Journal of Animal Breeding and Genetics最新文献

This study aimed to estimate (co)variance components and genetic parameters for accumulated profitability (AFP) and profit per kilogram of liveweight gain (PFT), and their relationships with weight at 450 days of age (W450), scrotal circumference at 365 days of age (SC365), age at first calving (AFC), probability of precocious calving at 30 months of age (PPC30), age at puberty in males (APM), stayability (STAY), accumulated cow productivity (ACP), rib eye area (REA), rump fat thickness (RFT), residual feed intake (RFI), dry-matter intake (DMI), residual live weight gain (RG), and frame score (FRAME). Data of profitability from 3614 Nelore (Bos indicus) animals were used. The (co)variance components and genetic parameters were estimated using Bayesian inference in a multi-trait animal model. The heritability estimates for AFP and PFT were 0.18 and 0.02, respectively. AFP and PFT exhibited moderate to high genetic correlations with growth traits (0.64 to 0.65), carcass (0.43 to 0.44), feed efficiency (0.72 to 0.88), and frame (0.44 to 0.77), except for the correlation with RFT (-0.10 to -0.68), RFI (-0.23 to 0.28), and between PFT and DMI (0.26). Low to high genetic correlations (-0.47 to 0.76) with female reproductive traits and low correlations (-0.03 to -0.21) with male reproductive traits were observed for both traits. These results provide important information for improving economic performance by including complementary AFP and PFT tools in the selection criteria. Such traits can be strategic tools for producers when identifying animals with greater genetic potential for profitability, supporting decision-making in genetic planning and herd management.

Age at conception is a critical factor in the intensification of production systems. It is crucial for intensifying production, optimising reproductive efficiency and boosting Nellore female productivity for genetic progress. However, the low heritability estimates currently limit the effectiveness of selection responses, thereby underscoring the necessity for more precise genetic parameter estimation methods. The aim of this study was to evaluate the genetic associations between body weights at weaning and yearling, and age at conception, in Nellore females. The methodology employed in this study involved the utilisation of a mixed polychotomous threshold model, a methodological framework that has been deemed appropriate for the analysis of categorical variables characterised by an underlying continuous distribution. The records of body weights at weaning and yearling, in conjunction with female age at conception, from 796 animals, were treated as a categorical variable associated with conception success. The (co)variance components were estimated via Bayesian inference using a Gibbs sampler. The mean heritability values were 0.43 (0.27; 0.60) for weaning weight, 0.63 (0.46; 0.81) for yearling weight, and 0.19 (0.06; 0.40) for the categorical variable of age at conception. While body weights exhibited a high additive genetic correlation were (0.79; 95% CI: 0.57; 0.96), correlations were lower between the categorical variable and weaning (-0.21; 95% CI: -0.75; 0.28) and yearling (0.34; 95% CI: -0.14; 0.71) weights. The study concluded that indicators of age at conception should incorporate additional selective criteria beyond body weight in order to improve the probability of conception.

In recent years, Beef-on-Dairy (BoD) crossbreeding programs have gained momentum to enhance dairy cattle's economic and genetic merits while meeting the demand for high-quality beef. However, bulls with superior growth potential can lead to calving problems; thus, Holstein dairy farmers must decide which semen to use to avoid calving problems while producing heavier BoD calves. In this study, our objective was to genetically evaluate beef sires using a BoD crossbred reference population for three major economic traits, i.e., gestation length (GL), birth weight (BW), and calving ease (CE). A population comprising 4420 BoD calves sired by bulls from Angus (ANG), Limousin (LIM), Wagyu (WAG), and White-Belgian Blue (WBB) was used to perform a joint genetic evaluation of the sire for traits. Univariate and bivariate (linear-linear or threshold-linear) models were applied to estimate variance components and genomic breeding values using single-step methods. Estimates from CE models were transformed from the liability to the observable scale for more straightforward interpretation. Direct heritabilities for GL, BW, and CE (after transformations) ranged from 0.33 to 0.35, 0.33 to 0.37, and 0.02 to 0.14, respectively, while maternal heritabilities ranged from 0.11 to 0.17 for all traits. Generally, male BoD calves had higher probabilities for calving difficulty, with calvings being more difficult if sired by LIM (13%) as compared to ANG (7%) and WBB (9%) when considering male calves. The bivariate models outperformed the univariate models. For CE, the accuracy of predictions was up by 95% with a reduction in bias and dispersion. WBB sires were preferred when crossing with higher parity cows compared to ANG sires. These findings demonstrate that a well-structured BoD reference population enables accurate genomic evaluation of beef sires, facilitating the selection of sires that produce economically viable calves with reduced calving difficulties.

