Genetic analysis of phenotypic indicators for heat tolerance in crossbred dairy cattle.

Climate change-induced rise in global temperatures has intensified heat stress on dairy cattle and is contributing to the generally observed low milk productivity. Selective breeding aimed at enhancing animals' ability to withstand rising temperatures while maintaining optimal performance is crucial for ensuring future access to dairy products. However, phenotypic indicators of heat tolerance are yet to be effectively factored into the objectives of most selective breeding programs. This study investigated the response of milk production to changing heat load as an indication of heat tolerance and the influence of calving season on this response in multibreed dairy cattle performing in three agroecological zones Kenya. First-parity 7-day average milk yield (65 261 milk records) of 1 739 cows were analyzed. Based on routinely recorded weather data that were accessible online, the Temperature-Humidity Index (THI) was calculated and used as a measure of heat load. THI measurements used represented averages of the same 7-day periods corresponding to each 7-day average milk record. Random regression models, including reaction norm functions, were fitted to derive two resilience indicators: slope of the reaction norm (Slope) and its absolute value (Absolute), reflecting changes in milk yield in response to the varying heat loads (THI 50 and THI 80). The genetic parameters of these indicators were estimated, and their associations with average test-day milk yield were examined. There were no substantial differences in the pattern of milk yield response to heat load between cows calving in dry and wet seasons. Animals with ≤50% Bos taurus genes were the most thermotolerant at extremely high heat load levels. Animals performing in semi-arid environments exhibited the highest heat tolerance capacity. Heritability estimates for these indicators ranged from 0.06 to 0.33 and were mostly significantly different from zero (P < 0.05). Slope at THI 80 had high (0.64-0.71) negative correlations with average daily milk yield, revealing that high-producing cows are more vulnerable to heat stress and vice versa. A high (0.63-0.74) positive correlation was observed between Absolute and average milk yield at THI 80. This implied that low milk-producing cows have a more stable milk production under heat-stress conditions and vice versa. The study demonstrated that the slope of the reaction norms and its absolute value can effectively measure the resilience of crossbred dairy cattle to varying heat load conditions. The implications of these findings are valuable in improving the heat tolerance of livestock species through genetic selection.

Modelling growth in Suffolk and Charollais sheep populations using random regression models and validation of constrained polynomial correlation values.

Random regression modelling has been used across multiple animal species to model longitudinal data. The random regression model for growth accounts for the genetic correlation between measures of the same trait over time and the wide environmental variability in growth, but this requires adequate weight records across the age range. However, contemporary management practices in sheep in the United Kingdom generally focus on growing lambs and neglect mature weight recordings. This study examined modelling strategies for growth data in Suffolk and Charollais sheep, provided by the Agriculture and Horticulture Development Board, with polynomial random regression modelling with many early life weight recordings but limited weight recordings in mature animals. Two methods were employed to model the data. In Method A, missing mature weight records were predicted for those animals that did not have a recorded mature weight. The animals were sorted into groups based on the identity of their sires and the year in which the animal was born. Mature weight values were predicted within each group with a multiple regression model. The dataset, including predicted values, was analysed with random regression models using polynomials and simple linear regression for animal and permanent environmental (PE) effects. In Method B, the dataset with missing mature weight records was analysed using a random linear regression animal model with random animal and PE effects. Due to problems of convergence because the parameters were close to the boundary space, fixing the correlation between the intercept and slope of the Legendre polynomial at different levels was investigated. The heritability values resulting from the model with a fixed correlation between intercept and slope parameters at 0.5 for the Suffolk dataset resulted in heritability values ranging from 0.2 to 0.5 from 1 to 619 days of age. Corresponding estimates for the Charollais dataset ranged from 0.18 to 0.49 from 1 to 640 days of age. For the Suffolk data, the genetic correlations ranged from 1.00 to 0.08 between weight at day 1 to weight at day 619, while for the Charollais, the correlations ranged from 1.00 to 0.05 from 1 to 640 days of age. Validation procedures were undertaken using a multitrait approach to examine the estimated breeding values when the correlation between the intercept and slope are fixed at different levels. The results indicated that fixing the correlation at 0.5 gave the most appropriate estimates for the Suffolk and Charollais datasets.

Genomic prediction of crossbred dairy cattle in Tanzania: A route to productivity gains in smallholder dairy systems.

