Reproductive efficiency is crucial for the long-term economic sustainability of beef cattle production. Pregnancy loss and stillbirth are complex reproductive traits that do not yet have their genomic background fully understood, especially in zebu breeds (Bos taurus indicus). Hence, this study aimed to perform a genome-wide association study (GWAS) and functional annotation for conception success (CS), pregnancy loss (PL), stillbirth (SB), and pre-weaning calf mortality (PWM) in Nellore cattle. In this study, 3,728 cows with 17,094 reproductive records and 11,785 calves were evaluated. A total of 3,351 genotyped animals and 383,739 SNP markers were considered for GWAS analyses. SNP effects were estimated using the weighted single-step GWAS (WssGWAS), which considered two iterations. The top ten genomic windows with the highest contribution to the additive genetic variance of the traits were selected for gene annotation. Candidate genes were then analyzed for Gene Ontology terms (GO) and metabolic pathways. The top ten genomic windows that explained the largest proportion of the direct additive genetic variance ( $$\:{{\sigma\:}}_{\text{a}}^{2}$$ ) for CS, PL, SB, and PWM accounted for 17.03% (overlapping with 79 genes), 16.76% (57 genes), 11.71% (73 genes), and 12.03% (65 genes) of the total $$\:{{\sigma\:}}_{\text{a}}^{2}$$ , respectively. For CS, significant GO terms included Somitogenesis (GO:0001756), Somite Development (GO:0061053), and Chromosome Segregation (GO:0007059). Considering PL, the processes annotated were the Regulation of Hormone Secretion (GO:0046883), and Hormone Transport (GO:0009914), along with the Glucagon Signaling Pathway (bta04922). Embryonic Development (GO:0045995), and Cerebellum Development (GO:0021549) were the main biological processes found in the gene enrichment analysis for SB. For PWM, the Regulation of Glucose metabolic processes (GO:0010906), Zinc Ion Homeostasis (GO:0055069), Lactation (GO:0007595), and Regulation of Insulin Secretion (GO:0050796) were the most significant GO terms observed. These findings provide valuable information on genomic regions, candidate genes, biological processes, and metabolic pathways that may significantly influence the expression of complex reproductive traits in Nellore cattle, offering potential contributions to breeding strategies and future genomic selection strategies.

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 ( h ² ) were highest for LnVar, ranging from 0.32 ± 0.03 (calf weaning) to 0.42 ± 0.03 (breeding). Moderate h ² 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.

Climate change has intensified the need to reduce greenhouse gas emissions, particularly methane (CH ₄ ) from enteric fermentation. Genetic selection has emerged as a promising mitigation strategy; however, studies on Bos taurus indicus , especially Nellore cattle, remain limited. This study aimed to estimate heritabilities and genetic correlations for CH ₄ emission traits and their relationships with feeding behaviour, feed efficiency, and performance, as well as to evaluate the direct and correlated responses to selection for lower CH ₄ emissions. Data were from 2418 Nellore cattle evaluated in feed efficiency trials. Traits included dry matter intake (DMI), feeding time per day (FTd), feed events per day (FEd), and feeding rate (FR), residual feed intake (RFI), average daily gain (ADG), and mid‐test body weight (MBW). Methane emissions were measured in 1153 animals using the SF ₆ tracer technique, providing daily CH ₄ emission (g/day), CH ₄ per unit of DMI (CH ₄ DMI, g/day), and residual CH ₄ (CH ₄ res). Variance components were estimated using the single‐step genomic BLUP (ssGBLUP) method through Bayesian inference. Heritability estimates were moderate for CH ₄ (0.25), CH ₄ DMI (0.14), CH ₄ res (0.14), and performance traits such as DMI (0.35), ADG (0.36), and MBW (0.40). Higher estimates were observed for feeding behaviour traits FTd (0.49) and FR (0.42). Genetic correlations between CH ₄ and production traits were high, particularly with DMI (0.79), ADG (0.90), and MBW (0.91), indicating that selection for reduced CH ₄ emissions may affect growth. Direct selection for CH ₄ led to a modest annual reduction in emissions but also a correlated decline in MBW. These results demonstrate that while CH ₄ emissions are heritable, their strong genetic association with productivity traits indicates that isolated selection for reduced emissions may lead to undesirable outcomes in feed intake and performance. Therefore, strategies aiming to reduce CH ₄ emissions should consider the genetic relationships with growth and efficiency traits to avoid compromising animal productivity.