Bayesian reanalysis of a quantitative trait locus accounting for multiple environments by scaling in broilers

A Bayesian method was developed to handle QTL analyses of multiple experimental data of outbred populations with heterogeneity of variance between sexes for all random effects. The method employed a scaled reduced animal model with random polygenic and QTL allelic effects. A parsimonious model specification was applied by choosing assumptions regarding the covariance structure to limit the number of parameters to estimate. Markov chain Monte Carlo algorithms were applied to obtain marginal posterior densities. Simulation demonstrated that joint analysis of multiple environments is more powerful than separate single trait analyses of each environment. Measurements on broiler BW obtained from 2 experiments concerning growth efficiency and carcass traits were used to illustrate the method. The population consisted of 10 full-sib families from a cross between 2 broiler lines. Microsatellite genotypes were determined on generations 1 and 2, and phenotypes were collected on groups of generation 3 animals. The model included a polygenic correlation, which had a posterior mean of 0.70 in the analyses. The reanalysis agreed on the presence of a QTL in marker bracket MCW0058-LEI0071 accounting for 34% of the genetic variation in males and 24% in females in the growth efficiency experiment. In the carcass experiment, this QTL accounted for 19% of the genetic variation in males and 6% in females.     

Genetic associations between feed efficiency measured in a performance test station and performance of growing cattle in commercial beef herds

Interest in selection for improved feed efficiency is increasing, but before any steps are taken toward selecting for feed efficiency, correlations with other economically important traits must first be quantified. The objective of this study was to quantify the genetic associations between feed efficiency measured during performance testing and linear type traits, BW, live animal value, and carcass traits recorded in commercial herds. Feed efficiency data were available on 2,605 bulls from 1 performance test station. There were between 10,384 and 93,442 performance records on type traits, BW, animal value, or carcass traits from 17,225 commercial herds. (Co)variance components were estimated using linear mixed animal models. Genetic correlations between the muscular type traits in commercial animals and feed conversion ratio (-0.33 to -0.25), residual feed intake (RFI; -0.33 to -0.22), and residual BW gain (RG; 0.24 to 0.27) suggest that selection for improved feed efficiency should increase muscling. This is further evidenced by the genetic correlations between carcass conformation of commercial animals and feed conversion ratio (-0.46), RFI (-0.37), and residual BW gain (0.35) measured in performance-tested animals. Furthermore, the genetic correlations between RFI and both ultrasonic fat depth and carcass fat score (0.39 and 0.33, respectively) indicated that selection for improved RFI will result in leaner animals. It can be concluded from the genetic correlations estimated in this study that selection for feed efficiency will have no unfavorable effects on the performance traits measured in this study and will actually lead to an improvement in performance for some traits, such as muscularity, animal price, and carcass conformation. Conversely, this suggests that genetic selection for traits such as carcass quality, muscling traits, and animal value might also be indirectly selecting for more efficient animals.     

Fine mapping quantitative trait loci for feed intake and feed efficiency in beef cattle

Feed intake and feed efficiency are economically important traits in beef cattle because feed is the greatest variable cost in production. Feed efficiency can be measured as feed conversion ratio (FCR, intake per unit gain) or residual feed intake (RFI, measured as DMI corrected for BW and growth rate, and sometimes a measure of body composition, usually carcass fatness, RFI(bf)). The goal of this study was to fine map QTL for these traits in beef cattle using 2,194 markers on 24 autosomes. The animals used were from 20 half-sib families originating from Angus, Charolais, and University of Alberta Hybrid bulls. A mixed model with random sire and fixed QTL effect nested within sire was used to test each location (cM) along the chromosomes. Threshold levels were determined at the chromosome and genome levels using 20,000 permutations. In total, 4 QTL exceeded the genome-wise threshold of P < 0.001, 3 exceeded at P < 0.01, 17 at P < 0.05, and 30 achieved significance at the chromosome-wise threshold level (at least P < 0.05). No QTL were detected on BTA 8, 16, and 27 above the 5% chromosome-wise significance threshold for any of the traits. Nineteen chromosomes contained RFI QTL significant at the chromosome-wise level. The RFI(bf) QTL results were generally similar to those of RFI, the positions being similar, but occasionally differing in the level of significance. Compared with RFI, fewer QTL were detected for both FCR and DMI, 12 and 4 QTL, respectively, at the genome-wise thresholds. Some chromosomes contained FCR QTL, but not RFI QTL, but all DMI QTL were on chromosomes where RFI QTL were detected. The most significant QTL for RFI was located on BTA 3 at 82 cM (P = 7.60 x 10(-5)), for FCR on BTA 24 at 59 cM (P = 0.0002), and for DMI on BTA 7 at 54 cM (P = 1.38 x 10(-5)). The RFI QTL that showed the most consistent results with previous RFI QTL mapping studies were on BTA 1, 7, 18, and 19. The identification of these QTL provides a starting point to identify genes affecting feed intake and efficiency for use in marker-assisted selection and management.     

