Journal of Animal Breeding and Genetics最新文献

Genetic, environmental, technological and financial resources are used differently in cattle herds that participate in the same breeding programme. The percentages of calves sired by sires within their own herd or from external herds vary across herds, as do the intensities of use of reproductive biotechnologies. These divergences may be related to differences in the indicators of genetic performance for economic traits. The aim of this study was to determine the factors related to herd structure and genetic resource utilization that exert the greatest influence on the genetic merit of seedstock herds within a Nellore breeding programme. The database comprised 21 factors, along with genomic-enhanced expected progeny differences (GE-EPDs) for growth, reproductive and carcass traits, as well as a selection index of animals from 128 herds. By combining principal component analysis and cluster analysis, we were able to group the herds. We identified statistically significant differences (p < 0.05) in the mean values of the factors, GE-EPDs and genetic trends among the groups of herds. Differences in the percentage of sires from external herds and in sire age between the groups of herds were the factors most associated with differences in mean GE-EPDs and genetic trends. Using young sires from other herds or lineages is an effective strategy in animal breeding. By enhancing genetic variability, this approach does not only improve the genetic quality of herds but also accelerates genetic progress in desired traits over time. Therefore, to ensure the success of this strategy, it is crucial that seedstock herds undergo a thorough selection process aimed at maximizing the genetic potential of future generations of beef cattle.

This study investigated the impact of temperature and humidity on milk production traits in Tunisian dairy cows, analysing population-level trends and individual cow responses using various modelling techniques and heat stress (HS) indices. Two distinct datasets were used for this purpose: the first included 551,139; 331,654 and 302,396 test-day records for milk, fat and protein yields, respectively. The second supplemented the production information with daily average (THIavg) and maximum (THImax) temperature-humidity index (THI) data. Three main parts of analyses were conducted simultaneously: classical least squares, identification of HS thresholds and associated production losses and assessment of individual cow responses using random regression models (RRM) fitting various continuous functions that include/exclude individual effects. The best model, determined by goodness-of-fit measurements, was a cubic polynomial function that accounted for individual variation and THIavg as a heat load measure. HS thresholds were established at THIavg/THImax of 70/74 for milk yield, 50/55 for fat percentage, 59/66 for protein percentage, 54/63 for fat yield and 56/66 for protein yield. According to the fitted polynomial models, daily milk production traits showed a curvilinear decline with accelerated loss rates beyond the established thermal thresholds. However, for all models and thermal indices, maximum daily production losses remained below 164 g/day, 4.4 g/day and 6.1 g/day for milk, fat and protein yields, respectively. Despite these losses, the relatively high thermal thresholds and lower associated production losses suggest that Tunisian dairy cows can tolerate high heat loads. Moreover, observed variations in response patterns indicate potential for selecting heat-tolerant individuals within this population.

The aim was to estimate the relative contribution of imprinting effects from both paternal and maternal sides to phenotypic variation in milk production traits including 305 days milk yield (MY), average daily milk production (ADM), fat percentage (F%), protein percentage (P%), 305 days fat yield (FY), 305 days protein yield (PY), ratio of fat percentage to protein percentage (F:P) and somatic cell score (SCS) in Iranian Holstein cows. To do this, each trait was analysed with a series of four animal models, which were identical for fixed and additive genetic effects but differed for combinations of paternal and maternal imprinting effects. The log-likelihood ratio test (LRT) and Akaike's information criteria (AIC) were used to select the best model for each trait. Correlations between traits due to additive and imprinting effects were estimated by bivariate analyses. For all traits studied, fitting the imprinting effect led to a better data fit. Also, it resulted in a noticeable decrease in additive genetic variance from 8% (SCS) to 28% (F:P). A significant maternal imprinting effect was detected on all traits studied. Estimates of maternal imprinting heritability ( $h_{\mathrm{mi}}^2$ ) were 0.07 ± 0.02, 0.04 ± 0.01, 0.06 ± 0.01, 0.05 ± 0.01, 0.5 ± 0.01, 0.09 ± 0.02, 0.07 ± 0.02 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. For F:P, in addition to the maternal imprinting effect, a significant paternal imprinting component was also detected with a 7% contribution to phenotypic variance of F:P. Estimates of direct heritability ( $h_a^2$ ) were 0.29 ± 0.02, 0.17 ± 0.01, 0.22 ± 0.02, 0.11 ± 0.01, 0.18 ± 0.02, 0.22 ± 0.02, 0.15 ± 0.04 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. Maternal imprinting correlations (r_mi) were in a wide range between -0.75 ± 0.15 (P%-SCS) and 0.95 ± 0.11 (MY-ADM). Additive genetic correlations (r_a) ranged between -0.54 ± 0.05 (MY-P%) and 0.99 ± 0.01 (MY-ADM) and phenotypic correlations (r_p) ranged from -0.30 ± 0.01 (MY-F%) to 0.93 ± 0.01 (MY-ADM). The Spearman's correlation between additive breeding values including and excluding imprinting effects deviated from unity especially for top-ranked animals implying re-ranking of top animals following the inclusion of imprinting effects in the model. Since including imprinting effects in the model resulted in better data fit and re-ranking of top animals, including these effects in the genetic evaluation models for milk production traits was recommended.

