Breed effects and heterosis for weight traits and tick count in a cross between an indigenous fat-tailed and a commercial sheep breed.

Ticks can compromise productivity and welfare in free-ranging sheep. Chemical tick control may not be sustainable in the long term. Alternative control measures must be sought for an integrated control programme. Birth and weaning weights as well as log transformed overall tick count of indigenous fat-tailed Namaqua Afrikaner (NA), commercial Dorper and NA x Dorper cross lambs were studied under extensive conditions. Relative to NA lambs, Dorper lambs were 22.2% heavier at weaning (P < 0.05). Geometric means for total tick count on Dorper lambs exceeded those of their Namaqua Afrikaner contemporaries by more than twofold (P < 0.05). Relative to the pure-breed midparent value, the mean performance of NA x Dorper lambs was 7.9% more for birth weight, 11.2% more for weaning weight and 26.2% less for the back transformed means for total tick count (P < 0.05). Heterosis for total tick count was slightly greater at -29.3% when data were adjusted for the larger size of NA x Dorper lambs. Crossing commercial Dorper sheep with a hardy, indigenous breed therefore resulted in lower levels of tick infestation without compromising live weight in progeny so derived. Hardy, indigenous genetic resources like the NA should be conserved and used in further studies of ovine genetics of resistance to ticks in South Africa.

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

Genetic parameters for test-day milk yield, lactation persistency, and fertility in low-, medium-, and high-production systems in Kenya.

Genetic parameters for test-day milk yield, lactation persistency, and age at first calving (as a fertility trait) were estimated for the first 4 lactations in multiple-breed dairy cows in low-, medium-, and high-production systems in Kenya. Data included 223,285 test-day milk yield records from 11,450 cows calving from 1990 to 2015 in 148 herds. A multivariate random regression model was used to estimate variance and covariance components. The fixed effects in the model included herd, year, and test month, and age as a covariate. The lactation profile over days in milk (DIM) was fitted as a cubic smoothing spline. Random effects included herd, year, and test month interaction effects, genetic group effects, and additive genetic and permanent environmental effects modeled with a cubic Legendre polynomial function. The residual variance was heterogeneous with 11 classes. Consequently, the variance components were varied over the lactation and with the production system. The estimated heritability for milk yield was lower in the low-production system (0.04-0.48) than in the medium- (0.22-0.59) and high-production (0.21-0 60) systems. The genetic correlations estimated between different DIM within lactations decreased as the time interval increased, becoming negative between the ends of the lactations in the low- and medium-production systems. Low (0.05) to medium (0.60) genetic correlations were estimated among first lactation test-day milk yields across the 3 production systems. Genetic correlations between the first lactation test-day milk yield and age at first calving ranged from 0.27 to 0.49, 0 to 0.81, and -0.08 to 0.27 in the low-, medium-, and high-production systems, respectively. Medium to high heritabilities (0.17-0.44) were estimated for persistency, with moderate to high (0.30-0.87) genetic correlations between 305-d milk yield and persistency. This indicates that genetic improvement in persistency would lead to increased milk yield. The low to medium genetic correlations between test-day milk yield between production systems indicate that sires may be re-ranked between production systems. Therefore, we conclude that sires should be selected based on a genetic evaluation within the target production system.

Genetic-based interactions among tree neighbors: identification of the most influential neighbors, and estimation of correlations among direct and indirect genetic effects for leaf disease and growth in Eucalyptus globulus.

