Search

Jeju black cattle (JBC) are recognised as one of Korea's native breeds that are well-suited to the local environmental conditions and known for their excellent meat quality. This study investigated the variance components and genomic prediction accuracy for carcase traits such as carcase weight (CWT), eye muscle area (EMA), backfat thickness (BF) and marbling score (MS) in JBC using various statistical models. We employed single-step genomic best linear unbiased prediction (ssGBLUP) and single-step Bayesian regression (SSBR) models, which utilised imputed genotypes for non-genotyped individuals. Phenotypic data were collected from 2045 JBC, and 3759 JBC individuals were genotyped using the Affymetrix 160K SNP Axiom array. Estimated heritability ranged from 0.24 to 0.40 for both single-trait and multi-trait models. Multi-trait models consistently demonstrated higher genomic prediction accuracy compared to single-trait models, underscoring the value of integrating multiple traits. For instance, CWT accuracy improved from 0.24 in single-trait PBLUP (st-PBLUP) to 0.47 in multi-trait ssGBLUP. Similarly, SSBR models showed significant improvements, with single-trait SSBR Bayes B achieving 0.46 and its multi-trait version reaching 0.49. EMA accuracy improved from 0.21 in st-PBLUP to 0.39 in the multi-trait SSBR Bayes C model. BF and MS also exhibited consistent accuracy improvements with multi-trait Bayesian regression models. These results demonstrate the superior accuracy of ssGBLUP and SSBR models compared to traditional BLUP, particularly for traits with higher heritability values. This findings offer valuable insights for enhancing genomic selection strategies and improving carcase traits in JBC breeding programs, contributing to the breed's economic sustainability and meat production efficiency. [ABSTRACT FROM AUTHOR]

Individual methane emissions are a potential breeding goal for selection plans aimed at improving the sustainability of ruminant farming systems. However, a large-scale recording of individual methane emissions is hampered by the high costs of the equipment and the logistics of the experiments. Equations have been developed to predict methane emissions in dairy cattle based on variables such as dry matter intake (DMI), gross energy intake, body weight and milk fatty acids (FAs) profile. No equations are currently available for dairy sheep. The aims of this study were: (i) to estimate the enteric methane yield (eMY, expressed as g/kg of DMI) and enteric methane intensity (eMI, expressed as g/kg of FPCM) of dairy sheep using equations developed for dairy cattle that consider as predictors milk FA; (ii) to evaluate the effect of stage of lactation, parity and month of lambing on eMY and eMI; (iii) to estimate the heritability of eMY and eMI. For this purpose, 964 individual milk samples from Sarda dairy ewes were analysed for milk composition and FA profile. Nine different equations were used to predict eMY and eMI. Values of eMY ranged from 19.4 g/kg to 20.4 g/kg of DMI, whereas values of eMI ranged from 15.1 g/kg to 21.0 g/kg of DMI, respectively. Stage of lactation was an important factor in the variability of eMY (p <.01). Indeed, this trait increased towards the end of lactation. Both eMY and eMI were negatively correlated with milk yield, lactose concentration and urea and positively correlated with fat concentration and NaCl, respectively. Heritability of eMI and eMY estimated using a single trait model was 0.13 ± 0.05 and 0.05 ± 0.04, respectively. The values of heritability estimated for eMY using a bivariate models ranged from 0.10 to 0.16 and they were higher than the value estimated for eMI. Results of the present study, even if based on prediction equations developed for cows, increased the knowledge about the phenotypic and genetic background of methane emissions in Sarda dairy sheep. Methane emissions were predicted from fatty acids using equation developed for cows. Estimates were in agreement with the values reported in literature. Methane emissions were lowly heritable. [ABSTRACT FROM AUTHOR]

