in 119 herds in Iran. Variance components were estimated using the restricted maximum likelihood procedure based on multiple-trait animal models. Udder depth (-0.32), fore udder attachment (-0.22), and udder width (0.34) showed moderate genetic correlation with SCS(150). Heart girth (0.17), body depth (0.14), chest width (0.26), and angularity (0.19), showed modest genetic correlation with SCS(150). The estimated heritabilities for SCS(150) and SCS(305) were 0.06 and 0.08, respectively. The heritability of the conformation traits ranged from 0.09 to 0.29. Genetic and environmental correlations between SCS(150) and SCS(305) were very high (means ± SE; 0.99±0.01 and 0.89±0.01, respectively), which indicates that recording SCS over a shorter period of lactation is an alternative approach for involving many herds in SCS data collection. The low heritability of SCS indicated that indirect selection for some of udder and body traits might be helpful to reduce the SCS. Additionally, selection for udder traits may help reduce SCS in developing countries where SCS data are sparsely recorded.

Simultaneous estimation of genotype by environment interaction accounting for discrete and continuous environmental descriptors in Irish dairy cattle

Genotype by environment interaction can be analyzed by using a multi-trait model in which a trait measured in different environments is considered as separate traits. Alternatively, it can be analyzed by using a reaction norm model, in which the trait is considered a function of an environmental descriptor. Here, a model is developed where the 2 approaches are combined such that the effect of a continuous environmental descriptor can be analyzed in 2 or more discrete environments. The model is applied to somatic cell score (SCS) in relation to average herd milk production in 2 production environments: spring calving and year-round calving in Ireland. Heritabilities and additive genetic variances for SCS increased somewhat with increasing milk production and were higher in year-round calving. Under the combined model, the genetic correlation between spring and year-round calving was estimated at 0.82 to 0.84, clearly lower than obtained in a bivariate analysis ignoring effects of herd milk production. Thus, when estimating the genetic correlation between environments, effects of one environmental descriptor may be obscured by another, but can be disentangled in an analysis combining the reaction norm and the multi-trait approach. Such models will be especially useful for analyzing questions such as whether the effect of increasing production or temperature is more severe in different production systems or geographic regions.

Effects of clustering herds with small-sized contemporary groups in dairy cattle genetic evaluations

Most test-day models used in genetic evaluations of dairy cattle define contemporary groups (CG) as the herd-test-date effect. Fitting this effect as fixed may minimize prediction bias, but requires a minimum number of observations per CG to simultaneously maximize the effective number of observations and minimize the residual error and prediction error variance. Nearly 4 million test-day records from the Portuguese Holstein database of 238,271 cows calving in 1,330 herds from 1994 through 2006 were used to evaluate the effect of clustering CG from small herds based on the similarity of their production environments. Principal component analysis was used to summarize 14 descriptive variables in 5 eigenvectors that explained 88% of the total variation. Based on the distance matrix, 2 different approaches were applied to group the herds. For each approach, 4 data sets were built having at least 3, 5, 10, or 15 observations per CG, respectively. For the data sets of group A, all herds, with or without the required number of observations per CG, were used in the clustering process. For the data sets of group B, only herds without the minimum number of observations were candidates to form clusters. All data sets were analyzed by an autoregressive test-day animal model fitting a fixed herd test date in a multiple-lactation setting, and results were compared with the current clustering procedure used in the Portuguese genetic evaluations. The data set from group B, with a minimum of 3 records per CG, was the one that provided the highest accuracy of prediction and the smaller within-CG variance, revealing a better fit for the data. This procedure also preserved the original herd structure of the database, better maximizing the number of herd groups. Correlations among EBV, rank, prediction error variance, and accuracies of prediction for this data set were high (0.97, 0.97, 0.85, and 0.82, respectively), suggesting that no major reranking is to be expected.

Short Communication: Changes in the Association Between Milk Yield and Age at First Calving in Holstein Cows with Herd Environment Level for Milk Yield

The aim of this study was to evaluate the effect of herd environment class on the genetic and phenotypic relationships of mature equivalent milk yield (MY) on age at first calving (AFC). Data analyzed were 248,230 first parity records of Holstein cows, daughters of 588 sires in 3,042 herds in the United States. Heritability for AFC was 0.33 +/- 0.01 and 0.20 +/- 0.01 in high and low environment herds, respectively, and 0.47 +/- 0.01 in the complete data set. The correlation between AFC sires' predicted breeding values of low and high classes was 0.69. Genetic correlations between MY and AFC were -0.52 +/- 0.02 and -0.31 +/- 0.03 in high and low environment herds, respectively, and -0.44 +/- 0.02 in the complete data set representing intermediate environments. If selection is based on the whole data set, expected correlated responses for AFC estimated as a result of 1,000 kg of genetic gain in MY, for high and low herd environment herds were -26.1 and -15.3 d, respectively, and -32.6 for the complete data set; hence the highest reduction in AFC occurs in intermediate environment herds. Different estimates of the heritability of AFC, the correlation between AFC breeding values of low and high classes as well as changes in the genetic correlation between MY and AFC across environments indicate genotype x environment interaction. Caution in interpretation is warranted because genetic relationships are dynamic, especially in populations undergoing selection. Current relationships may differ from those during the time period of the present study (1987-1994). Notwithstanding this possibility, methods and findings from the present study provide insight about the complexity of genetic association and genotype x environment interactions between AFC and MY.