An individual's genes may influence the phenotype of neighboring conspecifics. Such indirect genetic effects (IGEs) are important as they can affect the apparent total heritable variance in a population, and thus the response to selection. We studied these effects in a large, pedigreed population of Eucalyptus globulus using variance component analyses of Mycosphearella leaf disease, diameter growth at age 2 years, and post-infection diameter growth at ages 4 and 8 years. In a novel approach, we initially modeled IGEs using a factor analytic (FA) structure to identify the most influential neighbor positions, with the FA loadings being position-specific regressions on the IGEs. This involved sequentially comparing FA models for the variance-covariance matrices of the direct and indirect effects of each neighbor. We then modeled IGEs as a distance-based, combined effect of the most influential neighbors. This often increased the magnitude and significance of indirect genetic variance estimates relative to using all neighbors. The extension of a univariate IGEs model to bivariate analyses also provided insights into the genetic architecture of this population, revealing that: (1) IGEs arising from increased probability of neighbor infection were not associated with reduced growth of neighbors, despite adverse fitness effects being evident at the direct genetic level; and (2) the strong, genetic-based competitive interactions for growth, established early in stand development, were highly positively correlated over time. Our results highlight the complexities of genetic-based interactions at the multi-trait level due to (co)variances associated with IGEs, and the marked discrepancy occurring between direct and total heritable variances.

Genetic selection for increased mean and reduced variance of twinning rate in Belclare ewes.

It is sometimes possible to breed for more uniform individuals by selecting animals with a greater tendency to be less variable, that is, those with a smaller environmental variance. This approach has been applied to reproduction traits in various animal species. We have evaluated fecundity in the Irish Belclare sheep breed by analyses of flocks with differing average litter size (number of lambs per ewe per year, NLB) and have estimated the genetic variance in environmental variance of lambing traits using double hierarchical generalized linear models (DHGLM). The data set comprised of 9470 litter size records from 4407 ewes collected in 56 flocks. The percentage of pedigreed lambing ewes with singles, twins and triplets was 30, 54 and 14%, respectively, in 2013 and has been relatively constant for the last 15 years. The variance of NLB increases with the mean in this data; the correlation of mean and standard deviation across sires is 0.50. The breeding goal is to increase the mean NLB without unduly increasing the incidence of triplets and higher litter sizes. The heritability estimates for lambing traits were NLB, 0.09; triplet occurrence (TRI) 0.07; and twin occurrence (TWN), 0.02. The highest and lowest twinning flocks differed by 23% (75% versus 52%) in the proportion of ewes lambing twins. Fitting bivariate sire models to NLB and the residual from the NLB model using a double hierarchical generalized linear model (DHGLM) model found a strong genetic correlation (0.88 ± 0.07) between the sire effect for the magnitude of the residual (VE ) and sire effects for NLB, confirming the general observation that increased average litter size is associated with increased variability in litter size. We propose a threshold model that may help breeders with low litter size increase the percentage of twin bearers without unduly increasing the percentage of ewes bearing triplets in Belclare sheep.

Genetic parameters for meat quality traits of Australian lamb meat.

Genetic parameters were estimated for a range of meat quality traits recorded on Australian lamb meat. Data were collected from Merino and crossbred progeny of Merino, terminal and maternal meat breed sires of the Information Nucleus programme. Lambs born between 2007 and 2010 (n=8968) were slaughtered, these being the progeny of 372 sires and 5309 dams. Meat quality traits were found generally to be of moderate heritability (estimates between 0.15 and 0.30 for measures of meat tenderness, meat colour, polyunsaturated fat content, mineral content and muscle oxidative capacity), with notable exceptions of intramuscular fat (0.48), ultimate pH (0.08) and fresh meat colour a* (0.08) and b* (0.10) values. Genetic correlations between hot carcass weight and the meat quality traits were low. The genetic correlation between intramuscular fat and shear force was high (-0.62). Several measures of meat quality (fresh meat redness, retail meat redness, retail oxy/met value and iron content) appear to have potential for inclusion in meat sheep breeding objectives.

Estimating variance components and predicting breeding values for eventing disciplines and grades in sport horses.

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.

Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models.

