Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires

Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo® breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k SNP panels. After imputation and quality control, 61,666 SNP were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent SNPs across all chromosomes was 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

Influence of model selection and data structure on the estimation of genetic parameters in honeybee populations

Estimating genetic parameters of quantitative traits is a prerequisite for animal breeding. In honeybees, the genetic variance separates into queen and worker effects. However, under data paucity, parameter estimations that account for this peculiarity often yield implausible results. Consequently, simplified models which attribute all genetic contributions to either the queen (queen model) or the workers (worker model) are often used to estimate variance components in honeybees. However, the causes for estimations with the complete model (colony model) to fail and the consequences of simplified models for variance estimates are little understood. We newly developed the necessary theory to compare parameter estimates that were achieved by the colony model with those of the queen and worker models. Furthermore, we performed computer simulations to quantify the influence of model choice, estimation algorithm, true genetic parameters, rates of controlled mating, apiary sizes, and phenotype data completeness on the success of genetic parameter estimations. We found that successful estimations with the colony model were only possible if at least some of the queens mated controlledly on mating stations. In that case, estimates were largely unbiased if more than 20% of the colonies had phenotype records. The simplified queen and worker models proved more stable and yielded plausible parameter estimates for almost all settings. Results obtained from these models were unbiased when mating was uncontrolled, but with controlled mating, the simplified models consistently overestimated heritabilities. This work elucidates the requirements for variance component estimation in honeybees and provides the theoretical groundwork for simplified honeybee models.

Estimation of direct and maternal genetic parameters for weaning weight in Hungarian Simmental cattle

The aim of the current research was to estimate variance components and genetic parameters of weaning weight in Hungarian Simmental cattle. Weaning weight records were obtained from the Association of Hungarian Simmental Breeders. The dataset comprised of 44,278 animals born from 1975 to 2020. The data was analyzed using the restricted maximum likelihood methodology of the Wombat software. We fitted a total of six models to the weaning weight data of Hungarian Simmental cattle. Models ranged from a simple model with animals as the only random effect to a model that had maternal environmental effects as additional random effects as well as direct maternal genetic covariance. Fixed effects in the model comprised of herd, birth year, calving order and sex. Likelihood ratio test was used to determine the best fit model for the data. Results indicated that allowing for direct-maternal genetic covariance increases the direct and maternal effect dramatically. The best fit model had direct and maternal genetic effects as the only random effect with non-zero direct-maternal genetic correlation. Direct heritability, maternal heritability and direct maternal correlation of the best fit model was 0.57, 0.16 and -0.78 respectively. The result indicates that problem of (co-)sampling variation occurs when attempting to partition additive genetic variance into direct and maternal components.

Estimation of genetic parameters for early reproduction traits in Beetal goat

Summary The evaluation of early reproduction traits in Beetal goat was performed for possible effects of genetic and non-genetic factors on litter size at birth (LSB), litter size at weaning (LSW), litter weight at birth (LWB), litter weight at weaning (LWW) and age at first kidding (AFK). The data records consisted of information of pedigree and targeted traits pertained to 223 does born to 25 sires and 122 dams between the years 2004 to 2019. A general linear model was used for assessment of non-genetic factors such as period of birth, type of birth and dam’s weight at kidding on studied traits. Genetic evaluation of targeted traits was done to estimate variance components and genetic parameters under dyadic mixed modelling. The estimates of least-square means for LSB, LSW, LWB, LWW and AFK were observed as 1.27 ± 0.03, 1.25 ± 0.03, 3.24 ± 0.07 kg, 13.08 ± 0.30 kg and 27.56 ± 0.58 months, respectively. Only the period of birth showed significant (P < 0.05) effects for targeted traits in this study. The estimates of direct heritability for LSB, LSW, LWB, LWW and AFK were low in magnitude as 0.08, 0.03, 0.10, 0.03 and 0.06, respectively. The moderate to high genetic and phenotypic correlations among litter traits indicate simultaneous improvement for these traits. It was concluded that low ranged direct heritability estimates for targeted traits indicated modest scope for genetic improvement of reproductive efficiency in Beetal goat through selection and, therefore, adoption of improved managerial practices is necessary to improve reproductive efficiency of Beetal goat.