Bayesian analysis of the effect of selection for residual feed intake on growth and feed intake curves in Yorkshire swine

Gompertz growth functions were fitted to longitudinal measurements of daily feed intake (DFI) and BW of 586 boars and 495 gilts from a selection experiment in Yorkshire pigs for residual feed intake (RFI). The selection experiment consists of a line selected for low residual feed intake (LRFI) for 5 generations and a randomly selected control line (CTRL). The objectives of this study were to use Bayesian methods to estimate genetic parameters of the Gompertz curve parameters for DFI and BW, to evaluate the effect of selection for reduced RFI on the Gompertz parameters and shape of curves for DFI and BW, and to develop methodology for quantifying genetic variation at the level of the original phenotypes for DFI and BW based on the Bayesian analysis of the nonlinear model. Separate analyses were done for boars and gilts and for BW and DFI. A hierarchical model was specified in 2 levels: in the first level, the Gompertz function was modeled for each pig, and at the second level, a 3-trait linear mixed model was fitted to the 3 Gompertz parameters (asymptotic value, inflection point, and decay parameter), with fixed effects of line by generation and random effects of additive genetic and environmental effects. Bayesian methods were used to combine the 2 levels of modeling. A total of 30,000 random samples of the posterior distributions after convergence of Markov chains were used for inference. Posterior means of heritability within the first level of the model for the asymptotic value, inflection point, and decay parameter for DFI were 0.74, 0.66, and 0.82 for boars and 0.79, 0.70, and 0.57 for gilts; corresponding estimates for BW were 0.64, 0.58, and 0.60 for boars and 0.46, 0.35, and 0.33 for gilts. For DFI, LRFI boars had a reduced mature DFI (2.91 vs. 3.20 kg/d) and an earlier inflection point (85 vs. 95 d) compared with CTRL boars. For BW, LRFI boars had a lighter mature BW (279 vs. 317 kg), an earlier inflection point (184 vs. 198 d), and a decreased decay parameter (127 vs. 134 d) compared with CTRL boars. In contrast, LRFI gilts had a later inflection point (225 vs. 200 d) and a greater decay parameter (172 vs. 143 d) than CTRL gilts for BW. The other Gompertz curve parameters for DFI and BW for boars and gilts were considered not different between lines, with posterior probabilities of the line differences being greater than zero ranging from 0.1 to 0.9.     

Genetic parameters for residual feed intake in growing pigs, with emphasis on genetic relationships with carcass and meat quality traits

Data were collected over the first 4 generations of a divergent selection experiment for residual feed intake of Large White pigs having ad libitum access to feed. This data set was used to obtain estimates of heritability for residual feed intake and genetic correlations (r(a)) between this trait and growth, carcass, and meat quality traits. Individual feed intake of group-housed animals was measured by single-space electronic feeders. Upward and downward selection lines were maintained contemporarily, with 6 boars and 35 to 40 sows per line and generation. Numbers of records were 793 for residual feed intake (RFI1) of boar candidates for selection issued from first-parity (P1) litters and tested over a fixed BW range (35 to 95 kg) and 657 for residual feed intake (RFI2) and growth, carcass, and meat quality traits of castrated males and females issued from second-parity (P2) litters and tested from 28 to 107 kg of BW. Variance and covariance components were estimated using REML methodology applied to a series of multitrait animal models, which always included the criterion for selection as 1 of the traits. Estimates of heritability for RFI1 and RFI2 were 0.14 +/- 0.03 and 0.24 +/- 0.03, respectively, whereas the estimate of r(a) between the 2 traits was 0.91 +/- 0.08. Estimates of r(a) indicated that selection for low residual feed intake has the potential to improve feed conversion ratio and reduce daily feed intake, with minimal correlated effect for ADG of P2 animals. Estimates of r(a) between RFI2 and body composition traits of P2 animals were positive for traits related to the amount of fat depots (r(a) = 0.44 +/- 0.16 for carcass backfat thickness) and negative for carcass lean meat content (r(a) = -0.55 +/- 0.14). There was a tendency for a negative genetic correlation between RFI2 and carcass dressing percent (r(a) = -0.36 +/- 0.21). Moreover, selection for low residual feed intake is expected, through lower ultimate pH and lighter color, to decrease pork quality (r(a) = 0.77 +/- 0.14 between RFI2 and a meat quality index intended to predict the ratio of the weight of ham after curing and cooking to the weight of defatted and boneless fresh ham).     