The possibility of breeding for uniform individuals by selecting animals expressing a small response to environment has been studied extensively in animal breeding. Bayesian methods for fitting models with genetic components in the residual variance have been developed for this purpose, but have limitations due to the computational demands. We use the hierarchical (h)-likelihood from the theory of double hierarchical generalized linear models (DHGLM) to derive an estimation algorithm that is computationally feasible for large datasets. Random effects for both the mean and residual variance parts of the model are estimated together with their variance/covariance components. An important feature of the algorithm is that it can fit a correlation between the random effects for mean and variance. An h-likelihood estimator is implemented in the R software and an iterative reweighted least square (IRWLS) approximation of the h-likelihood is implemented using ASReml. The difference in variance component estimates between the two implementations is investigated, as well as the potential bias of the methods, using simulations. IRWLS gives the same results as h-likelihood in simple cases with no severe indication of bias. For more complex cases, only IRWLS could be used, and bias did appear. The IRWLS is applied on the pig litter size data previously analysed by Sorensen & Waagepetersen (2003) using Bayesian methodology. The estimates we obtained by using IRWLS are similar to theirs, with the estimated correlation between the random genetic effects being -0·52 for IRWLS and -0·62 in Sorensen & Waagepetersen (2003).

Estimation of maternal variance components considering cow-calf contacts under extensive pastoral systems.

Traditional methods of variance component estimation for traits under maternal influence consist of partitioning the variance into direct additive genetic, maternal additive genetic, permanent maternal environmental, and error variance components. This partitioning is based on the assumption that each calf is nurtured and fed exclusively by its own dam. However, under extensive pastoral systems, voluntary cross-suckling may occur and could be quantified by using contact loggers recording cow-calf affiliations. A simulation study was conducted to test several variance models for partitioning maternal variation by including information on cow-calf contacts. The results indicated that weighting maternal genetic and permanent maternal environmental effects by the relative time calves spent with particular cows, including their own mothers, is feasible and significantly increased the log-likelihood of the models. However, the interpretation of the variance components in terms of traditional direct and maternal heritability is no longer straightforward. The need for further research and implications for the industry are discussed.

Reproductive performance and genetic parameters in first cross ewes from different maternal genotypes.

The reproduction of 2,846 crossbreed ewes with 7,899 records is reported. The ewes were progeny of mainly Merino dams and 91 sires from several maternal sire breeds including Border Leicester, East Friesian, Finnsheep, Coopworth, Corriedale, Booroola Leicester, and several others. There were 3 cohorts of ewes at each of 3 sites that were bred naturally to meat-type rams for each of 3 yr to evaluate reproduction and lamb production. At 2 sites, the ewes were mated in the autumn, first at 7 mo of age, and at 2 sites the ewes were mated in the spring, first at 14 or 17 mo of age. The cohorts of ewes and sites were genetically linked by 3 common sires. Mixed linear models were used to analyze ultrasound scanned pregnancy rate, fetal number, fertility (ewes lambing), litter size, lamb survival, number of lambs born (NLBj), number of lambs weaned (NLWj), and total weight of lamb weaned (TWWj) per ewe bred. Fixed effects included sire breed (1 to 10), environment (1 to 4, site and season of breeding: autumn, spring), breeding (1 to 3), cohort (1 to 3), and their interactions. The REML procedures were used to estimate (co)variance components. Ewe sire breed effects were significant (P < 0.01) for all the reproductive traits and breed means ranged from 0.75 to 0.96 for fertility, 1.22 to 2.08 for litter size, 0.70 to 0.90 for lamb survival, 0.99 to 1.66 for NLBj, 0.87 to 1.26 for NLWj, and 22.9 to 33.8 kg for TWWj, with the ranking of sire breeds varying for different traits. For all traits except lamb survival, the contrast between breeding 1 vs. 2 and 3 was considerably greater than the contrast between breeding 2 vs. 3, with significant environment x breeding interactions (P < 0.01). Estimates of heritability for the components of reproduction ranged from 0.03 +/- 0.02 for lamb survival to 0.19 +/- 0.05 for litter size, and those for the composite traits were 0.17 +/- 0.04 for NLBj, 0.13 +/- 0.04 for NLWj, and 0.17 +/- 0.04 for TWWj, with repeatability ranging from 0.10 to 0.19. Genetic and phenotypic correlations among the traits are reported. The significant variation among sire breeds of the crossbred ewes can be used to improve reproduction, although there was a change in the rank of the breeds for the various traits. There was considerable overlap between the breeds, and additional improvement could be achieved by exploiting the genetic variation between sires within breeds for all the ewe reproductive traits.

