Genetic Evaluation Of Dairy Cattle Research Articles

Heterogeneity in convergence behaviour of the single-step SNP-BLUP model across different effects and animal groups

BackgroundThe single-step model is becoming increasingly popular for national genetic evaluations of dairy cattle due to the benefits that it offers such as joint breeding value estimation for genotyped and ungenotyped animals. However, the complexity of the model due to a large number of correlated effects can lead to significant computational challenges, especially in terms of accuracy and efficiency of the preconditioned conjugate gradient method used for the estimation. The aim of this study was to investigate the effect of pedigree depth on the model's overall convergence rate as well as on the convergence of different components of the model, in the context of the single-step single nucleotide polymorphism best linear unbiased prediction (SNP-BLUP) model.ResultsThe results demonstrate that the dataset with a truncated pedigree converged twice as fast as the full dataset. Still, both datasets showed very high Pearson correlations between predicted breeding values. In addition, by comparing the top 50 bulls between the two datasets we found a high correlation between their rankings. We also analysed the specific convergence patterns underlying different animal groups and model effects, which revealed heterogeneity in convergence behaviour. Effects of SNPs converged the fastest while those of genetic groups converged the slowest, which reflects the difference in information content available in the dataset for those effects. Pre-selection criteria for the SNP set based on minor allele frequency had no impact on either the rate or pattern of their convergence. Among different groups of individuals, genotyped animals with phenotype data converged the fastest, while non-genotyped animals without own records required the largest number of iterations.ConclusionsWe conclude that pedigree structure markedly impacts the convergence rate of the optimisation which is more efficient for the truncated than for the full dataset.

Ignoring genotype by environment interaction in the genetic evaluation of dairy cattle reduces accuracy but may increase selection intensity

Genotype by environment interaction (G×E) may exist for traits that are expressed in different environments. The G×E is often ignored in the genetic evaluation of selection candidates. We hypothesized that genetic gain in 2 environments is always higher when the true value of the genetic correlation (rg) between traits expressed in different environments is considered in the genetic evaluation. We tested this hypothesis by stochastic simulation of dairy cattle breeding programs in a mainstream and a niche environment. The rg was varied from 0 to 1 in steps of 0.1. We simulated the following 3 scenarios: 1Trait_1Index, 2Traits_1Index, and 2Traits_2Indices. The G×E was ignored in the genetic evaluation in the scenario with 1Trait and included in scenarios with 2Traits. Selection was based on the mainstream selection index in both environments in scenarios with 1Index. Selection in the mainstream environment was based on the mainstream selection index and selection in the niche environment was based on the niche selection index in the scenario with 2Indices. With moderate G×E (rg between 0.6 and 0.9), the highest genetic gain was achieved in the niche environment by selecting for the mainstream selection index and ignoring G×E. At lower rg, the highest genetic gain was achieved when considering G×E and selecting for the niche selection index. For the mainstream environment, it was never an advantage to ignore G×E. Therefore, although our hypothesis was confirmed in most cases, there were cases where ignoring G×E was the better option, and using the correct evaluation led to inferior genetic gain. The results of the current study can be used in animal breeding programs that encompass multiple environments.

Heat stress level as an alternative to fixed regression modeling for fat and protein yield traits in Holstein cattle

To date, no study has tested the inclusion of bioclimatic variables or heat stress indicators in the formation of fixed regression for the genetic evaluation of dairy cattle. The objective of the present study was to investigate the inclusion of heat stress level in modeling of fixed regression in random regression models to predict population response and individual genetic components for fat and protein yield in test-day, using data from Holstein cattle in tropical environment. The data comprised 52,012 test-day fat and protein records of 9,858 first-parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (temperature-humidity index – THI, and diurnal temperature variation – DTV) from 18 weather stations. Least square linear regression models were used to determine THI and DTV thresholds for fat and protein yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were analyzed as fixed effects (considering the average of two days before the test-day control), and heat stress level was considered in formation of fixed regression, totaling three models for each trait. THI and DTV thresholds for fat and protein losses was THI = 74 (−0.030 kg/day/ THI) and DTV = 16 (−0.020 kg/day/ DTV), for both traits. The model that included THI and DTV as fixed effects, and used heat stress level in formation of fixed regression (fixed curve), presented a better fit (Akaike's information criterion, Schwarz's Bayesian information criterion, and residual variance). Estimated breeding values (EBV) are improved when using this model, and there is an increase in reliability of estimates. There is an important reranking of sires when heat stress indicators are included in the model to evaluate fat and protein yield in test-day. The increase in reliability of EBV by including heat stress level in the fixed regression shows a better fit of model to the data. It is possible to conclude that the inclusion of heat stress level in the formation of fixed regression is an important factor to be considered in dairy cattle reared in tropical regions to minimize environmental effects and optimize the process of evaluation and genetic selection.

