Reproductive efficiency is a key determinant of profitability in beef cattle systems, and hormonal protocols are widely used to improve reproductive performance in tropical breeds. The objective of this study was to assess the effect of hormonal stimulation on the estimation of variance components and genomic prediction ability of the probability of calving before 30 months of age (PC30) of Nelore heifers. The database contained information from 31 554 heifers of the National Association of Breeders and Researchers, Brazil. Among them, 22 105 heifers were not genotyped, and 9 449 were genotyped. These animals were categorised based on mating type (MT), with and without hormonal stimulation. For the estimation of genetic parameters and variance components in the complete database, two models were used: one with the inclusion of MT and one without. A single-step genomic BLUP animal threshold model was used. The Linear Regression (LR) validation method was employed to assess model performance. The dataset was divided into training subsets with 31 554 animals with phenotype records and validation subsets with 1 184 genotyped animals with phenotype records, but without progeny records. The subsets were further stratified by mating type. The heritability (h2) estimated for PC30 was of low magnitude and similar for both models (0.175 without MT and 0.18 with MT). Only the group of stimulated heifers with the inclusion of MT in the model exhibited a higher partial genomic breeding value (GEBV) than the complete GEBV. For LR estimators, Spearman correlation was not influenced by the inclusion of MT in the model. In animals with hormonal stimulation, a correlation of 0.95 was observed, whereas without hormonal stimulation, it was 0.96. Accuracy was similar across mating types (MT), and the inclusion of MT in the model did not alter the estimation (animals without hormonal stimulation: 0.36 without MT and 0.37 with MT; animals with hormonal stimulation: 0.38 with and without MT). The bias (± SD) for stimulated animals (-0.09 ± 0.05 without MT and 0.04 ± 0.05 with MT) compared to non-stimulated animals (-0.01 ± 0.05 without MT and -0.02 ± 0.06 with MT). In conclusion, the estimation of variance components and h2 for early pregnancy before 30 months is not influenced using hormonal stimulation and/or the inclusion of mating type in the evaluation model. However, there is a bias effect when considering hormonal stimulation in genomic prediction for PC30.

Prenatal environmental conditions may influence offspring development through fetal programming, particularly in tropical beef cattle systems. This study evaluated associations between late-gestation climatic stress and growth, reproductive, and accumulated productivity traits in Brahman cattle raised on tropical pastures in northern Bolivia. The dataset included 79,222 animals born between 2010 and 2026, totaling 418,621 valid phenotypic records. Climatic exposure was characterized retrospectively for the last 90 days of gestation using NASA POWER meteorological data. Fetal thermal exposure was quantified as the number of days in which THI exceeded the farm-specific historical 90th percentile, and hydric exposure was quantified as cumulative precipitation during the same window. Exposures were independently ranked across the analytical population and classified into tertiles (TS1–TS3; HS1–HS3). Linear mixed models tested TS, HS, and TS × HS effects. The main contrast compared high thermal load with low precipitation (TS3 + HS1) versus low thermal load with intermediate precipitation (TS1 + HS2). TS, HS, and their interaction affected all growth traits. In the main contrast, TS3 + HS1 was associated with lower yearling weight, lower female weaning weight, reduced scrotal circumference at 455 days, longer gestation length, lower age at first calving, and reduced accumulated productivity, particularly accumulated cow productivity up to 10 years. Although causality cannot be inferred, late-gestation climatic exposure was associated with persistent phenotypic variation in Brahman cattle.