Traits such as meat quality and composition are becoming valuable in modern pork production; however, they are difficult to include in genetic evaluations because of the high phenotyping costs. Combining genomic information with multiple-trait indirect selection with cheaper indicator traits is an alternative for continued cost-effective genetic improvement. Additionally, gut microbiome information is becoming more affordable to measure using targeted rRNA sequencing, and its applications in animal breeding are becoming relevant. In this paper, we investigated the usefulness of microbial information as a correlated trait in selecting meat quality in swine. This study incorporated phenotypic data encompassing marbling, colour, tenderness, loin muscle and backfat depth, along with the characterization of gut (rectal) microbiota through 16S rRNA sequencing at three distinct time points of the animal's growth curve. Genetic progress estimation and cross-validation were employed to evaluate the utility of utilizing host genomic and gut microbiota information for selecting expensive-to-record traits in crossbred individuals. Initial steps involved variance components estimation using multiple-trait models on a training dataset, where the top 25 associated operational taxonomic units (OTU) for each meat quality trait and time point were included. The second step compared the predictive ability of multiple-trait models incorporating different numbers of OTU with single-trait models in a validation set. Results demonstrated the advantage of including genomic information for some traits, while in some instances, gut microbial information proved advantageous, namely, for marbling and pH. The study suggests further investigation into the shared genetic architecture between microbial features and traits, considering microbial data's compositional and high-dimensional nature. This research proposes a straightforward method to enhance swine breeding programs for improving costly-to-record traits like meat quality by incorporating gut microbiome information.

The current work aimed to identify genomic regions and candidate genes associated with resilience in pigs. In previous work, we proposed the body weight deviation from the expected growth curve (ΔBW) and the increase of the positive acute-phase protein haptoglobin (ΔHP) after a vaccine challenge as resilience indicators which may be improved through selective breeding in pigs. Individuals with steady growth rate and minor activation of haptoglobin (high ΔBW and low ΔHP values) were considered resilient. In contrast, pigs with perturbed growth rate and high activation of haptoglobin (low ΔBW and high ΔHP values) were considered susceptible. Both ∆BW and ∆HP were simultaneously considered to select the most resilient (N = 40) and susceptible (N = 40) pigs. A genome-wide association study was carried out for the pigs' response classification to the challenge test using whole-genome sequence data (7,760,720 variants). Eleven associated genomic regions were identified, harbouring relevant candidate genes related to the immune response (such as pro- and anti-inflammatory responses) and growth pathways. These associated genomic regions harboured 41 potential functional mutations (frameshift, splice donor, splice acceptor, start loss and stop loss/gain) in candidate genes. Overall, this study advances our knowledge about the genetic determinism of resilience, highlighting its polygenic nature and strong relationship with immunity and growth.