Selection based on genomic predictions has become the method of choice for genetic improvement in dairy cattle. This offers huge opportunity for developing countries with little or no pedigree data, and preliminary studies have shown promising results. The African Dairy Genetic Gains (ADGG) project initiated a digital system of dairy performance data collection, accompanied by genotyping in Tanzania in 2016. Currently, ADGG has the largest body of dairy performance data generated in East Africa from a smallholder dairy system. This study examines the use of genomic best linear unbiased prediction (GBLUP) and single-step (ss)GBLUP for the estimation of genetic parameters and accuracy of genomic prediction for daily milk yield and body weight in Tanzania. The estimates of heritability for daily milk yield from GBLUP and ssGBLUP were essentially the same, at 0.12 ± 0.03. The heritability estimates for daily milk yield averaged over the whole lactation from random regression model (RRM) GBLUP or ssGBLUP were 0.22 and 0.24, respectively. The heritability of body weight from GBLUP was 0.24 ± 04 but was 0.22 ± 04 from the ssGBLUP analysis. Accuracy of genomic prediction for milk yield from a forward validation was 0.57 for GBLUP based on fixed regression model or 0.55 from an RRM. Corresponding estimates from ssGBLUP were 0.59 and 0.53, respectively. Accuracy for body weight, however, was much higher at 0.83 from GBLUP and 0.77 for ssGBLUP. The moderate to high levels of accuracy of genomic prediction (0.53-0.83) obtained for milk yield and body weight indicate that selection on the basis of genomic prediction is feasible in smallholder dairy systems and most probably the only initial possible pathway to implementing sustained genetic improvement programs in such systems.

Evaluating the impact of heat stress as measured by temperature-humidity index (THI) on test-day milk yield of small holder dairy cattle in a sub-Sahara African climate.

This study evaluates the effect of heat stress on milk production and describes the pattern of response of milk yield to increasing heat load, using temperature-humidity index (THI) on test-day milk records of small holder dairy cattle herds in the sub-Saharan African climate of Tanzania. Climate data obtained from aWhere, an agricultural weather data platform (http://www.awhere.com) was analysed with 14,367 first lactation test day milk records of 3511 dairy cows collected between 2016 and 2019. THI was calculated from daily maximal temperatures and daily minimum humidity. Three sets of analysis were performed. In the first and second analysis, two mixed effect repeatability models were fitted with THI treated as a categorical variable grouped into 5 classes (THI1= [61 - 66], THI2= [67 - 71], THI3= [72 - 78], THI4=[79 - 81], THI5=[82 - 86]), to obtain least squares estimates of THI effect on milk production, and as a continuous variable within THI classes to identify THI thresholds at which milk yield started to decline. In the third analyses, one quadratic polynomial regression (POL) and three regression spline functions namely piecewise linear spline function (PLF), natural splines function (NSF) and cubic splines function (CSF) were fitted to determine the average effect of THI on milk yield in the population and describe the pattern of response of milk yield to increasing head load. The results show that heat stress reduced milk yield by 4.16% to 14.42% across THI groups, with daily milk yield being the highest in THI1 (7.40±0.39 litres) and the lowest in THI4 (6.33±0.32). Regression coefficients within groups showed significant daily milk yield decrease in THI2 (-0.09) and THI3 (-0.06), but not for other THI classes, indicating that cows experienced heat stress between THI values of 67 and 76 and milk loss plateaued afterwards, suggesting that the animals acclimatized to heat stress conditions beyond THI value of 76. At the population level, THI and its squared term were significantly negatively and positively (-0.61, 0.004) associated with milk production, indicating a non-linear relationship between milk yield and THI. The CSF model showed better goodness of fit and predictive ability than other models for predicting future population response of milk yield to heat stress in small holder dairy farms in Tanzania. Herd management strategies and animal husbandry measures are needed in small holder dairy farms in Tanzania to minimize the impact of heat stress on milk yield and income of the farmers.

Genetic evaluation of test-day milk yields from smallholder dairy production systems in Kenya using genomic relationships.

