Journal of Animal Breeding and Genetics最新文献

Evaluating optimal mating combinations in large populations poses significant combinatorial and computational challenges. To address this, we propose a method to optimise mating combinations in composite cattle populations, incorporating heterosis and genetic variability. Leveraging integer linear programming, our approach maximises expected offspring merit, outperforming random mating systems. A robust mathematical model and specialised software were developed to implement the method, demonstrating its effectiveness on a real dataset. Notably, results reveal a 14.8% superiority over random mating averages and a 12.4% advantage over random mating maxima. The method's flexibility and adaptability enable constraint inclusion and application to diverse species and genomic data, making it an indispensable tool for enhancing mating selection efficiency and effectiveness in composite beef cattle breeding programmes.

Fitness traits described as a ratio often display non-normal distributions; consequently, transformations are frequently applied to improve normality prior to the estimation of genetic parameters. However, the impact of different transformations on genetic parameter estimates depends on the dataset at hand. The objective of this study was to evaluate the effects of eight common transformations (z-score, log, square root, probit, arcsine, logit, Box-Cox and Yeo-Johnson) on genetic parameter estimates for non-normal fitness traits in turkeys. Three fertility traits in turkeys were analysed. Egg production rate, egg fertility rate and hatch of fertile eggs rate phenotypes were collected on 6667 turkeys. All three phenotypes exhibited a significant level of non-normality. An informative pedigree file for the phenotyped birds was generated and consisted of 8612 animals. A mixed linear model that included the hatch year and regression on body weight at 18 weeks of age as fixed effects was used to analyse the transformed and untransformed phenotypes. To make the untransformed and transformed data comparable, they were all standardised to the same mean and variance. Results showed that the transformations significantly impacted genetic parameter estimates. In fact, the percentage variations in the estimates of the heritabilities of the three traits compared to the non-transformed data ranged from -80% to 45%. Across the different comparison criteria, the Box-Cox transformation seems to have the advantage compared to the other methods. Furthermore, it resulted in the highest heritability estimates. Although the genetic correlations showed fewer differences across transformations, the Spearman rank correlations ranged between 0.87 and 1, indicating some re-ranking. These findings suggest that the choice of data transformation impacts inferences on the genetic properties of non-normal traits, and careful consideration of the transformation method is needed prior to genetic analysis of skewed fitness data in turkeys and potentially other agricultural species. The results provide guidelines for the appropriate choice of transformations given observed levels of deviation from normality.

In genomic selection, balancing genetic gain with the preservation of genetic diversity is a critical challenge, requiring innovative approaches to parent selection. Traditional methods risk losing valuable genetic diversity by not fully accounting for the complex patterns of haplotype distribution. To address this, we developed a novel haplotype similarity measure that estimates the genetic similarity amongst offspring from parent pairs by analysing segregating marker patterns and the covariance of additive genetic effects between potential parental gametes. This measure is encapsulated in a novel similarity matrix that quantifies parental genetic relationships and their Mendelian sampling variance, facilitating the selection of parents with diverse haplotypes to maintain genetic diversity. Our method was evaluated through simulation studies and empirical data analysis, indicating that the similarity matrix can help preserve haplotype diversity and potentially improve long-term genetic gains compared to traditional selection methods. These results suggest that the similarity matrix could contribute to more efficient and sustainable genomic selection programs, although further research is necessary to fully understand its impact.

The calving ease (CE) trait has recently been introduced in animal breeding programs, and studies on its genetic variability have proven essential for the genetic advancement of animals. An increase in dystocia rates in primiparous heifers has been observed due to the birth of heavier calves. As this is a new trait, no established model exists for its analysis. Thus, this study developed different statistical models to evaluate CE, aiming to estimate genetic parameters and perform genomic predictions for this trait. A total of 39,664 records of CE from primiparous Nellore heifers born between 2010 and 2017 were collected, belonging to the animal breeding program of the Nellore breed in Brazil, managed by the National Association of Breeders and Researchers (ANCP, Ribeirão Preto, Brazil). The results showed that direct heritability estimates ranged from 0.11 to 0.24, while maternal heritability estimates ranged from 0.09 to 0.11. Despite these low to moderate heritability estimates, the trait has potential for direct selection. Models incorporating the heifer category (HC) (early or traditional) and birth weight (BW), as well as the dam age at calving (DAC) and BW, were more suitable for estimating variance components. On the other hand, the model that considered only the HC and the model that included the DAC excelled in predictive ability, making them more appropriate for genomic predictions.

