Journal of Animal Breeding and Genetics最新文献

Indicators of heat stress response are heritable complex traits with polygenic inheritance. Copy number variations (CNV) are important genomic structural variations that may be linked to climatic adaptation by influencing the phenotypic variability of traits related to thermal stress and disease resistance in animals. Therefore, the primary objectives of this study were to detect CNV and CNV regions (CNVR) in pigs and explore their associations with physiological and anatomical indicators of HS response in lactating sows. A total of 4184 autosomal genome CNV (4012 deletions and 172 duplications) were detected in 969 animals. CNVR were identified by merging CNV with at least 1-bp overlap, which enabled the identification of 236 autosomal CNVR. The association analyses led to the identification of three CNVR significantly associated with ear skin temperature and one CNVR significantly associated with vaginal temperature considering all records, vaginal temperature measured at 8:00 h, and hair density. Eleven genes harboured the CNVR with significant associations. In summary, various CNV and CNVR were identified in crossbred maternal-line pigs, including CNVR significantly associated with physiological and anatomical heat stress response indicators in lactating sows. Candidate genes involved in immune and stress responses overlapped with the significant CNVR, suggesting that they may contribute to climatic resilience in pigs. The findings of this study contribute to better understanding the genetic background of heat stress response in lactating sows.

Indirect predictions (IP) are used for young genotyped animals that lack phenotypes (of their own or from progeny) or are from commercial herds. The former can be left behind because they do not contribute to the official genomic evaluations. The latter are often excluded from the evaluations because they are not registered and may not have pedigree information. Including such animals could result in inflated and biased genomic breeding values (GEBV). In Brazil, pedigree, phenotype and genotype information is scarce for important breeds like Brahman, Guzerat, and Tabapua, while the Nellore breed has a substantial amount of information. IP for young animals of these breeds based on a larger reference population could enhance genomic selection accuracy. Our objective in this study was to compute IP for young genotyped Nellore, Brahman, Guzerat, and Tabapua animals using single- and multi-breed analyses, with or without metafounders (MF) to model genetic differences across breeds. Records from the four breeding programs of the National Association of Breeders and Researchers (ANCP—Ribeirão Preto, SP, Brazil) were used. Data included pedigree (4.2 M), phenotypes (329 K), and genotypes (63.5 K) across all breeds. The number of genotyped animals within each breed was 58,574 for Nellore, 3102 for Brahman, 1389 for Guzerat, and 427 for Tabapua. The analysed traits were adjusted weight at 210 (W210) and 450 (W450) days of age and the scrotal circumference at 365 (SC365) days of age. IP were derived as the sum of the SNP effects weighted by the gene content using different reference populations: multi-breed with or without MF, Nellore, or within-breed. Scenarios were compared using the linear regression (LR) method for bias, dispersion, and accuracy. Adding MF decreased bias and under- or overdispersion and slightly increased the accuracy of IP. Combining breeds increased the accuracy of IP, mainly benefiting breeds with a small number of genotypes. These findings suggest that when young genotyped animals are not included in an official multi-breed evaluation in zebuine cattle from Brazil, robust IP can be obtained with proper modelling, regardless of the breed. This helps obtain fast genomic predictions for young animals without overwhelming the evaluation system with too many animals.