Efforts to improve dairy production in smallholder farming systems of East Africa over the past decade have had limited impact because of the lack of records on performance to guide targeted breeding programs. Estimates of genetic parameters in these systems are lacking. Using data generated through a project ("Germplasm for Dairy Development in East Africa") in Kenya and a genomic relationship matrix from genotypic records, we examined the potential impact of different models handling contemporary groups or herd effects on estimates of genetic parameters using a fixed regression model (FRM) for test-day (TD) milk yields, and the covariance structure for TD milk yield at various stages of lactation for animals using a random regression model (RRM). Models in which herd groups were defined using production levels derived from the data fitted the data better than those in which herds were grouped depending on management practices or were random. Lactation curves obtained for animals under different production categories did not display the typical peak yield characteristic of improved dairy systems in developed countries. Heritability estimates for TD milk yields using the FRM varied greatly with the definition of contemporary herd groups, ranging from 0.05 ± 0.03 to 0.27 ± 0.05 (mean ± standard error). The analysis using the RRM fitted the data better than the FRM. The heritability estimates for specific TD yields obtained by the RRM were higher than those obtained by the FRM. Genetic correlations between TD yields were high and positive for measures within short consecutive intervals but decreased as the intervals between TD increased beyond 60 d and became negative with intervals of more than 5 mo. The magnitude of the genetic correlation estimates among TD records indicates that using TD milk records beyond a 60-d interval as repeated measures of the same trait for genetic evaluation of animals on smallholder farms would not be optimal. Although each individual smallholder farmer retains only a few animals, using the genomic relationship between animals to link the large number of farmers operating under specified environments provides a sufficiently large herd-group for which a breeding program could be developed.

Current situations of animal data recording, dairy improvement infrastructure, human capacity and strategic issues affecting dairy production in sub-Saharan Africa.

An online survey on the state of existing dairy data, dairy improvement infrastructure and human capacity in sub-Saharan Africa (SSA) was undertaken with the aim of assessing whether the state of existing animal recording, dairy improvement methods and key issues facing dairy production together with means of addressing the issues differ among countries and regions of SSA. Respondents comprised experts and practitioners in livestock production and genetic resources from research institutes, animal breeding companies, universities, non-governmental organisations and government agricultural ministries. The main dairy farming system in which the respondents were involved was mixed crop-livestock system (30.2%), and this was mainly practised in the private land tenure system (46.3%). Data were analysed using linear model and paired Student t test in R software package. Respondents identified key issues affecting dairy production as poor genetic assessment of imported exotic breeds and crosses in Africa (62.3%), fluctuations in milk prices within both the formal and informal markets (50.9%), no comprehensive sire ranking systems (39.6%), housing and health management regimes which adversely affect milk yield (32.1%), poor market networks for dairy products (25.5%), poor feeding (13.3%), inadequate genetic technologies (9.4%) and poor animal performance recording systems (9.4%). Respondents emphasised the need for updated breeding policies, sire ranking systems, adequate farm management systems, capacity building, across-country collaborations and joint genetic assessments of dairy breeds found in sub-Saharan Africa. The current situation of dairy production though similar for the different countries, differed in order of emphasis and magnitude across the countries and regions in sub-Saharan Africa.

The feasibility of using low-density marker panels for genotype imputation and genomic prediction of crossbred dairy cattle of East Africa.

Cost-effective high-density (HD) genotypes of livestock species can be obtained by genotyping a proportion of the population using a HD panel and the remainder using a cheaper low-density panel, and then imputing the missing genotypes that are not directly assayed in the low-density panel. The efficacy of genotype imputation can largely be affected by the structure and history of the specific target population and it should be checked before incorporating imputation in routine genotyping practices. Here, we investigated the efficacy of imputation in crossbred dairy cattle populations of East Africa using 4 different commercial single nucleotide polymorphisms (SNP) panels, 3 reference populations, and 3 imputation algorithms. We found that Minimac and a reference population, which included a mixture of crossbred and ancestral purebred animals, provided the highest imputation accuracy compared with other scenarios of imputation. The accuracies of imputation, measured as the correlation between real and imputed genotypes averaged across SNP, were around 0.76 and 0.94 for 7K and 40K SNP, respectively, when imputed up to a 770K panel. We also presented a method to maximize the imputation accuracy of low-density panels, which relies on the pairwise (co)variances between SNP and the minor allele frequency of SNP. The performance of the developed method was tested in a 5-fold cross-validation process where various densities of SNP were selected using the (co)variance method and also by alternative SNP selection methods and then imputed up to the HD panel. The (co)variance method provided the highest imputation accuracies at almost all marker densities, with accuracies being up to 0.19 higher than the random selection of SNP. The accuracies of imputation from 7K and 40K panels selected using the (co)variance method were around 0.80 and 0.94, respectively. The presented method also achieved higher accuracy of genomic prediction at lower densities of selected SNP. The squared correlation between genomic breeding values estimated using imputed genotypes and those from the real 770K HD panel was 0.95 when the accuracy of imputation was 0.64. The presented method for SNP selection is straightforward in its application and can ensure high accuracies in genotype imputation of crossbred dairy populations in East Africa.