Genetic analysis of longitudinal measurements of performance traits in selection lines for residual feed intake in Yorkshire swine

A 5-generation selection experiment in Yorkshire pigs for feed efficiency consists of a line selected for low residual feed intake (LRFI) and a random control line (CTRL). The objectives of this study were to use random regression models to estimate genetic parameters for daily feed intake (DFI), BW, backfat (BF), and loin muscle area (LMA) along the growth trajectory and to evaluate the effect of LRFI selection on genetic curves for DFI and BW. An additional objective was to compare random regression models using polynomials (RRP) and spline functions (RRS). Data from approximately 3 to 8 mo of age on 586 boars and 495 gilts across 5 generations were used. The average number of measurements was 85, 14, 5, and 5 for DFI, BW, BF, and LMA. The RRP models for these 4 traits were fitted with pen × on-test group as a fixed effect, second-order Legendre polynomials of age as fixed curves for each generation, and random curves for additive genetic and permanent environmental effects. Different residual variances were used for the first and second halves of the test period. The RRS models were fitted with the same fixed effects and residual variance structure as the RRP models and included genetic and permanent environmental random effects for both splines and linear Legendre polynomials of age. The RRP model was used for further analysis because the RRS model had erratic estimates of phenotypic variance and heritability, despite having a smaller Bayesian information criterion than the RRP model. From 91 to 210 d of age, estimates of heritability from the RRP model ranged from 0.10 to 0.37 for boars and 0.14 to 0.26 for gilts for DFI, from 0.39 to 0.58 for boars and 0.55 to 0.61 for gilts for BW, from 0.48 to 0.61 for boars and 0.61 to 0.79 for gilts for BF, and from 0.46 to 0.55 for boars and 0.63 to 0.81 for gilts for LMA. In generation 5, LRFI pigs had lower average genetic curves than CTRL pigs for DFI and BW, especially toward the end of the test period; estimated line differences (CTRL-LRFI) for DFI were 0.04 kg/d for boars and 0.12 kg/d for gilts at 105 d and 0.20 kg/d for boars and 0.24 kg/d for gilts at 195 d. Line differences for BW were 0.17 kg for boars and 0.69 kg for gilts at 105 d and 3.49 kg for boars and 8.96 kg for gilts at 195 d. In conclusion, selection for LRFI has resulted in a lower feed intake curve and a lower BW curve toward maturity.     

Individual efficiency for the use of feed resources in rabbits

A Bayesian procedure, which allows consideration of the individual variation in the feed resource allocation pattern, is described and implemented in 2 sire lines of rabbit (Caldes and R). The procedure is based on a hierarchical Bayesian scheme, where the first stage of the model consists of a multiple regression model of feed intake on metabolic BW and BW gain. In a second stage, an animal model was assumed including batch, parity order, litter size, and common environmental litter effects. Animals were reared during the fattening period (from weaning at 32 d of age to 60 d of age) in individual cages on an experimental farm, and were fed ad libitum with a commercial diet. Body weight (g) and cumulative feed intake (g) were recorded weekly. Individual BW gain (g) and average BW (ABW, g) were calculated from these data for each 7-d period. Metabolic BW (g(0.75)) was estimated as ABW(0.75). The number of animals actually measured was 444 and 445 in the Caldes and R lines, respectively. Marginal posterior distributions of the genetic parameters were obtained by Gibbs sampling. Posterior means (posterior SD) for heritabilities for partial coefficients of regression of feed intake on metabolic BW and feed intake on BW gain were estimated to be 0.35 (0.17) and 0.40 (0.17), respectively, in the Caldes line and 0.26 (0.19) and 0.27 (0.14), respectively, in line R. The estimated posterior means (posterior SD) for the proportion of the phenotypic variance due to common litter environmental effects of the same coefficients of regression were respectively, 0.39 (0.14) and 0.28 (0.13) in the Caldes line and 0.44 (0.22) and 0.49 (0.14) in line R. These results suggest that efficiency of use of feed resources could be improved by including these coefficients in an index of selection.     

