Making a morphological pre-selection of Pura Raza Español horses (PRE) for dressage is a challenging task within its current breeding program. The aim of our research was to design an early genetic selection morphological linear traits index to improve dressage performance, using 26 morphological linear traits and six dressage traits (walk, trot, canter, submission, general impression – partial scores – and total score) as selection criteria. The data set included morphological linear traits of 10 127 PRE (4159 males and 5968 females) collected between 2008 and 2013 (one record per horse) and 19 095 dressage traits of 1545 PRE (1476 males and 69 females; 12.4 records of average) collected between 2004 and 2014. A univariate animal model was applied to predict the breeding values (PBV). A partial least squares regression analysis was used to select the most predictive morphological linear traits PBV on the dressage traits PBV. According to the Wold Criterion, the 13 morphological linear traits (width of head, head–neck junction, upper neck line, neck–body junction, width of chest, angle of shoulder, lateral angle of knee, frontal angle of knee, cannon bone perimeter, length of croup, angle of croup, ischium–stifle distance and lateral hock angle) most closely related to total score PBV, partial scores PBV and gait scores PBV (walk, trot and canter) were selected. A multivariate genetic analysis was performed among the 13 morphological linear traits selected and the six dressage traits to estimate the genetic parameters. After it, the selection index theory was used to compute the expected genetic response using different strategies. The expected genetic response of total score PBV (0.76), partial scores PBV (0.04) and gait scores PBV (0.03) as selection objectives using morphological linear traits PBV as criteria selection were positive, but lower than that obtained using dressage traits PBV (1.80, 0.16 and 0.14 for total score PBV, partial scores PBV and gait scores PBV) or dressage traits PBV and morphological linear traits PBV (2.97, 0.16 and 0.15 for total score PBV, partial scores PBV and gait scores PBV), as selection criteria. This suggests that it is possible to preselect the PRE without dressage traits PBV using as selection criteria the morphological linear traits PBV, but the expected genetic response will be lower.

In 2010, a routine genetic evaluation on occurrence of clinical mastitis in three main dairy cattle breeds – Montbéliarde (MO), Normande (NO) and Holstein (HO) – was implemented in France. Records were clinical mastitis events reported by farmers to milk recording technicians and the analyzed trait was the binary variable describing the occurrence of a mastitis case within the first 150 days of the first three lactations. Genetic parameters of clinical mastitis were estimated for the three breeds. Low heritability estimates were found: between 2% and 4% depending on the breed. Despite its low heritability, the trait exhibits genetic variation so efficient genetic improvement is possible. Genetic correlations with other traits were estimated, showing large correlations (often>0.50, in absolute value) between clinical mastitis and somatic cell score (SCS), longevity and some udder traits. Correlation with milk yield was moderate and unfavorable (ρ=0.26 to 0.30). High milking speed was genetically associated with less mastitis in MO (ρ=−0.14) but with more mastitis in HO (ρ=0.18). A two-step approach was implemented for routine evaluation: first, a univariate evaluation based on a linear animal model with permanent environment effect led to pre-adjusted records (defined as records corrected for all non-genetic effects) and associated weights. These data were then combined with similar pre-adjusted records for others traits in a multiple trait BLUP animal model. The combined breeding values for clinical mastitis obtained are the official (published) ones. Mastitis estimated breeding values (EBV) were then combined with SCSs EBV into an udder health index, which receives a weight of 14.5% to 18.5% in the French total merit index (ISU) of the three breeds. Interbull genetic correlations for mastitis occurrence were very high (ρ=0.94) with Nordic countries, where much stricter recording systems exist reflecting a satisfactory quality of phenotypes as reported by the farmers. They were lower (around 0.80) with countries supplying SCS as a proxy for the international evaluation on clinical mastitis.