Variance components due to direct genetic, maternal genetic and permanent environmental effect for growth and feed-efficiency traits in young male Japanese Black cattle.

Variance components and genetic parameters were estimated using data recorded on 740 young male Japanese Black cattle during the period from 1971 to 2003. Traits studied were feed intake (FI), feed-conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG), metabolic body weight (MWT) at the mid-point of the test period and body weight (BWT) at the finish of the test (345 days). Data were analysed using three alternative animal models (direct, direct + maternal environmental, and direct + maternal genetic effects). Comparison of the log likelihood values has shown that the direct genetic effect was significant (p < 0.05) for all traits and that the maternal environmental effects were significant (p < 0.05) for MWT and BWT. The heritability estimates were 0.20 +/- 0.12 for FI, 0.14 +/- 0.10 for FCR, 0.33 +/- 0.14 for RFI, 0.19 +/- 0.12 for ADG, 0.30 +/- 0.14 for MWT and 0.30 +/- 0.13 for BWT. The maternal effects (maternal genetic and maternal environmental) were not important in feed-efficiency traits. The genetic correlation between RFI and ADG was stronger than the corresponding correlation between FCR and ADG. These results provide evidence that RFI should be included for genetic improvement in feed efficiency in Japanese Black breeding programmes.

Genetic correlations among and between wool, growth and reproduction traits in Merino sheep.

Data from seven research resource flocks across Australia were combined to provide accurate estimates of genetic correlations among production traits in Merino sheep. The flocks represented contemporary Australian Merino fine, medium and broad wool strains over the past 30 years. Over 110,000 records were available for analysis for each of the major wool traits, and 50,000 records for reproduction and growth traits with over 2700 sires and 25,000 dams. Individual models developed from the single trait analyses were extended to the various combinations of two-trait models to obtain genetic correlations among six wool traits [clean fleece weight (CFW), greasy fleece weight, fibre diameter (FD), yield, coefficient of variation of fibre diameter and standard deviation of fibre diameter], four growth traits [birth weight, weaning weight, yearling weight (YWT), and hogget weight] and four reproduction traits [fertility, litter size, lambs born per ewe joined, lambs weaned per ewe joined (LW/EJ)]. This study has provided for the first time a comprehensive matrix of genetic correlations among these 14 wool, growth and reproduction traits. The large size of the data set has also provided estimates with very low standard errors. A moderate positive genetic correlation was observed between CFW and FD (0.29 +/- 0.02). YWT was positively correlated with CFW (0.23 +/- 0.04), FD (0.17 +/- 0.04) and LWEJ (0.58 +/- 0.06), while LW/EJ was negatively correlated with CFW (-0.26 +/- 0.05) and positively correlated with FD (0.06 +/- 0.04) and LS (0.68 +/- 0.04). These genetic correlations, together with the estimates of heritability and other parameters provide the basis for more accurate prediction of outcomes in complex sheep-breeding programmes designed to improve several traits.

The impact of homogenizer type and speed on the determination of myofibrillar fragmentation.

The myofibrillar fragmentation index (MFI) was determined by the turbidity method on five samples of ovine Longissimus muscle, aged for either 1 or 5 days and processed from the frozen state over 3 weeks. These samples were homogenized at one of four speeds (11,000, 16,000, 19,000 or 22,000 rpm) with a shaft type homogenizer (Ystral) and at 15,000 rpm with a blade type homogenizer (Omni mixer). At all speeds except 15,000 or 22,000 rpm samples were homogenized for either one or two bursts of 30 s giving a total of eight different treatments. There was a significant interaction between ageing and treatment (P<0.05) and ageing and muscle sample (P<0.001). Regarding the Omni mixer result as a reference standard, the closest result from the Ystral homogenizer was obtained at 19,000 rpm after two bursts of 30 s. These two treatments gave similar mean values for samples aged 5 days. Repeatability between duplicates did not differ significantly for the various treatments (r=0.55). A large difference was found between samples aged for 1 and 5 days and the difference was greater for the Ystral homogenizer than the Omni mixer. The Omni mixer values for 1 day aged samples were 38 units less than for samples aged for 5 days while this difference for the Ystral homogenizer was 63 units. As speed of homogenization increased with the Ystral homogenizer the overall mean values increased and the values were always greater after two bursts of 30 s compared to one burst of 30 s. The results suggest that the Ystral homogenizer may be better for detecting differences between treatments than the Omni mixer. At the slower speeds myofibrils consist of more sarcomeres and intermyofibril linkages could still be observed. This contrast was also seen between samples aged for 1 or 5 days with much greater degradation visually observed in the latter samples. This was supported by the quantitative data.