Age at first calving (AFC) is an important indicator of reproductive efficiency in beef cattle. Studies that simultaneously estimate genetic parameters for reproductive traits and neonatal performance in Zebu populations are still scarce. This study aimed to estimate variance components and genetic parameters for AFC, calving interval (CI), longevity (LONG), productive life (PL), neonatal vigor (VIG), and pre-weaning mortality (MT) in Nellore cattle. Data from 33,445 animals were analyzed, with a pedigree of 67,970 animals and genotypes of 2623 individuals evaluated using a medium-density SNP panel. Single-trait linear and threshold animal models and two-trait models were fitted using Bayesian inference. The posterior heritability estimates were 0.19 ± 0.03 for AFC, 0.13 ± 0.02 for CI, 0.23 ± 0.03 for LONG, 0.17 ± 0.03 for PL, 0.31 ± 0.07 for VIG, and 0.14 ± 0.04 for MT, indicating low heritability for most traits, except for VIG, but with sufficient genetic variability to allow selection response. Posterior estimates of genetic correlation coefficients between AFC and the other traits were 0.35 ± 0.13 (AFC-CI), -0.27 ± 0.11 (AFC-LONG), -0.28 ± 0.15 (AFC-PL), -0.19 ± 0.16 (AFC-VIG), and 0.47 ± 0.13 (AFC-MT), indicating that females with a lower AFC tend to have shorter first calving intervals, remain productive in the herd for longer, and produce calves with lower mortality before four months of age. These results pointed out that management and selection strategies aimed at reducing age at first calving may contribute to more efficient and economically sustainable production systems.

This study evaluated the impact of progressively reducing phenotypic data collection on variance components and the genomic prediction ability of productive traits in Nellore cattle. A total of 288,456 phenotypic records and 51,471 genotypes were obtained from the National Association of Breeders and Researchers (ANCP), covering three traits: rib eye area (REA), residual feed intake (RFI), and adjusted weight at 450 days (W450). Two data-reduction scenarios were simulated: a retrospective scenario, involving the sequential removal of the most recent data by year, and a prospective scenario, involving the annual exclusion of historical data. In both scenarios, animals born in 2021 comprised the validation set. Analyses were performed using the single-step genomic best linear unbiased prediction method (ssGBLUP), with prior estimation of variance components adjusted for each scenario and year. Prediction accuracy, bias, and dispersion were evaluated using linear regression (LR) between genomic estimated breeding values (GEBV) from the complete and reduced datasets. In the retrospective scenario, starting from the complete 2021 database and removing contemporary records down to 2013, prediction accuracy declined from 0.72 to 0.299 for REA, from 0.51 to 0.11 for RFI, and from 0.77 to 0.34 for W450, accompanied by reduced heritability estimates and increased underdispersion. These effects were mainly attributed to the loss of genomic connectivity and the low proportion of genotyped animals (<1% in recent years). For RFI, genetic variance also increased due to the concentration of data on farms with strong genomic structure, although the absence of contemporary records limited predictive ability. Conversely, in the prospective scenario, starting from 2013 dataset and progressively removing the recent records through 2021, accuracy remained essentially stable, changing from 0.72 to 0.73 for REA, from 0.51 to 0.50 for RFI, and from 0.76 to 0.77 for W450, while bias and dispersion stayed within acceptable limits. This outcome was supported by the presence of up-to-date phenotypes and a higher proportion of genotyped animals (exceeding 70% by 2018). These findings demonstrate that while the exclusion of historical data can be a viable strategy to reduce computational demands, maintaining a contemporary and representative phenotypic dataset is critical to ensuring the reliability of genomic predictions and the long-term success of breeding programs.

Background/Objectives: Accurate estimates of variance components are essential in breeding programs. In this context, the main objective of this study was to estimate variance components for growth traits in the Montana Composite® beef population, which was developed in Brazil by crossing various taurine and indicine breeds. After 30 years of selection, the impact of recombination, heterosis, and inbreeding may have influenced the genetic background of the population. Methods: We analyzed data of birth weight, weaning weight, post-weaning weight gain, and yearling weight using 124,255 phenotypic records, 193,129 pedigree records, and 3911 genotyped individuals. Ten single-trait animal models (M1-M10) were compared, differing in the relationship matrix (pedigree- or genome-based relationships) and the inclusion of direct/maternal breed composition, heterosis, and recombination effects. Results: Models incorporating genomic information consistently yielded better fit and lower residual variances than pedigree-based models, highlighting the advantage of genomic information in capturing Mendelian sampling and realized genetic relationships. The inclusion of heterosis effects improved model fit and led to a partial reallocation of genetic variance from additive to non-additive components. In contrast, the inclusion of recombination effects in the models minimally influenced variance component estimates. Nevertheless, more complex models affected animal rankings and altered the breed composition of top-ranked selection candidates, with selection overlap between pedigree- and genomic-based evaluations ranging from moderate to high. Conclusions: Overall, genome-based models accounting for breed composition, heterosis, and recombination provided the most robust variance component estimates and the best support for long-term selection goals in the studied tropical composite beef cattle population.