Detecting selection footprints offers valuable insight into evolutionary processes and the mechanisms underlying phenotypic diversity in selected traits. Domestication, natural and artificial selection, and breeding have produced indigenous goats well-adapted to their local environments, making them crucial genetic resources. Understanding the genetic foundation of these adaptations can guide the development of effective breeding strategies to preserve and improve local goat breeds. This study investigated selection signatures in Lao native goats using Illumina's Goat SNP50 BeadChip, analysing 420 Lao native goats, 87 goats from three Chinese breeds, and 51 Teddi goats from Pakistan as test populations. We applied the de-correlation composite multiple signals (DCMS) method, incorporating p values from nine statistical tests, including runs of homozygosity in the Lao goat population, and fixation index and cross-population extended haplotype homozygosity between Lao goats and test populations. Significant genomic regions were identified using a 0.05 threshold adjusted for multiple testing. Our results uncovered 24 genomic regions harbouring 68 unique-coding genes. Analysis revealed both annotated and novel candidate genes linked to a variety of characteristics, including adaptation to the tropical monsoon climate (e.g., ABHD6, GATA4 and MSRA) and economic traits like growth and status (e.g., CNTNAP5, FAM135B and GATA4), reproduction (e.g., NPHP3, ARSJ and GATA4), milk production (e.g., MRPL32, PRSS51 and EPHA7), and carcass characteristics (e.g., GNAI1, SOX7 and FAM135B). These results offered insightful information about genetic mechanisms driving economic traits and tropical climate adaptation of Lao native goats. Combining p values from various statistical tests into a single DCMS framework effectively assists in selecting and prioritising candidate genes for further analysis.

Marchigiana, Chianina, and Romagnola are three Italian autochthonous beef cattle breeds that have been historically selected for meat production. Recent advancements suggest that the use of genomic data and multi-breed (MB) models to combine information from different breeds may help to increase the accuracies of genomic predictions, in particular if the available data per breed is limited. This study aimed to evaluate and compare the accuracies of genomic predictions for average daily gain (ADG) in the three Italian breeds. We implemented different scenarios using phenotypes collected on 5303 young bulls in performance tests across the three breeds, 23,793 pedigree records, and 4593 genotypes, and then validated through the linear regression method. The implemented scenarios were: pedigree Best Linear Unbiased Prediction (pBLUP) and single-step Genomic BLUP (ssGBLUP) single-trait single-breed evaluations where each breed was modelled separately; pBLUP and ssGBLUP single-trait multi-breed evaluations where ADG was modelled as the same trait for all breeds, and ssGBLUP multi-trait multi-breed evaluations where ADG was considered as a different correlated trait across breeds. In addition, single- and multi-breed pBLUP and ssGBLUP evaluations were implemented including weight at 1 year of age and muscularity as correlated traits of ADG in a multi-trait approach. Results highlighted the improved accuracies (an average of 5% in ssGBLUP models compared to corresponding pBLUP ones) when incorporating genomic data in the prediction models. Moreover, single-trait multi-breed scenarios resulted in higher accuracy for breeds with lower heritabilities for ADG (an average of 4% for single-trait multi-breed models compared to single-breed ones), confirming the importance of leveraging data from populations with higher heritabilities. Lastly, adding two correlated traits next to ADG in the single- and multi-breed ssGBLUP yielded even higher accuracies than the scenarios only encompassing ADG. The observed increases in accuracy when leveraging data from more populations and/or more traits could be helpful when implementing genomic predictions for innovative traits with limited records per individual or low heritabilities, and for the genetic improvement of local populations where limited data availability represents a challenge for traditional genetic selection.