Importance of Genetic Evaluation of Dairy Cattle for Functional Traits: A Review

The general practice of selection of dairy animals concentrating high milk production has unwanted consequences like occurrence of health problems and fertility issues. Functional traits, including health and fertility, have significant genetic association with production and reproduction performance traits. Functionally superior dairy cows are healthy and sustained producers of healthful food for calves and human consumption. Improvement in functional traits rises the economy of dairy farmers by reducing the cost of input. These facts emphasised the inclusion of functional traits along with other economically important traits in selection criteria for the improvement of dairy animals. Hence, most of the dairy advanced countries have included genetic evaluation of functional traits as a routine practice in their dairy cattle selection programmes. In India, comparatively limited works have been carried out on functional traits and it suggested exploring more on this area for further development of the dairy sector. This review details the importance of inclusion of functional traits in genetic evaluation of dairy cattle for overall development of the dairy industry.

Non-genetic effects affecting fertility traits in local Reggiana cattle

Submitted 2020-07-24 | Accepted 2020-09-22 | Available 2020-12-01 https://doi.org/10.15414/afz.2020.23.mi-fpap.338-346 The objectives of this study were to investigate non-genetic sources of variation of fertility in Reggiana cattle and identify the best model for future genetic analysis. Moreover, the variation of target fertility traits in the different parities and months of first service was evaluated. The investigated fertility traits were interval between calving and first insemination, interval between calving and conception, number of inseminations per conception, and calving interval. The dataset included 22,731 records of 10,502 cows, collected between 1986 and 2019. Four different models were tested: Model 1 included the fixed effects of herd-year-month of first service and parity; Model 2 separately accounted for herd-year and month of first service, in addition to parity; Model 1a and Model 2a were Model 1 and Model 2 with the addition of the effect of age at first insemination. Model 1 had an average coefficient of determination of 0.40 for the traits, and this value increased to 0.48 in Model 1a. Moving to Models 2 and 2a, the coefficient of determination dropped to average values of 0.21 and 0.27, respectively. Regarding the effect of month of first service, the best fertility performances were observed in March and April, whereas for parity effect the best performance was in third lactation. Model 1a presented the best R2 for all studied traits but data editing for age at first insemination was too strict. On the other hand, in Models 2 and 2a variance absorbed by fixed effects was low, and this resulted in potentially biased estimates. Model 1 was therefore the best trade off between data loss and predictivity. Keywords: fertility, local cattle, Reggiana, source of variation, modelling References Cavestany, D. et al. (1985). Effect of season and high environmental temperature on fertility of Holstein cattle. Journal of Dairy Science, 68(6), 1471–1478. https://doi.org/10.3168/jds.S0022-0302(85)80985-1 Forabosco, F., Mantovani, R. and Meneghini, B. (2011). European and Indigenous Cattle Breeds in Italy. Schiel & Denver Publishing Limited. Retrieved from https://books.google.it/books?id=BiA0YAAACAAJ Gandini, G. et al. (2007). Comparing local and commercial breeds on functional traits and profitability: The case of Reggiana dairy cattle. Journal of Dairy Science, 90(4), 2004–2011. https://doi.org/10.3168/jds.2006-204 Gonzalez-Recio, O. and Alenda, R. (2005). Genetic parameters for female fertility traits and a fertility index in Spanish dairy cattle. Journal of Dairy Science, 88(9), 3282–3289. https://doi.org/10.3168/jds.S0022-0302(05)73011-3 Groen, A. F. et al. (1997). Economic values in dairy cattle breeding, with special reference to functional traits. Report of an EAAP-working group. Livestock Production Science, 49(1), 1–21. https://doi.org/10.1016/S0301-6226(97)00041-9 R Core Team. (2017). R: A Language and Environment for Statistical Computing. Vienna, Austria. Schaeffer, L. R. (2018). Necessary changes to improve animal models. Journal of Animal Breeding and Genetics, 135(2), 124–131. https://doi.org/10.1111/jbg.12321 Tiezzi, F. et al. (2012). Genetic parameters for fertility of dairy heifers and cows at different parities and relationships with production traits in first lactation. Journal of Dairy Science, 95(12), 7355–7362. https://doi.org/10.3168/jds.2012-5775 Tiezzi, F. et al. (2011). Genetic analysis of fertility in the Italian Brown Swiss population using different models and trait definitions. Journal of Dairy Science, 94(12), 6162–6172. https://doi.org/10.3168/jds.2011-4661 Van Bebber, J. et al. (1997). Accounting for herd, year and season effects in genetic evaluations of dairy cattle: A review. Livestock Production Science, 51(1–3), 191–203. https://doi.org/10.1016/S0301-6226(97)00058-4