Heat stress represents a major limitation for livestock production systems, negatively affecting feed efficiency, animal health and welfare, and overall performance. In this context, the objective of this study was to identify genomic regions, candidate genes, biological pathways, and functional networks associated with dry matter intake (DMI) and residual feed intake (RFI) in Nellore cattle exposed to varying levels of thermal stress. The dataset comprised records from 22,838 animals, with genotypes available for 18,567 individuals. The data were collected during 296 feed efficiency trials between 2011 and 2023 across 21 Brazilian farms. Genome-wide association studies (GWAS) were performed using the single-step GBLUP (ssGBLUP) approach to account for genotype-by-environment (G×E) interactions in Nellore cattle. Environmental variation was modeled using the temperature-humidity index (THI) as the environmental gradient, with analyses stratified across three environmental gradients (EG): low (THI = 66), medium (THI = 74), and high (THI = 81). Fifty-one SNPs were significantly associated with RFI, including 27 shared across all three EGs, 10 exclusive to the low EG, one to the high EG, and 13 shared between the moderate and high EGs. These associations were mapped to 44 candidate genes, with 19 genes commonly identified across all EGs, including key candidates such as PIPOX, GTF2F2, KCTD4, MYO18A, and NFIA. For DMI, 136 significant SNPs were identified: 12 and 39 exclusive to the low and moderate EGs, respectively; 28 shared across all EGs; and 57 shared between the moderate and high EGs. These variants were linked to 58 candidate genes, of which 19 were common to all EGs, including NCAPG, LCORL, FAM13A, HERC3, CCND1, and FGF19. Gene network analyses revealed a clear reconfiguration of interaction structures across thermal gradients, particularly for RFI, where gene connectivity declined with increasing THI levels. For DMI, gene networks remained highly integrated, especially in the lowest THI level. Functional annotation highlighted both conserved and environment-specific regulatory architectures, involving key biological processes such as growth regulation, lipid and protein metabolism, intracellular signaling, stress response, and neuroendocrine control. These findings uncover the environmental sensitivity of RFI and DMI, highlight the complex and dynamic genomic basis of these traits under varying climatic conditions, and support the identification of candidate genes for genomic selection programs aiming to enhance climatic resilience in tropical beef cattle.

Brahman cattle are widely used in tropical production systems due to their adaptability; however, reproductive efficiency remains constrained by pregnancy loss (PL), particularly under heat stress. This study evaluated genotype-by-environment (G × E) interactions for PL using reaction norm models incorporating the temperature-humidity index (THI) as an environmental descriptor. Records from Brahman heifers in two Bolivian herds were combined with weather data to calculate accumulated THI for the 30 d before and after rectal palpation. Pregnancy loss, defined from diagnoses performed between 60 and 150 d of gestation, was analyzed as a binary trait using threshold RNM fitted with either a pedigree-based relationship matrix ( A ) or a combined pedigree-genomic matrix ( H ). Genetic variation was detected for both the intercept and slope of the reaction norm. Heritability of the intercept increased from 0.23 ( A ) to 0.37 ( H ), whereas the slope showed moderate heritability (0.32 for A ; 0.33 for H ). Mean heritability of PL across accumulated THI levels was 0.10 ( A ) and 0.13 ( H ). Negative genetic correlations between intercept and slope (-0.41 for A ; -0.34 for H ) and between extreme THI levels (-0.65 for A ; -0.49 for H ) indicate substantial reranking of animals under contrasting thermal conditions. Although variance component estimates were broadly similar between models, incorporating genomic information improved prediction accuracy and reduced dispersion of breeding values. These results demonstrate that accumulated THI captures meaningful environmental sensitivity for PL and that integrating genomic information with reaction norm models enhances selection for reproductive resilience in Brahman cattle under tropical conditions.