We investigated the effect of maternal sire on early pregnancy failure (between D0, day of insemination and D90) in their progeny during the first and second lactations (n=3508) in the Holstein breed. The estimated breeding value (EBV) for cow fertility of 12 bulls (reliability⩾0.95) was used to create the following three groups: low, medium and high EBV (EBV from −0.7 to 1 expressed as genetic standard deviation relative to the mean of the breed). In their daughters (93 to 516 per bull), progesterone measurement was carried out on the day of artificial insemination (AI; D0) to check whether the cows were in the follicular phase and on D18 to 25 to assess non-fertilisation-early embryonic mortality (NF-EEM). Late embryonic mortality (LEM) and early foetal death (FD) were determined by ultrasonography on D45 and D90 and by the return to oestrus after the first AI. Frequencies of NF-EEM, LEM, FD and pregnancy were 33.3%, 11.7%, 1.4% and 48.5% and incidences were 35.1, 19.0, 2.7 and 51.1, respectively. Sire EBV was significantly related to the incidences of pregnancy failure between D0 and D90, fertilisation failure-early embryonic mortality (FF-EEM) and LEM but not to the incidence of FD between D45 and D90 of pregnancy. The relative risk (RR) of FF-EEM was significantly higher (RR=1.2; P<0.05) for the progeny group of low EBV bulls when compared with high EBV bulls. The same effect was observed when comparing LEM of the progeny groups from the low EBV bulls to those from moderate and high EBV bulls (RR, respectively, of 1.3 and 1.4; P<005). The incidence of FF-EEM was significantly higher when cows were inseminated before 80 days postpartum compared with later, and for the extreme values of the difference between milk fat and protein content measured during the first 3 months of lactation. FF-EEM was also significantly related to the year of observation. The incidence of LEM was higher for the highest producing cows and was influenced by interaction between milk yield×lactation rank and milk yield×milk protein content. In conclusion, this study showed large differences in early pregnancy failure between progeny groups and highlights the interest of accurate characterisation of embryonic death in order to identify potential candidate genes for female fertility.

The use of molecular genetic information in the evaluation of livestock has become more common. This study looks at the efficacy of using such information to improve the genetic evaluation of a rare breed of dual-purpose cattle. Data were available in the form of pedigree information on the Gloucester cattle breed in the United Kingdom and recorded milk and beef performance on a small number of animals. In addition, molecular genetic information in the form of multi-marker, multiple regression results converted to a 1 to 10 score (Igenity scores) and 123 single nucleotide polymorphism (SNP) genotypes for 199 non-recorded animals were available. Appropriate mixed-animal models were explored for the recorded traits and these were used to calculate estimated breeding values (EBV), and their accuracies, for 6527 animals in the breed’s pedigree file. Various ways to improve the accuracy of these EBV were explored. This involved using multivariate BLUP analyses, genomic estimated breeding values (GEBV) and combining Igenity scores with recorded traits in a series of bivariate genetic analyses. Using the milk recording traits as an example, the accuracy of a number of traits could be improved using multivariate analyses by up to 14%, depending on the combination of traits used. The level of increase in accuracy largely corresponded to the absolute difference between the genetic and residual correlations between two traits, but this was not always symmetrical. The use of GEBV did not increase the accuracy of milk trait EBV owing to the low proportion of variance explained by the 101 SNPs used. Using Igenity scores in bivariate analyses with the recorded data was more successful in increasing EBV accuracy. The largest increases were found in genotyped animals with no recorded performance (e.g. a 58% increase in fat weight in milk); however, the size of the increase depended on the level of the genetic correlation between the recorded trait and the Igenity score for that trait. Lower levels of improvements in accuracy were seen in animals that were recoded but not genotyped, and ancestors which were neither genotyped nor recorded. This study demonstrated that it was possible to improve the accuracy of EBV estimation by including Igenity score information in genetic analyses but it also concluded that increasing the level of performance recording in the breed would be beneficial.