Genotype by Environment Interaction for Somatic Cell Score Across Bulk Milk Somatic Cell Count and Days in Milk

The objective of this paper was to investigate the importance of a genotype x environment interaction (G x E) for somatic cell score (SCS) across levels of bulk milk somatic cell count (BMSCC), number of days in milk (DIM), and their interaction. Variance components were estimated with a model including random regressions for each sire on herd test-day BMSCC, DIM, and the interaction of BMSCC and DIM. The analyzed data set contained 344,029 test-day records of 24,125 cows, sired by 182 bulls, in 461 herds comprising 13,563 herd test-days. In early lactation, considerable G x E effects were detected for SCS, indicated by 3-fold higher genetic variance for SCS at high BMSCC compared with SCS at low BMSCC, and a genetic correlation of 0.72 between SCS at low and at high BMSCC. Estimated G x E effects were smaller during late lactation. Genetic correlations between SCS at the same level of BMSCC, across DIM, were between 0.43 and 0.89. The lowest genetic correlation between SCS measures on any 2 possible combinations of BMSCC and DIM was 0.42. Correlated responses in SCS across BMSCC and DIM were, on some occasions, less than half the direct response to selection in the response environment. Responses to selection were reasonably high among environments in the second half of the lactation, whereas responses to selection between environments early and late in lactation tended to be low. Selection for reduced SCS yielded the highest direct response early in lactation at high BMSCC.

Genetic correlations between milk production and health and fertility depending on herd environment

High milk production in dairy cattle can have negative side effects on health and fertility traits. This paper explores the genetic relationship of milk yield with health and fertility depending on herd environment. A total of 71,720 lactations from heifers calving in 1997 to 1999 in the Netherlands were analyzed. Herd environment was described by 4 principal components: intensity, average fertility, farm size, and relative performance indicating whether herds had good (poor) health and fertility despite a high (low) production. Fertility was evaluated by days to first service and number of inseminations (NINS); somatic cell score was used as a measure of udder health. Data were analyzed with a multitrait reaction norm model. Genetic correlation within traits across environments ranged from 0.84 to unity. Genetic correlations of the 3 traits with milk yield were antagonistic but varied over environments. Genetic correlation of milk yield with days to first service varied from 0.30 in small herds to 0.48 in herds with low average fertility. Correlations with NINS varied from 0.18 in large herds to 0.64 in high fertility herds, and with somatic cell score from 0.25 in herds with a high fertility relative to production to 0.47 in herds with a relative low fertility. Selection in environments of average value resulted in different predicted responses over environments. For example, selection for a decrease of NINS of 0.1 in an average production environment decreased milk yield by 35 kg in low production herds, but by 178 kg in high production herds.

Genotype by Environment Interaction for Milk Production Traits Between Organic and Conventional Dairy Cattle Production in The Netherlands

Estimates of genetic parameters for organic dairy farming have not been published previously, and neither is information available on the magnitude of genotype by environment interaction (GxE) between organic and conventional farming. However, organic farming is growing worldwide and basic information about genetic parameters is needed for future breeding strategies for organic dairy farming. The goal of this study was to estimate heritabilities of milk production traits under organic farming conditions and to estimate the magnitude of GxE between organic and conventional dairy farming. For this purpose, production records of first-parity Holstein heifers were used. Heritabilities of milk, fat and protein yield, and somatic cell score (SCS) were higher under organic farming conditions. For percentages of fat and protein, heritabilities of organic and conventional production were very similar. Genetic correlations between preorganic and organic, and organic and conventional milk production were 0.79 and 0.80, respectively. For fat yield, these correlations were 0.86 and 0.88, and for protein yield, these were 0.78 and 0.71, respectively. Our findings indicate that moderate GxE was present for yield traits. For percentage of fat and protein and SCS, genetic correlations between organic and conventional and preorganic production were close to unity, indicating that there was no GxE for these traits.

