This study aimed to investigate the effect of leptin gene polymorphism and some environmental factors on milk production traits. Blood samples from 212 Holstein Friesian dairy cattle reared on a private farm were used. The intron 2 region of the leptin gene was digested with Sau3AI restriction enzyme using the PCR-RFLP method. A and B alleles and AA, AB, and BB genotype frequencies for the Sau3AI polymorphism were determined as 0.8821 and 0.1179, and 0.764, 0.236 and 0.000, respectively. Chi-square analysis revealed that the leptin gene polymorphism followed the Hardy–Weinberg equilibrium, including the absence of animals with the BB genotype. The effect of leptin gene polymorphism on all milk production traits was insignificant. For milk production traits, direct heritability (ha2) varied between 0.03 ± 0.283 (for the dry period) and 0.50 ± 0.183 (for milk conductivity). Regarding the milking time (MT), the estimated breeding values (EBVs) of cattle with the AA genotype were higher than the AB genotype (P < 0.05). As a result of this study, in the selection program, allele or genotype could not be suggested as a marker for milk yield characteristics except for the possible exception of milking time and its relationship to mastitis incidence.

Our objective was to evaluate the genetic merit of Holstein cattle population in southern Brazil in response to variations in the regional temperature by analyzing the genotype by environment interaction using reaction norms. Fat yield (FY) and protein yield (PY) data of 67 360 primiparous cows were obtained from the database of the Paraná Holstein Breeders Association, Brazil (APCBRH). The regional average annual temperature was used as the environmental variable. A random regression model was adopted applying mixed models with Restricted Maximum Likelihood (REML) algorithm using WOMBAT software. The genetic merit of the 15 most representative bulls, depending on the temperature gradient, was evaluated. Heritability ranged from 0.21 to 0.27 for FY and from 0.14 to 0.20 for PY. The genetic correlation observed among the environmental gradients proved to be higher than 0.80 for both traits. Slight reranking of bulls for both traits was detected, demonstrating that non-relevant genotype by environment interaction for FY and PY were observed. Consequently, no inclusion of the temperature effect in the model of genetic evaluation in southern Brazilian Holstein breed is required.

Ranking trait was used as a selection criterion for competition horses to estimate racing performance. In the literature the most common approaches to estimate breeding values are the linear or threshold statistical models. However, recent studies have shown that a Thurstonian approach was able to fix the race effect (competitive level of the horses that participate in the same race), thus suggesting a better prediction accuracy of breeding values for ranking trait. The aim of this study was to compare the predictability of linear, threshold and Thurstonian approaches for genetic evaluation of ranking in endurance horses. For this purpose, eight genetic models were used for each approach with different combinations of random effects: rider, rider–horse interaction and environmental permanent effect. All genetic models included gender, age and race as systematic effects. The database that was used contained 4065 ranking records from 966 horses and that for the pedigree contained 8733 animals (47% Arabian horses), with an estimated heritability around 0.10 for the ranking trait. The prediction ability of the models for racing performance was evaluated using a cross-validation approach. The average correlation between real and predicted performances across genetic models was around 0.25 for threshold, 0.58 for linear and 0.60 for Thurstonian approaches. Although no significant differences were found between models within approaches, the best genetic model included: the rider and rider–horse random effects for threshold, only rider and environmental permanent effects for linear approach and all random effects for Thurstonian approach. The absolute correlations of predicted breeding values among models were higher between threshold and Thurstonian: 0.90, 0.91 and 0.88 for all animals, top 20% and top 5% best animals. For rank correlations these figures were 0.85, 0.84 and 0.86. The lower values were those between linear and threshold approaches (0.65, 0.62 and 0.51). In conclusion, the Thurstonian approach is recommended for the routine genetic evaluations for ranking in endurance horses.

Intramuscular fat percentage (IMF%) has been shown to have a positive influence on the eating quality of red meat. Selection of Australian lambs for increased lean tissue and reduced carcass fatness using Australian Sheep Breeding Values has been shown to decrease IMF% of the Muscularis longissimus lumborum. The impact this selection has on the IMF% of other muscle depots is unknown. This study examined IMF% in five different muscles from 400 lambs (M. longissimus lumborum, Muscularis semimembranosus, Muscularis semitendinosus, Muscularis supraspinatus, Muscularis infraspinatus). The sires of these lambs had a broad range in carcass breeding values for post-weaning weight, eye muscle depth and fat depth over the 12th rib (c-site fat depth). Results showed IMF% to be highest in the M. supraspinatus (4.87±0.1, P<0.01) and lowest in the M. semimembranosus (3.58±0.1, P<0.01). Hot carcass weight was positively associated with IMF% of all muscles. Selection for decreasing c-site fat depth reduced IMF% in the M. longissimus lumborum, M. semimembranosus and M. semitendinosus. Higher breeding values for post-weaning weight and eye muscle depth increased and decreased IMF%, respectively, but only in the lambs born as multiples and raised as singles. For each per cent increase in lean meat yield percentage (LMY%), there was a reduction in IMF% of 0.16 in all five muscles examined. Given the drive within the lamb industry to improve LMY%, our results indicate the importance of continued monitoring of IMF% throughout the different carcass regions, given its importance for eating quality.

