Summary List

2015 Advances in Animal Biosciences  
Implications Carcase trait EBVs produced from national abattoir data, for traits of direct economic importance to commercial breeders, will enable the beef industry to increase the rates of genetic improvement for carcase traits. Furthermore it will help to link the different sectors of the beef industry as they will all be using the same trait definitions from breeder to finisher to abattoir. Introduction Farmers are paid for carcases using the EUROP system; however, pedigree animals are
more » ... ed based on ultrasound muscle and fat depth. This reduces genetic progress as the traits are different reducing the efficiency of selection. In addition, the lack of clear signals between pedigree and commercial farmers often make it difficult for farmers to select the most appropriate sires. The aim of this study was to produce EBVs for the abattoir carcase traits providing tools to enable the industry to make genetic improvement for carcase traits of importance. Material and methods Using the software MiX99, EBVs were produced for carcase weight and visually assessed EUROP fat and conformation class (converted to a numeric scale where higher values for both traits indicate more muscular and fatter carcases) using statistical models and genetic parameters previously developed (unpublished results; Pritchard et al, 2013). The animal models were adjusted for age and genetic parameters were moderately heritable with moderate positive genetic correlations between weight and conformation and moderate negative genetic correlations between fat and the other traits. Nearly 4 million abattoir records were available and matched to the BCMS database to obtain further information about the fixed effects and movement information. These data were reduced to just over half a million records from nearly 40,000 sires from 31 different breeds after data edits. The data edits applied removed records of non-prime slaughter animals (this accounted for the greatest loss of records), were incomplete (i.e. recent records not yet included in the BCMS data snapshot) or in error. A three generation pedigree was built and EBVs produced for approximately 1.4 million animals. Results The first ever UK EBVs for abattoir carcase traits are shown in Table 1 . As these EBVs have not been re-based the average EBVs are near 0, with some differences due to differences in breed. Within different breed subsets, the EBVs were shown to be normally distributed (results not shown) with a trend for those animals with the higher EBVs also being those with higher phenotypic values for the traits of interest. Figure 1 shows the average EBVs for high accuracy sires for individual breeds within breed types. It can be seen that for carcase weight, the continental breeds had the higher EBVs and the dairy breeds the lowest. 1 numerical score from 3 to 45 where higher values indicate carcases with more muscle and/or more fat Figure 1 Average carcase weight EBVs of high accuracy sires within breed and breed type Conclusion These EBVs are the first of their kind in the UK and can be used to assist both pedigree and commercial farmers to produce animals that better meet market specification contributing to a more profitable and efficient beef industry. Implications VIA provides an objective way to measure carcass traits. Genetic evaluation of VIA carcass traits will bring better opportunities for genetic selection, providing many benefits for the beef industry including processors and retailers, breeders and farmers. Introduction The UK pedigree beef cattle sector's breeding program for carcass traits is based on Estimated Breeding Values (EBV) for live weight and ultrasound scans (proxies for conformation and fat) from a limited number of performance recorded live animals (Moore et al, 2014). The carcass trait genetic evaluations can be improved by utilising imaging technologies such as Video Image Analysis (VIA) to routinely collect data on the slaughter population. VIA machines can be placed into the abattoir's processing chain, allowing for carcass traits to be routinely collected with a relatively high throughput, and the information provided can be used as phenotypes for the genetic evaluations (Pabiou et al, 2011). The aim of this study was to estimate genetic parameters of routinely collected VIA data to facilitate its use in genetic evaluations. Material and methods 111,394 records were collected from two abattoirs for animals slaughtered between 2012 and 2014. These records included all animals slaughtered during this time, including older cull cows. After edits, 17,765 records were kept from one abattoir as the second abattoir had only recently installed the machinery. The carcass traits considered were: carcass weight, conformation and fat (converted to a numeric scale (3-45) with higher values indicating carcasses of high conformation and/or fat), and seven VIA primal cut yields: Topside, Silverside, Knuckle, Rump, Striploin, Fillet and Flank. All animals were progeny of purebred sires. The most common breeds represented were Charolais (24.9%), Limousin (18.3%), Simmental (18.7%), Aberdeen Angus (17.7%), and Holstein Friesian (10.7%). Genetic parameters were estimated using restricted maximum likelihood (REML) as implemented in ASReml (Gilmour et al. 2006) using an animal model that included an adjustment for age, sex and birth-herd-season. Also heterosis and