Change in climate over the past years and its impact on the environment have necessitated the inclusion of resilience traits in the breeding objectives of dairy cattle. However, the relationship between resilience and other traits of economic importance in dairy production is currently not well known. This study examined the genetic parameters and relationships among resilience, fertility and milk production traits in dairy cattle in Kenya. Indicators of general resilience and heat tolerance were defined from the first parity test-day milk yield records. Indicators of general resilience included variance of actual deviations (LnVar1), variance of standardised deviations (LnVar2), lag-1 autocorrelation (r_auto) and skewness (Skew) of standardised deviations in milk yield. Heat tolerance indicators at temperature-humidity index 80 included the slope of the reaction norm (Slope), absolute slope of the reaction norm (Absolute), and the intercept of the reaction norm model (Intercept). Cows with > 50% taurine genes had lower age at first calving (AFC), longer calving intervals (CI) and higher test-day milk yield (MY). The heritability estimates of AFC, CI and MY were 0.17 ± 0.033, 0.06 ± 0.012 and 0.35 ± 0.021, respectively. The repeatability estimates of CI and MY were 0.06 ± 0.012 and 0.47 ± 0.009, respectively. The low heritability and non-significant permanent environmental variance of CI showed that CI is heavily influenced by external factors, such as management practices. AFC was negatively genetically correlated with both CI (-0.88 ± 0.077) and MY (-0.53 ± 0.059) showing that animals that attain sexual maturity earlier exhibit longer CI and higher milk production. A positive genetic correlation (0.62 ± 0.077) between CI and MY shows that high-yielding cows face challenges in maintaining shorter calving intervals. Heritability estimates of nearly all resilience indicators were significant and ranged from 0.05 to 0.34. Heat tolerance indicators showed low to non-significant genetic correlations with general resilience indicators, suggesting that different genetic factors are involved in responses to different types of disturbances. There was a generally positive genetic correlation between resilience and fertility, implying that resilient animals might have better fertility. All indicators, except LnVar1 and LnVar2, revealed an antagonistic genetic relationship between resilience and milk production. The findings present an opportunity for including resilience in the development and application of selection indices in dairy cattle, especially for the tropics.

Barrel racing is a competitive timed rodeo event that challenges horses and riders to complete a cloverleaf pattern around three barrels in the fastest time possible. In this study, we aimed to estimate the genetic parameters of barrel racing time (BRT) and evaluate the most suitable statistical model for its analysis. We compared a repeatability model and three random regression models (RRM) to analyse the longitudinal BRT data in Brazilian Quarter Horses. A total of 356,877 BRT records from 14,108 horses that competed in various events held across Brazil between 2010 and 2024 were analysed. The cubic RRM provided the best fit to the data, and therefore, the results from this model were presented in detail. Heritability estimates for BRT varied by age (0.15-0.24), with the highest estimates observed between 36 and 54 months, suggesting that selection at younger ages could be most effective. Genetic correlations between BRT at different ages were generally strong (> 0.8). The lowest mean genetic correlation of 0.65 (0.09) was observed between BRT at 36 and 144 months of age. Thus, selecting the best-performing horses at younger ages should result in favourable genetic progress at older ages. Phenotypic trends showed an improvement in BRT over the years, although no significant genetic progress was observed, likely due to the absence of an official breeding programme and the lack of use of estimated breeding values for BRT. These findings highlight the need for a more strategic approach to genetic selection in Quarter Horses to optimise BRT performance. The substantial genetic variation identified for BRT indicates that, if properly exploited, this trait could be significantly improved in the future, ultimately enhancing competition outcomes for Brazilian Quarter Horses in barrel racing.