Heritable variation in swimming performance in Nile tilapia (Oreochromis niloticus) and negative genetic correlations with growth and harvest weight

Nile tilapia is predominantly produced in smallholder ponds without aeration. We hypothesize that Nile tilapia with high oxygen uptake efficiency (O2UE) may perform better under these conditions than Nile tilapia with low O2UE. Critical swimming speed (Ucrit, in cm s−1) is a potential indicator for O2UE. Our objectives were to estimate variance components for Ucrit and fish size at swim testing early in life, and genetic correlations (rg) between Ucrit with harvest weight (HW) and daily growth coefficient (DGC) later after grow-out in a non-aerated pond. Substantial heritability was found for absolute Ucrit (0.48). The estimated rg between absolute Ucrit and fish size at testing were all strong and positive (range 0.72–0.83). The estimated rg between absolute Ucrit and HW, and absolute Ucrit and DGC were − 0.21 and − 0.63 respectively, indicating that fish with higher absolute Ucrit had lower growth in the non-aerated pond as compared to fish with lower absolute Ucrit. These results suggest a juvenile trade-off between swimming and growth performance where fish with high Ucrit early in life show slower growth later under conditions of limited oxygen availability. We conclude that Ucrit in Nile tilapia is heritable and can be used to predict growth performance.

Estimates of Genetic Variation for Angus Gestation Length

The objective of this study was to estimate variance components for gestation length within the American Angus breed. Data (n = 148,649) from the American Angus Association, containing cattle born between 2000 to 2020, were used for the analysis. Based on a cow’s reproductive biology, gestation length records were determined acceptable if between 266 and 290 days (n = 114,857). Gestation length mean and standard deviation were 278.6 and 4.6 days, respectively. Average Information Restricted Maximum Likelihood (AIREML) was used to estimate variance components for the gestation length. A single trait animal model included random effects of direct and maternal additive genetic variance and fixed effects of dam age rounded to the nearest year, calf gender and contemporary group. Contemporary group was determined as herd, year of birth and season of birth combinations. Contemporary groups containing less than five animals were excluded from analysis. Phenotypic variance for gestation length was estimated at 18.9. Direct and maternal heritability estimates for gestation length were 0.59±0.01 and 0.10±0.01, respectively. Further analysis evaluated the fixed effects of year and dam age on gestation length. From 2000 to 2020, an increase in one year decreased (P &lt; 0.01) gestation length by 0.09 days. Gestation length differed (P &lt; 0.01) by age of dam. Gestation length LSMEANS for 2, 3, 4, 5, 6, 7 and 8 year old cows were 277.7, 278.6, 279.0, 279.2, 279.3, 279.5 and 279.6, respectively. Heritability estimates within Angus breed suggest gestation length has a high capacity for genetic change. Results suggest gestation length has decreased over the past two decades and is shorter in younger cows.

Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials

Identification of stable and high-yielding genotypes is a real challenge in peach breeding, since genotype-by-environment interaction (GE) masks the performance of the materials. The aim of this work was to evaluate the effectiveness of parameter estimation and genotype selection solving the LMM under frequentist and Bayesian approaches. Fruit yield of 308 peach genotypes were assessed under different seasons and replication numbers arranged in a completely randomized design. Under the frequentist framework the restricted maximum likelihood method to estimate variance component and genotypic prediction was used. Different models considering environment, genotype and GE effects according to the likelihood ratio test and Akaike information criteria were compared. In the Bayesian approach, the mean and the variance components were assumed to be random variables having a priori non-informative distributions with known parameters. According the deviance information criteria the most suitable Bayesian model was selected. The full model was the most appropriate to calculate parameters and genotypic predictions, which were very similar in both approaches. Due to imbalance data, Cullis’s method was the most appropriate to estimate heritability. It was calculated at \(0.80,\) and selecting above 5% of the genotypes, the realized gain of 14.81 kg.tree1 was attained. Genotypic frequentist and Bayesian predictions showed a positive correlation (r = 0.9991; P = 0.0001). Since the Bayesian method incorporates the credible interval for genetic parameters, genotypic Bayesian prediction would be a more useful tool than the frequentist approach and allowed the selection of 17 high-yielding and stable genotypes.