Several studies have shown that computation of genomic estimated breeding values (GEBV) with accuracies significantly greater than parent average (PA) estimated breeding values (EBVs) requires genotyping of at least several thousand progeny-tested bulls. For all published analyses, GEBV computed from the selected samples of markers have lower or equal accuracy than GEBV derived on the basis of all valid single nucleotide polymorphisms (SNPs). In the current study, we report on four new methods for selection of markers. Milk, fat, protein, somatic cell score, fertility, persistency, herd life and the Israeli selection index were analyzed. The 972 Israeli Holstein bulls genotyped with EBV for milk production traits computed from daughter records in 2012 were assigned into a training set of 844 bulls with progeny test EBV in 2008, and a validation set of 128 young bulls. Numbers of bulls in the two sets varied slightly among the nonproduction traits. In EFF12, SNPs were first selected for each trait based on the effects of each marker on the bulls’ 2012 EBV corrected for effective relationships, as determined by the SNP matrix. EFF08 was the same as EFF12, except that the SNPs were selected on the basis of the 2008 EBV. In DIFmax, the SNPs with the greatest differences in allelic frequency between the bulls in the training and validation sets were selected, whereas in DIFmin the SNPs with the smallest differences were selected. For all methods, the numbers of SNPs retained varied over the range of 300 to 6000. For each trait, except fertility, an optimum number of markers between 800 and 5000 was obtained for EFF12, based on the correlation between the GEBV and current EBV of the validation bulls. For all traits, the difference between the correlation of GEBV and current EBV and the correlation of the PA and current EBV was >0.25. EFF08 was inferior to EFF12, and was generally no better than PA EBV. DIFmax always outperformed DIFmin and generally outperformed EFF08 and PA. Furthermore, GEBV based on DIFmax were generally less biased than PA. It is likely that other methods of SNP selection could improve upon these results.

The objectives of the present study were: (1) to evaluate the importance of genotype×production environment interaction for the genetic evaluation of birth weight (BW) and weaning weight (WW) in a population of composite beef cattle in Brazil, and (2) to investigate the importance of sire×contemporary group interaction (S×CG) to model G×E and improve the accuracy of prediction in routine genetic evaluations of this population. Analyses were performed with one, two (favorable and unfavorable) or three (favorable, intermediate, unfavorable) different definitions of production environments. Thus, BW and WW records of animals in a favorable environment were assigned to either trait 1, in an intermediate environment to trait 2 or in an unfavorable environment to trait 3. The (co)variance components were estimated using Gibbs sampling in single-, bi- or three-trait animal models according to the definition of number of production environments. In general, the estimates of genetic parameters for BW and WW were similar between environments. The additive genetic correlations between production environments were close to unity for BW; however, when examining the highest posterior density intervals, the correlation between favorable and unfavorable environments reached a value of only 0.70, a fact that may lead to changes in the ranking of sires across environments. The posterior mean genetic correlation between direct effects was 0.63 in favorable and unfavorable environments for WW. When S×CG was included in two- or three-trait analyses, all direct genetic correlations were close to unity, suggesting that there was no evidence of a genotype×production environment interaction. Furthermore, the model including S×CG contributed to prevent overestimation of the accuracy of breeding values of sires, provided a lower error of prediction for both direct and maternal breeding values, lower squared bias, residual variance and deviance information criterion than the model omitting S×CG. Thus, the model that included S×CG can therefore be considered the best model on the basis of these criteria. The genotype×production environment interaction should not be neglected in the genetic evaluation of BW and WW in the present population of beef cattle. The inclusion of S×CG in the model is a feasible and plausible alternative to model the effects of G×E in the genetic evaluations.