This study estimated genetic parameters for pregnancy loss (PL) in Brahman cattle and evaluated the genetic correlation of PL with growth and reproductive traits using both the pedigree relationship matrix (A) and pedigree plus genomic relationship matrix (H). Data were collected from two herds in Bolivia, focusing on three age groups: heifers, primiparous and multiparous cows. Threshold animal models were fitted to each group. Multitrait models were fitted between the PL at different age group and between PL and the following traits: adjusted weights at 450 (W450) and 550 (W550) days, scrotal circumference adjusted at 450 (SC450) and 550 (SC550) days, accumulated cow productivity (ACP), age at first calving (AFC) and stayability (STAY). The H matrix increased the heritability for PL in heifers from 0.06 to 0.11. The genetic correlation between PL in heifers and primiparous cows changed using H (from 0.18 to 0.7), and it was high between heifers and multiparous cows. Moderate-high negative genetic correlation was observed between PL and STAY, with changes in heifers when using H (-0.17 to -0.57). AFC and PL presented medium-high positive genetic correlations. Negative correlations between PL and SC450 or SC550 were found in primiparous and multiparous cows. Using H, the correlation between PL in heifers and ACP shifted from 0.08 to -0.31, showing medium-high negative correlations for the other two age groups. Genetic correlations were low between PL and W450 or W550. Genomic information allows the use of PL as a selection criterion in heifers. Selection for major sexual precocity, longevity and productivity would enable the reduction of pregnancy loss.

Reducing the number of days from birth to slaughter is one strategy to improve animal feed and environmental efficiency. The export market use yearling sheep weighing 22-30 kg and usually purchases from large and small-scale traders at an early age (lambs weighing ≥ 15 kg) for mutton production. Hence, this study aimed to derive a new trait phenotype, which helps to reduce sheep market age without adverse effects on the market weight of Menz sheep and to evaluate the extent of exploitable genetic variation in this new trait. To this end, 11,258 lambs weighing ≥ 15 kg between 66 and 395 days of age were considered in this study. Co (variance) components and heritability estimates for novel traits were estimated using the average information restricted maximum likelihood method in WOMBAT fitting the animal model. The best-fitted model was selected from six models based on likelihood ratio test and Akaike's information criterion. The days-to-market weight of 16.3% of the animals was shorter by 48.2 days (with an estimated breeding value of -26.2 days) compared to the mean of the contemporary groups. The market weight of 15.5% of the animals was higher by 1.73 kg compared to the mean of the contemporary groups. There was a phenotypic variability of deviation in age at market weight (DAMW) and deviation in weight at market age (DWMA) for the sheep population in Molalie village compared to other villages. Likewise, the genetic standard deviation for DAMW and DWMA was 25 days and 0.79 kg, respectively. Based on the best-fitted model, the direct heritability estimate for DAMW and DWMA was 0.65 and 0.57, respectively. In addition, the maternal genetic effect explains 28% of the phenotypic variation in DAMW and 26% of the phenotypic variation in DWMA. The DAMW of Menz sheep in Dargegn and Molalie villages decreased significantly by 2.113 and 1.192 days year^-1, respectively. The observed additive genetic variance for DAMW suggests further scope for genetic improvement in the flock to reduce the days-to-market weight of Menz sheep. Including this novel trait in a breeding objective could shorten days to market weight without necessarily reducing the genetic merit of the live weight included in the breeding objective.

The aim of this study was to evaluate the impact of incorporating genomic information on the estimation of genetic (co)variance components and the accuracy of breeding values for milk yield under varying thermal environments, and to identify SNPs associated with genes that play significant roles in heat tolerance. We analysed 58,070 test-day milk yield records from 3459 first lactations, collected between 1987 and 2018 from six herds. Genotypic data consisted of 870 animals genotyped for 45,405 SNP markers. Climatic data were obtained from INMET and combined into a temperature-humidity index (THI). Breeding values for test-day milk yield across THI values and days in milk were estimated using both genomic and pedigree-based random regression animal models. The model incorporating genomic information yielded higher estimates of heritability and additive genetic variance, along with improved accuracy under heat stress conditions and better modelling of genotype-by-environment interaction, making it a promising approach for predicting breeding values. GWAS results were reported based on the proportion of genetic variance explained by sliding windows of five consecutive SNPs, with regions explaining more than 1% of the variance in heat tolerance selected for further consideration. The ESRRG, IGSF5 and PCP4 genes emerged as strong candidates associated with heat tolerance in milk yield.