Enhancing reproductive performance is a key strategy to mitigate involuntary culling rates, thereby extending productive life (PL) and ultimately improving profitability in dairy cattle herds. A piecewise Weibull proportional hazards model was used to investigate the effect of several important reproductive traits on PL in Holstein dairy cows. Data comprised 200,747 lactation records from 82,505 cows sired by 1952 bulls across 36 dairy herds. PL was defined as the number of days from the first calving to the last milk record or censoring. The statistical model accounted for the time-dependent fixed effects of changes in herd size, year-season, milk production, fat and protein contents, and the time-independent fixed effect of age at first calving. Herd-year and sire effects were also included as random effects. Reproductive traits include calving traits such as calving ease (CE), calf size (CZ), and calf survival (CS), as well as female fertility traits such as number of inseminations per conception (NI), days from calving to first service (CFS), days from first service to conception (FSC), and days open (DO). All reproductive traits had a significant effect on PL (p < 0.001). Each reproductive trait was analysed separately. The relative risk (RR) of being culled increased as the severity of calving difficulties increased in both primiparous and multiparous cows. Cows that calved small or large calves showed a higher risk of being culled compared with those that calved medium size calves. The increased RR of culling was observed only for primiparous cows that gave birth to dead calves. In addition, cows that required more NI, a longer CFS, FSC, and DO had shorter longevity. These insights can deepen our comprehension of the factors affecting PL and provide information for refining management and breeding strategies, which could lead to increased profitability and sustainability in Iranian dairy farming.

This study utilized Bayesian inference in a genome-wide association study (GWAS) to identify genetic markers associated with traits relevant to the adaptation of Hereford and Braford cattle breeds. We focused on eye pigmentation (EP), weaning hair coat (WHC), yearling hair coat (YHC), and breeding standard (BS). Our dataset comprised 126,290 animals in the pedigree. Out of these, 233 sires were genotyped using high-density (HD) chips, and 3750 animals with medium-density (50 K) single-nucleotide polymorphism (SNP) chips. Employing the Bayes B method with a prior probability of π = 0.99, we identified and tagged single nucleotide polymorphisms (Tag SNPs), ranging from 18 to 117 SNPs depending on the trait. These Tag SNPs facilitated the construction of reduced SNP panels. We then evaluated the predictive accuracy of these panels in comparison to traditional medium-density SNP chips. The accuracy of genomic predictions using these reduced panels varied significantly depending on the clustering method, ranging from 0.13 to 0.65. Additionally, we conducted functional enrichment analysis that found genes associated with the most informative SNP markers in the current study, thereby providing biological insights into the genomic basis of these traits.

Age at first calving (AFC) is a measure of sexual maturity associated with the start of productive life of dairy animals. Additionally, a lower AFC reduces the generation interval and early culling of females. However, AFC has low heritability, making it a trait highly influenced by environmental factors. In this scenario, one way to improve the reproductive performance of buffalo cows is to select robust animals according to estimated breeding value (EBV) using models that include genotype-environment interaction (GEI) with the application of reaction norm models (RNMs). This can be achieved by understanding the genomic basis related to GEI of AFC. Thus, in this study, we aimed to predict EBV considering GEI via the RNM and identify candidate genes related to this component in dairy buffaloes through genome-wide association studies (GWAS). We used 1795 AFC records from three Murrah buffalo herds and formed environmental gradients (EGs) from contemporary group solutions obtained from genetic analysis of 270-day cumulative milk yield. Heritability estimates ranged from 0.15 to 0.39 along the EG. GWAS of the RNM slope parameter identified important genomic regions. The genomic window that explained the highest percentage of genetic variance of the slope (0.67%) was located on BBU1. After functional analysis, five candidate genes were detected, involved in two biological processes. The results suggested the existence of a GEI for AFC in Murrah buffaloes, with reclassification of animals when different environmental conditions were considered. The inclusion of genomic information increased the accuracy of breeding values for the intercept and slope of the reaction norm. GWAS analysis suggested that important genes associated with the AFC reaction norm slope were possibly also involved in biological processes related to lipid metabolism and immunity.

