Selection of genetically resistant animals is one alternative to reduce the negative impact of gastrointestinal nematodes (GIN) on sheep production. The aim of this study was to identify genomic regions associated with GIN resistance in Corriedale sheep by single-step genome-wide association studies (ssGWAS) using 170, 507 and 50K single nucleotide polymorphisms (SNPs). Analysis included 19,547 lambs with faecal egg counts (FEC) records, a pedigree file of 40,056 animals and 454, 711 and 383 genotypes from 170, 507 and 50K SNPs, respectively. Genomic estimated breeding values (GEBV) were obtained with single-step genomic BLUP methodology (ssGBLUP), using a univariate animal model, which included contemporary group, type of birth and age of dam as class fixed effects and age at FEC recording as covariate. The SNP effects as wells as p-values were estimated with POSTGSF90 program. Significance level was defined by a chromosome-wise False Discovery Rate of 5%. Significant genomic regions were identified in chromosomes 1, 3, 12 and 19 with the 170 SNP set, in chromosomes 7, 12 and 24 using the 507 SNP chip and only in chromosome 7 with the 50K SNP chip. Candidate genes located in these regions, using Oar_v4.0 as reference genome, were TIMP3, TLR5, LEPR and TLR9 (170 SNPs), SYNDIG1L and MGRN1 (507 SNP chip) and INO80, TLN2, TSHR and EIF2AK4 (50K SNP chip). These results validate genomic regions associated with FEC previously identified in Corriedale and other breeds and report new candidate regions for further investigation.

10 páginas, 7 tablas.Ultrasound tissue depths, similar to those measured commercially as part of UK beef genetic evaluation schemes, were measured on 52 crossbred steers and 10 heifers at the start and end of the finishing period (average age 476 and 568 days, respectively). Animals were slaughtered at commercial target weights and carcass grades, and one carcass side fully dissected. Combining live weights and ultrasound tissue depths measured both before and after finishing provided the best predictions of carcass composition. Dissected fat weights and proportions in the carcass side, the forequarter and the hindquarter were predicted with high accuracy (R-2 similar to 0.8), muscle weights with moderate accuracy (R-2=0.5-0.6), but muscle proportions were less accurately predicted (R-2=0.23-0.49). Meat and Livestock Commission (EUROP) carcass conformation and fatness classes could also be predicted with moderate accuracy (R-2=0.4 and 0.6, respectively). Fat and muscle weights in each quarter expressed as proportions of the total weight of that tissue in the carcass side were not predicted accurately (R-2<0.4). Live weight and ultrasound data measured only at the start of finishing also gave good predictions of fat proportions in the carcass side and quarters (R-2=0.64-0.67). and moderate predictions of fat and muscle weights and fatness class (R-2=0.380.53), but low prediction accuracies for muscle proportions and conformation classes. In comparison, live weights and tissue depths measured only at the end of finishing explained 12 to 19% more of the variation in fat weights and fatness class, but predicted muscle weight, conformation class and fat proportions with similar accuracies and muscle proportions with similar or less accuracy. Including ultrasound fat depths, alongside muscle depth and live weight, in equations to predict muscle weights and proportions increased prediction accuracies to similar values as those estimated for fat traits, but increased confounding between predictions of different tissues.\ud Derived prediction equations were validated on data from another 32 heifers and 48 steers, relating ultrasound-predicted weights to carcass tissue weights predicted from fore-rib sample joint dissections. Prediction accuracies (R-2) of between 0.52 and 0.64 were achieved for fat and muscle weights and fat proportion in the carcass side, when the model included post-finishing measurements, but were lower (0.29-0.52) using only pre-finishing measurements. Predictions of muscle proportions and conformation classes were of poor accuracy, regardless of the model used.Scottish Government.Peer reviewe