Pregnancy losses (PL) affect calf production and economic efficiency in beef cattle operations. Here, PL was defined as failure from pregnancy diagnosis to calving, capturing a window approximately 60 d after the breeding season. In Zebu breeds, the genetic understanding of this phenomenon remains limited. The objective of this study was to estimate genetic parameters for PL in Nellore heifers and its associations with reproductive, growth, feed efficiency, and carcass traits. The dataset comprised 23,507 Nellore heifers with pregnancy diagnosis records (17,052 genotyped) from the Animal Genetics and Breeding Program (GMA), developed by the Animal Breeding and Biotechnology Group (GMAB) at the University of São Paulo, Brazil. Variance components and genetic parameters were estimated using a two-trait threshold-threshold animal model for PL jointly with the probability of early calving at 30 mo of age (PP30) and stayability (STAY), and a two-trait threshold-linear animal model for PL with age at first calving (AFC), scrotal circumference adjusted to 365 d (SC365), cumulative cow productivity (CCP), body weight adjusted to 240 (W240) and 455 d of age (W455), rib eye area (REA), rump fat thickness (RFT), marbling (MAR), residual feed intake (RFI), and dry matter intake (DMI). For all traits, the single-step GBLUP methodology was applied. The two-trait analyses were carried out using Bayesian inference through the Gibbs sampling algorithm implemented in the GIBBSF90+ software. The estimated heritability (h2) for PL was low (0.033), indicating strong environmental influence. Genetic correlations between PL and reproductive traits (PP30, STAY, AFC, CCP, and SC365) ranged from -0.15 to 0.93. For growth (W240 and W455) and carcass (REA, RFT, and MAR), genetic correlation ranged from -0.16 to 0.01. For RFI and DMI, genetic correlations were -0.44 and 0.07, respectively. The direct genetic gain for PL was of low magnitude. The relative efficiency of selection indicates that direct selection for PL tends to be less efficient than indirect selection. Therefore, the results of this study demonstrate that, although PL shows low h2, it is favorably correlated with sexual precocity in females. Selection for growth and carcass traits is not expected to affect PL, whereas the observed antagonistic correlation with RFI suggests a potential increase in PL among more feed-efficient animals, although this effect is small and not conclusive.

Background/Objectives: Genome-wide association studies (GWAS) based on single-step genomic BLUP (ssGBLUP) commonly assume equal single nucleotide polymorphism (SNP) variances, which may not reflect the biological architecture of complex traits. Alternative weighting strategies can increase detection power but may affect stability. This study evaluated how different SNP weighting approaches influence genomic region detection and biological interpretation of ribeye area (REA) and subcutaneous fat thickness (SFT) in Guzerá cattle. Methods: Phenotypic records from 2729 animals and genotypes from 1405 individuals (43,039 SNPs after quality control) were analyzed. Heritabilities were estimated using Restricted Maximum Likelihood (REML), and GWAS were conducted under five approaches: unweighted method (UM), quadratic method (QM), and three Non-Linear A strategies with weighting constants (1.125, 1.2, and 1.5). Genomic windows of 20 adjacent SNPs explaining ≥0.5% of the additive genetic variance (AGV) were considered significant. Recurrent regions were prioritized, and functional enrichment analyses (KEGG, GO, and MeSH) were performed. Results: Heritability estimates were moderate for REA (0.26 ± 0.05) and SFT (0.22 ± 0.04). Weighted approaches increased detection sensitivity. For REA, UM identified 10 windows, whereas QM and A_1.5 detected 24 and 31 windows. For SFT, UM identified 8 windows, while QM and A_1.5 detected 30 and 23 windows. Recurrent chromosomes included 2, 4, 6, 12, 16, 19, and 22 for REA, and 2, 3, 5, 7, 11, 17, and 22 for SFT. Key genes included AKT3, NOS2, and MSTN. Enrichment highlighted pathways related to muscle growth and lipid metabolism. Conclusions: SNP-weighted GWAS increased detection sensitivity but involved trade-offs between signal amplification and stability. Integrating weighting strategies improves biological interpretation and supports robust candidate gene identification for genomic selection.