Cluster and meta‐analyses of genetic parameters for feed intake traits in growing beef cattle

A data set based on 50 studies including feed intake and utilization traits was used to perform a meta‐analysis to obtain pooled estimates using the variance between studies of genetic parameters for average daily gain (ADG); residual feed intake (RFI); metabolic body weight (MBW); feed conversion ratio (FCR); and daily dry matter intake (DMI) in beef cattle. The total data set included 128 heritability and 122 genetic correlation estimates published in the literature from 1961 to 2012. The meta‐analysis was performed using a random effects model where the restricted maximum likelihood estimator was used to evaluate variances among clusters. Also, a meta‐analysis using the method of cluster analysis was used to group the heritability estimates. Two clusters were obtained for each trait by different variables. It was observed, for all traits, that the heterogeneity of variance was significant between clusters and studies for genetic correlation estimates. The pooled estimates, adding the variance between clusters, for direct heritability estimates for ADG, DMI, RFI, MBW and FCR were 0.32 ± 0.04, 0.39 ± 0.03, 0.31 ± 0.02, 0.31 ± 0.03 and 0.26 ± 0.03, respectively. Pooled genetic correlation estimates ranged from ?0.15 to 0.67 among ADG, DMI, RFI, MBW and FCR. These pooled estimates of genetic parameters could be used to solve genetic prediction equations in populations where data is insufficient for variance component estimation. Cluster analysis is recommended as a statistical procedure to combine results from different studies to account for heterogeneity.     

Quantitative trait locus detection in commercial broiler lines using candidate regions

A QTL that explained a large proportion of the phenotypic difference between broiler and layer chickens in an experimental cross was evaluated in a commercial broiler line. A three-generation design, consisting of 15 grandsires, 608 half-sib hens, and more than 50,000 third-generation offspring, was implemented within the existing breeding scheme of a broiler breeding company. Four markers from a candidate region on chicken chromosome 4 were selected for their informativeness in the grandsires and used to genotype the first two generations. Using half-sib analyses, linkage was studied between these markers and 13 growth and carcass traits. The QTL analyses confirmed the presence of significant QTL for body weight (P < 0.01) and residual feed intake (P < 0.05) on chicken chromosome 4. Furthermore, evidence was found for QTL affecting the relative weight of bone and muscle in the thigh. Four more markers were added to increase resolution of the QTL positions. This increased the significance of the QTL for body weight (P < 0.001) and residual feed intake (P < 0.01) and showed evidence (P < 0.05) for additional QTL affecting carcass weight and conformation score. This study showed for the first time that a QTL that explains differences between broilers and layers was segregating in lines that have been selected for body weight over 50 generations. A possible explanation could be a pleiotropic or closely linked effect on fitness-related traits that are not part of the present study. The results demonstrate the feasibility of QTL detection and the potential for marker-assisted selection within a commercial broiler line without altering the existing breeding scheme.     

Feed intake,digestibility, body weight and carcass parameters of Afar rams fed tef (Eragrostis tef) straw supplemented with graded levels of concentrate mix