Invited review: Genomic selection for small ruminants in developed countries: how applicable for the rest of the world?

Improved management and use of estimated breeding values in breeding programmes, have resulted in rapid genetic progress for small ruminants (SR) in Europe and other developed countries. The development of single nucleotide polymorphisms chips opened opportunities for genomic selection (GS) in SR in these countries. Initially focused on production traits (growth and milk), GS has been extended to functional traits (reproductive performance, disease resistance and meat quality). The GS systems have been characterized by smaller reference populations compared with those of dairy cattle and consisting mostly of cross- or multi-breed populations. Molecular information has resulted in gains in accuracy of between 0.05 and 0.27 and proved useful in parentage verification and the identification of QTLs for economically important traits. Except for a few established breeds with some degree of infrastructure, the basic building blocks to support conventional breeding programmes in small holder systems are lacking in most developing countries. In these systems, molecular data could offer quick wins in undertaking parentage verification and genetic evaluations using G matrix, and determination of breed composition. The development of next-generation molecular tools has prompted investigations on genome-wide signatures of selection for mainly adaptive and reproduction traits in SR in developing countries. Here, the relevance of the developments and application of GS and other molecular tools in developed countries to developing countries context is examined. Worth noting is that in the latter, the application of GS in SR will not be a 'one-size fits all' scenario. For breeds with some degree of conventional genetic improvement, classical GS may be feasible. In small holder systems, where production is key, community-based breeding programmes can provide the framework to implement GS. However, in fragile growth systems, for example those found in marginal environments, innovative GS to maximize adaptive diversity will be required. A cost-benefit analysis should accompany any strategy of implementing GS in these systems.

Pseudopregnancy and aseasonal breeding in dairy goats: genetic basis of fertility and impact on lifetime productivity.

Until recently, the main selection focus in UK dairy goats has been on milk yield. To develop a selection index suitably weighted for a variety of traits, it is important to understand the genetic relationships between production, health and fertility traits. This study focussed on three aspects of reproduction that are of interest to goat breeders. (1) Out of season (OOS) kidding ability: goats are highly seasonal breeders so achieving consistent, year-round dairy production presents a challenge. It may be possible to select for extended or shifted breeding cycles, however, there are no published studies on the genetic basis of seasonal kidding ability, and a genetic correlation with milk production in dairy goats; (2) age at first kidding (AFK): a reduced AFK offers the opportunity for more rapid genetic improvement, as well as reducing the amount of time and resources required to raise the animals to producing age; (3) pseudopregnancy (PPG): as it is difficult to diagnose pregnancy within 30 days of mating, high herd levels of PPG could add a significant delay in breeding replacement animals, or commencing a new lactation. Using records from 9546 goats, the objective of this study was to investigate the genetic relationships between the reproductive traits described above, and the production traits 520-day milk yield (MY520), lifetime milk yield (MYLife) and lifetime number of days in milk (DIMLife). The 'out of season' phenotype was defined as week of kidding relative to the 4 weeks of the year where the highest average number of births occur. Incidences of PPG that occurred during the first lactation were used as cases, while goats with none were assigned as controls. Relevant fixed and random effects were fitted in the models. In line with other reproduction traits, heritability estimates were low ranging from 0.08 to 0.11. A negative genetic correlation was found between AFK and MY520 (-0.22±0.10), whereas a positive genetic correlation was found between PPG and DIMLife (0.58±0.11). Pseudopregnancy and OOS were positively genetically correlated (0.36±0.15). All other genetic correlations were very low. The results of this study indicate that selection for the reproductive traits analysed is feasible, without adversely affecting MYLife.