The aim was to evaluate the genetic association between sexual precocity and productive performance in Nellore cattle. Records of standardized body weights at 120 (W120), 210 (W210), 365 (W365), and 450 (W450) days of age, Longissimus Muscle Area (LMA), backfat thickness (BFT), and age at first calving (AFC) were used. The mixed linear model included the fixed effects of contemporary groups (composed of herd, year, and season of birth, as well as the animal’s sex), in addition to the random effects of direct additive genetic, maternal genetic (for W120 and W210), and maternal permanent environment. The variance components were estimated by the restricted maximum likelihood method. The obtained direct heritability estimates were 0.45, 0.49, 0.42, 0.40, 0.33, 0.19, and 0.08 for W120, W210, W365, W450, LMA, BFT, and AFC, respectively. The Spearman correlations of the animals’ breeding values between the weight development estimates were of a high order, and for the traits of backfat thickness and age at first calving, it was 0.27, indicating a relationship at older ages with the onset of the acceleration of adipogenesis. The genetic gain obtained in AFC (0.48 days) with the correlated response related to LMA and BFT was − 0.05 and − 0.19 days, respectively. The genetic correlations obtained between AFC and the weight development traits were positive, however negative with the carcass-associated traits, indicating that the offspring are reproductively precocious, yet lighter and with a greater finish of subcutaneous fat.

Genomic prediction in beef cattle is particularly challenging in breeds with limited phenotypic and genotypic data. In this context, multi-breed methodologies that integrate information from genetically related populations have emerged as a promising strategy to improve prediction accuracy and calibration under data-scarce conditions. This study evaluated different genomic prediction approaches in a multi-breed population of Zebu cattle, including Nellore, Guzerat, Brahman, and Tabapua, using 653 785 phenotypic records, 190 865 genotypic records, and 3 681 158 pedigree records. The traits analyzed were rib eye area (REA), rump fat thickness (RFT), age at first calving (AFC), and accumulated productivity (ACP). Genomic estimated breeding values were obtained using the single-step GBLUP method under four models: single-breed (GSB), standard multi-breed (G0), metafounders (MF), and an adjusted genomic relationship matrix (AGR). Model performance was assessed using the linear regression method, which compares predictions from complete and partial datasets to estimate accuracy, bias, and dispersion. Multi-breed models, particularly MF and AGR, produced higher accuracy than the single-breed approach in several analyses for underrepresented breeds such as Guzerat, Brahman, and Tabapua, especially for carcass and reproductive-related traits. For example, in Guzerat, REA accuracy increased from 0.44 with GSB to 0.62 with AGR, while in Tabapua, ACP accuracy improved substantially from 0.23 with GSB to 0.51 with AGR. These results highlight the importance of leveraging genetically related breeds and well-structured reference populations to improve the reliability of genomic predictions in Zebu cattle under limited data conditions.

