Journal of Animal Breeding and Genetics最新文献

Herd health is one of the key problems influencing the efficiency of the dairy industry. Genetic selection, with a focus on animal health, is important for herd improvement. This study aimed to estimate genetic parameters for health traits and their correlations with fertility and milk production traits in dairy cattle. Based on records from 58,549 lactating cows calved between 2015 and 2021, a total of 24 health traits (six composite health traits and 18 independent health traits), four fertility traits and five milk production traits were analysed. First, linear and threshold animal models were used to estimate the variance components and heritabilities of the health traits. Second, a bivariate linear animal model was used to estimate genetic correlations among all 24 health traits. Finally, a bivariate linear animal model based on records from the first lactation was used to estimate the correlations between health traits and fertility or milk production traits. The results showed that all health traits had low heritabilities, ranging from 0.002 (0.001) to 0.048 (0.004) in the linear model and from <0.001 (0.021) to 0.226 (0.035) in the threshold model. Genetic correlations between health traits across categories were generally low, whereas the relatively high genetic correlations were found between health traits within the same category. In this study, only a few significant and moderate genetic correlations were observed between health traits and fertility or milk production traits. Clinical mastitis showed relatively moderate correlations with fertility traits, ranging from 0.277 (0.113) (interval from first to last insemination) to 0.401 (0.104) (calving interval). Moreover, there were moderate genetic correlations between hoof health and milk production traits. The results from the current study will support balanced dairy breeding to genetically improve disease resistance in dairy cows.

Multiple trait animal models (MTM) allow to estimate the breeding values (BV) of several traits simultaneously while accounting for genetic and environmental correlations among them. However, relationships among traits may not be reciprocal but rather causal in nature. In these cases, and given a causal network, structural equations models (SEM) arise as a more appropriate methodology. Although MTM and SEM have been shown to be parametrically equivalent, the estimated breeding value (EBV) obtained from either one or the other should be interpreted differently. In this study, we investigated the impact of using these estimates on the response to selection for a causal network comprising five different traits through a stochastic simulation experiment. Three different selection targets were assayed, involving traits located upstream, midstream and downstream this causal network. We first considered the case in which traits were causally related but not genetically correlated. The current results support our hypothesis that MTM will absorb causal relationships as genetic correlations and, consequently, change the response to selection achieved as compared with SEM. We found no differences on the response to selection when the target trait was located at the top of the causal network, but noticeable differences were detected on upstream traits when selection pressure was placed on midstream or downstream traits. We also assayed a scenario in which causal effects and genetic correlations act simultaneously and found that selection based on BVs estimated using SEM diminished the indirect response in traits upstream the causal network.

Jinhua pigs have excellent meat quality and intramuscular fat content (IMF). Crossbreeding of Jinhua with Duroc pigs with high productivity was conducted to develop a new composite breed that possesses the beneficial characteristics of both breeds. The objective of this study was to estimate the crossbreeding parameters (additive breed, dominance, and epistatic loss effects) using the Kinghorn model and genetic parameters (heritability and genetic correlation) for carcass and meat quality traits by analysing the phenotypic data of Jinhua, Duroc, and their crossbred pigs. Backfat thickness at the thinnest point above the 9th to 13th thoracic vertebrae (BF), longissimus muscle area between the 4th and 5th thoracic vertebrae (LMA), meat shear force value (SFV), and IMF were measured. The additive breed effects were significant for all traits: 1.59 cm, −8.30 cm², −6.38 lb/cm², and 1.76% for BF, LMA, SFV, and IMF, respectively. The dominance effect was significant for LMA (7.41 cm²) and IMF (−2.46%), whereas the epistatic loss effect was significant for only LMA (−15.18 cm²). The estimated heritability values were high, ranging from 0.58 for IMF to 0.76 for LMA. A negative but non-significant genetic correlation of −0.11 was estimated between BF and IMF; however, previous studies have reported that the genetic correlation between these traits is moderately positive in modern western pigs. Our results imply that, with the estimation of crossbreeding and genetic parameters, genetic improvement could be implemented to produce a new composite breed with good meat quality and productivity, to meet Japanese market requirements, by crossbreeding Jinhua and Duroc pigs.

The inheritance of racing performance in Thoroughbreds is of interest to breeders and geneticists. Therefore, the genetic parameters of racing performance have been investigated in various populations of Thoroughbreds. However, the predictability of the racing performance of a racehorse has not been assessed well. In this study, we built mixed-effects models for Japanese Thoroughbreds and assessed their predictability of racing performance. We used the average velocity as an index of racing performance and treated each category of racecourse and distance as different traits. Model selection using the deviance information criterion showed that explanatory variables, such as race, age and jockey effects are important for racing performance. Using the selected models, the phenotypic values of horses born after 2009, adjusted using the entire dataset, were predicted with the breeding values estimated from a partial dataset until 2010. The correlation coefficients ranged from 0.000 to 0.235 (average of 0.084 ± 0.066) and were higher for longer distances. When predicting the graded race winners born after 2009 from the partial dataset until 2010, the area under the curve values ranged from 0.516 to 0.776 (average of 0.613 ± 0.073) and were also higher for longer distances. Although these results indicate the predictability of racing performance, further efforts, including exploring more suitable racing performance indices and refining statistical modelling, are required for improvement.