recombination effects were included in the model. Results Heritability and phenotypic variance estimates for each trait are presented in Table 1 . Moderate to strong heritabilities were estimated for Conformation, Carcass Weight, Striploin and Fillet ranging from 0.41 to 0.44. High phenotypic variance estimate was obtained for Carcass Weight due to a wide range of beef and dairy breeds present in this population. A moderate heritability was estimated for Fat (0.31). While still heritable, when the traits were expressed as a percentage of net weight (and thus now also adjusted for carcass weight) the heritabilities were lower: 0.28 for Pc Striploin, 0.22 for Pc Fillet and 0.23 for Retail yield (the sum of 7 VIA yields expressed as percentage of carcass weight). Not only was the heritability lower, but the phenotypic variation for the traits expressed as percentages were also lower. Conclusion The results of this study showed that there was moderate to large heritability and genetic variability for the VIA carcass traits for this British commercial population. Therefore, using VIA carcass traits, breeding values can be estimated using real carcass data rather than proxy measurements. This evidence suggests that VIA carcass traits are suitable for genetic evaluation allowing for increased genetic gain for carcass traits more closely related to value received by farmers. Acknowledgements The authors acknowledge their project partners ABP and BLCS, Rural payments Agency's British Cattle Movement Service (BCMS) for supplying data. Funding was received from Innovate UK and BBSRC. Implications These estimates of genetic parameters are a first step before their inclusion into comprehensive selection indices for dairy goats. Genetic antagonisms exist between a number of conformation traits and milk yield, indicating that it is essential for conformation scoring to be included in future dairy goat breeding programmes. Introduction Improving the functional fitness of dairy goats, such as animal mobility and structural correctness, is important for the improvement of animal health if genetic selection for increased productivity is actively pursued. The aim of this work is to quantify genetic and phenotypic properties of udder, teat, legs and feet scores with milk yield in UK dairy goats. Material and methods The data set comprised 2,429 conformation records (udder, legs, and feet) and 126,262 milk yield records on 2,429 first lactation mixed breed and age dairy goats, scored in 2013. The pedigree file contained 30,139 individuals. In total 10 conformation traits were scored on a linear scale (1-9) by one scorer. The scoring system was similar to that developed by the French dairy goat breeders' association CAPGENES and used by Manfredi et al. (2001) and Rupp et al. (2011). Covariance components were estimated with the AI-REML algorithm in the DMU package (Madsen and Jensen 2008) with a random regression model for milk yield (Mucha et al. 2014) and the following model for the conformation traits: y = Xb + Za + e where: y -vector of observations for the analysed conformation score; b -vector of fixed effects: farm, lactation stage, and birth year; a -vector of random additive animal effects; e -vector of random residuals. X and Z -incidence matrices. Results Heritability estimates for the conformation traits, ranged from 0.02 to 0.45. The genetic and phenotypic correlations estimated between all conformation traits along with trait explanation are shown in Table 1 . The highest genetic correlation was between UF and TS (0.96) and the lowest between FF and BF (0.004). The standard errors associated with the genetic correlations were relatively high, being between 0.02 and 0.79. The phenotypic correlations ranged from 0 to 0.39. Genetic correlations estimated between milk yield and udder and teat conformation traits were negative, ranging from -0.60 to -0.20, and from -0.50 to -0.20, respectively. Genetic correlations with feet and leg conformation were between -0.30 and 0.30. Implications The results can contribute towards defining a breeding strategy for enhancing (sub)clinical hypocalcaemia resistance in dairy cows. Introduction Hypocalcaemia is the most important macromineral disorder that affects transition dairy cows and is related to many early-lactation health disorders (Goff, 2008). Prevention has been attempted mainly with acidifiers for transition rations and calcium drenches after calving. So far, genetic studies have focused on the genetic parameters of clinical hypocalcaemia. Our objective was to estimate genetic parameters of Ca and other related macromineral (P, Mg, K) serum concentration in Holstein dairy cows and to investigate the correlation between serum Ca concentrations/changes and disease incidence after parturition. Materials and methods The study included 1,021 Holstein dairy cows (1-4+ lactations) in 9 herds in Northern Greece. Clinical examination and blood sampling was carried out at 1 st , 2 nd , 4 th and 8 th DIM. Milk fever (MF), mastitis, metritis, retained foetal membranes (RFM) and displaced abomasum (LDA, RDA) were recorded during the same time. Ca and Mg serum concentration was determined with atomic absorption spectrophotometry (AAS Perkin Elmer A 100), while for P and K with a biochemical (Vitalab Flexor E) and electrolyte analyser (Roche 9180). Total number of repeated records summed to 4,060. Subclinical hypocalcaemia/hypophosphatemia was