Including fat thickness as a covariate in the regression model used to calculate residual feed intake (RFI) could help preserve carcass quality traits, such as marbling, flavour and juiciness, by accounting for variation in fat deposition. This study aimed to: (1) investigate the benefits of adjusting RFI for rump fat thickness (RFT); (2) estimate variance components and genetic correlations between RFI-calculated with (RFIF) and without (RFIW) adjustment for RFT-and growth, reproduction and carcass traits using genomic information in beef cattle; and (3) compute accuracy, bias and dispersion of RFIF and RFIW genomic breeding values predicted using single-step GBLUP (ssGBLUP). We hypothesised that adjusting for RFT would account for a small proportion of RFI variability, and that genetic parameter estimates would support more balanced selection decisions. Phenotypic records were collected from 9094 Nellore animals (3253 females and 5952 males) over 14 feed efficiency tests conducted from 2011 to 2024. The pedigree included 17,407 animals, of which 5812 were genotyped. Linear and threshold animal models were applied for continuous and categorical traits, respectively. Heritability estimates were low for RFIW (0.17) and RFIF (0.16), with a strong genetic correlation between them (0.98), and a weak genetic correlation between RFIW and RFT (0.15). Spearman correlations between RFIF and RFIW breeding values were high: 0.98 in females and 0.95 in males. Genetic correlations of RFIW and RFIF with growth, reproduction and carcass traits ranged from -0.33 to 0.35. Prediction accuracy was similar for RFIF (0.43) and RFIW (0.44), whereas bias (0.00 for RFIw and 0.00 for RFIF) and dispersion (0.05 for RFIw and 0.03 for RFIF) showed minor differences. Although RFIF captured slightly more genetic variability, the impact was minimal and no differences were observed between RFIF and RFIW. The genetic correlations between RFI and traits related to growth, reproduction and carcass were close to zero to moderate, indicating that selection for RFI is unlikely to negatively impact these other traits. However, it is essential to consider the full set of traits in the selection process to avoid potential drawbacks to the overall genetic progress of the herd.

Sustainable beef production requires identifying animals with superior feed efficiency to reduce environmental impact and production costs. This study aimed to estimate heritability and genetic correlations between residual gain (RG) and growth, reproductive, carcass and feed efficiency traits in Nellore cattle. A total of 217,333 phenotypic records from animals born between 1980 and 2024, raised in diverse Brazilian regions, were analysed using Bayesian inference with a multi-trait mixed animal model. Heritability estimates for reproductive traits were low, while feed efficiency, growth and carcass traits showed moderate heritabilities. Residual gain exhibited a moderate positive genetic correlation with adjusted weight at 450 days (W450), supporting its potential as a selection criterion for growth efficiency. Genetic correlations between RG and carcass, reproductive and feed efficiency traits were generally low or near zero. However, a negative low genetic correlation and a moderate positive phenotypic correlation with early conception probability indicate complex effects on reproduction. Selection for RG may increase yearling weight and maintenance energy requirements, which can reduce early reproductive performance under restricted nutritional conditions, presenting trade-offs to consider in extensive systems. Conversely, RG may be particularly suitable for intensive systems such as feedlot or finishing operations, where nutritional management can mitigate these limitations. The negative genetic correlation between RG and residual feed intake further highlights RG's ability to identify animals that grow efficiently without increased feed intake. These results confirm that RG is a largely independent trait and shows sufficient genetic variability to respond to selection in Nellore cattle. Using it as a selection criterion can enhance feed efficiency without negatively impacting other economically important traits.

Genotype imputation is a technique used to infer unobserved genotypes based on reference panels, allowing increased marker density and cost-effective optimization for genomic selection. This study aimed to evaluate whether the inclusion of genotypes from the founder breeds Nelore (NE) and Charolais (CH) improves the imputation accuracy in the composite beef cattle breed Canchim (CA). The populations studied consisted of 804 NE, 897 CH, and 392 CA animals, all genotyped using high-density panels (777,962 SNP – single nucleotide polymorphisms). CA animals had their genotypes masked to simulate a medium-density panel (54,609 SNP). Fourteen imputation scenarios were evaluated, varying according to breed, sex, year of birth, and lineage. Imputation accuracy was determined based on the percentage of correctly imputed genotypes (PERC) and the squared Pearson’s correlation between observed and imputed genotypes (R2). PERC values ranged from 66.52

