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.

Missing pedigrees are a common problem in most populations. Animals with unknown ancestors are usually treated as founders; however, this can underestimate inbreeding, not properly account for different base populations, and bias breeding values. We aimed to assess the use of unknown parent groups (UPG) or metafounders (MF) to model missing pedigrees in a beef cattle population. Phenotypic and genotypic data from the Nellore improvement programme of the Brazilian Breeders and Researchers Association were used. The pedigree contained 3.8 M animals born between 1970 and 2022, of which 51,752 were genotyped. Records for scrotal circumference at 365 days old (SC365, N = 239,806), age at first calving (AFC, N = 560,785) and accumulated cow productivity (ACP, N = 269,330) were used. Four models were implemented: single‐step GBLUP without explicitly dealing with missing pedigree (G0), with UPG (G1), with MF (G2) and with accounting for group‐specific allele frequencies (G3). UPG and MF were assigned based on commercial and registered herds (S1), uncertain paternity (S2) and patriarchs (S3). The accuracy and bias of predictions were assessed using the linear regression (LR) method. Linear, single‐trait animal models were used for SC365 and AFC, and multi‐trait for ACP. Heritability estimates ranged from 0.07 to 0.40. Compared to G0, accuracy was slightly higher in G2 _S2 and G2 _S3 (0.70 vs. 0.71) for SC365, G2 _S3 (0.49 vs. 0.51) for AFC, G1 _S2 for ACP (0.67 vs. 0.71). Bias was small in all the scenarios (≤ 0.06 SD), except of ACP that presented a great bias, including MF. Overall, G1 and G2 had similar accuracy, possibly because of the limited number of genotyped animals linked to MF. Centring the genomic relationship matrix by patriarchs' allelic frequencies resulted in similar accuracy and bias to the MF models. Replicating the study with a larger database containing more genotyped animals connected to MF could help improve the MF estimates, and thus, prediction accuracy and bias.

Enhancing female longevity through regular calvings improves herd replacement rates and reduces the costs associated with replacing low reproductive efficiency animals. Stayability ( STAY ), defined as a cow's ability to remain productive in the herd, is a categorical trait that challenges traditional genetic evaluation due to its non‐normal distribution. This study aimed to estimate genomic predictions for different STAY definitions—based on the number of calvings at specific ages in Nellore females—and to compare the predictive ability of linear and threshold models using the linear regression ( LR ) method. Phenotypic and genotypic data from 187 herds provided by the Nellore Brazil breeding program ( ANCP , Ribeirão Preto, Brazil) were used. Four STAY definitions ( STAY48 ‐2, STAY48 ‐3, STAY54 ‐2, STAY54 ‐3, STAY72 ‐3) were evaluated. Genomic estimated breeding values ( GEBV ) were obtained using univariate linear and threshold models implemented in the BLUPF90 software family. Variance components were transformed from liability to observed scale. Heritability estimates ranged from 0.16 to 0.22 on the liability scale and 0.07 to 0.09 on the observed scale. Threshold models showed superior predictive ability compared to linear models, with higher accuracies (0.531 to 0.698 vs. 0.451 to 0.532), lower bias (−0.0004 to 0.008 vs. 0.027 to 0.096) and dispersion values closer to the ideal (0.932 to 1.000 vs. 0.811 to 0.848). Among the definitions, STAY48 with at least two or three calvings demonstrated the most consistent performance, representing a promising criterion for genetic evaluation in Nellore cattle.

Ncluding 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 (RFI_F) and without (RFI_W) adjustment for RFT—and growth, reproduction and carcass traits using genomic information in beef cattle; and (3) compute accuracy, bias and dispersion of RFI_F and RFI_W 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 RFI_W (0.17) and RFI_F (0.16), with a strong genetic correlation between them (0.98), and a weak genetic correlation between RFI_W and RFT (0.15). Spearman correlations between RFI_F and RFI_W breeding values were high: 0.98 in females and 0.95 in males. Genetic correlations of RFI_W and RFI_F with growth, reproduction and carcass traits ranged from −0.33 to 0.35. Prediction accuracy was similar for RFI_F (0.43) and RFI_W (0.44), whereas bias (0.00 for RFI_w and 0.00 for RFI_F) and dispersion (0.05 for RFI_w and 0.03 for RFI_F) showed minor differences. Although RFI_F captured slightly more genetic variability, the impact was minimal and no differences were observed between RFI_F and RFI_W. 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.

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 (G _SB ), standard multi‐breed (G ₀ ), 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 G _SB to 0.62 with AGR, while in Tabapua, ACP accuracy improved substantially from 0.23 with G _SB 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.