The Deviation between Dairy Cow Metabolizable Energy Requirements and Pasture Supply on a Dairy Farm Using Proximal Hyperspectral Sensing

This study aimed at determining the extent to which the deviation of daily total metabolizable energy (MEt) requirements of individual cows from the metabolizable energy (ME) supplied per cow (DME) varied throughout the production season in a pasture-based dairy farm using proximal hyperspectral sensing (PHS). Herd tests, milk production, herbage and feed allocation data were collected during the 2016–2017 and 2017–2018 production seasons at Dairy 1, Massey University, New Zealand. Herbage ME was determined from canopy reflectance acquired using PHS. Orthogonal polynomials were used to model lactation curves for yields of milk, fat, protein and live weights of cows. Daily dietary ME supplied per cow to the herd and ME requirements of cows were calculated using the Agricultural Food and Research Council (AFRC) energy system of 1993. A linear model including the random effects of breed and cow was used to estimate variance components for DME. Daily herd MEt estimated requirements oscillated between a fifth above or below the ME supplied throughout the production seasons. DME was mostly explained by observations made within a cow rather than between cows or breeds. Having daily estimates of individual cow requirements for MEt in addition to ME dietary supply can potentially contribute to achieving a more precise fit between supply and demand for feed in a pasture-based dairy farm by devising feeding strategies aimed at reducing DME.

Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods

The generated signals generally contain a large amount of background noise when the mechanical bearing fails, and the fault signals present nonlinear and non-Gaussian feature, which have heavy tail and belong to α -stable distribution ( 1 < α < 2 ); even the background noises are also α -stable distribution process. Then it is difficult to obtain reliable conclusion by using the traditional bispectral analysis method under α -stable distribution environment. Two improved bispectrum methods are proposed based on fractional lower-order covariation in this paper, including fractional low-order direct bispectrum (FLODB) method, fractional low-order indirect bispectrum (FLOIDB) method. In order to decrease the estimate variance and increase the bispectral flatness, the fractional lower-order autoregression (FLOAR) model bispectrum and fractional lower-order autoregressive moving average (FLOARMA) model bispectrum methods are presented, and their calculation steps are summarized. We compare the improved bispectrum methods with the conventional methods employing second-order statistics in Gaussian and S α S distribution environments; the simulation results show that the improved bispectrum methods have performance advantages compared to the traditional methods. Finally, we use the improved methods to estimate the bispectrum of the normal and outer race fault signal; the result indicates that they are feasible and effective for fault diagnosis.

Estimation of variance components for body weight of grasscutters (Thryonomys swinderianus) using a repeatability model

Genetic parameters for growth and other economically important traits of grasscutters are scant in literature. Therefore, the aim of this study was to estimate variance components,heritability and repeatability of body weight of grasscutters using restricted maxim um likelihood method in a repeatability animal model. Sixteen grasscutter families were used for the study. Each family was made up of one male and four females. Each grasscutter has four repeated records giving a total of 320. The pedigree consisted of 80 animals, progenies of 16 sires and 16 dams. Fixed factors included in the model were family and sex. The WOMBAT program was used for the analysis. The heritability of body weight of grasscutters ranged from 0.23±0.04 to 0.68±0.10, thus implying that mass selection will be appropriate for this population. The repeatability estimates ranged from 0.82±0.08 to 0.93±0.11. It can be concluded that the number of body weight records was a good indicator of the animal's growth potential and that mass selection will be reliable.