Fertility responses of Mexican Holstein cows to US sire selection

Genetic relationships between 2 fertility traits and milk production were investigated using mature-equivalent lactation records of 55,162 daughters of 1,339 Holstein sires in Mexico and 499,401 daughters of 663 Holstein sires in the northeastern United States. Data sets contained yields in first and second lactation, age at first calving (AFC), and calving interval (CI). There were 474 US sires in common between countries. A herd-year standard deviation criterion defined nonoverlapping low- (< or = 1,300 kg) and high- (> or = 1,600 kg) opportunity Mexican herd environments and a low-opportunity (< or = 1,024 kg) US environment. Genetic variances for the average Mexican herd (all data) for AFC and CI were 65 and 85% as large as those obtained from half-sisters in the average US herd. Genetic correlations for first-lactation milk in the average US herd and AFC and CI in the average Mexican environment were unfavorable (0.18 and 0.10). Regression coefficients of AFC in Mexican environments on US genetic gain in milk ranged from 2 to 7 d/1,000 kg. However, the favorable predicted response in AFC from genetic gain in milk in Mexican environments, like those in average US herds, ranged from -4 to -7 d/1,000 kg (r(g) = - 0.20). This unequal AFC response may indicate genotype by environment interaction in fitness performance or differential breeding management of high and low yielding Mexican cows. The potential effects of age at first service of breeding females need to be disentangled to accurately assess genetic improvement needs for Mexican Holstein herds.

Genetic Relationships for Dairy Performance Between Large-Scale and Small-Scale Farm Conditions

Genotype by environment interaction can be detected via the estimation of genetic correlations between environments under an animal model based on data comprising genetic links between the strata. Genetic correlations were estimated for protein yield of Holstein cows within and across regions of Germany using REML under an animal model for lactation and test-day records. Subsets of the entire data were created, stratified by region or herd size within region, and comprised between 16,307 and 132,972 cows with first-lactation records. Substantial heterogeneity exists between regions in Western and Eastern Germany. In Western states, most farms are small, with typical herd sizes of 30 to 60 cows, whereas in Eastern states, mostly large herds with herd sizes of 500 to 2000 cows are common. The results show drastic differences for residual and permanent environmental variance components between Eastern and Western regions with increases of around 30% for Eastern regions. Additive genetic variances were of similar magnitude in both regions. Genetic correlations between Eastern and Western states were between 0.90 and 0.95 but dropped to 0.79 when data from an Eastern state were reduced to contain large herds only. The results indicate that differences in herd size account for more of the differences in genetic correlation than do geographic regional differences.

Adjustment for Heterogeneous Covariance due to Herd Milk Yield by Transformation of Test-Day Random Regressions

A method of accounting for differences in covariance components of test-day milk records was developed based on transformation of regressions for random effects. Preliminary analysis indicated that genetic and nongenetic covariance structures differed by herd milk yield. Differences were found for phenotypic covariances and also for genetic, permanent environmental, and herd-time covariances. Heritabilities for test-day milk yield tended to be lower at the end and especially at the start of lactation; they also were higher (maximum of approximately 25%) for high-yield herds and lower (maximum of 15%) for low-yield herds. Permanent environmental variances were on average 10% lower in high-yield herds. Relative herd-time variances were approximately 10% at start of lactation and then began to decrease regardless of herd yield; high-yield herds increased in midlactation followed by another decrease, and medium-yield herds increased at the end of lactation. Regressors for random regression effects were transformed to adjust for heterogeneity of test-day yield covariances. Some animal reranking occurred because of this transformation of genetic and permanent environmental effects. When genetic correlations between environments were allowed to differ from 1, some additional animal re-ranking occurred. Correlations of variances of genetic and permanent-environmental regression solutions within herd, test-day, and milking frequency class with class mean milk yields were reduced with adjustment for heterogeneous covariance. The method suggests a number of innovative solutions to issues related to heterogeneous covariance structures, such as adjusted estimates in multibreed evaluation.

Associations Among Descriptors of Herd Management and Phenotypic and Genetic Levels of Health and Fertility

The objective of this paper was to investigate the association of descriptors of herd environment with phenotypic levels and breeding values of fertility and health traits. Analyses were performed for 82,080 first-lactation heifers and 173,787 multiparous cows. Fourteen environmental parameters were defined that described herd environment, such as average protein production, average somatic cell score (SCS), average calving interval, and average body condition score (BCS). Herds with lower average SCS had, in general, more desirable values for almost all analyzed traits (i.e., days to first service was 7 d shorter), as did herds with lower average calving interval (i.e., 2.8% lower incidence of predicted mastitis). Herds with higher average protein production had slightly poorer fertility but more desirable values for all other analyzed traits (i.e., 5.1% less predicted mastitis, 0.4 lower SCS, and 0.6 higher BCS). Variance components and breeding values of sires were estimated by applying a random regression on the environmental parameters. In general, genetic variances varied generally only slightly across environments. However, based on data exclusively for heifers, the genetic variance for number of inseminations was 4.1 times higher in herds with a higher number of inseminations, 1.9 times higher for survival in herds with higher fat to protein ratio, and 1.7 times higher for predicted mastitis in herds with higher number of inseminations. Based on the heifer data, the lowest estimated genetic correlation across environments was 0.76 (SE 0.21) for first-service conception between herds with differing average BCS. The minimum based on the cow data was 0.65 (SE 0.10) for survival between herds with differing average ages at calving. The relative importance of some fertility traits compared with yield traits doubled across environments. Possible reranking of individual animals within a population and the changes in genetic variance across environments suggests that environment-specific breeding values should be estimated for use in customized selection indices.