The experiment was conducted at Alamata Agricultural Research Center, Ethiopia using 20 Afar rams with an initial body weight (BW) of 18.2 ± 1.76 (mean ± SD) kg. The objectives were to study the effect of supplementation with concentrate mix consisting of wheat bran (WB), noug seed cake (NSC) and sesame seed cake (SSC) at the ratio of 2:1:1 on dry matter (DM) basis, respectively on feed intake, digestibility, BW gain and carcass parameters of Afar rams fed tef (Eragrostis tef) straw basal diet. The experiment was arranged with four treatments and five replications in a randomized complete block design. The treatments included feeding sole tef straw (T1, control), and daily supplementation with the concentrate mix offered at 150 (T2, low), 250 (T3, medium) and 350 (T4, high) g DM per head. Total DM intake, crude protein (CP) digestibility, daily BW gain (P < 0.001), DM and organic matter (OM) digestibility, and carcass parameters (P < 0.05) were higher in the supplemented than in the control treatment. Intake of tef straw reduced as the level of supplementation increased, whereas the contrary was true for CP intake. Performance in carcass parameters was better for the medium compared to the low level of concentrate mix supplementation. Moreover, the medium level of supplementation did not substitute tef straw intake. Therefore, it is concluded that the medium level of concentrate mix supplement maintained the utilization of the roughage feed and resulted in better carcass parameters.     

Genetic and phenotypic relationships of feed intake and measures of efficiency with growth and carcass merit of beef cattle

Feed intake and efficiency of growth are economically important traits of beef cattle. This study determined the relationships of daily DMI, feed:gain ratio [F:G, which is the reciprocal of the efficiency of gain (G:F) and therefore increases as the efficiency of gain decreases and vice versa, residual feed intake (RFI), and partial efficiency of growth (efficiency of ADG, PEG) with growth and carcass merit of beef cattle. Residual feed intake was calculated from phenotypic regression (RFIp) or genetic regression (RFIg) of ADG and metabolic BW on DMI. An F1 half-sib pedigree file containing 28 sires, 321 dams, and 464 progeny produced from crosses between Alberta Hybrid cows and Angus, Charolais, or Alberta Hybrid bulls was used. Families averaged 20 progeny per sire (range = 3 to 56). Performance, ultrasound, and DMI data was available on all progeny, of which 381 had carcass data. Phenotypic and genetic parameters were obtained using SAS and ASREML software, respectively. Differences in RFIp and RFIg, respectively, between the most and least efficient steers (i.e., steers with the lowest PEG) were 5.59 and 6.84 kg of DM/d. Heritabilities for DMI, F:G, PEG, RFIp, and RFIg were 0.54 +/- 0.15, 0.41 +/- 0.15, 0.56 +/- 0.16, 0.21 +/- 0.12, and 0.42 +/- 0.15, respectively. The genetic (r = 0.92) and phenotypic (r = 0.97) correlations between RFIp and RFIg indicated that the 2 indices are very similar. Both indices of RFI were favorably correlated phenotypically (P < 0.001) and genetically with DMI, F:G, and PEG. Residual feed intake was tendentiously genetically correlated with ADG (r = 0.46 +/- 0.45) and metabolic BW (r = 0.27 +/- 0.33), albeit with high SE. Genetically, RFIg was independent of ADG and BW but showed a phenotypic correlation with ADG (r = -0.21; P < 0.05). Daily DMI was correlated genetically (r = 0.28) and phenotypically (r = 0.30) with F:G. Both DMI and F:G were strongly correlated with ADG (r > 0.50), but only DMI had strong genetic (r = 0.87 +/- 0.10) and phenotypic (r = 0.65) correlations with metabolic BW. Generally, the phenotypic and genetic correlations of RFI with carcass merit were not different from zero, except genetic correlations of RFI with ultrasound and carcass LM area and carcass lean yield and phenotypic correlations of RFI with backfat thickness (P < 0.01). Daily DMI had moderate to high phenotypic (P < 0.01) and genetic correlations with all the ultrasound and carcass traits. Depending on how RFI technology is applied, adjustment for body composition in addition to growth may be required to minimize the potential for correlated responses to selection in cattle.     

Impact of variation in length of individual testing periods on estimation of (co)variance components of a random regression model for feed intake of growing pigs