Procambarus acanthophorus is a freshwater crayfish species endemic to Mexico with high aquaculture potential. Monitoring genetic diversity during the early stages of domestication is crucial to avoid the erosion of adaptive potential and ensure the success of breeding programmes. This study analysed changes in genetic variability across four consecutive populations: a wild founder population (G0) and three captive-bred generations (G1, G2, G3), using five mitochondrial markers (COI, COII, COIII, tRNA-Asn/12S and D-loop) and one nuclear marker (elongation factor 2, EF-2). A total of 21 haplotypes were detected, with decreasing haplotype richness from G0 (n = 10) to G3 (n = 5), although haplotype and nucleotide diversity remained within moderate to high ranges (Hd = 0.65-0.90; π = 0.0020-0.0466), consistent with diversity levels reported in other early-stage domestication studies. AMOVA revealed that 68% of the total genetic variation was among generations, and pairwise Fst values confirmed progressive differentiation. These findings suggest that while some genetic erosion is occurring, the implemented breeding practices have preserved a substantial portion of genetic diversity during early domestication. This study provides a preliminary genetic baseline for the management of P. acanthophorus breeding programmes and underscores the need for continued monitoring using complementary nuclear markers.

General resilience in livestock can be estimated from the variability in longitudinal data and may support balanced breeding objectives by helping animals better cope with environmental stress. However, its economic value and inclusion within multi-trait selection indexes remain largely unexplored. Current genetic improvement programs for sheep primarily focus on wool growth, reproduction and lean meat production. This study developed a bioeconomic model to estimate the economic value of general resilience, using the natural logarithm of variance of wool fibre diameter as a resilience indicator trait in sheep. A commercial Merino sheep enterprise was considered, and the economic value of resilience was derived from its association with health, mortality, and energy allocation for production. The breeding objective included key traits related to meat production, reproduction, and wool quality and quantity, to compare the economic impact of resilience with these traits. Among 13 traits, resilience contributed 2.02% to the total value of the index and ranked 11th in economic importance. After 10 years of simulated selection, resilience was expected to improved by 0.07 genetic standard deviations, translating to a modest economic gain of $0.08 per ewe. A sensitivity analysis was conducted to assess the impacts of increasing the relative contribution of resilience within the index on selection responses under four scenarios: (i) base genetic correlations, (ii) stronger correlations, (iii) weaker correlations, and (iv) adding genomic information. Resilience responded 50%-75% more effectively to selection when it had stronger genetic correlations with production traits or when genomic information was incorporated. However, placing greater emphasis on resilience (> 10% of the index) reduced progress in key production traits. Future research should explore these trade-offs in industry selection indexes to integrate resilience effectively without compromising productivity.

This study employed participatory methods to identify breeding objectives and define desired genetic gains for economically important traits in the Russian sturgeon (Acipenser gueldenstaedtii). Two structured questionnaires were distributed to all Russian sturgeon farmers in Iran. The first questionnaire collected farm management information and asked farmers to prioritise five important traits from a list of thirteen. The top-ranked traits were ovarian fat lobe weight (OFW), total caviar weight (TCW), body weight of broodstock (BWB), larval body area at hatching (LBA), and yolk sac area (YSA). In the second questionnaire, pairwise comparisons were applied to derive individual trait preferences through the Analytical Hierarchy Process (AHP). Social group preference (Soc-p) values were computed for each social group using the weighted goal programming (WGP) model implemented in LINGO software. The greatest disagreement in Soc-p values emerged between the commercial product and water temperature categories. Subsequently, the extended WGP models were employed to derive consensus preference (Con-p) values for these categories. The average of the Con-p values was 0.28 (OFW), 0.22 (BWB), 0.14 (TCW), 0.13 (LBA), and 0.05 (YSA). These Con-p values were then used to determine the desired genetic gains, which were highest for TCW (1.39%) and lowest for YSA (0.34%). The use of AHP and WGP, rather than economic indices, was justified by the limited availability of reliable economic data in Iranian sturgeon aquaculture and the need for farmer-driven, consensus-based breeding goals. This research demonstrates that participatory approaches can successfully define genetic priorities, improve consensus among diverse farmer groups, and guide sustainable breeding strategies for Russian sturgeon in Iran.