defined as Ca/P serum concentrations below 8.3 and 4.2 mg/dL, respectively. Each trait (Ca, P, Mg, K serum concentration and respective changes from DIM 1 to 8, and postpartum health disorders) was analysed with a univariate random regression model, including the fixed effects of herd, year-season of calving, parity, age at calving and DIM, and the random regressions on DIM from calving associated with the animal additive genetic effect. All pedigree available was included in the analysis bringing the total number of animals to 4,262. Estimates of (co)variance components from this model were used to calculate heritabilities for each trait. Correlations between Ca, P, Mg and K serum concentration/changes and health disorders were estimated with bivariate analysis using the same model. The ASREML software was used for all statistical analyses (Gilmour et al., 2006). Results Mean serum Ca, P, Mg and K concentration (±s.e.m.) was 8.92±0.018 mg/dL, 5.21±0.020 mg/dL, 2.24±0.006 mg/dL, and 4.58±0.009 mmol/L, respectively. Daily heritability estimates for serum concentration ranged from 0.23 to 0.32 (Ca), 0.30 to 0.43 (P), 0.20 to 0.39 (Mg) and 0.15 to 0.23 (K) and were all statistically significant (P<0.05). Daily heritability estimates for subclinical hypocalcaemia and hypophosphatemia were 0.13 -0.25 and 0.18 -0.33, respectively. Regarding concentration changes, only Mg change between DIM 1 and 8 had a significant (P<0.05) heritability of 0.18. Postpartum health disorders that had significant daily heritabilities (P<0.05) were mastitis (0.15 -0.41), LDA (0.19 -0.31) and MF (0.07 -0.11). No significant genetic correlations between serum Ca, P, Mg and K concentration or changes and health traits were found; significant (P<0.05) phenotypic correlations between serum Ca, P, Mg and K concentrations/changes and health traits are shown in Table 1 . Conclusion Serum concentrations of Ca, P, Mg and K, at the first critical days after parturition, are heritable. Selection against (sub) clinical hypocalcaemia may be achieved using these biochemical traits. All but three phenotypic correlations are minor (except Ca/MF) and negative (desirable), however, between P serum concentration and mastitis and Change Ca 1-4/P 1-4 and MF -although small-are positive (undesirable). Preventive measures (management/nutrition) establishing normal serum macromineral concentrations must apply during the close up period in order to avoid major health problems. Implications Calculating the genetic parameters and heritability of methane production in dairy cows will help determine the potential to select for less methane production as part of broader breeding goals. Introduction Ruminants release enteric methane primarily via eructation as a by-product of digestion and may account for up to 6% of gross energy intake (de Haas et al., 2011). Such inefficiency in feed utilisation and the role of methane as a greenhouse gas means there is a focus on mitigation of methane emission from ruminants. Understanding the genetics of methane production is hindered by the difficulty of collecting data on sufficient numbers of animals. Results from the Laser Methane Detector (LMD) have shown strong correlation with results from the respiratory chambers (r = 0.80; Chagunda and Yan, 2011) whilst allowing methane production to be recorded without disturbing cow activity. Improving the amount of data on methane emissions from dairy cattle could allow selection for lower emitters and more efficient feeders via genetic improvement (Wall et al., 2010). This study examines the feasibility of using data on individual animal methane production sampled using a LMD to estimate genetic parameters for the methane emissions (ppm-m). Material and methods The Holstein-Friesians cows in this study were from the Langhill selection experiment housed at SRUC Dairy Research & Innovation Centre, subject to a long term experiment of 2 diets X selection lines. The LMD was used to take 2 recordings every second for approx. 5 minutes after midday milking. In this study, an average of 500 records was taken for each of 207 Holstein Friesian cows on approx. each of 3 recording periods from 2010, 2011 and 2014. Average methane measurements were calculated for 780 animal-testdates once values lower than 1 standard deviation from the mean were removed and within 450 days in milk. Average methane measures were log transformed and modelled in a univariate model which included week in milk with a 2nd order polynomial, season of calving, feed group, genetic group and the interaction between feed and genetic group as fixed effects and random effect of animal using a pedigree. A bivariate model was also constructed on the same dataset to estimate the correlation between methane and the concurrent daily dry matter intake (DMI, data for 571/780 methane testdates). Analyses used ASReml 3.0 (Gilmour et al., 2009). Results Despite the small size of the dataset, univariate modelling estimated the heritability of enteric methane in this study at 0.041 (±0.028, P=0.13) and diet type was consistently a significant variable in explaining this. The bivariate model between enteric methane and DMI intake showed a strong positive correlation (±0.48, P =0.16) and a significant phenotypic correlation (0.098) ( Table 1 ). The heritabilities reduced slightly in the bivariate analysis (methane=0.038 and DMI=0.22).
doi:10.1017/s2040470015000035 fatcat:wn5aexrsbngznagosn2zvxttjm