The genetics of antibody response to paratuberculosis in dairy cattle.

Genetic parameters were estimated for antibody response to paratuberculosis (Mycobacterium avium ssp. paratuberculosis) using milk ELISA test results, collected and analyzed by National Milk Records, from Holstein Friesian cows on UK dairy farms in their first 3 lactations. Milk ELISA test results were obtained from 2007 to 2012 and combined with milk recording data and pedigree information. The reduced data set edited for the purposes of genetic parameter estimation consisted of 148,054 milk ELISA records from 64,645 lactations in 40,142 cows of 908 sires, recorded in 641 herds. Milk ELISA test results were loge-transformed and univariate analysis of 3 alternative animal models and equivalent sire models were considered. The most appropriate model included additive genetic and permanent environmental random effects, whereas maternal effects were significant according to likelihood ratio test and Akaike's information criterion but not for Bayesian information criterion. Heritability and repeatability estimates were 0.06 and 0.37, respectively, for the chosen animal model and its equivalent sire model. A subset of the data including herds with greater than 10% positive tests gave a slightly higher heritability of 0.08. Favorable but generally low significant genetic correlations were obtained between antibody response with 305-d milk yield (-0.16), 305-d protein yield (-0.16), loge-transformed lactation-average somatic cell count (0.15), and the number of mastitis episodes (0.22). Thus, selection on the antibody response to paratuberculosis, should not be detrimental to production or udder health traits. Testing cattle for paratuberculosis is important for its use in control programs and although the heritability of antibody response was low, breeding against the disease might be a good prospect as a preventative measure to assist together with other approaches in an overall control strategy.

Genetic parameters of Visual Image Analysis primal cut carcass traits of commercial prime beef slaughter animals.

Visual Image analysis (VIA) of carcass traits provides the opportunity to estimate carcass primal cut yields on large numbers of slaughter animals. This allows carcases to be better differentiated and farmers to be paid based on the primal cut yields. It also creates more accurate genetic selection due to high volumes of data which enables breeders to breed cattle that better meet the abattoir specifications and market requirements. In order to implement genetic evaluations for VIA primal cut yields, genetic parameters must first be estimated and that was the aim of this study. Slaughter records from the UK prime slaughter population for VIA carcass traits was available from two processing plants. After edits, there were 17 765 VIA carcass records for six primal cut traits, carcass weight as well as the EUROP conformation and fat class grades. Heritability estimates after traits were adjusted for age ranged from 0.32 (0.03) for EUROP fat to 0.46 (0.03) for VIA Topside primal cut yield. Adjusting the VIA primal cut yields for carcass weight reduced the heritability estimates, with estimates of primal cut yields ranging from 0.23 (0.03) for Fillet to 0.29 (0.03) for Knuckle. Genetic correlations between VIA primal cut yields adjusted for carcass weight were very strong, ranging from 0.40 (0.06) between Fillet and Striploin to 0.92 (0.02) between Topside and Silverside. EUROP conformation was also positively correlated with the VIA primal cuts with genetic correlation estimates ranging from 0.59 to 0.84, whereas EUROP fat was estimated to have moderate negative correlations with primal cut yields, estimates ranged from -0.11 to -0.46. Based on these genetic parameter estimates, genetic evaluation of VIA primal cut yields can be undertaken to allow the UK beef industry to select carcases that better meet abattoir specification and market requirements.

Genetic evaluation for bovine tuberculosis resistance in dairy cattle.