The evaluation of breeding schemes against established objectives and selection traits is essential for assessing the performance, outputs, and overall impacts of breeding programmes. In Ethiopia, most Community-Based Breeding Programmes (CBBPs) have prioritised growth traits, particularly live weight, as the main selection criteria. However, since productivity relies on both reproductive and growth traits, it is critical to evaluate how these traits are evolving to make necessary adjustments in management practices and breeding schemes. This study considered five indigenous sheep breeds (Menz, Semein, Horro, Bonga and Doyogena), managed under CBBPs since 2009. Fixed effects for reproductive traits were estimated using the GLM procedures of SAS 9.4. Genetic parameters were estimated for all traits using the restricted maximum likelihood (REML) method with WOMBAT software, employing a multivariate repeated model, except for age at first lambing (AFL), which was analysed using a non-repeated multivariate model. Significant effects (p < 0.001) were observed for year of birth, breed of ewe, parity and birth season across all traits in the breeds studied. A general trend of improvement in litter size at birth (LSB), total litter weight at birth (TLWB), litter size at weaning (LSW), total litter weight at weaning (TLWW) and annual reproductive rate (ARR) was noted with increasing ewe parity until the seventh parity, followed by a decline thereafter. Direct heritability estimates for the traits according to the ewe breeds ranged from 0.03 to 0.25 for LSB, 0.02 to 0.16 for LSW, 0.08 to 0.21 for TLWB, 0.07 to 0.22 for TLWW, 0.03 to 0.19 for LI, 0.08 to 0.32 for ARR and 0.15 to 0.36 for AFL. Estimates of direct heritability and repeatability varied by breed and location, generally falling within small to medium ranges. Moderate to high genetic correlations were found between TLWW and other traits suggesting that selection for TLWW may significantly influence reproductive performances across most sheep breeds, with the exception of Menz sheep. The variations in genetic estimates across different breeds and locations indicate that genetic influences may vary depending on the specific context. Moderate to high genetic correlations between TLWW and other reproductive traits suggest that prioritising selection for TLWW could have a significant positive impact on reproductive performance across most sheep breeds, though the Menz breed may not exhibit the same expected benefits. These findings emphasise the need to integrate genetic selection with effective management practices tailored to each breed's specific needs, recommending the culling of unproductive ewes after the seventh parity to enhance the sustainability and productivity of CBBPs in Ethiopia.

Mature cow size, often defined by mature cow weight (MWT), height (MHT) and body condition score (BCS), is crucial to cow-calf profitability, maintenance efficiency and reproductive performance. Although MWT and MHT are often included in national cattle evaluations by many breed organisations, BCS adjustments are applied less consistently. This study investigated the impact of different modelling approaches on the estimation of breeding values for MWT, with a focus on how BCS is accounted for across models. The dataset provided by American Angus Association comprised 382,156 MWT and BCS records from 209,491 cows. Four modelling approaches were evaluated: Model 1 does not consider BCS; Model 2 treated BCS as a categorical fixed effect; Model 3 used pre-adjusted records standardised for BCS and age; and Model 4 used a recursive model to assess MWT as a genetically independent trait from BCS. Spearman correlations between breeding values predicted across models ranged from 0.79 (between Models 1 and 4) to 0.95 (between Models 1 and 2), indicating that 5%–21% of bulls could have different rankings depending on the model used. Concordance in sire selection was assessed between the top 10% of sires in each model, and model-pair comparison revealed differences ranging from 19% (between Models 1 and 2) to 40% (between Models 3 and 4). These differences highlight the potential for model choice to influence the selection outcomes. Model selection can significantly affect the sire rankings, highlighting the importance of carefully selecting the model that best aligns with the selection objectives and the underlying biology of the traits being evaluated. Although Model 4 offers theoretical advantages, this study does not allow for a definitive conclusion on its overall effectiveness, as no simulations were performed. Additional research is needed to confirm its advantages.