Impact of Interbeef on national beef cattle evaluations

International evaluation models for beef cattle allow to compare animals’ estimated breeding values (EBV) across different countries, thanks to sires having offspring in more than one country. In this study we aimed to provide an up-to-date picture of the Interbeef international beef cattle evaluations from a national perspective, considering both large and small populations. Limousin age-adjusted weaning weight (AWW) phenotypes were available for 3,115,598 animals from 10 European countries, born between 1972 and 2017. EBV and reliabilities were obtained using a multi-trait animal model including maternal effects where AWW from different countries are modelled as different traits. We investigated the country of origin of the sires with internationally publishable EBV and, among them, the country of origin of the top 100 sires for each country scale. All countries had 20 to 28,557 domestic sires whose EBV were publishable, according to Interbeef’s rules, on the scale of other countries. All countries, except one, had domestic sires that ranked among the top 100 sires on other country scales. Across countries, inclusion of information from relatives recorded in other countries increased the reliability of EBV for domestic animals on average by 9.6 percentage points for direct EBV, and 8.3 percentage points for maternal EBV. In conclusion, international evaluations provide small countries access to a panel of elite foreign sires with EBV on their country scale and a more accurate estimation of EBV of domestic animals, while large countries obtain EBV for their sires on the scale of different countries which helps to better promote them.

Joint genetic analysis for dairy cattle performance across countries in sub-Saharan Africa

This study assessed the feasibility of across-country genetic evaluation of dairy cattle in sub-Saharan Africa where data on livestock production are scarce. Genetic parameters were estimated for the 305-day milk yield in the first lactation and across five lactations, for age at first calving and for interval between first and second calving. Estimated breeding values of individual animals for these traits were calculated. There were records from 2 333, 25 208, and 5 929 Holstein cows in Kenya, South Africa, and Zimbabwe, and 898 and 65134 Jersey cows from Kenya and South Africa. Genetic gain from sire selection within and across countries. was predicted Genetic links between countries were determined from sires with daughters that had records in two or more countries, and from common ancestral sires across seven generations on both the maternal and paternal sides of the pedigree. Each country was treated as a trait in the across-country evaluation. The results showed that genetic variance and heritability were not always estimable within country, but were significantly different from zero in the across-country evaluation. In all three countries, there was greater genetic gain in all traits from an across-country genetic evaluation owing to greater accuracy of selection compared with within country. Kenya stood to benefit most from an across-country evaluation, followed by Zimbabwe, then South Africa. An across-country breeding programme using joint genetic evaluation would be feasible, provided that there were genetic links across countries, and would provide a platform for accelerated genetic progress through selection and germplasm exchange between sub-Saharan African countries.Keywords: across-country genetic evaluation, genetic connectedness, genetic progress

Genetic parameters of somatic cell scores using random regression test-day models with Legendre polynomials in Tunisian dairy cattle