A simulation study was conducted to assess the influence of differences in the length of individual testing periods on estimates of (co)variance components of a random regression model for daily feed intake of growing pigs performance tested between 30 and 100 kg live weight. A quadratic polynomial in days on test with fixed regressions for sex, random regressions for additive genetic and permanent environmental effects and a constant residual variance was used for a bivariate simulation of feed intake and daily gain. (Co)variance components were estimated for feed intake only by means of a Bayesian analysis using Gibbs sampling and restricted maximum likelihood (REML). A single trait random regression model analogous to the one used for data simulation was used to analyse two versions of the data: full data sets with 18 weekly means of feed intake per animal and reduced data sets with the individual length of testing periods determined when tested animals reached 100 kg live weight. Only one significant difference between estimates from full and reduced data (REML estimate of genetic covariance between linear and quadratic regression parameters) and two significant differences from expected values (Gibbs estimates of permanent environmental variance of quadratic regression parameters) occurred. These differences are believed to be negligible, as the number lies within the expected range of type I error when testing at the 5% level. The course of test day variances calculated from estimates of additive genetic and permanent environmental covariance matrices also supports the conclusion that no bias in estimates of (co)variance components occurs due to the individual length of testing periods of performance‐tested growing pigs. A lower number of records per tested animal only results in more variation among estimates of (co)variance components from reduced compared with full data sets. Compared with the full data, the effective sample size of Gibbs samples from the reduced data decreased to 18% for residual variance and increased up to five times for other (co)variances. The data structure seems to influence the mixing of Gibbs chains.     

Genetic and genomic analyses for predicted methane‐related traits in Japanese Black steers

The objectives of this study were to estimate genetic parameters and to perform a genome‐wide association study (GWAS) for predicted methane‐related traits in Japanese Black steers. The methane production and yield traits were predicted using on‐farm measurable traits, such as dry matter intake and average daily gain. A total of 4,578 Japanese Black steers, which were progenies of 362 sires genotyped with imputed 551,995 single nucleotide polymorphisms (SNPs), had phenotypes of predicted methane‐related traits during the total fattening period (52 weeks). For the estimation of genetic parameters, the estimated heritabilities were moderate (ranged from 0.57 to 0.60). In addition, the estimated genetic correlations of methane production traits with most of carcass traits and feed‐efficiency traits were unfavorable, but those of methane yield traits were favorable or low. For the GWAS, no genome‐wide significant SNP was detected, but a total of four quantitative trait locus (QTL) regions that explained more than 5.0% of genetic variance were localized on the genome, and some candidate genes associated with growth and feed‐efficiency traits were located on the regions. Our results suggest that the predicted methane‐related traits are heritable and some QTL regions for the traits are localized on the genome in Japanese Black steers.     

Effects of forage and protein source on feedlot performance and carcass traits of Holstein and crossbred beef steers.

Fifty-eight Holstein and 58 crossbred beef steers were individually fed one of four isonitrogenous diets to evaluate the effects of forage source (corn silage and alfalfa haylage) and protein source (soybean meal and fish meal) on feedlot performance. Phase 1 diets (up to 354 kg of BW) were 40% forage and 60% concentrates and were fed for 70 to 136 d (depending on diet and breed group). Phase 2 diets (354 kg of BW until slaughter) were 20% forage and 80% concentrates and were fed for 127 to 150 d (depending on diet and breed group). Slaughter end points were .6 cm of 12th rib fat for Holsteins and 1.0 cm of rib fat for crossbreds using real-time ultrasonic estimates. The steers were fed for a maximum of 330 d each year. Forage source was a significant component of variation for most growth, efficiency, and carcass traits. Holstein and crossbred steers fed alfalfa haylage had significantly lower average daily gain, feed efficiency, dressing percentage, and empty body fat and required more days on feed to reach slaughter end points, but had higher total feed energy intake available for production. Steers fed corn silage diets had significantly greater energetic efficiency (P less than .05) than those fed alfalfa haylage, due to increased use of ME to produce fat in the carcass. Protein type did not influence gain, feed or energetic efficiency, energy intake, or most carcass traits. A significant protein system x forage source interaction among the four diets was detected for crossbred steers fed corn silage and fish meal, for which there was significantly greater feed conversion with lower energy intake above maintenance, possibly due to better fiber digestion and(or) amino acid flow to the lower tract. Alfalfa haylage plus soybean meal diets decreased (P less than .05) the percentage of Holsteins grading USDA Choice or higher. These results indicate that corn silage, because of greater energy concentration, was a more desirable forage in feedlot diets composed of less than or equal to 40% forage and that protein type (soybean meal and fish meal) in growing diets is not an important factor in feedlot performance or carcass traits of Holstein or crossbred steers that are fed these diets.     