The objective of this study was to identify genomic regions and genes associated with resistance to gastrointestinal nematodes in Australian Merino sheep in Uruguay, using the single-step GWAS methodology (ssGWAS), which is based on genomic estimated breeding values (GEBVs) obtained from a combination of pedigree, genomic, and phenotypic data. This methodology converts GEBVs into SNP effects. The analysis included 26,638 animals with fecal egg count (FEC) records obtained in two independent parasitic cycles (FEC1 and FEC2) and 1700 50K SNP genotypes. The comparison of genomic regions was based on genetic variances (gVar(%)) explained by non-overlapping regions of 20 SNPs. For FEC1 and FEC2, 18 and 22 genomic windows exceeded the significance threshold (gVar(%) ≥ 0.22%), respectively. The genomic regions with strong associations with FEC1 were located on chromosomes OAR 2, 6, 11, 21, and 25, and for FEC2 on OAR 5, 6, and 11. The proportion of genetic variance attributed to the top windows was 0.83% and 1.9% for FEC1 and FEC2, respectively. The 33 candidate genes shared between the two traits were subjected to enrichment analysis, revealing a marked enrichment in biological processes related to immune system functions. These results contribute to the understanding of the genetics underlying gastrointestinal parasite resistance and its implications for other productive and welfare traits in animal breeding programs.

Context: Residual feed intake is a relevant trait for beef cattle, given the positive impact on reducing feeding costs and greenhouse gas emissions. The lack of large databases is a restriction when estimating accurate genetic parameters for dry matter intake (DMI) and residual feed intake (RFI), and combining different data sets could be an alternative to increase the amount of data and achieve better estimations. Aim: The main objective was to compare Uruguayan data (URY; 780 bulls) and Canadian data (CAN; 1597 bulls), and to assess the adequacy of pooling both data sets (ALL) for the estimation of genetic parameters for DMI and RFI. Methods: Feed intake and growth traits phenotypes in both data sets were measured following the same protocols established by the Beef Improvement Federation. Pedigree connections among data sets existed, but were weak. Performance data were analysed for each data set, and individual partial regression coefficients for each energy sink on DMI were obtained and compared. Univariate and multivariate variance components were estimated by the restricted maximum likelihood (REML) for DMI, RFI and their energy sinks traits (average daily gain, metabolic mid weight and back fat thickness). Key results: There were some differences in phenotypic performance among data (P < 0.01); however, no differences (P > 0.1) were observed for phenotypic values of RFI between sets. Heritability estimates for DMI were 0.42 (URY), 0.41 (CAN) and 0.45 for ALL data, whereas heritability estimates for RFI were 0.34 (URY), 0.20 (CAN) and 0.25 for ALL data. The results obtained indicate selection on reducing RFI could lead to a decrease in DMI, without compromising other performance traits, as genetic correlations between RFI, growth and liveweight were low or close to 0 (-0.12–0.07). Conclusions: As genetic parameters were similar between national data sets (URY, CAN), pooling data (ALL) provided more accurate parameter estimations, as they presented smaller standard deviations, especially in multivariate analysis. Implications: Parameters estimated here may be used in international or national genetic evaluation programs.

Feed efficiency significantly impacts the economics of beef production and is influenced by biological and environmental factors. The rumen microbiota plays a crucial role in efficiency, with studies increasingly focused on its relationship with different rearing systems. This study analyzed 324 rumen samples from bulls and steers categorized as high and low efficiency based on residual feed intake. The animals were fed two diets (postweaning and finishing) and rumen samples were sequenced using a reduced representation sequencing (RRS) based approach. The results indicated that diet significantly affected microbial diversity and abundance. In postweaning diets, Actinomycetota, particularly Bifidobacterium, were prevalent, aiding carbohydrate fermentation. In contrast, Acetoanaerobium was identified in finishing diets, likely contributing to acetate production. Additionally, Bacteroides and Butyrivibrio were abundant during postweaning, known for fiber degradation and volatile fatty acid production. Notably, Prevotella and Fibrobacter succinogenes were associated with high feed intake and nutrient utilization, indicating their potential as microbial biomarkers. However, alpha diversity indices showed no significant relationship with feed efficiency, suggesting that diversity alone may not adequately reflect the complexity of feed efficiency phenotypes. These findings highlight the importance of diet and microbial interactions on feed efficiency and suggest further research to explore these microbial contributions to precision feeding strategies.