The aim of the present study is to carry out a risk analysis and Bayesian estimates of genetic effects on calf survival in Hardhenu cattle using data records of 2593 calves born to 102 sires and 790 dams over 25 years. The Bayesian analysis using Gibbs sampling was employed towards threshold animal models to estimate direct and maternal effects on animal survival of studied population. The results showed that mortality from birth to 3 months of age (S1), birth to 6 months of age (S2) and birth to 12 months of age (S3) was 10.22, 12.88 and 14.65%, respectively. It was revealed from the results of logistic regression analysis that the male animals had greater risk (1.41–1.61 times) of mortality during S1, S2 and S3 as compared to female animals. However, calves born during rainy season had higher risk (1.36 to 1.44 times) of mortality than calves born during winter season. Among died animals, the simultaneous infection of respiratory and digestive diseases had leading contribution (26.84%–30.19%) to deaths while alone of them contributed to 18%–20% only. On evaluation of six threshold animal models, model 1 was found to be most appropriate model and the Bayesian estimates (95% highest posterior density confidence intervals) of direct additive heritability for S1, S2 and S3 under model 1 were 0.15 ± 0.07 (0.04–0.23), 0.23 ± 0.12, (0.02–0.44) and 0.26 ± 0.06 (0.08–0.41), respectively. It was concluded that the inclusion of survival traits in existing selection criteria may be helpful to increase calf survival and ultimately economic gain in the dairy herd.

The breeding objectives of Central Highland goats rearing under a low-input production system were defined through a participatory proportional piling method and bio-economic model. Additionally, the economic values and relative economic value of the breeding objective traits were derived. A participatory proportional piling method was used to estimate the relative weights of farmers attached to a list of goat traits identified, and the relative weights were statistically evaluated using a generalized multinomial logit model analysis. A bio-economic model was used to compute the economic values of the identified traits. The most important traits for selection of does according to farmer's preference were body size, coat colour, post-weaning growth rate and weaning rate with a relative weight (odds ratio) of 1.58, 1.38, 1.37 and 1.13, respectively. Goats with dark red followed by light red coat colour were the most preferred (p < 0.001) by goat keepers compared with white-coloured goats. Farmers were more likely (p < 0.001) to allocate higher scores for does-bearing twins than for single and triplet-bearing does. Using the bio-economic model (economic value and relative economic value), post-weaning growth rate, weaning rate, and six-month weight (body size) were identified as the most important traits and if the mean of these traits is changed by one genetic standard deviation, the change in profit will range from 2.06 to 3.03 $ doe⁻¹ year⁻¹. Therefore, the most important traits for the selection of Central Highland goats according to the economic-based method were post-weaning weight gain, weaning rate and body size (six-month weight). Besides, coat colour was the second preferred trait by goat keepers next to body size. Thus, this aesthetical trait should be included in the designed breeding programme besides economically important quantitative traits. The combination of the participatory proportional piling method and bio-economic model would give better insights to explore the trait preferences of farmers and enhance profitability. The economic values of traits estimated in this study can be used for the construction of selection indices for Central Highland goats.

Nitrogen (N) use efficiency (NUE) is an economically important trait for dairy cows. Recently, we proposed a new N efficiency index (NEI), that simultaneously considers both NUE and N pollution. This study aimed to validate the genomic prediction for NEI and its composition traits and investigate the relationship between SNP effects estimated directly from NEI and indirectly from its composition traits. The NEI composition included genomic estimated breeding value of N intake (NINT), milk true protein N (MTPN) and milk urea N yield. The edited data were 132,899 records on 52,064 cows distributed in 773 herds. The pedigree contained 122,368 animals. Genotypic data of 566,294 SNP was available for 4514 individuals. A total of 4413 cows (including 181 genotyped) and 56 bulls (including 32 genotyped) were selected as the validation populations. The linear regression method was used to validate the genomic prediction of NEI and its composition traits using best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP). The mean theoretical accuracies of validation populations obtained from ssGBLUP were higher than those obtained from BLUP for both NEI and its composition traits, ranging from 0.57 (MTPN) to 0.72 (NINT). The highest mean prediction accuracies for NEI and its composition traits were observed for the genotyped cows estimated under ssGBLUP, ranging from 0.48 (MTPN) to 0.66 (NINT). Furthermore, the SNP effects estimated from NEI composition traits, multiplied by the relative weight were the same as those estimated directly from NEI. This study preliminary showed that genomic prediction can be used for NEI, however, we acknowledge the need for further validation of this result in a larger dataset. Moreover, the SNP effects of NEI can be indirectly calculated using the SNP effects estimated from its composition traits. This study provided a basis for adding genomic information to establish NEI as part of future routine genomic evaluation programs.