A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep

Genetic parameters for a range of sheep production traits have been reviewed from estimates published over the last decade. Weighted means and standard errors of estimates of direct and maternal heritability, common environmental effects and the correlation between direct and maternal effects are presented for various growth, carcass and meat, wool, reproduction, disease resistance and feed intake traits. Weighted means and confidence intervals for the genetic and phenotypic correlations between these traits are also presented. A random effects model that incorporated between and within study variance components was used to obtain the weighted means and variances. The weighted mean heritability estimates for the major wool traits (clean fleece weight, fibre diameter and staple length) and all the growth traits were based on more than 20 independent estimates, with the other wool traits based on more than 10 independent estimates. The mean heritability estimates for the carcass and meat traits were based on very few estimates except for fat (27) and muscle depth (11) in live animals. There were more than 10 independent estimates of heritability for most reproduction traits and for worm resistance, but few estimates for other sheep disease traits or feed intake. The mean genetic and phenotypic correlations were based on considerably smaller numbers of independent estimates. There were a reasonable number of estimates of genetic correlations among most of the wool and growth traits, although there were few estimates for the wool quality traits and among the reproduction traits. Estimates of genetic correlations between the groups of different production traits were very sparse. The mean genetic correlations generally had wide confidence intervals reflecting the large variation between estimates and relatively small data sets (number of sires) used. More accurate estimates of genetic parameters and in particular correlations between economically important traits are required for accurate genetic evaluation and development of breeding objectives.     

Genetic correlations between males, females and castrates for residual feed intake, feed conversion ratio, growth rate and carcass composition traits in Large White growing pigs

Data were collected in the course of a divergent selection experiment for residual feed intake (RFI) of Large White growing pigs. This data set was used to estimate (i) heritability for RFI and genetic correlations of RFI with growth and carcass traits within the three sexes (male, castrate and female) and (ii) genetic correlations between sexes for these traits. Individual feed intake of animals raised in collective pens was measured by single-place electronic feeders on 1121 males (candidates for selection), 508 females and 535 castrates (sibs of candidates). Variance components were estimated using the REML methodology applied to a multitrait animal model. Estimates of heritability for RFI were 0.16 ± 0.04, 0.16 ± 0.08 and 0.23 ± 0.10 for males, females and castrates, respectively. Estimates of genetic correlations between sexes for homologous traits were not significantly different from 1 (0.88 to 0.99 for RFI, 0.79 to 0.99 for growth traits and 0.65 to 0.99 for carcass composition traits). The relatively low genetic correlations between castrates and males or females for backfat thickness (0.65 and 0.69, respectively) suggest the presence of genotype by sex interactions for this trait.     

Generalized marker regression and interval QTL mapping methods for binary traits in half-sib family designs

A Generalized Marker Regression Mapping (GMR) approach was developed for mapping Quantitative Trait Loci (QTL) affecting binary polygenic traits in a single-family half-sib design. The GMR is based on threshold-liability model theory and regression of offspring phenotype on expected marker genotypes at flanking marker loci. Using simulation, statistical power and bias of QTL mapping for binary traits by GMR was compared with full QTL interval mapping based on a threshold model (GIM) and with a linear marker regression mapping method (LMR). Empirical significance threshold values, power and estimates of QTL location and effect were identical for GIM and GMR when QTL mapping was restricted to within the marker interval. These results show that the theory of the marker regression method for QTL mapping is also applicable to binary traits and possibly for traits with other non-normal distributions. The linear and threshold models based on marker regression (LMR and GMR) also resulted in similar estimates and power for large progeny group sizes, indicating that LMR can be used for binary data for balanced designs with large families, as this method is computationally simpler than GMR. GMR may have a greater potential than LMR for QTL mapping for binary traits in complex situations such as QTL mapping with complex pedigrees, random models and models with interactions.     

Comparison of the supplemental effects of Saccharomyces cerevisiae and antibiotics in low-protein and high-fibre diets fed to broiler chickens.