Genetic evaluations for resistance to bovine tuberculosis (bTB) were calculated based on British national data including individual animal tuberculin skin test results, postmortem examination (presence of bTB lesions and bacteriological culture for Mycobacterium bovis), animal movement and location information, production history, and pedigree records. Holstein cows with identified sires in herds with bTB breakdowns (new herd incidents) occurring between the years 2000 and 2014 were considered. In the first instance, cows with a positive reaction to the skin test and a positive postmortem examination were defined as infected. Values of 0 and 1 were assigned to healthy and infected animal records, respectively. Data were analyzed with mixed models. Linear and logit function heritability estimates were 0.092 and 0.172, respectively. In subsequent analyses, breakdowns were split into 2-mo intervals to better model time of exposure and infection in the contemporary group. Intervals with at least one infected individual were retained and multiple intervals within the same breakdown were included. Healthy animal records were assigned values of 0, and infected records a value of 1 in the interval of infection and values reflecting a diminishing probability of infection in the preceding intervals. Heritability and repeatability estimates were 0.115 and 0.699, respectively. Reliabilities and across time stability of the genetic evaluation were improved with the interval model. Subsequently, 2 more definitions of "infected" were analyzed with the interval model: (1) all positive skin test reactors regardless of postmortem examination, and (2) all positive skin test reactors plus nonreactors with positive postmortem examination. Estimated heritability was 0.085 and 0.089, respectively; corresponding repeatability estimates were 0.701 and 0.697. Genetic evaluation reliabilities and across time stability did not change. Correlations of genetic evaluations for bTB with other traits in the current breeding goal were mostly not different from zero. Correlation with the UK Profitable Lifetime Index was moderate, significant, and favorable. Results demonstrated the feasibility of a national genetic evaluation for bTB resistance. Selection for enhanced resistance will have a positive effect on profitability and no antagonistic effects on current breeding goal traits. Official genetic evaluations are now based on the interval model and the last bTB trait definition.

Incorporating the single-step strategy into a random regression model to enhance genomic prediction of longitudinal traits.

In prediction of genomic values, the single-step method has been demonstrated to outperform multi-step methods. In statistical analyses of longitudinal traits, the random regression test-day model (RR-TDM) has clear advantages over other models. Our goal in this study was to evaluate the performance of a model that integrates both single-step and RR-TDM prediction methods, called the single-step random regression test-day model (SS RR-TDM), in comparison with the pedigree-based RR-TDM and genomic best linear unbiased prediction (GBLUP) model. We performed extensive simulations to exploit the potential advantages of SS RR-TDM over the other two models under various scenarios with different levels of heritability, number of quantitative trait loci, as well as selection scheme. SS RR-TDM was found to achieve the highest accuracy and unbiasedness under all scenarios, exhibiting robust prediction ability in longitudinal trait analyses. Moreover, SS RR-TDM showed better persistency of accuracy over generations than the GBLUP model. In addition, we also found that the SS RR-TDM had advantages over RR-TDM and GBLUP in terms of its being a real data set of humans contributed by the Genetic Analysis Workshop 18. The findings of our study demonstrated the feasibility and advantages of SS RR-TDM, thus enhancing the strategies for genomic prediction of longitudinal traits in the future.

Short communication: Genomic selection in a crossbred cattle population using data from the Dairy Genetics East Africa Project.

Due to the absence of accurate pedigree information, it has not been possible to implement genetic evaluations for crossbred cattle in African small-holder systems. Genomic selection techniques that do not rely on pedigree information could, therefore, be a useful alternative. The objective of this study was to examine the feasibility of using genomic selection techniques in a crossbred cattle population using data from Kenya provided by the Dairy Genetics East Africa Project. Genomic estimated breeding values for milk yield were estimated using 2 prediction methods, GBLUP and BayesC, and accuracies were calculated as the correlation between yield deviations and genomic breeding values included in the estimation process, mimicking the situation for young bulls. The accuracy of evaluation ranged from 0.28 to 0.41, depending on the validation population and prediction method used. No significant differences were found in accuracy between the 2 prediction methods. The results suggest that there is potential for implementing genomic selection for young bulls in crossbred small-holder cattle populations, and targeted genotyping and phenotyping should be pursued to facilitate this.

Genetic parameters of linear conformation type traits and their relationship with milk yield throughout lactation in mixed-breed dairy goats.