This study tested the hypothesis that crossbreeding Rubia Gallega with Nelore cattle alters lipid composition through differences in biochemical, hormonal, and molecular parameters of lipid metabolism. The objective was to compare fatty acid profiles, serum metabolites and hormones, and the expression of genes related to lipid metabolism in Rubia Gallega × Nelore (RGN) and purebred Nelore (N) cattle. Sixteen Nelore and sixteen RGN non-castrated males (11 months old; 280 ± 15 kg initial body weight) were finished in a feedlot for 120 days prior to slaughter. Nelore cattle exhibited higher total lipid content, greater concentrations of saturated fatty acids 14:0 and 16:0, and higher Δ9-desaturase activity compared with RGN (P ≤ 0.05). In contrast, RGN cattle showed higher total polyunsaturated fatty acids (ΣPUFA) and a higher health index (P ≤ 0.05). Serum total cholesterol, high-density lipoprotein (HDL), and low-density lipoprotein (LDL) concentrations were lower in RGN than in Nelore cattle (P ≤ 0.05). Additionally, RGN cattle had higher circulating insulin-like growth factor 1 (IGF-1) and lower insulin concentrations (P ≤ 0.05). Gene expression analysis showed higher lipoprotein lipase (LPL) expression in RGN cattle (P ≤ 0.05), while acyl-CoA oxidase (ACOX) expression tended to be higher (P = 0.0998). These results demonstrate that Rubia Gallega × Nelore crossbreeding modifies lipid composition and is associated with differences in metabolic and molecular indicators of lipid metabolism.

Subclinical mastitis is still one of the most important health issues in dairy herds, leading to substantial economic losses due to reduced milk yield, treatment costs, and premature culling. Understanding the genetic basis of this disorder is essential in supporting selection strategies to enhance udder health and herd productivity. The aim of this study was to estimate the genetic and phenotypic parameters for subclinical mastitis occurrence using information from 748 lactations of 279 pasture-based Girolando cows from the Federal Institute of Triângulo Mineiro (IFTM)-Uberaba research herd, calved between 2012 and 2024. Variance components were obtained using a Bayesian approach under a complete animal model. The heritability estimates (posterior medians [highest posterior density interval]) for subclinical mastitis were 0.14 [0.09-0.20] and 0.15 [0.10-0.20] for the right (RF) and left (LF) front quarters, and 0.13 [0.08-0.18] and 0.15 [0.09-0.20] for the right (RR) and left (LR) rear quarters, respectively. The repeatabilities were 0.41 [0.37-0.45] and 0.35 [0.31-0.39] for RF and LF, and 0.35 [0.32-0.39] and 0.41 [0.37-0.45] for the RR and LR quarters, respectively. The low to moderate heritabilities indicate that subclinical mastitis is highly influenced by environmental factors, and the moderate repeatabilities estimates indicate that these traits will benefit from a more frequent data collection. The results suggest that the genetic progress for subclinical mastitis in pasture-based Girolando cows will be slow. Genetic and phenotypic correlations among quarters were also estimated using a multiple-trait model. Genetic correlations were consistently high, exceeding 0.80 for all quarter combinations, indicating a largely shared genetic background controlling susceptibility to subclinical mastitis across udder quarters. In contrast, phenotypic correlations were moderate ranging from 0.50 [0.47-0.54] between RF and LR, and 0.59 [0.56-0.62] between RF and LF, suggesting that environmental and management-related factors contribute more substantially to differences in mastitis occurrence among quarters than genetic effects. Overall, these results suggest that selection for reduced subclinical mastitis in one quarter is expected to result in favorable correlated responses in the others. However, given the limited dataset size, further studies using larger multiherd populations in tropical production systems, and genomic information, are necessary to validate these correlations and to investigate potential breed composition differences in udder and teat morphology that can affect mastitis susceptibility.