Eventing competitions in Great Britain (GB) comprise three disciplines, each split into four grades, yielding 12 discipline-grade traits. As there is a demand for tools to estimate (co)variance matrices with a large number of traits, the aim of this work was to investigate different methods to produce large (co)variance matrices using GB eventing data. Data from 1999 to 2008 were used and penalty points were converted to normal scores. A sire model was utilised to estimate fixed effects of gender, age and class, and random effects of sire, horse and rider. Three methods were used to estimate (co)variance matrices. Method 1 used a method based on Gibbs sampling and data augmentation and imputation. Methods 2a and 2b combined sub-matrices from bivariate analyses; one took samples from a multivariate Normal distribution defined by the covariance matrix from each bivariate analysis, then analysed these data in a 12-trait multivariate analysis; the other replaced negative eigenvalues in the matrix with positive values to obtain a positive definite (co)variance matrix. A formal comparison of models could not be conducted; however, estimates from all methods, particularly Methods 2a/2b, were in reasonable agreement. The computational requirements of Method 1 were much less compared with Methods 2a or 2b. Method 2a heritability estimates were as follows: for dressage 7.2% to 9.0%, for show jumping 8.9% to 16.2% and for cross-country 1.3% to 1.4%. Method 1 heritability estimates were higher for the advanced grades, particularly for dressage (17.1%) and show jumping (22.6%). Irrespective of the model, genetic correlations between grades, for dressage and show jumping, were positive, high and significant, ranging from 0.59 to 0.99 for Method 2a and 0.78 to 0.95 for Method 1. For cross-country, using Method 2a, genetic correlations were only significant between novice and pre-novice (0.75); however, using Method 1 estimates were all significant and low to moderate (0.36 to 0.70). Between-discipline correlations were all low and of mixed sign. All methods produced positive definite 12 × 12 (co)variance matrices, suitable for the prediction of breeding values. Method 1 benefits from much reduced computational requirements, and by performing a true multivariate analysis.

In genetic evaluations, the definition of unknown parent groups (UPG) is usually based on time periods, selection path and flows of foreign founders. The definition of UPG may be more complex for populations presenting genetic heterogeneity due to both, large national expansion and coexistence of artificial insemination (AI) and natural service (NS). A UPG definition method accounting for beef bull flows was proposed and applied to the French Charolais cattle population. It assumed that, at a given time period, unknown parents belonged to the same UPG when their progeny were bred in herds that used bulls with similar origins (birth region and reproduction way). Thus, the birth period, region and AI rate of a herd were pointed out to be the three criteria reflecting genetic disparities at the national level in a beef cattle population. To deal with regional genetic disparities, 14 regions were identified using a factorial approach combining principal component analysis and Ward clustering. The selection nucleus of the French cattle population was dispersed over three main breeding areas. Flows of NS bulls were mainly carried out within each breeding area. On the contrary, the use and the selection of AI bulls were based on a national pool of candidates. Within a time period, herds of different regions were clustered together when they used bulls coming from the same origin and with an estimated difference of genetic level lower than 20% of genetic standard deviation (σg) for calf muscle and skeleton scores (SS) at weaning. This led to the definition of 16 UPG of sires, which were validated as robust and relevant in a sire model, meaning numerically stable and corresponding to distinct genetic subpopulations. The UPG genetic levels were estimated for muscle and SS under sire and animal models. Whatever the trait, differences between bull UPG estimates within a time period could reach 0.5 σg across regions. For a given time period, bull UPG estimates for muscle and SS were generally larger by 0.30 to 0.75 σg than those of cows. Including genetic groups in the evaluation model increased the estimated genetic trends by 20% to 30%. It also provoked re-ranking in favor of bulls and cows without pedigree.

A genetic analysis of longitudinal binary clinical mastitis (CM) data recorded on about 90 000 first-lactation Swedish Holstein cows was carried out using linear random regression models (RRM). This method for genetic evaluation of CM has theoretical advantages compared to the method of linear cross-sectional models (CSM), which is currently being used. The aim of this study was to investigate the feasibility and suitability of estimating genetic parameters and predicting breeding values for CM with a linear sire RRM. For validation purposes, the estimates and predictions from the RRM were compared to those from linear sire longitudinal multivariate models (LMVM) and CSM. For each cow, the period from 10 days before to 241 days after calving was divided into four 1-week intervals followed by eight 4-week intervals. Within each interval, presence or absence of CM was scored as ‘1’ or ‘0’. The linear RRM used to explain the trajectory of CM over time included a set of explanatory variables plus a third-order Legendre polynomial function of time for the sire effect. The time-dependent heritabilities and genetic correlations from the chosen RRM corresponded fairly well with estimates obtained from the linear LMVM for the separate intervals. Some discrepancy between the two methods was observed, with the more unstable results being obtained from the linear LMVM. Both methods indicated clearly that CM was not genetically the same trait throughout lactation. The correlations between predicted sire breeding values from the RRM, summarized over different time periods, and from linear CSM were rather high. They were, however, less than unity (0.74 to 0.96), which indicated some re-ranking of sires. Sire curves based on the time-specific breeding values from the RRM illustrated differences in intercept and slope among the best and the worst sires. To conclude, a linear sire RRM seemed to work well for genetic evaluation purposes, but was sensitive for estimation of genetic parameters.