Long-term directional selection in a population can severely reduce the additive genetic variability for the desired trait. Therefore, it is really important to assess the genetic parameters of a population at definite time intervals for designing effective breeding programmes. The present study was designed for the genetic evaluation of a White Leghorn strain (IWI) which has been intensely selected for higher egg numbers up to 64 weeks of age at ICAR-Directorate of Poultry Research, Hyderabad, Telangana, India. The genetic parameters were estimated for egg production up to 24 (EP24), 32 (EP32), 40 (EP40), 52 (EP52), 64 (EP64) and 72 (EP72) weeks of age along with other traits (egg weight, reproductive and body weight traits) utilising six models with different random effects in a Bayesian framework. The normalised mean value for the primary selection trait, EP64, was 218.16 ± 1.24 eggs while the total egg production up to 72 weeks was 242.85 ± 1.72. Comparative evaluation of different models based on Deviance Information Criterion (DIC) revealed that model 6 (including direct additive, maternal genetic and maternal permanent environment effects) was the most accurate for early production traits like EP24, whereas model 3 (including direct additive and maternal genetic effects) was the best-fitted for egg production traits like EP32 and EP40. The trait variance for late egg production traits like EP52, EP64 and EP72 was best defined by model 1, which only included the direct additive effect. Furthermore, it was found that the posterior mean additive heritability of egg production traits declined as the laying cycle progressed. Particularly, for later traits like egg production up to 52 (EP52), 64 (EP64) and 72 (EP72) weeks, the direct additive heritability estimate was very low (0.02 ± 0.009; 0.04 ± 0.01 and 0.02 ± 0.0009 respectively). Subsequently, posterior genetic correlations (r_G) were estimated between late egg production traits and the rest of the traits. It was found that there was a highly negative r_G between egg weight at 40 weeks (EW40), body weight at 52 weeks (BW52) and the later egg production traits (EP52, EP64 and EP72). Therefore, depending on the trait correlations, multivariate analysis was done for improving the accuracy of evaluations. Posterior estimates of direct additive heritability for EP52 increased to 0.08 ± 0.05 when analysed together with EW40 and BW52 traits in a multivariate model, whereas the corresponding estimate for EP64 increased to 0.11 ± 0.05 when analysed with EW40 and BW52. Based on these results, we can conclude that although the additive genetic variability for the selection trait is very low in the population, multitrait evaluations can be more effective for making selection decisions for higher egg production in White Leghorns.

In chickens, economically important traits are commonly controlled by multiple genes and are often correlated. The genetic mechanisms underlying the correlated phenotypes likely involve pleiotropy or linkage disequilibrium, which is not handled properly in single-trait genome-wide association studies (GWAS). We employed factor analytical models to estimate the value of latent traits to reduce the dimensionality of the adjusted phenotypes. The dataset included phenotypes from 369 F2 chickens, categorised into six observable classes, namely body weight (BW), feed intake (FI), feed efficiency (FE), immunity (IMU), blood metabolites (BMB), and carcass (CC) traits. All birds were genotyped using a 60K SNP Beadchip. A Bayesian network (BN) algorithm was used to discern the recursive causal relationships among the inferred latent traits. Multi-Trait (MT) and Structural Equation Model (SEM) were applied for association analysis. Several candidate genes were detected across six phenotypic classes, namely the IPMK gene for BW and FI, and, the MTERF2 gene for BW and FE. The rs14565514 SNP, close to genes IPMK, UBE2D1, and CISD1, was recognised as a pleiotropic marker by both models. The NRG3 gene, located on chromosome 6, was associated with FI. CRISP2, RHAG, CYP2AC1, and CENPQ genes, located on chromosome 3, were detected for BMB through both MT- and SEM-GWAS. In general, the results indicated that the SEM-GWAS is superior to MT-GWAS due to considering the causal relationships among the traits, correcting the effects of the traits on each other, and also leading to the identification of pleiotropic SNP markers.

