Farm-animal species play crucial roles in satisfying demands for meat on a global scale, and they are genetically being developed to enhance the efficiency of meat production. In particular, one of the important breeders’ aims is to increase skeletal muscle growth in farm animals. The enhancement of muscle development and growth is crucial to meet consumers’ demands regarding meat quality. Fetal skeletal muscle development involves myogenesis (with myoblast proliferation, differentiation, and fusion), fibrogenesis, and adipogenesis. Typically, myogenesis is regulated by a convoluted network of intrinsic and extrinsic factors monitored by myogenic regulatory factor genes in two or three phases, as well as genes that code for kinases. Marker-assisted selection relies on candidate genes related positively or negatively to muscle development and can be a strong supplement to classical selection strategies in farm animals. This comprehensive review covers important (candidate) genes that regulate muscle development and growth in farm animals (cattle, sheep, chicken, and pig). The identification of these genes is an important step toward the goal of increasing meat yields and improves meat quality.

(Co)variance components for milk, fat, and protein yield of 8075 first-parity Danish Holsteins (DH) were estimated in random regression models by REML. For all analyses, the fixed part of the model was held constant, whereas four different functions were applied to model the additive genetic effect and the permanent environment effect. Homogeneous residual variance was assumed throughout lactation. Univariate models were compared using a minimum of -2 ln(restricted likelihood) as the criterion for best fit. Heritabilities as a function of time were calculated from the estimated curve parameters from univariate analyses. Independent of the function applied and the trait in question, heritabilities were lowest in the beginning of the lactation. Heritabilities for persistency of fat yield were slightly higher than heritabilities for persistency of milk and protein yield. Genetic correlations between persistency and 305-d production were higher for protein and milk yield than for fat yield. Bivariate analyses between the production traits were carried out in sire models using the models with the best 3-parameter curve fit in the univariate analyses. Correlations between traits were calculated from covariance components for curve parameters estimated in bivariate analyses. Genetic correlations between milk and protein yield were higher than between milk and fat yield.

Genotype by environment interaction for production and fertility was studied by use of a reaction norm model. Milk recording data, comprising 927 929 records, were analysed to predict reaction norms for young bulls of the Nordic Red dairy breeds. Random regressions were estimated for each bull, regressing phenotypic values of daughters on herd environment. The phenotypic measures were 305 days kg protein production and days open in first lactation. The herd environment was defined as the herd-year average of protein production and days open, respectively. Heritability of protein production and days open and genetic correlation between the two traits were estimated as functions of the herd environment. The results showed that the genetic parameters change over environments, which are measured on a continuous scale across countries. Grouping of observations is avoided and thereby the problem of genetic connectedness between groups or countries may be avoided. Although significant genetic variation for the slope of the reaction norm was found, there was little reranking of sires, except between extreme environments. More appropriate models and methods need to be developed for further studies of genetic variation in reaction norms.

The aim of this study was to characterize patterns of energy balance through lactation of cows kept under constant feeding conditions. Danish Holstein, Danish Red, and Jersey cows were studied during consecutive lactations and remained on the same dietary treatment throughout. They were fed a normal (13.55 MJ of digestible energy/kg of dry matter) or a lower energy diet (12.88 MJ of digestible energy/kg of dry matter) ad libitum throughout lactation. Energy balance was calculated using the effective energy (EE) system in such a way that energy balance equated to body energy reserve change. In the EE system the energy values assigned to feeds are directly equivalent to the energy requirements of the animal; 1 MJ of EE supply has the same energy value as 1 MJ of lipid loss from the body. The resulting body energy change data were analyzed using a linear spline model. There was no evidence to suggest that different combinations of breed and parity required different knot placements. The Holstein mobilized significantly more body energy in early lactation than the Danish Red and Jersey breeds. Parity 1 cows mobilized significantly less than parity 2 and 3 cows. There was a significant interaction between breed and parity in the first half of lactation due to parity 1 Jersey cows having a greater mobilization than would be expected of the difference between parities in the other breeds. As lactation progressed, the differences between parities and between breeds decreased. Cows on the higher energy diet had a more positive energy balance. Within breed and parity, the following possible predictors of individual differences in body energy change were examined: fatness-corrected live weight, condition score at calving, and genotype. There was no difference in the predicted cow effect or residual energy balance profile when grouped according to quartiles of corrected live weight or according to condition score at calving. During the period of most negative energy balance (d 14) there was no significant relationship between live weight and intake, suggesting that, within diet type, the systematic patterns of body energy change through lactation in cows that were kept under stable and sufficient nutritional conditions cannot be accounted for by environmental factors such as constrained intake or condition score at calving. Thus, these patterns appear to have a genetic basis. The proportion of the phenotypic variation (remaining after accounting for fixed effects) accounted for by additive genetic effects varied through lactation from 4.2 to 13.0%. Genetic correlations between early and late lactation energy balances were low and close to zero, suggesting that body energy changes in early and late lactation are genetically independent traits.