Genetic evaluation based on information from phenotypes, pedigree and markers can be implemented using a recently developed single-step method. In this paper we compare accuracies of predicted breeding values for daily gain and feed conversion ratio (FCR) in Danish Duroc pigs obtained from different versions of single-step methods, the traditional pedigree-based method and the genomic BLUP (GBLUP) method. In particular, we present a single-step method with an adjustment of the genomic relationship matrix so that it is compatible to the pedigree-based relationship matrix. Comparisons are made for both genotyped and non-genotyped animals and univariate and bivariate models. The results show that the three methods with marker information (two single-step methods and GBLUP) produce more accurate predictions of genotyped animals than the pedigree-based method. In addition, single-step methods provide more accurate predictions for non-genotyped animals. The results also show that the single-step method with adjusted genomic relationship matrix produce more accurate predictions than the original single-step method. Finally, the results for the bivariate analyses show a somewhat improved accuracy and reduced inflation of predictions for FCR for the two single-step methods compared with the univariate analyses. The conclusions are: first, the methods with marker information improve prediction compared with the pedigree-based method; second, a single-step method, contrary to GBLUP, provides improved predictions for all animals compared to the pedigree-based method; and third, a single-step method should be used with an adjustment of the genomic relationship matrix.

Designing breeding schemes suitable for smallholder livestock production systems in developing regions has hitherto been a challenge. The suggested schemes either do not address farmers’ breeding goals (centralized station-based nucleus schemes) or yield slow genetic progress (village-based schemes). A new breeding scheme that integrates the merits of previously suggested schemes has been designed for Menz sheep improvement in Ethiopia. It involves selection based on breeding values in nucleus flocks to produce elite rams, a one-time only provision of improved rams to villagers and a follow-up village-based selection to sustain genetic progress in village flocks. Here, we assessed whether conventional selection of breeding rams based on breeding values for production traits, which is the practice in station-based nucleus flocks, meets farmers’ breeding objectives. We also elicited determinants of farmers’ ram choice. Low but significant correlations were found between rankings of rams based on farmers’ selection criteria, estimated breeding values (EBV) and body weight (BW). Appearance traits (such as color and horn) and meat production traits (BW and linear size traits) significantly determined farmers’ breeding ram choice. The results imply that conventional selection criteria based solely on EBV for production traits do not address farmers’ trait preferences fully, but only partially. Thus, a two-stage selection procedure involving selection on breeding values in nucleus centers followed by farmers’ selection among top- ranking candidate rams is recommended. This approach accommodates farmers’ preferences and speeds up genetic progress in village-based selection. The Menz sheep scheme could be applied elsewhere with similar situations to transform conventional station-based nucleus breeding activities into participatory breeding programs.

For Swedish Warmblood sport horses, breeding values (BVs) are predicted using a multiple-trait animal model with results from competitions and young horse performance tests. Data go back to the beginning of the 1970s, and earlier studies have indicated that some of the recorded traits have changed through the years. The objective of this study was to investigate the effects of including all performance data or excluding the older ones compared to a bivariate model (BM) considering performance traits in early and late periods as separate traits. The bivariate approach was assumed to give the most correct BVs for the actual breeding population. Competition results in dressage and show jumping for almost 40 000 horses until 2006 were available. For riding horse quality test (RHQT), data of 14 000 horses judged between 1973 and 2007 were used. Genetic correlations of 0.69 to 1.00 were estimated between traits recorded at different time periods (RHQT data) or different birth year groups (competition data). A cross-validation study and comparison of BVs using different sets of data showed that most accurate and similar results were obtained when BVs were predicted from either the BM or the univariate model including all data from the beginning of the recording. We recommend using all data and applying the univariate model to minimise the computational efforts for genetic evaluations and for provision of reliable BVs for as many horses as possible.

Survival data were simulated under the Weibull model in a half-sib family design, and about 50% of the records were censored. The data were analysed using the proportional hazard model (PHM) and, after transformation to survival scores, using a linear and a binary (logit) model (LIN and BIN, respectively), where the survival scores are indicators of survival during time period t given survival up to period t – 1. Correlations between estimated and true breeding values of sires (accuracies of selection) were very similar for all three models (differences were smaller than 0·3%). Daughter effects were however less accurately predicted by the LIN model, i.e.taking proper account of the distribution of the survival data yields more accurate predictions of daughter effects. The estimated variance components and regressions of true on estimated breeding values were difficult to compare for the LIN models, because estimated breeding values were expressed as additive effects on survival scores while the simulated true breeding values were expressed on the underlying scale. Also the differences in accuracy of selection between sire and animal model breeding value estimates were small, probably due to the half-sib family design of the data. To estimate breeding values for functional survival, i.e. the component of survival that is genetically independent of production (here milk yield), two methods were compared: (i) breeding values were predicted by a single-trait linear model with a phenotypic regression on milk yield; and (ii) breeding values were predicted by a two-trait linear model for survival and milk yield, and breeding values for survival corrected for milk yield were obtained by a genetic regression on the milk yield breeding value estimates. Both methods yielded very similar accuracies of selection for functional survival, and are expected to be equivalent.