Conformation traits are of interest to many dairy goat breeders not only as descriptive traits in their own right, but also because of their influence on production, longevity, and profitability. If these traits are to be considered for inclusion in future dairy goat breeding programs, relationships between them and production traits such as milk yield must be considered. With the increased use of regression models to estimate genetic parameters, an opportunity now exists to investigate correlations between conformation traits and milk yield throughout lactation in more detail. The aims of this study were therefore to (1) estimate genetic parameters for conformation traits in a population of crossbred dairy goats, (2) estimate correlations between all conformation traits, and (3) assess the relationship between conformation traits and milk yield throughout lactation. No information on milk composition was available. Data were collected from goats based on 2 commercial goat farms during August and September in 2013 and 2014. Ten conformation traits, relating to udder, teat, leg, and feet characteristics, were scored on a linear scale (1-9). The overall data set comprised data available for 4,229 goats, all in their first lactation. The population of goats used in the study was created using random crossings between 3 breeds: British Alpine, Saanen, and Toggenburg. In each generation, the best performing animals were selected for breeding, leading to the formation of a synthetic breed. The pedigree file used in the analyses contained sire and dam information for a total of 30,139 individuals. The models fitted relevant fixed and random effects. Heritability estimates for the conformation traits were low to moderate, ranging from 0.02 to 0.38. A range of positive and negative phenotypic and genetic correlations between the traits were observed, with the highest correlations found between udder depth and udder attachment (0.78), teat angle and teat placement (0.70), and back legs and back feet (0.64). The genetic correlations estimated between conformation traits and milk yield across the first lactation demonstrated changes during this period. The majority of correlations estimated between milk yield and the udder and teat traits were negative. Therefore, future breeding programs would benefit from including these traits to ensure that selection for increased productivity is not accompanied by any unwanted change in functional fitness.

Estimation of genomic breeding values for milk yield in UK dairy goats.

The objective of this study was to estimate genomic breeding values for milk yield in crossbred dairy goats. The research was based on data provided by 2 commercial goat farms in the UK comprising 590,409 milk yield records on 14,453 dairy goats kidding between 1987 and 2013. The population was created by crossing 3 breeds: Alpine, Saanen, and Toggenburg. In each generation the best performing animals were selected for breeding, and as a result, a synthetic breed was created. The pedigree file contained 30,139 individuals, of which 2,799 were founders. The data set contained test-day records of milk yield, lactation number, farm, age at kidding, and year and season of kidding. Data on milk composition was unavailable. In total 1,960 animals were genotyped with the Illumina 50K caprine chip. Two methods for estimation of genomic breeding value were compared-BLUP at the single nucleotide polymorphism level (BLUP-SNP) and single-step BLUP. The highest accuracy of 0.61 was obtained with single-step BLUP, and the lowest (0.36) with BLUP-SNP. Linkage disequilibrium (r(2), the squared correlation of the alleles at 2 loci) at 50 kb (distance between 2 SNP) was 0.18. This is the first attempt to implement genomic selection in UK dairy goats. Results indicate that the single-step method provides the highest accuracy for populations with a small number of genotyped individuals, where the number of genotyped males is low and females are predominant in the reference population.

The effects of different farm environments on the performance of Texel sheep.

In order to assess the extent of genotype by environment interactions (G×E) and environmental sensitivity in sheep farm systems, environmental factors must be identified and quantified, after which the relationship with the traits(s) of interest can be investigated. The objectives of this study were to develop a farm environment (FE) scale, using a canonical correlation analysis, which could then be used in linear reaction norm models. Fine-scale farm survey data, collected from a sample of 39 Texel flocks across the United Kingdom, was combined with information available at the national level. The farm survey data included information on flock size and concentrate feed use. National data included flock performance averages for 21-week-old weight (21WT), ultrasound back-fat (UFD) and muscle (UMD) depths, as well as regional climatic data. The FE scale developed was then combined with 181 555 (21WT), 175 399 (UMD) and 175 279 (UFD) records from lambs born between 1990 and 2011, on 494 different Texel flocks, to predict reaction norms for sires used within the population. A range of sire sensitivities estimated across the FE scale confirmed the presence of genetic variability as both 'plastic' and 'robust' genotypes were observed. Variations in heritability estimates were also observed indicating that the rate genetic progress was dependent on the environment. Overall, the techniques and approaches used in this study have proven to be useful in defining sheep FEs. The results observed for 21WT, UMD and UFD, using the reaction norm models, indicate that in order to improve genetic gain and flock efficiency, future genetic evaluations would benefit by accounting for the G×E observed.

Genetic parameters for predicted methane production and laser methane detector measurements.