Although alfalfa (Medicago sativa L.) is a leguminous herbage widely used in temperate regions as animal feed, there is not much research in tropical regions to develop cultivars adapted to these environmental conditions. The utilization of adapted cultivars with adequate management practices is important to improve productivity, quality and persistence of cultivated pastures. The objectives of this study were to verify the genetic variability among alfalfa cultivars and to rank them using mixed model methodology. A total of 35 alfalfa cultivars were evaluated in the rainy and dry seasons, from 1996 to 2000, in plots of 2.8 m2 in Sertãozinho, São Paulo, Brazil. The experimental design was a randomized complete block with three replications. Longitudinal data of dry matter yield were analyzed using PROC MIXED of SAS® System. Several covariance structures were tested and the spherical spatial structure was selected. The results show that the genetic variability was statistically significant only for the dry season. Moreover, the interaction among cultivars and harvests variance was highly significant for both seasons. The empirical best linear unbiased predictions of cultivar effects were obtained, allowing for the selection of the superior cultivars MH 15, 5715, SW 8210, Rio, High, 5888, Monarca, Victoria, Florida 77 and Falcon. Crioula, the most common cultivar in Brazil, showed low forage potential in Sertãozinho. Results indicate potential for use of more productive cultivars of alfalfa to produce animal feed in tropical environments.

El presente trabajo presenta un análisis de la variabilidad y el potencial genético de las poblaciones de bovinos criollos utilizados en un Programa Nacional de Fomento, desarrollado por organimos gubernamentales (Ministerio de Agricultura y Desarrollo Rural, Instituto Colombiano Agropecuario y Corpoica). Se hizo el análisis de la información productiva de poblaciones de las razas Romosinuano, Costeño con Cuernos, Sanmartinero y Blanco Orejinegro, mediante el uso de la metodología BLUP, se calcularon los valores genéticos directos para peso al nacimiento, destete, 16 meses y ganancia de peso al destete y se realizó el análisis de la distribución de los animales por sus valores genéticos. Se encontró que en cada una de las razas existe una alta proporción de animales con valores genéticos positivos, siempre superior al 50%, lo cual indica una amplia variabilidad genética y posibilidad de selección de individuos mejoradores, que asegurarán el progreso genético en el programa de fomento que será desarrollado.