The aim of this study was to analyse the genetic parameters of test-day somatic cell scores (TDSCS) in Tunisian dairy cattle using random regression models (RRM) with homogeneous residual variances. Data included 43,647 test-day somatic cell count (TDSCC) records collected on 4825 Holstein-Friesian cows with parities up to the fifth, and calving dates between 2000 and 2014. Records of the same animal belonging to different lactations were treated as repeated records. Additive genetic (AG) and permanent environmental (PE) random effects were fitted using Legendre polynomials functions (LP) up to the fifth-order. The models included fixed regressions on days-in-milk (DIM), modelling the average phenotypic curve using Ali-Schaeffer's lactation function. Overall, three models (LP3, LP4 and LP5) where compared using the -2log marginal density for Bayes factor (-2logp) and the deviance information criterion (DIC). Models were also compared by similarities between the estimated breeding values and genetic trends. For all models, the total variances tended to decrease during the first month of the lactation, then they maintained a nearly constant trend. All models showed the largest AG and PE variances at the beginning and at the end of lactation with an oscillating pattern in the middle. The heritability estimates of from the three models ranged from 0.01 to 0.08 with the lowest heritability estimates being in the mid-lactation period. Genetic correlations between adjacent DIM were high and then decreased as the intervals widened. All the best statistical criteria were obtained with the LP5 model, however, no differences were observed between the estimated 305 average breeding values (EBV305) from the three models. This assumes that the LP3 model can be selected as a parsimonious model since it contains the smallest number of parameters. This RRM study provided a good insight into the changes in genetic merit of the somatic cell score trait in Tunisian dairy cattle and that an EBV for the SCS should be included in the selection programs. It also showed the benefits given of using RRMs to perform national genetic evaluations of dairy cattle for milk production traits in Tunisia.

Inclusion of bioclimatic variables in genetic evaluations of dairy cattle.

ObjectiveConsidering the importance of dairy farming and the negative effects of heat stress, more tolerant genotypes need to be identified. The objective of this study was to investigate the effect of heat stress via temperature-humidity index (THI) and diurnal temperature variation (DTV) in the genetic evaluations for daily milk yield of Holstein dairy cattle, using random regression models.MethodsThe data comprised 94,549 test-day records of 11,294 first parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (THI and DTV) from 18 weather stations. Least square linear regression models were used to determine the THI and DTV thresholds for milk yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were combined in various ways and tested for different days, totaling 41 models.ResultsThe THI and DTV thresholds for milk yield losses was THI = 74 (−0.106 kg/d/THI) and DTV = 13 (−0.045 kg/d/DTV). The model that included THI and DTV as fixed effects, considering the two-day average, presented better fit (−2logL, Akaike information criterion, and Bayesian information criterion). The estimated breeding values (EBVs) and the reliabilities of the EBVs improved when using this model.ConclusionSires are re-ranking when heat stress indicators are included in the model. Genetic evaluation using the mean of two days of THI and DTV as fixed effect, improved EBVs and EBVs reliability.

Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle.

Simple SummaryMethane is a greenhouse gas with a global warming potential 28 times that of CO2. Enteric methane accounts for 17% of global methane emissions and 3.3% of total global greenhouse gas emissions from human activities. There is, therefore, significant research interest in finding ways to reduce enteric methane emissions by ruminants. Partners in Expert Working Group 2 (WG2) of the European Cooperation in Science and Technology (COST) Action METHAGENE have used several methods for measuring methane output by individual dairy cattle under various environmental conditions. Methods included respiration chambers, the sulphur hexafluoride (SF6) tracer technique, breath sampling during milking or feeding, the GreenFeed system, and the laser methane detector. Respiration chambers are considered the ‘gold standard’, but are unsuitable for large-scale measurements of methane emissions, which are needed for genetic evaluations. In this study, the suitability of methods for large-scale studies was reviewed and compared. All methods showed high correlations with respiration chambers, but comparisons among alternative methods generally had lower correlations. Results confirm, however, that there is sufficient correlation between methods for measurements from all methods to be combined, with appropriate weightings, for use in international genetic studies. This will pave the way for breeding cattle with lower methane emissions.Partners in Expert Working Group WG2 of the COST Action METHAGENE have used several methods for measuring methane output by individual dairy cattle under various environmental conditions. Methods included respiration chambers, the sulphur hexafluoride (SF6) tracer technique, breath sampling during milking or feeding, the GreenFeed system, and the laser methane detector. The aim of the current study was to review and compare the suitability of methods for large-scale measurements of methane output by individual animals, which may be combined with other databases for genetic evaluations. Accuracy, precision and correlation between methods were assessed. Accuracy and precision are important, but data from different sources can be weighted or adjusted when combined if they are suitably correlated with the ‘true’ value. All methods showed high correlations with respiration chambers. Comparisons among alternative methods generally had lower correlations than comparisons with respiration chambers, despite higher numbers of animals and in most cases simultaneous repeated measures per cow per method. Lower correlations could be due to increased variability and imprecision of alternative methods, or maybe different aspects of methane emission are captured using different methods. Results confirm that there is sufficient correlation between methods for measurements from all methods to be combined for international genetic studies and provide a much-needed framework for comparing genetic correlations between methods should these become available.