Enteric ruminant methane is the most important greenhouse gas emitted from the pastoral agricultural systems. Genetic improvement of livestock provides a cumulative and permanent impact on performance, and using high-density SNP panels can increase the speed of improvement for most traits. In this study, a data set of 1,726 dairy cows, collected since 1990, was used to calculate a predicted methane emission (PME) trait from feed and energy intake and requirements based on milk yield, live weight, feed intake, and condition score data. Repeated measurements from laser methane detector (LMD) data were also available from 57 cows. The estimated heritabilities for PME, milk yield, DMI, live weight, condition score, and LMD data were 0.13, 0.25, 0.11, 0.92, 0.38, and 0.05, respectively. There was a high genetic correlation between DMI and PME. No SNP reached the Bonferroni significance threshold for the PME traits. One SNP was within the 3 best SNP for PME at wk 10, 20, 30, and 40. Genomic prediction accuracies between dependent variable and molecular breeding value ranged between 0.26 and 0.30. These results are encouraging; however, more work is required before a PME trait can be implemented in a breeding program.

Characterisation of terminal sire sheep farm systems, based on a range of environmental factors: a case study in the context of genotype by environment interactions using Charollais lambs.

The objective of this study was to define different terminal sire flock environments, based on a range of environmental factors, and then investigate the presence of genotype by environment interactions (G×E) between the environments identified. Data from 79 different terminal sire flocks (40 Texel, 21 Charollais and 18 Suffolk), were analysed using principal coordinate and non-hierarchical cluster analyses, the results of which identified three distinct environmental cluster groups. The type of grazing, climatic conditions and the use of vitamins and mineral supplements were found to be the most important factors in the clustering of flocks. The presence of G×E was then investigated using data from the Charollais flocks only. Performance data were collected for 12 181 lambs, between 1990 and 2010, sired by 515 different sires. Fifty six of the sires had offspring in at least two of the three different cluster groups and pedigree information was available for a total of 161 431 animals. Traits studied were the 21-week old weight (21WT), ultrasound muscle depth (UMD) and log transformed backfat depth (LogUFD). Heritabilities estimated for each cluster, for each trait, ranged from 0.32 to 0.45. Genetic correlations estimated between Cluster 1 and Cluster 2 were all found to be significantly lower than unity, indicating the presence of G×E. They were 0.31 (±0.17), 0.68 (±0.14) and 0.18 (±0.21) for 21WT, UMD and LogUFD, respectively. Evidence of sires re-ranking across clusters was also observed. Providing a suitable strategy can be identified, there is potential for the optimisation of future breeding programmes, by taking into account the G×E observed. This would enable farmers to identify and select animals with an increased knowledge as to how they will perform in their specific farm environment thus reducing any unexpected differences in performance.

Estimation of genetic parameters for milk yield across lactations in mixed-breed dairy goats.

Currently, breeding values for dairy goats in the United Kingdom are not estimated and selection is based only on phenotypes. Several studies from other countries have applied various methodologies to estimate breeding values for milk yield in dairy goats. However, most of the previous analyses were based on relatively small data sets, which might have affected the accuracy of the parameter estimates. The objective of this study was to estimate genetic parameters for milk yield in crossbred dairy goats in lactations 1 to 4. The research was based on data provided by 2 commercial goat farms in the United Kingdom comprising 390,482 milk yield records on 13,591 dairy goats kidding between 1987 and 2012. The population was created by crossing 3 breeds: Alpine, Saanen, and Toggenburg. In each generation, the best-performing animals were selected for breeding and, as a result, a synthetic breed was created. The pedigree file contained 28,184 individuals, of which 2,414 were founders. The data set contained test-day records of milk yield, lactation number, farm, age at kidding, and year and season of kidding. Data on milk composition was unavailable. Covariance components were estimated with the average information REML algorithm in the ASReml package (VSN International Ltd., Hemel Hempstead, UK). A random regression animal model for milk yield with fixed effects of herd test day, year-season, and age at kidding was used. Heritability was the highest at 200 and 250d in milk (DIM), reaching 0.45 in the first lactation and between 0.34 and 0.25 in subsequent lactations. After 300 DIM, the heritability started decreasing to 0.23 and 0.10 at 400 DIM in the first and subsequent lactations, respectively. Genetic correlation between milk yield in the first and subsequent lactations was between 0.16 and 0.88. This study found that milk yields in first and subsequent lactations are highly correlated, both at the genetic and phenotypic level. Estimates of heritability for milk yield were higher than most of the values reported in the literature, although they were in the range reported in this species. This should facilitate genetic improvement for the population studied as part of a broader multi-trait breeding program.