Brazilian livestock breeding programmes strive to enhance the genetics of beef cattle, with a strong emphasis on the Nellore breed, which has an extensive database and has achieved significant genetic progress in the last years. There are other indicine breeds that are economically important in Brazil; however, these breeds have more modest sets of phenotypes, pedigree and genotypes, slowing down their genetic progress as their predictions are less accurate. Combining several breeds in a multi-breed evaluation could help enhance predictions for those breeds with less information available. This study aimed to evaluate the feasibility of multi-breed, single-step genomic best linear unbiased predictor genomic evaluations for Nellore, Brahman, Guzerat and Tabapua. Multi-breed evaluations were contrasted to the single-breed ones. Data were sourced from the National Association of Breeders and Researchers of Brazil and included pedigree (4,207,516), phenotypic (328,748), and genomic (63,492) information across all breeds. Phenotypes were available for adjusted weight at 210 and 450 days of age, and scrotal circumference at 365 days of age. Various scenarios were evaluated to ensure pedigree and genomic information compatibility when combining different breeds, including metafounders (MF) or building the genomic relationship matrix with breed-specific allele frequencies. Scenarios were compared using the linear regression method for bias, dispersion and accuracy. The results showed that using multi-breed evaluations significantly improved accuracy, especially for smaller breeds like Guzerat and Tabapua. The validation statistics indicated that the MF approach provided accurate predictions, albeit with some bias. While single-breed evaluations tended to have lower accuracy, merging all breeds in multi-breed evaluations increased accuracy and reduced dispersion. This study demonstrates that multi-breed genomic evaluations are proper for indicine beef cattle breeds. The MF approach may be particularly beneficial for less-represented breeds, addressing limitations related to small reference populations and incompatibilities between G and A₂₂. By leveraging genomic information across breeds, breeders and producers can make more informed selection decisions, ultimately improving genetic gain in these cattle populations.

Temperament (docility) is a key breeding goal in the cattle industry due to its direct relationship with animal welfare, cattle handler's safety and animal productivity. Over the past six decades, numerous studies have reported heritability estimates for temperament-related traits in cattle populations ranging from low to high values. Therefore, the primary objective of this study was to perform a comprehensive systematic review with meta-analysis to obtain weighted estimates of heritability for temperament-related traits in worldwide cattle populations. After data editing and quality control, 106 studies were included in the systematic review, of which 29.2% and 70.8% reported estimates of heritability for temperament-related traits in dairy and beef cattle populations, respectively. Meta-analyses were performed for 95 heritability estimates using a random model approach. The weighted heritability estimates were as follow: (a) flight score at weaning = 0.23 (95% CI: 0.15-0.32); (b) flight speed at weaning = 0.30 (95% CI: 0.26-0.33); (c) joint analysis of flight speed and flight score at weaning = 0.27 (95% CI: 0.22-0.31); (d) flight speed at yearling = 0.26 (95% CI: 0.21-0.30); (e) joint analysis of flight speed at weaning and yearling = 0.27 (95% CI: 0.24-0.30); (f) movement score = 0.12 (95% CI: 0.08-0.15); (g) crush score at weaning = 0.21 (95% CI: 0.17-0.25); (h) pen score at weaning = 0.27 (95% CI: 0.19-0.34); (i) pen score at yearling = 0.20 (95% CI: 0.17-0.23); (j) joint analysis of pen score at weaning and yearling = 0.22 (95% CI: 0.18-0.26); (k) cow's aggressiveness at calving = 0.10 (95% CI: 0.01-0.19); (l) general temperament = 0.13 (95% CI: 0.06-0.19); (m) milking temperament = 0.16 (95% CI: 0.11-0.21); and (n) joint analysis of general and milking temperament = 0.14 (95% CI: 0.11-0.18). The heterogeneity index ranged from 0% to 77%, and the Q-test was significant (p < 0.05) for four single-trait meta-analyses. In conclusion, temperament is moderately heritable in beef cattle populations, and flight speed at weaning had the highest weighted heritability estimate. Moreover, between-study heterogeneity was low or moderate in beef cattle traits, suggesting reasonable standardization across studies. On the other hand, low-weighted heritability and high between-study heterogeneity were estimated for temperament-related traits in dairy cattle, suggesting that more studies are needed to better understand the genetic inheritance of temperament in dairy cattle populations.