Genetic analyses of novel temperament and milkability traits in Norwegian Red cattle based on data from automatic milking systems

The number of dairy cows milked in automatic milking systems (AMS) is steadily increasing in Norway. Capacity and efficiency of AMS are highly dependent on the individual cow's milking efficiency, such as milking speed and occupation time in the milking robot. Cows meet new challenges in herds utilizing AMS. Consequently, new or revised traits may be needed for genetic evaluation of dairy cattle. The AMS records relevant information on an individual cow basis. The aims of this study were to estimate genetic parameters of new automatically recorded milkability and temperament traits. Data from 77 commercial herds with Norwegian Red dairy cattle were analyzed by mixed linear animal models. The final data set contained 1,012,912 daily records from 4,883 cows in first to ninth lactation. For variance component estimation, univariate and bivariate models were used. Daily records of box time (BT), average flow rate (FR), kilograms of milk per minute of box time (MEF), handling time (HT), log-transformed HT, milking frequency, and milking interval were analyzed with repeatability models. Among these traits, FR, BT, and MEF showed the highest heritabilities of 0.48, 0.27, and 0.22, respectively, whereas heritability of log-transformed HT, HT, milking frequency, and milking interval was low (0.02-0.07). Unsuccessful milkings expressed as rejected milkings, incomplete milkings (IM), milkings with kick-offs (KO), and teat not found also showed low heritabilities (0.002-0.06). Due to low frequency, KO, rejected milkings, IM, and teat not found were also analyzed as proportions per lactation, which resulted in slightly higher heritability estimates. Genetic correlations were favorable and intermediate to strong between BT, HT, MEF, and FR with absolute values above 0.50. Intermediate and favorable correlations were found for IM and KO with BT, HT, MEF, and FR. Cow milkability in AMS can be improved by selection for reduced number of unsuccessful milkings, faster FR, increased MEF, and shorter BT and HT. Our results confirm that automatically recorded data on milkability and temperament can be valuable sources of information for routine genetic evaluations and that milking efficiency in AMS can be genetically improved.

Comparison of statistical models for estimation of methane emission in dairy Simmentals based on animal recording data

In the last decades we have witnessed increasingly pronounced climate change worldwide resulting in environment transformation in various regions by making it not convenient for agricultural and livestock production. The global livestock sector contributes to anthropogenic greenhouse gas emission, but on the other hand, it can also deliver a significant share of the necessary mitigation effort. One of the most significant greenhouse gas is methane. Mitigation methods for the methane emissions in cattle can be classified as short and long term. Short-term methods imply increase of production per animal, reduction of number of animals and feeding optimization, while long-term methods imply genetic evaluation and selection based on methane emission variation. Prerequisite for genetic evaluation is selection of optimal indicators and models with high accuracy and easy applicability in routine Animal Recording. Therefore, the objective of this study was to evaluate different models for methane emission estimation in dairy cows based on Animal Recording data. The results obtained indicate that data from regular Animal Recording could be used in estimation of methane emission of dairy Simmental cows enabling the population analysis and genetic evaluation of dairy cattle for methane emission. Given the very high variability determined in estimated methane emission values regarding the used statistical models and aiming high accuracy of genetic evaluation it is recommended to define estimation models for body weight, dry matter intake and methane emission based on parameters (type traits and test-day records) of particular dairy cattle population. The stated will enable genetic evaluation of dairy cattle for methane emission as well as selection of cows with lower methane emission intensity. Finally, this will lead to environmentally sustainable milk production.

Autoregressive single-step test-day model for genomic evaluations of Portuguese Holstein cattle