The aim of this study was to estimate genetic parameters and investigate the genomic background of scrotal circumference and semen parameters in the Italian Simmental bulls. Scrotal circumference, number of normal spermatozoa, ejaculate volume, spermatozoa motility, and total spermatozoa were measured on 622 bulls, of which 603 had genotypes for 42,141 SNP. Variance components of scrotal circumference were estimated with an animal model that included the fixed effects of birth year, animal age, and measurement method, and the random effects of day of measurement and animal. In the model for the other traits, the scrotal circumference was added as a covariate to account for its influence on the semen parameters. A genome-wide association study was carried out using the ssGBLUP-approach. Heritabilities ranged from 0.07 ± 0.05 (spermatozoa motility) to 0.50 ± 0.14 (scrotal circumference). A total of 13 SNP passed the Bonferroni correction threshold and the number of significantly associated markers ranged from 1 (ejaculate volume and spermatozoa motility) to 5 (total spermatozoa). Genes already associated with reproduction parameters were retrieved close to the significant SNP. Results of the present study gave preliminary insights about the genetic determinism of semen quality in Italian Simmental bulls. Low to moderate heritabilities were estimated for semen traits. Few markers were associated with the phenotypes, suggesting their polygenic determinism. Candidate genes already associated in literature with sperm traits were found. [ABSTRACT FROM AUTHOR]

Single-step genomic best linear unbiased predictor (ssGBLUP) is a methodology for estimating breeding values jointly for genotyped and non-genotyped animals. Since its development in the early 2010s, ssGBLUP faced challenges like modelling missing pedigrees, efficiently computing accuracies, ensuring the compatibility between genomic and pedigree information, implementing large-scale genetic evaluations, and using non-genotyped animals for genome-wide association studies, among others. Because of the extensive research and the availability of efficient software packages, those challenges for ssGBLUP were solved. Nowadays, ssGBLUP is the chosen methodology estimating values in almost all livestock populations. This review aims to report the progress of ssGBLUP, outline the current state of the art, and hypothesise about the future of this methodology. Single-step genomic BLUP is the most popular methodology for genetic evaluations including genotyped and non-genotyped animals. The development of theories and efficient software allows to use single-step for virtually any real dataset. Continuous research in single-step will allow the use of massive amount of data like video recording, omics, among others. [ABSTRACT FROM AUTHOR]

The aim of the present study was to develop a model to predict muscularity and body condition score (BCS) during the lactation of Italian Simmental dairy cows in Emilia Romagna herds. A total of 2656 Italian Simmental primiparous cows from 324 dairy herds were linear classified between 2002 and 2020. Lactation curves for muscularity and BCS were modelled for each cow using random regression model. The model included the fixed effects of age at linear scoring and days in milk modelled with a Legendre polynomial, and the random effects of herd-year of classification, cow and days in milk for each cow modelled with Legendre polynomials. The most parsimonious model included a fixed cubic Legendre polynomial and a random linear polynomial for cow effects. Results indicated that, on an average, BCS nadir was anticipated to that of muscularity, and, in both cases, this moment was around the lactation peak, when animals have the greatest nutrients requirement. After this period, both BCS and muscularity recovered up to post-partum levels. Moreover, after the 9 month of lactation, the absolute growth rate of muscularity and BCS was negative, suggesting that late-gestating cows could potentially enter a phase of body conformation loss. Results reported in the current research indicate that random regression using Legendre polynomials can be successfully employed to predict muscularity and BCS during the lactation of dairy cows. Modelling dairy cows' muscularity and BCS allows to use these parameters as indicator traits for functionality in dairy cows. The use of prediction model of muscularity and BCS allows to understand the evolution of these conformation traits during the lactation. The analysis of muscularity and BCS allows to assess health and welfare status of dairy cows, which is essential to maximise production performances. [ABSTRACT FROM AUTHOR]