Breed additive and non-additive effects, and genetic parameters of lactation milk yield (LYD), 305-day milk yield (305YD), lactation length (LL), milk yield per day of lactation (DM) and lifetime milk yield (LTYD) were estimated in Ethiopian Boran cattle and their crosses with Holstein in central Ethiopia. The data analyzed included 2360 lactation records spread over 15 years. Ethiopian Boran cattle were consistently inferior (P < 0.01) to the Ethiopian Boran–Holstein crosses for the dairy traits studied. When the crosses were compared, LYD, 305YD and DM were higher (P < 0.01) for 75% and 87.5% crosses compared to 50% and 62.5% ones. However, the 50% crosses had higher (P < 0.01) LTYD than the other genetic groups. The individual additive genetic breed differences for milk production traits were all significant (P < 0.01). The estimates, in favor of Holstein, were 2055 ± 192 kg for LYD, 1776 ± 142 kg for 305YD, 108 ± 24 days for LL, 5.9 ± 0.5 kg for DM and 3353 ± 1294 kg for LTYD. Crossbreeding of the Holstein with the Ethiopian Boran resulted in desirable and significant (P < 0.01) individual heterosis for all milk production traits. The heterosis estimates were, 529 ± 98, 427 ± 72 kg, 44 ± 12 days 1.47 ± 0.23 kg and 3337 ± 681 kg, for LYD, 305YD, LL, DM and LTYD, respectively. The maternal heterotic effects were non-significant (P > 0.05) for all traits. Heritabilities of LYD, 305YD, LL, DM and LTYD for Ethiopian Boran were 0.20 ± 0.03, 0.18 ± 0.03, 0.26 ± 0.03, 0.13 ± 0.03 and 0.02 ± 0.04, respectively. The corresponding estimates for crosses were 0.10 ± 0.002, 0.11 ± 0.003, 0.63 ± 0.02, 0.45 ± 1.05 and 0.24 ± 0.11, respectively. Selection within each of the genetic groups and crossbreeding should substantially improve the milk production potential of the Ethiopian Boran breed under such production system.

A large number of environmental factors affect the daily milk production of a cow. Lactation curves included in the French test-day model are modelled as a function of days in milk with semi-parametric curves (splines). The proper modelling of environmental effects in the test-day analysis was investigated using test-day records collected from the first three lactations of French Montbéliarde cows from 1988 to 2005. Four lactation-curve effects describing calving month, length of dry period, age at calving and gestation defined within parity-class were fitted. The shape of lactation curves did not depend on year of calving, which can be modelled as a constant over the whole lactation. To reduce computational requirements and time, data were pre-adjusted in a first step for fixed effects with no year interaction, and then used for genetic evaluation. Correlations for each lactation between 305-day estimates of genetic and permanent environment effects computed using pre-adjustment factors obtained at a 4-year interval were virtually one. The use of a two-step procedure had a very limited impact on the estimates of genetic and permanent environment effects. The minimum correlations with values estimated with a one-step procedure were 0.9984 and 0.9974, respectively. The knowledge of systematic environmental effects affecting the cow daily yield through lactation curves offers interesting perspectives to predict future daily milk production.

Clinical mastitis records on 36 178 first-lactation Norwegian dairy cattle (NRF) cows, daughters of 245 sires from 5286 herds, were analysed to study the impact of trait definition on estimates of genetic parameters and sire evaluations for clinical mastitis. The opportunity interval for infection, going from 30 days pre-calving to 300 days post partum, was divided into either 11 periods (each 30 days long); four periods ((-30, 0), (1, 30), (31, 120), (121, 300)); a single period (-30, 300) or defined as the interval currently used for sire evaluation in Norway (-15,120). Within each period, clinical mastitis was scored as 1 if it occurred at least once and 0 otherwise. Analysis was with Bayesian threshold models, assuming that mastitis (presence v. absence) was a different trait in each period. By use of multivariate or univariate normal link functions, unobserved liabilities to disease were modelled as a linear function of year of calving, age-season of calving, herd, sire of cow and residual effects. Estimates of heritability of liability to clinical mastitis ranged from 0-06 to 0-14, depending on the model and stage of lactation. In multi-period models, estimates of genetic correlations between periods were positive and ranged from 0-13 to 0-55. This suggests that clinical mastitis resistance is not the same trait in different periods of the first lactation, which is not captured by the single-interval models. The single-interval (-30, 300) model gave slightly smaller sire-specific posterior probabilities of clinical mastitis during the first lactation than the multi-period models. Furthermore, the interval used in current Norwegian sire evaluation understated the posterior probabilities of clinical mastitis, relative to the multi-period specifications. This led to some differences in sire rankings between the four models, although there was agreement between the four- and 11-period models. In conclusion, the multi-period models captured more genetic variation than the single-interval models, but the four-period model gave sire rankings that differed little from those obtained with an 11-period definition of clinical mastitis.