For developing beef cattle breeding programmes, it is essential to understand the genetic basis of economically relevant traits, such as carcass, meat quality and female sexual precocity. However, the direct selection of most of these traits is a challenge for producers because of the high cost and measurement difficulty. Genetic correlation estimates between carcass and meat quality traits obtained after slaughter and sexual precocity indicator traits in Nellore are limited in the literature. Thus, this study aimed to estimate genetic parameters for longissimus muscle area (LMA), backfat thickness (BF), hot carcass weight (HCW), shear force tenderness (SF), marbling score (MARB), intramuscular fat content (IMF), age at first calving (AFC), heifer pregnancy (HP) and scrotal circumference (SC) in Nellore cattle, using pedigree and genomic information. For this, data from 6910 young bulls with phenotypic information for carcass and meat traits, 230,682 for sexual precocity indicator traits, and 17,850 animals genotyped with or imputed to the Illumina Bovine HD BeadChip were used. The (co)variance components and genetic parameters were estimated considering BLUP and single-step GBLUP (ssGBLUP) models via Bayesian inference using the GIBBSF90+ software. The multi-trait animal model included additive and residual genetic effects as random; the fixed effects of contemporary group (for all traits) and date of analysis as classes (for BF, SF and MARB); and the linear effects of age at slaughter (all carcass and meat traits) and age at yearling (YW and SC) as covariates. Heritability estimates ranged from 0.13 to 0.34 for carcass and meat quality traits, and for SC, AFC and HP, were 0.33, 0.07 and 0.29, respectively. Favourable genetic correlations were estimated between YW-HCW (0.79 ± 0.03), YW-LMA (0.28 ± 0.05), YW-SC (0.35 ± 0.03), HCW-LMA (0.44 ± 0.05), HCW-SF (-0.22 ± 0.09), HCW-SC (0.19 ± 0.05), MARB-IMF (0.90 ± 0.07), SF-IMF (-0.20 ± 0.11), BF-MARB (0.29 ± 0.08), BF-IMF (0.22 ± 0.09), BF-AFC (-0.21 ± 0.07) and BF-HP (0.24 ± 0.10). In general, the correlations between carcass traits and those of meat quality were low to moderate. Additionally, carcass and meat quality traits did not exhibit strong genetic correlations with female precocity indicators. So, to achieve significant genetic advances in female sexual indicator traits, carcass composition and meat quality, these traits must compose selection indices for Nellore cattle.

The current study investigated the application of structural equation models for genetic analysis of lifetime reproductive traits and latent variable modelling in the Murciano-Granadina goat breed. In the current investigation, data collected between 2016 and 2023 in a private dairy farm of the Murciano-Granadina goat breed in Ghale-Ganj city, located in the southern area of Kerman Iranian province were used. The investigated lifetime reproductive traits included overall litter size at birth (OLSB), overall litter size at weaning (OLSW), overall litter weight at birth (OLWB), and overall litter weight at weaning (OLWW). Four multivariate animal models, including standard (SMM), Inductive Causation algorithm-based structural equation (ICM), ICM with biological modification (ICM-BM), and fully recursive (FRM) models were fitted on the data and compared in terms of predictive ability measures including mean squared prediction error (MSE) and Pearson's correlation coefficient between the observed and predicted values (r(y, $\hat{y}$ )) of records. ICM-BM performed better than other models in terms of the lowest MSE and the highest r(y, $\hat{y}$ ). Under ICM-BM, heritability estimates were low values of 0.08, 0.08, 0.11, and 0.10 for OLSB, OLSW, OLWB, and OLWW, respectively. Genetic correlations among lifetime reproductive traits were positive and varied from 0.72 (OLSB-OLWW) to 0.95 (OLSB-OLWB). The confirmatory factor analysis technique was used to construct a latent variable named reproductive performance (RP) from the investigated lifetime reproductive traits. The posterior mean for heritability of RP was estimated at 0.06. The genetic correlations between RP and the investigated lifetime reproductive traits were high and positive, ranging from 0.92 (RP-OLSB) to 0.99 (RP-OLSW). The corresponding phenotypic correlations were also high and positive, ranging from 0.81 (RP-OLWB) to 0.95 (RP-OLSW). Considering causal structure among the traits detected via ICM-BM had more advantages for genetic evaluation of the lifetime reproductive traits in the Murciano-Granadina goat compared with SMM. The low heritability estimates implied that the studied lifetime reproductive traits and RP were mainly controlled by non-additive genetic and environmental effects which limits the efficiency of direct genetic selection for improving these traits. Furthermore, positive genetic and phenotypic correlations favoured using RP latent variable for breeding purposes.