The aim of this study was to test the feasibility of genomic selection in the Italian Mediterranean water buffalo, which is farmed mainly in the south Italy for milk, and mozzarella, production. A total of 498 animals were genotyped at 49,164 loci. Test day records (80,417) of milk (MY), fat (FY) and protein (PY) yields from 4127 cows, born between 1975 and 2009, were analysed in a three-trait model. Cows born in 2008 and 2009 with phenotypes and genotypes were selected as validation animals (n = 50). Variance components (VC) were estimated with BLUP and ssGBLUP. Heritabilities for BLUP were 0.25 ± 0.02 (MY), 0.16 ± 0.01 (FY) and 0.25 ± 0.01 (PY). Breeding values were computed using BLUP and ssGBLUP, using VC estimated from BLUP. ssGBLUP was applied in five scenarios, each with a different number of genotypes available: (A) bulls (35); (B) validation cows (50); (C) bulls and validation cows (85); (D) all genotyped cows (463); (E) all genotypes (498). Model validation was performed using the LR method: correlation, accuracy, dispersion, and bias statistics were calculated. Average correlations were 0.71 ± 0.02 and 0.82 ± 0.01 for BLUP and ssGBLUP-E, respectively. Accuracies were also higher in ssGBLUP-E (0.75 ± 0.03) compared to BLUP (0.57 ± 0.03). The best dispersions (i.e. closer to 1) were found for ssGBLUP-C. The use of genotypes only for the 35 bulls did not change the validation values compared to BLUP. Results of the present study, even if based on small number of animals, showed that the inclusion of genotypes of females can improve breeding values accuracy in the Italian Buffalo. The genotypes of males did not improve the predictions. Genotypes of females improve breeding values accuracy. Slight increase in prediction accuracy with weighted ssGBLUP. [ABSTRACT FROM AUTHOR]

Residual feed intake (RFI) is the most used measure of feed efficiency. However, considering the importance of concentrates in the ration, a new index, the residual concentrate intake (RCI), was here defined. RCI aims to measure the individual efficiency in converting the concentrate into animal products. Brown Swiss young bulls (N = 736) were genotyped at 41,183 loci. Animals were housed in pens equipped with an automatic feeding system able to recognise the animal and record the concentrate intake. The diet consisted of concentrate and hay (ad libitum). The new RCI index was calculated as the residuals of the linear regression of concentrate intake on metabolic live weight and average daily gain. Animals were ranked according to their corrected RCI and divided into low (LRCI) and high phenotypes (HRCI). A low heritability (0.06 ± 0.03) was estimated using only genomics for this new index. Results from multivariate (M-GWAS) and Bayesian (B-GWAS) approaches were combined to identify SNP associated with RCI. The M-GWAS selected 698 SNPs potentially associated, whereas no significant markers were obtained in B-GWAS. Markers in the last approach were ranked according to their posterior inclusion probability and the first 698 were retained. Only SNPs in common between sorted B-GWAS and M-GWAS (N = 11) were considered associated with RCI. A total of 48 candidate genes were retrieved near these SNPs. Most of them were previously reported to be associated with feed efficiency and RFI. The combined use of multivariate and Bayesian techniques allow to identify SNPs associated with the investigated trait. RCI could be promising to select animals 48 candidate genes were found associated with RCI Multivariate technique allowed to identify significant SNPs [ABSTRACT FROM AUTHOR]

Genomic selection, that is based on the prediction of the breeding value (the genomic breeding value or GEBV) of the animals considering genomic information, is changing breeding strategies and approaches in dairy cattle and in several other livestock species. The starting points for the application of genomic selection in pigs were the development of a first commercial single-nucleotide polymorphism panel for high throughput genotyping, the sequencing of the pig genome and the application of statistical and methodological approaches first developed in dairy cattle and then adapted to the peculiarities of the pig breeding industry. In this review, we focused on the specific applications of the genomic selection in pigs, considering its limits, its advantages and its perspectives throughout an analysis of the simulation studies and field applications already available targeting many different traits (with high and low heritability). In addition, we presented an overview of the problems related to the implementation of genomic selection in crossbred and multi-breed pig populations and the potential solutions taking into account the economic aspects that should be faced in including genomic selection in pig breeding programmes. [ABSTRACT FROM PUBLISHER]