In tropical extensive beef cattle systems, heifers raised on pasture are exposed to various environmental challenges that affect their growth and reproductive performance during the first breeding. Resilience indicators derived from deviations in longitudinal traits can quantify the magnitude of these challenges and the ability of an animal to recover after disturbances. Hence, this study aimed to estimate genetic parameters for resilience indicators derived from weight deviations across growth in Nellore heifers, and their genetic correlations with yearling weight (YW), reproductive traits, calf performance and pre-weaning survival (PWS^c). Phenotypic records were available for 3072 heifers, while 3226 animals were genotyped with 383,856 SNP markers (after quality control). A total of 30,720 weight records were used for growth curve modelling across three developmental phases: yearling, first breeding and first calf weaning. The resilience indicators derived and analysed were as follows: (i) natural logarithm of residual variance (LnVar); (ii) lag⁻¹ autocorrelation of residuals (r_auto); and (iii) skewness of residuals. The weight, reproductive, calf performance and survival traits analysed were as follows: YW, weight at the beginning of the breeding season (WBS), heifer pregnancy (HP), calves birth weight (BW^c), calves weaning weight (WW^c), calves average daily gain from birth to weaning (ADG^c) and PWS^c. Genetic parameters were estimated using the ssGBLUP method under a Bayesian framework. Heritability estimates (h²) were highest for LnVar, ranging from 0.32 ± 0.03 (calf weaning) to 0.42 ± 0.03 (breeding). Moderate h² values were observed for r_auto (0.22 ± 0.03 to 0.29 ± 0.03), whereas skewness had low heritability (0.08 ± 0.02 to 0.13 ± 0.02). Genetic correlations (r_g) between LnVar and weight traits were unfavourable. In contrast, r_auto exhibited favourable correlations with YW (−0.29 ± 0.08 to −0.50 ± 0.08). LnVar at breeding showed favourable and moderate r_g with HP (−0.37 ± 0.10). All resilience indicators were favourably correlated with PWS^c, with the strongest estimate observed for LnVar at calf weaning (−0.28 ± 0.15). These findings provide novel insights into the genetic basis of resilience in growing beef heifers. LnVar and r_auto, in particular, emerge as promising traits for selecting animals better adapted to environmental variability. Additionally, favourable genetic correlations with fertility and survival traits suggest that more resilient heifers are more likely to become pregnant during their first breeding season and raise calves with higher survival rates until weaning.

Foot conformation is one of the main breeding goals in recent beef cattle breeding programs because it directly affects productivity, economic losses, animal welfare and longevity. Genetic heterogeneity of residual variance can be used to improve production uniformity in animal breeding programs because recent studies have shown that residual variance is partially under genetic control, allowing reduction of variability through selection. Despite being an important breeding goal, research on genetic heterogeneity of residual variance for conformation traits, such as foot angle and foot claw, is still scarce in livestock species. The objectives of our study were (1) to investigate the extent of genetic heterogeneity of residual variances on two conformation traits: foot angle (FA) and claw set (CS) in Angus cattle using genetic homogeneity (M1) and two genetic heterogeneity of residual variance models, including a double hierarchical generalised linear model (DHGLM, M2) and a genetically structured environmental variance model (M3). Genetic parameters for means and residual variances were estimated using M2 and M3. The dataset included 45,667 phenotypic records for FA and CS (scores from 1 to 9 with 5 being ideal) of American Angus cattle recorded from 2009 to 2021. M1 and M2 were fitted using average information restricted maximum likelihood, and M3 was fitted using Markov chain Monte Carlo. Heritability estimates for the means of FA (0.19 ± 0.007 for M1, 0.11 ± 0.005 for M2 and 0.09 ± 0.003) and CS (0.16 ± 0.005 for M1, 0.10 ± 0.004 for M2 and 0.08 ± 0.03) were within the range reported in the literature, but M2 and M3 estimates were lower than M1. Genetic heterogeneity of residual variance was assessed using three parameters: heritability for residual variance, genetic coefficient of variation, and correlation between mean and residual variance. Although heritability estimates for residual variance in M2 were low (0.08 for FA and 0.001 for CS), our results suggest that residual variance is partially under genetic control. The genetic coefficients of variation estimates were 0.08 (M2) and 0.06 (M3) for FA, and 0.06 (M2) and 0.02 (M3) for CS, indicating that selection on the trait mean would also change the residual variance. Our results for FA and CS showed moderate positive genetic correlations in M2 (0.52 for FA and 0.41 for CS) and M3 (0.35 for FA and 0.33 for CS) between mean and residual variance. Positive correlations may limit the response to selection unless other breeding strategies, such as selection indices, are used. FA and CS are promising traits for uniformity or resilience indicators because they are phenotypes that can be collected throughout the production cycle using traditional or digital data recording systems. Our results demonstrate the potential to modulate variability through breeding strategies and present an opportunity to evaluate the uniformity of foot score traits in beef cattle.