The multiple-lactation autoregressive test-day (AR) model is the adopted model for the national genetic evaluation of dairy cattle in Portugal. Under this model, animals' permanent environment effects are assumed to follow a first-order autoregressive process over the long (auto-correlations between parities) and short (auto-correlations between test-days within lactation) terms. Given the relevance of genomic prediction in dairy cattle, it is essential to include marker information in national genetic evaluations. In this context, we aimed to evaluate the feasibility of applying the single-step genomic (G)BLUP to analyze milk yield using the AR model in Portuguese Holstein cattle. In total, 11,434,294 test-day records from the first 3 lactations collected between 1994 and 2017 and 1,071 genotyped bulls were used in this study. Rank correlations and differences in reliability among bulls were used to compare the performance of the traditional (A-AR) and single-step (H-AR) models. These 2 modeling approaches were also applied to reduced data sets with records truncated after 2012 (deleting daughters of tested bulls) to evaluate the predictive ability of the H-AR. Validation scenarios were proposed, taking into account young and proven bulls. Average EBV reliabilities, empirical reliabilities, and genetic trends predicted from the complete and reduced data sets were used to validate the genomic evaluation. Average EBV reliabilities for H-AR (A-AR) using the complete data set were 0.52 (0.16) and 0.72 (0.62) for genotyped bulls with no daughters and bulls with 1 to 9 daughters, respectively. These results showed an increase in EBV reliabilities of 0.10 to 0.36 when genomic information was included, corresponding to a reduction of up to 43% in prediction error variance. Considering the 3 validation scenarios, the inclusion of genomic information improved the average EBV reliability in the reduced data set, which ranged, on average, from 0.16 to 0.26, indicating an increase in the predictive ability. Similarly, empirical reliability increased by up to 0.08 between validation tests. The H-AR outperformed A-AR in terms of genetic trends when unproven genotyped bulls were included. The results suggest that the single-step GBLUP AR model is feasible and may be applied to national Portuguese genetic evaluations for milk yield.

English

This study evaluated morning and afternoon test day records for genetic evaluation of dairy cattle. The data were taken form 128,087 test day yield records for the first four lactations of Holstein cows from 2007 to 2017, from Nucleus Breeding Center of dairy cattle in Indonesia. The records consisted of morning and afternoon and total milk yields from 823 cows, resulting from 133 sires and 520 dams; records were restricted to Day Interval Milk (DIM) between 5 and 305 days production. The genetic parameters were estimated with REML by using animal model with fixed regression. Ali and Schaeffer has a good fit for morning, afternoon and total test day yields with the coefficient of determination ranging from 0.980 to 0.995. Estimates of heritability were 0.177, 0.220, and 0.213 for morning, afternoon, and total test day records, respectively. Spearman rank correlations of breeding values between total yield and morning and afternoon yields, for both animals and sires, ranged between 0.953 and 0.968. In conclusion, morning and afternoon yields can be used for genetic evaluation of dairy cattle. Key words: Genetic parameters, heritability, morning and afternoon yields, fixed regression model, dairy cattle.

Alternative subclinical mastitis traits for genetic evaluation in dairy cattle

Chronic subclinical mastitis (SCM), characterized by changes in milk composition and high somatic cell count (SCC) in milk for a prolonged period of time, is often caused by a bacterial infection. Different levels of SCC have been suggested and used as threshold to identify subclinical infection. The aim of this study was to examine different definitions of SCM based on test-day SCC and estimate genetic parameters for these traits and their genetic correlation to milk production. Test-day SCC records from 1,209,128 Norwegian Red cows in lactation 1 to 3 were analyzed. Twelve SCM traits were defined as binary with 2 test-day SCC in a row above SCC thresholds from 50,000 to 400,000 cells/mL (SCM50, SCM100, SCM150, SCM200, SCM250, SCM300, SCM350, and SCM400), with 3 test-day SCC in a row above 200,000 and 400,000 cells/mL (SCM200_3 and SCM400_3), and the number of days before the first case with SCM50 (D50) or SCM400 (D400). The heritability and genetic correlations were estimated for SCM traits and the mean lactation-average somatic cell score (LSCS) using linear animal repeatability models. The total mean frequency of SCM ranged from 1.2% to 51.8%, for different trait definitions, high for low SCC threshold (SCM50) and low for the highest SCC threshold (SCM400_3). For the 2 traits based on number of days, the mean values were 104 (D50) and 117 (D400) days. The mean LSCS was 4.4 (equivalent to around 82,000 SCC). Heritabilities for the 12 alternative SCM traits were low and varied from 0.01 (SCM400_3) to 0.1 (SCM100), whereas for LSCS the estimated heritability was 0.3 and standard error varied from 0.001 to 0.003. Genetic correlations among the SCM traits ranged from 0.7 (D50 and SCM400) to 1 (SCM350 and SCM400), whereas between SCM traits and milk production the correlation ranged from 0.07 (LSCS) to 0.3 (D400). The standard error for genetic correlations varied from 0.001 to 0.06. The heritability was low and the genetic correlations were strong among SCM traits. Genetic correlations lower than 1 suggest that the alternative SCM traits are genetically different from LSCS, the trait currently used in genetic evaluations for Norwegian Red. Hence, the alternative traits will add information and improve breeding for better udder health.