Data comprised information on herd life from two different sources: actual lifespan and type data. The optimum way of predicting breeding values (BVs) for herd life from both these sources is a multivariate best linear unbiased predictor (BLUP) analysis of lifespan and the linear type traits most closely related to it. To reduce computing requirements, we suggest a bivariate BLUP analysis, where the direct information is lifespan, measured in lactations and the indirect information is a phenotypic index of type traits weighted by their economic values. Such an index is shown to be almost as efficient as using the individual traits.

Genetic correlations between functional lifespan and the linear type traits were estimated, and based on these foot angle (rg = 0·22) udder depth (rg = 0·24) and teat length (rg = -0·44) were chosen to predict herd life. Breeding values for herd life were predicted in a BLUP analysis for around 500 000 heifers. The traits were lifespan and the phenotype index offoot angle, udder depth and teat length. For bulls with a minimum of 10 daughters BVs ranged from -0·9 lactations to +1·0 lactations, indicating a difference of approximately one lactation between the daughters of extreme bulls. For sires with at least 50 daughter records for lifespan, type information contributed little to the accuracy of herd life BVs.

The main objective of this review is to present the advantages and disadvantages of applying the best linear unbiased prediction (BLUP) procedure with an animal model to the evaluation of laying fowl within a breeding programme. Following an outline of the traditional selection index-based procedure there are descriptions of mixed model methodology including modelling and data transformation, genetic parameter estimation and breeding value prediction. Some computer programs for carrying out BLUP animal model algorithms are described and the results of empirical and simulation comparisons involving different livestock species are given. New possibilities arising from the application of the animal model to breeding strategy are discussed. Poultry breeding evaluations using the traditional selection index do not always result in the same conclusions as arise when using BLUP. However it seems likely that, as has happened with other livestock species, the BLUP animal model method will be increasingly applied in breeding programmes to improve egg production.

A selection index for overall economic merit and five subindexes for maternal and lamb traits were constructed. For estimation of breeding values, two computer programs were developed. A multiple-trait animal model was programmed for the ewe traits including number of lambs born ofl-, 2- and 3-years-old ewes and ewe fleece weight of yearlings. In order to reduce the memory requirement, the lamb data (growth rate, carcass weight, carcass grade, fat% and lamb fleece iveight) were analysed by a reduced multiple-trait animal model including genetic maternal effect. Experiences with the computer programs in genetic evaluation of approximately 619 000 animals are presented. It is concluded that the models and computing strategy may be used for genetic evaluation in the national sheep breeding programme in Norway.

Relative economic values for all traits were derived from a profit equation on a 3-year-old ewe basis. The sensitivity towards changed economic values urns found to be low. Further, the genetic change in the ewe traits of the Norwegian breeds has been estimated, and a significant improvement was found, which was two to four times higher in the ram circles than outside. For breeding rams, the genetic change for number of lambs born was estimated to be approximately 0·01 lambs per ewe and year.

Formulas were derived to predict asymptotic response to selection on best linear unbiased predictors (BLUP) of breeding values that account for selection-induced gametic phase disequilibrium, also known as the ‘Bulmer’ effect. Breeding programmes in populations of infinite size and with discrete generations were considered. For two-path breeding programmes with equal male and female accuracy of selection, relative reductions in response due to gametic phase disequilibrium were independent of heritability. Reductions in response depended only on intensities of selection and ranged from 0·22 to 0·27 when selected proportions were less than 04 for both males and females. With unequal accuracy of males and females, relative reductions in response depended on the ratio of accuracies, in addition to selection intensities. For given selection intensities, reductions were up to proportionately 0·08 larger with unequal accuracies than reductions obtained for equal accuracies. Relative reductions in response for breeding programmes with four paths of selection depended on intensity and accuracy of selection in each path, but were within a range similar to that observed for two-path programmes. Gametic phase disequilibrium will, therefore, not greatly affect ranking and relative differences among alternative breeding programmes.

Gametic phase disequilibrium had a larger effect on response with selection on BLUP than on phenotype, which is largely due to larger relative reductions in accuracy of selection with BLUP. Despite larger relative reductions, asymptotic response to selection on BLUP is expected to be larger than selection on phenotype.