The Montana composite was developed in Brazil from crosses between Bos indicus and Bos taurus and structured into four biological types: Zebu (N), adapted taurine (A), British taurine (B), and continental taurine (C). This study aimed to characterize the genetic diversity and population structure of the Montana composite using genomic data through principal component analysis (PCA), admixture analysis, and Wright’s FST statistic. The PCA revealed a clear separation between Bos indicus and Bos taurus groups, with Montana animals distributed in an intermediate position. The first two principal components explained 69.48

BACKGROUND: Reproductive efficiency is an important component of profitability and sustainability in beef cattle production, particularly in tropical environments where animals are routinely exposed to environmental stressors. This study aimed to identify environmentally sensitive SNPs associated with sexual precocity traits in Nellore cattle and characterize candidate genes and biological pathways regulating sexual precocity under variable environmental conditions. For this purpose, three sexual precocity indicators were analyzed; one in heifers (heifer early calving probability at 30 months, HC30), and two in young males (scrotal circumference at 365 days, SC365; age at puberty, APM). Reaction norm models were integrated with genome-wide association studies (GWASs) to identify genomic regions associated with both genetic potential (intercept) and environmental sensitivity (slope). RESULTS: A total of 46 significant SNPs were identified across the three traits, with SC365 showing the highest genetic complexity (36 SNPs), followed by HC30 (7 SNPs), and APM (3 SNPs). The analyses revealed distinct genetic architectures among the traits. For HC30, candidate genes showed clear functional partitioning between those associated with baseline fertility (e.g., ERBB4, SNAI2) and those associated with environmental sensitivity (e.g., TRIB1, NSMCE2), with no overlap between intercept and slope components. In contrast, SC365 exhibited substantial genetic overlap, with five genes (i.e., GRB14, SLC9A8, SPATA2, MGRN1, SEPTIN12) significantly associated with both intercept and slope, indicating a robust trait with shared genetic control of baseline potential and environmental responsiveness. For APM, genetic associations were predominantly related to baseline potential, with genes involved in DNA repair (CHEK2), endoplasmic reticulum stress response (XBP1), and immune regulation (TNIP3). Functional enrichment analyses revealed trait-specific biological pathways, whereas QTL enrichment demonstrated biologically coherent overlaps with reproductive and metabolic traits. CONCLUSION: These findings demonstrate that sexual precocity traits exhibit distinct genetic architectures reflecting their underlying biological mechanisms and environmental responsiveness, providing valuable insights for developing climate-resilient breeding strategies in tropical beef cattle production systems.

This study aimed to estimate the variance components, heritabilities and genetic correlations between four new different categories of stayability (STAY48-2, STAY48-3, STAY54-2, STAY54-3) with weight at 240 days of age (W240), weight at 450 days of age (W450), scrotal circumference at 365 days of age (SC365), age at puberty in males (APM), traditional stayability (STAY72), probability of precocious calving at 30 months of age (PPC30), ribeye area (REA), rump fat thickness (RFT), residual feed intake (RFI), dry-matter intake (DMI), residual live weight gain (RG) and frame score (FRAME). Records from 80,958 females born between 2000 and 2019, exposed to mating starting at 10 months of age, raised on pasture from 508 farms in the central-west, southeast, northeast and northern regions of Brazil that participate in the National Association of Breeders and Researchers (ANCP), were analysed. The (co)variance components were estimated by Bayesian inference in a two-trait animal model. The posterior means of heritability estimates for STAY48-2, STAY48-3, STAY54-2 and STAY54-3 were moderate to low, 0.20, 0.20, 0.22 and 0.22 respectively. The traits related to different categories of stayability showed low genetic correlations with male reproductive traits (-0.22 to 0.19), feed efficiency (-0.03 to 0.13), carcass (0.11 to 0.18) and body composition (-0.09 to -0.6), moderate with growth (0.04 to 0.29) and high with sexual precocity (0.88 to 0.93) and fertility (0.65 to 0.73). The heritability estimates of the different categories of stayability indicate genetic improvement for longevity in early challenged females. The genetic correlations with sexual precocity showed that its use as selection criteria is recommended for production systems of younger females challenged in the reproductive season rather than traditional stayability to increase the probability of stayability in the herd.