The accuracy of genetic selection in dairy can be increased by the adoption of new technologies, such as the inclusion of sequence data. In simulation studies, assigning different weights to causative single-nucleotide polymorphism (SNP) markers led to better predictions depending on the genomic prediction method used. However, it is still not clear how the weights should be calculated. Our objective was to evaluate the accuracy of a multi-step method (GBLUP) and single-step GBLUP with simulated data using regular SNP, causatives variants (QTN) and the combination of both. Additionally, we compared the accuracies of all previous scenarios using alternatives for SNP weighting. The data were simulated assuming a single trait with a heritability of 0.3. The effective population size (Ne) was approximately 200. The pedigree contained 440,000 animals, and approximately 16,800 individuals were genotyped. A total of 49,974 SNP markers were evenly placed throughout the genome, and 100, 1000 and 2000 causative QTN were simulated. Both GBLUP and ssGBLUP were used in this study. We evaluated quadratic and nonlinear SNP weights in addition to the unweighted G. The inclusion of QTN to panels led to significant accuracy gains. Nonlinear A was demonstrated to be superior to quadratic weighting and unweighted approaches; however, results from Nonlinear A were dependent on the equation parameters. The unweighted approach was more suitable for less polygenic scenarios. Finally, SNP weighting might help elucidate trait architecture features based on changes in the accuracy of genomic prediction.

Following the recent domestication process of the European rabbit (Oryctolagus cuniculus), many different breeds and lines, distinguished primarily by exterior traits such as coat colour, fur structure and body size and shape, have been constituted. In this study, we genotyped, with a high-density single-nucleotide polymorphism panel, a total of 645 rabbits from 10 fancy breeds (Belgian Hare, Champagne d'Argent, Checkered Giant, Coloured Dwarf, Dwarf Lop, Ermine, Giant Grey, Giant White, Rex and Rhinelander) and three meat breeds (Italian White, Italian Spotted and Italian Silver). ADMIXTURE analysis indicated that breeds with similar phenotypic traits (e.g. coat colour and body size) shared common ancestries. Signatures of selection using two haplotype-based approaches (iHS and XP-EHH), combined with the results obtained with other methods previously reported that we applied to the same breeds, we identified a total of 5079 independent genomic regions with some signatures of selection, covering about 1777 Mb of the rabbit genome. These regions consistently encompassed many genes involved in pigmentation processes (ASIP, EDNRA, EDNRB, KIT, KITLG, MITF, OCA2, TYR and TYRP1), coat structure (LIPH) and body size, including two major genes (LCORL and HMGA2) among many others. This study revealed novel genomic regions under signatures of selection and further demonstrated that population structures and signatures of selection, left into the genome of these rabbit breeds, may contribute to understanding the genetic events that led to their constitution and the complex genetic mechanisms determining the broad phenotypic variability present in these untapped rabbit genetic resources.

In most cases, inbreeding is expected to have unfavourable effects on traits in livestock. The consequences of inbreeding depression could be substantial, primarily in reproductive and sperm quality traits, and thus lead to decreased fertility. Therefore, the objectives of this study were (i) to compute inbreeding coefficients using pedigree (F_PED) and genomic data based on runs of homozygosity (ROH) in the genome (F_ROH) of Austrian Pietrain pigs, and (ii) to assess inbreeding depression on four sperm quality traits. In total, 74,734 ejaculate records from 1034 Pietrain boars were used for inbreeding depression analyses. Traits were regressed on inbreeding coefficients using repeatability animal models. Pedigree-based inbreeding coefficients were lower than ROH-based inbreeding values. The correlations between pedigree and ROH-based inbreeding coefficients ranged from 0.186 to 0.357. Pedigree-based inbreeding affected only sperm motility while ROH-based inbreeding affected semen volume, number of spermatozoa, and motility. For example, a 1% increase in pedigree inbreeding considering 10 ancestor generations (F_PED10) was significantly (p < 0.05) associated with a 0.231% decrease in sperm motility. Almost all estimated effects of inbreeding on the traits studied were unfavourable. It is advisable to properly manage the level of inbreeding to avoid high inbreeding depression in the future. Further, analysis of effects of inbreeding depression for other traits, including growth and litter size for the Austrian Pietrain population is strongly advised.