Residual feed intake (RFI) is commonly used as a measure of feed efficiency at a given level of production. A total of 16,872 pigs with their pedigree traced back as far as possible was used to estimate genetic parameters for RFI, growth performance, food conversion ratio (FCR), body conformation, and feeding behavior traits in 3 Danish breeds [Duroc (DD), Landrace (LL), and Yorkshire (YY)]. Two measures of RFI were considered: residual feed intake 1 (RFI1) was calculated based on regression of daily feed intake (DFI) from 30 to 100 kg on initial test weight and ADG from 30 to 100 kg (ADG2). Residual feed intake 2 (RFI2) was as RFI1, except it was also regressed with respect to backfat (BF). The estimated heritabilities for RFI1 and RFI2 were 0.34 and 0.38 in DD, 0.34 and 0.36 in LL, and 0.39 and 0.40 in YY, respectively. The heritabilities ranged from 0.32 (DD) to 0.54 (LL) for ADG2, from 0.54 (DD) to 0.67 (LL) for BF, and from 0.13 (DD) to 0.19 (YY) for body conformation. Feeding behavior traits including DFI, number of visits to feeder per day (NVD), total time spent eating per day (TPD), feed intake rate (FR), feed intake per visit (FPV), and time spent eating per visit (TPV) were moderately to highly heritable. Residual feed intake 2 was genetically independent of ADG2 and BF in all breeds, except it had low genetic correlation to ADG2 in YY (0.2). Residual feed intake 1 was also genetically independent of ADG2 in DD and LL. Both RFI traits had strong genetic correlations with DFI (0.85 to 0.96) and FCR (0.76 to 0.99). They had low or no genetic correlations with feeding behavior traits. Unfavorable genetic correlations were found between ADG2 and both BF and DFI. Among feeding behavior traits, DFI had low genetic correlations to other traits in all breeds. High and negative genetic correlations were also found between TPD with FR (–0.79 in YY to –0.88 in DD), NVD, and TPD (–0.91 in DD to –0.94 in YY) and between NVD and FPV (–0.83 in DD to –0.91 in YY) in all breeds. The genetic trend for feed efficiency was favorable in all breeds regardless of the definition of feed efficiency used. In summary, RFI1 and RFI2 were heritable and selection for reduced RFI2 can be performed without adversely affecting ADG and BF and could replace FCR in the selection index for the Danish pig breeds. Selection could also be based on RFI1 for breeds with fewer concerns about a negative effect of BF or for breeds that do not have BF records.

Recently there has been considerable interest in modeling individual test-day records (TDR) for genetic evaluation of dairy cattle as a replacement for the traditional use of estimated accumulated 305-d yields. Some advantages of test-day models (TDM) include the ability to account for environmental effects of each test day, the ability to model the trajectory of the lactation for individual genotypes or groups of animals, and the possibility of genetic evaluations for persistency of production. Also, the use of test-day models avoids the necessity of extending short lactations on culled animals and animals with records in progress. The disadvantages of TDM include computational difficulties associated with analyzing much larger datasets and the need to estimate many more parameters than in a traditional 305-d lactation model. Several different models have been proposed to model the trajectory of the lactation, including so-called "biological functions," various polynomials and character process models. At present, there is not universal agreement on which models to use in routine prediction of breeding values and better methods to compare models are desirable. Obtaining accurate estimates of the dispersion parameters to use in TDM remains a challenge. Methods used include a two-step procedure in which the dispersion parameters are estimated in a series of multivariate models followed by a reduction in order of fit using covariance functions, and a one-step procedure in which the parameters of TDM are estimated using restricted maximum likelihood or Bayesian methods in a random regression model. Further research should focus on including multiple lactation data and accounting for heterogeneity variance.