Development of genetic evaluation for milk production traits of Holsteins in Japan

The procedure used for the genetic evaluation of dairy cattle in Japan has developed from a lactation sire–MGS model to a multiple‐lactation random regression test‐day animal model. Genetic evaluation of Holstein bulls in Japan began in 1989 with the use of field‐style progeny testing; dairy herd improvement program data from all over Japan were used, along with a sire and maternal grandsire model. In 1993, an animal model was introduced to estimate breeding values for yield and type traits. A random regression test‐day model was first applied in 2010. In the business of breeding dairy cattle, it is very important to users that estimated breeding values are reliable and stable among subsequent routine evaluations. With experience in the genetic evaluation of dairy cattle in Japan, Japanese researchers have found ways to improve the stability of estimated breeding values. These modifications involve changes in data editing, development of evaluation models, changes to the structures of unknown‐parent groups, awareness of the problems of predicting lactation yield from partial test‐day records, and adjustment for heterogeneity within herd variances. Here, I introduce developments in, and our experiences with, the genetic evaluation of yield traits of Holstein cattle in Japan.

Application of single-step genomic evaluation using multiple-trait random regression test-day models in dairy cattle

Test-day traits are important for genetic evaluation in dairy cattle and are better modeled by multiple-trait random regression models (RRM). The reliability and bias of genomic estimated breeding values (GEBV) predicted using multiple-trait RRM via single-step genomic best linear unbiased prediction (ssGBLUP) were investigated in the 3 major dairy cattle breeds in Canada (i.e., Ayrshire, Holstein, and Jersey). Individual additive genomic random regression coefficients for the test-day traits were predicted using 2 multiple-trait RRM: (1) one for milk, fat, and protein yields in the first, second, and third lactations, and (2) one for somatic cell score in the first, second, and third lactations. The predicted coefficients were used to derive GEBV for each lactation day and, subsequently, the daily GEBV were compared with traditional daily parent averages obtained by BLUP. To ensure compatibility between pedigree and genomic information for genotyped animals, different scaling factors for combining the inverse of genomic (G-1) and pedigree (A-122) relationship matrices were tested. In addition, the inclusion of only genotypes from animals with accurate breeding values (defined in preliminary analysis) was compared with the inclusion of all available genotypes in the analyzes. The ssGBLUP model led to considerably larger validation reliabilities than the BLUP model without genomic information. In general, scaling factors used to combine the G-1 and A-122 matrices had small influence on the validation reliabilities. However, a greater effect was observed in the inflation of GEBV. Less inflated GEBV were obtained by the ssGBLUP compared with the parent average from traditional BLUP when using optimal scaling factors to combine the G-1 and A-122 matrices. Similar results were observed when including either all available genotypes or only genotypes from animals with accurate breeding values. These findings indicate that ssGBLUP using multiple-trait RRM increases reliability and reduces bias of breeding values of young animals when compared with parent average from traditional BLUP in the Canadian Ayrshire, Holstein, and Jersey breeds.

Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle.

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage (0.213±0.007). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran.

A new way to measure milk yield persistency: a genetic point of view with application to Gyr (Bos indicus) cattle.

The objective of this work was to estimate genetic parameters for a measure of persistency of milk yield and to evaluate its association with 305-d cumulative milk yield and lactation length. 12 346 records from 8202 dairy Gyr cows including lactations up to fifth calving were used. The measure of persistency was obtained from one of the parameters of a quadratic model that describes the cumulative yield across lactation as a function of days in milk. A three-trait multivariate analysis was done. Heritability and repeatability for persistency were 0.08 and 0.21, respectively. Deviance Information Criterion provided evidence that the additive genetic covariance between the measure of persistency studied and 305-d cumulative yield is zero. Genetic correlations between persistency and lactation length were 0.50 and 0.27 for first or all lactations, respectively. Milk yield persistency as measured in this study has low heritability. Selection for persistency can increase lactation length. The measure of milk yield persistency studied here is genetically independent of total milk yield and can be included in routine genetic evaluations of dairy cattle.

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

BackgroundGenotype 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.MethodsGenetic 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.ResultsMultiple-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.ConclusionsNo 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.