Use of a generalized linear mixed model to reduce excessive heterogeneity in petroleum spray oil bioassay data.

High heterogeneity (variance) is a consistent and significant problem in petroleum spray oil derived bioassay data. It can mask small statistical differences sought by researchers in relative toxicity or potency analysis. To compensate for excessive heterogeneity, researchers often use very large sample sizes to improve statistical accuracy. We present a statistical method of modeling heterogeneity extending the conventional probit model by adding random effects to it. We illustrate this by reanalyzing 26 of our own published experiments. Twelve of these had excessive heterogeneity that was significantly reduced in ten cases by including random replicate effects with or without random slopes. Five were further improved by allowing a nonlinear (spline) response. The result was tighter confidence intervals for the estimates of lethal dose.

Post blocking gone too far! Recovery of information and spatial analysis in field experiments.

Federer (1998, Biometrics 54, 471-481) presents two analyses of field data in which high-order polynomials are fitted as random regressions to remove spatial variation. We challenge the justification of this approach and suggest some alternatives.

The analysis of the NSW wheat variety database. I. Modelling trial error variance.

The retrospective analysis of a large database on wheat variety testing in New South Wales (NSW) is considered. This analysis involved three key steps. Initially error variance heterogeneity is modelled, indicating significant differences in error variance due to trial location, year of trialling, sowing date and trial mean yield. The implication of this modelling for the estimaion of variance components is discussed.

The analysis of the NSW wheat variety database. II. Variance component estimation.

The efficiency of various trialling systems for wheat variety evaluation in New South Wales (NSW) is considered. This involved the estimation of the variance components due to genotype, genotype-by-year, genotype-by-location and genotype-by-year-by-location. It is shown that there is a significant reduction in the magnitude of these variance components by the inclusion of the interaction of genotype maturity, winter habit and aluminium tolerance with environment.

Investigations into the toxicity of Echium plantagineum in sheep. 1. Field grazing experiments.

A field grazing trial was undertaken to monitor the health and production of crossbred sheep grazing pasture where Echium plantagineum constituted a considerable proportion of the available forage. The trial, conducted for 19 months over successive grazing seasons, demonstrated a significant difference in production, with sheep on the E. plantagineum pasture being lighter and growing less wool compared with sheep on Echium-free pasture. No mortalities involving pyrrolizidine alkaloid poisoning were recorded in sheep grazing E. plantagineum, although there was histological evidence of moderately severe liver damage associated with high liver copper concentrations in at least one sheep following the grazing of large quantities of the plant.

Seasonality of ovulation and estrus, and the ram effect in poll dorset ewes.

The effects of month on the proportion of Poll Dorset ewes expressing estrus and ovulating and on their ovulation rate when continually exposed to vasectomised rams and the effects of isolating the ewes from rams in winter/early spring were examined during 15 months in New South Wales, Australia. The percentage of ewes ovulating and their ovulation rate varied from 23% and 1.10 in November to an average of 99% and 1.85 in April and June, and the proportion of ewes expressing estrus followed a similar trend. Some ewes (6.8%) ovulated throughout the 15 months, and the average breeding season was 294 (SE = 6.1) days. Isolating ewes from rams in late winter/early spring significantly decreased the proportion of ewes ovulating from September to November, increased the proportion in December, but did not significantly alter ovulation rate. The variability in reproductive measures during spring offers scope for selection; with the ram effect, improvements in spring joining results from Dorset ewes are possible.