Two experiments were completed to compare the supplemental effects of yeast Saccharomyces cerevisiae and sub-therapeutic antibiotics in high-fibre and low-protein diets for broiler chicks. In experiment 1, yeast was added at 1.5, 3.0 and 6.0 g kg-1 while penicillin, tylosin or neoterramycin were added at 150 mg kg-1 into different batches of a high-fibre diet containing 250 g kg-1 palm kernel meal. Saccharomyces cerevisiae and antibiotics increased BW, BWG, FCR, feed intake, carcass and breast weights above the control. Small intestine weight was reduced by antibiotics, while yeast caused a lower deposition of abdominal fat. Liver weights of different treated broilers were similar. The performance indices were superior on penicillin and 3.0 g kg-1 yeast compared to other supplements evaluated. In the second experiment, 1.5 and 3.0 g kg-1 of yeast, and 0.75 mg kg-1 of procaine penicillin, zinc bacitracin and tylosin were separately added to a 180 g kg-1 crude protein diet. Performance of the chicks with additives was compared with unsupplemented negative and positive controls containing 180 and 210 g kg-1 crude protein respectively. Broilers fed with supplements had superior BW, BWG, FCR and feed intake compared to the negative control, but carcass data were similar. Dietary penicillin increased BW, BWG, feed intake more than other supplements, but yeast stimulated higher FCR. Data from both studies indicated that Saccharomyces cerevisiae comparatively increased growth and carcass weight and reduce abdominal fat, therefore, it can serve as a natural substitute for antibiotics in broiler diet.     

Genetic parameters for dry matter, energy and protein intake, and their relationships with performance and carcass traits in Japanese Black cattle

Genetic parameters for feed intake and performance traits of 514 bulls and carcass traits of 22 099 of their progeny, and the relationships of measures of feed intake with performance and carcass traits were estimated. Feed intake traits were dry matter intake (DMI), concentrate intake (CONI), roughage intake, ratio of roughage intake to DMI, metabolizable energy intake (MEI) and digestible crude protein intake (DCPI). Performance traits included daily gain, metabolic weight, live weight at the end of test, dry matter conversion ratio and residual feed intake. Progeny carcass traits were carcass weight, percentage of meat yield, rib eye area (REA), subcutaneous fat, marbling score, meat colour (MCS), fat colour (FCS) and meat quality grade. All the feed intake and performance traits were moderately heritable. The heritabilities for REA and MCS were moderate, and that for FCS was low, while those for the other carcass traits were high. Selection against DMI, CONI and DCPI would reduce excessive intake of feed, but would have undesirable effects on growth and most of the carcass traits. Selection against MEI would lead to improvements in feed efficiency and growth traits. Selection against DCPI would also improve feed efficiency; however, responses in growth traits would decrease. Results indicate that selection against MEI might be better than any other measures of feed intake to improve feed efficiency with simultaneous improvement in growth and most of the carcass traits.     

Genetic and phenotypic parameter estimates for feed intake and other traits in growing beef cattle, and opportunities for selection

Growth, feed intake, and temperament indicator data, collected over 5 yr on a total of 1,141 to 1,183 mixed-breed steers, were used to estimate genetic and phenotypic parameters. All steers had a portion of Hereford, Angus, or both as well as varying percentages of Simmental, Charolais, Limousin, Gelbvieh, Red Angus, and MARC III composite. Because the steers were slaughtered on various dates each year and the animals thus varied in days on feed, BW and feed data were adjusted to a 140-d feeding period basis. Adjustment of measures of feed efficiency [G:F or residual feed intake (RFI), intake adjusted for metabolic body size, and BW gain] for body fatness recorded at slaughter had little effect on the results of analyses. Average daily gain was less heritable (0.26) than was midtest BW (MBW; 0.35). Measures of feed intake had greater estimates of heritability, with 140-d DMI at 0.40 and RFI at 0.52; the heritability estimate for G:F was 0.27. Flight speed (FS), as an indicator of temperament, had an estimated heritability of 0.34 and a repeatability of 0.63. As expected, a strong genetic (0.86) correlation was estimated between ADG and MBW; genetic correlations were less strong between DMI and ADG or MBW (0.56 and 0.71). Residual feed intake and DMI had a genetic correlation of 0.66. Indexes for phenotypic RFI and genotypically restricted RFI (no correlation with BW gain) were compared with simple economic indexes incorporating feed intake and growth to elucidate expected selection responses under different criteria. In general, few breed differences were detected across the various measurements. Heterosis contributed to greater DMI, RFI, and MBW, but it did not significantly affect ADG, G:F, or FS. Balancing output (growth) with input costs (feed) is needed in practicing selection, and FS would not be recommended as an indicator trait for selection to change feed efficiency. An index including BW gain and RFI produced the best economic outcome.