Influence of Herd Environment on Health and Fertility and Their Relationship with Milk Production

High levels of milk production in dairy cattle can have negative side effects on health and fertility traits. This paper explores the relationships among milk yield, health, and fertility traits both across and within herd environments on a national scale. A total of 456,574 lactations from 3904 herds recorded from 1995 to 1999 in The Netherlands were analyzed. Herd environment was defined by 41 variables derived from production records and the annual national agricultural survey. Principal components analysis reduced this set to 4 components: intensity, defined as average production per cow, average fertility, farm size, and relative performance indicating whether herds had good (poor) health and fertility despite a high (low) production. Both fertility and health were better for some traits in high-intensity herds and for other traits in low-intensity herds. In high-intensity herds, somatic cell count (SCC) levels were lower, drops in production occurred more often, and first service took place earlier but with lower success. High fertility occurred more often in herds located on sandy soils and in those that had lower SCC levels, had fewer drops in production and higher cow survival. On large farms, drops in production were less frequent and fertility was somewhat better. The within-herd analysis showed that the relationship of milk yield with health and fertility was stronger in herds with high production, fertility, or both. In herds with poor relative performance, there was no difference in production levels between animals with good health or fertility and those with poor health or fertility.

Genetic Parameters for Conformation Traits in Herds that Differ in Mean Final Score and Completeness of Pedigree and Performance Data

The objectives of this study were to assess differences in the heritability of type (conformation) traits between herds that differ in mean final score and completeness of pedigree and performance data and to estimate genetic correlations among these environments. Measurement of subjective characteristics, such as conformation traits, may be more difficult in herds with poor management conditions, and genetic evaluation of sires using data from such herds could lead to inaccurate selection decisions. Furthermore, missing pedigree data is a significant problem in many herds, and a lack of phenotypic data from maternal relatives may reduce the effectiveness of animal model evaluation systems. These hypotheses were examined using type classification scores of 1,728,836 first-parity Holstein cows (from 54,223 sires) that calved from 1993 to 2002 in 24,207 US dairy herds. These data included 480,927 records from progeny test daughters, but only 254,891 (47%) were from dams that had valid sire identification, and only 132,953 (28%) were from dams that had also been classified. Herds were grouped into quartiles by mean classification score, percentage of known maternal grandsires, and percentage of classified dams. Estimated heritability of final score was 0.20 in herds with mean score <74.5, 0.17 in herds with <25% known maternal grandsires, and 0.19 in herds with <18% classified dams. Conversely, estimates were 0.39 in herds with mean score >78.7, 0.35 in herds with 100% known maternal grandsires, and 0.37 in herds with >71% classified dams. Estimated genetic correlations between quartiles ranged from 0.86 to 0.95. Based on this study, it appears that improvements in animal identification and data collection in progeny test herds would lead to greater accuracy and stability of genetic evaluations for conformation traits in US Holstein cattle.

Prediction of Daily and Lactation Yields of Milk, Fat, and Protein Using an Autoregressive Repeatability Test Day Model

We evaluated the accuracy of an autoregressive multiple-lactation test day (ATD) model to predict missing test day yields of milk, fat, and protein to obtain cumulative 305-d records for cows with incomplete or in-progress lactations. The data consisted of more than one million observations of daily yields on test days in the first 3 lactations of over 75,000 Portuguese Holstein cows. Differences between actual (estimates from complete lactations using the test interval method) and ATD-predicted 305-d yields were negligible and smaller than those predicted by the test interval method. The ATD procedure tended to slightly underestimate cumulative lactation yields, whereas the test interval method substantially overestimated them. Smaller differences obtained by the ATD procedure resulted in less biased estimates of lactation yield, which also implies greater accuracy. As expected, the correlations between actual and predicted lactation yields increased with the number of test days from 0.831 to 0.997. Average correlations (by parity) between actual and ATD-predicted yields ranged from 0.977 to 0.984. Correlations between actual test day yields and corresponding predicted yields exceeded 0.5 for up to 7 time-intervals from the last test day yield used to predict cumulative yield of projected lactations. These correlations indicate the good predictive ability of the ATD method. From a producer's viewpoint, these advantages underwrite management because most on-farm selection decisions are based on the producing abilities of cows. Implementation of ATD methodology does not require special computing capability and is easily transferable to the farm level.