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.

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.

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.

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

This study assessed the impact of breed effects, heterosis, and recombination on genomic predictions for growth, reproduction, and body composition traits in Montana (R) cattle. The database included 124,547 records for Birth Weight (BW), 111,103 for Weaning Weight (WW), 87,740 for Weight at 12 months (W12), 49,249 for Postweaning Weight Gain (WG), 87,740 for Scrotal Circumference (SC), and 44,873 for Muscularity (MUSC), with 3911 genotyped animals from the Montana (R) composite program. Models M1 to M5 included fixed effects of contemporary group, embryo transfer, and cow age at calving (linear and quadratic). The effects of direct and maternal breed composition, heterozygosity, and recombination varied across models. From model M2 onward, covariates for biological type, heterosis (direct, maternal, specific), and recombination (direct, maternal, specific) were added. All genomic analyses used the ssGBLUP method, and the LR (Linear Regression) validation method was used to assess predictive ability and model effect influence. Heritability estimates ranged from 0.19 to 0.22 for WW, 0.15 to 0.20 for WG, 0.36 to 0.37 for BW, 0.23 to 0.29 for W12, 0.28 to 0.29 for SC, and 0.17 to 0.19 for MUSC. The most parameterized models showed the best fit by AIC, with M5 best for WW, W12, WG, and SC; M4 for BW; and M3 for MUSC. Model M1 showed the best prediction ability for WW and W12, with the highest accuracies (0.407 and 0.456), best dispersions (1.01 and 0.897), and lowest biases (0.098 and 0.068), respectively. For WG, M1 had the highest accuracy (0.452), M5 the best dispersion (0.940), and M4 the lowest bias (0.028). For BW, M5 showed the highest accuracy (0.452), M4 the best dispersion (1.001), and M3 the lowest bias (-0.006). For SC, M1 had the highest accuracy (0.501), M3 the best dispersion (1.004), and M4 the lowest bias (0.092). For MUSC, M4 had the highest accuracy (0.415) and lowest bias (0.057), while M2 showed the best dispersion (0.979). More parameterized models provided a better fit for variance component estimation. In general, genomic predictions with M1 displayed the highest accuracies for WW, W12, WG, and SC, and lower bias for most traits.

This study evaluated the influence of variance component (VC) estimates, obtained from different models and two relationship matrices, pedigree-based (BLUP) and genomic information-based (ssGBLUP), on genomic predictions of carcass traits in Montana composite cattle. Phenotypic records from 14,422 animals were analysed for ribeye area, rump fat thickness, backfat thickness and marbling, along with pedigree information from 193,129 animals and genomic data from 3911 animals genotyped with 49,457 SNPs. Variance components and heritability estimates were calculated using restricted maximum likelihood under single-trait linear models. Across five models (M1-M5), fixed effects included contemporary group, embryo transfer, age at ultrasound and cow age at calving, while random effects included direct genetic effect and residual. From model M2 onwards, biological type, heterosis and both combined and specific recombination effects were also considered. The Akaike information criterion (AIC) was used to identify the best-fitting model. Different VC estimates were applied in ssGBLUP predictions to evaluate predictive ability based on accuracy, bias and dispersion. Variance component and heritability estimates were similar between methods, although ssGBLUP yielded higher direct additive genetic variances and heritabilities. More parameterised models using ssGBLUP provided a better fit according to AIC. However, less parameterised models showed superior predictive ability, regardless of whether VCs were estimated by BLUP or ssGBLUP. When comparing predictive ability across sources, pedigree-based VC estimates resulted in more accurate predictions. Thus, the choice of model complexity should be guided by the analysis objective and the available data structure.

Nellore cattle (Bos taurus indicus), the predominant beef cattle breed raised in Brazil, has substantial economic importance in the global beef market. Ultrasonography is commonly used to measure carcass traits, which facilitate the genetic evaluation of live animals and provides indicators of meat quality. Male reproductive efficiency assessed by semen quality and scrotal circumference traits directly influences farm profitability. This study estimated variance components and genetic correlations between ultrasound-based carcass and semen quality and scrotal circumference traits in Nellore cattle. We used a dataset of 116,011 animals with phenotypic records, 13,632 genotyped animals, and a pedigree with 635,175 animals. Heritability estimates for scrotal circumference at 18 and 24 months were moderate (0.39 f 0.008 and 0.38 f 0.02, respectively). Semen quality traits, including motility, vigor, and mass activity were found to be lowly heritable with estimates ranging from 0.03 f 0.01 to 0.05 f 0.01. The heritability estimates for sperm defects were also low and ranged from 0.01 f 0.007 to 0.12 f 0.02. Carcass traits, including ribeye area and rump fat thickness, were moderately heritable (0.34 f 0.05 and 0.39 f 0.06), while subcutaneous fat thickness had a heritability of 0.18 f 0.04. Genetic correlations between carcass with semen quality and scrotal circumference traits were of low to moderate magnitude and unfavorable. These findings indicate that carcass traits can be improved through genetic selection and the generally low genetic correlations with sperm quality traits and scrotal circumference traits suggest that selection for improved carcass traits, measured using ultrasonography technology, is expected to have minimal impact on semen quality. Therefore, simultaneous selection for both trait groups is recommended to ensure optimal long-term genetic improvement in Nellore cattle breeding programs.

The Montana Tropical cattle, a Taurine and Indicine composite, were developed in Brazil since 1994 and were based on crossing four biological types of cattle: zebu (mainly Nelore), tropical adapted taurine (mainly Senepol and Romosinuano), British taurine (mainly Angus) and continental taurine (as Charolais, Simental and Limousin). This study aimed to characterise the genetic ancestry of this composite breed at the genomic level. Principal component analysis revealed the composite in intermediate space between indicine and taurine but closer to the Taurine cluster, which is consistent with its multi-breed origin. The ADMIXTURE analysis indicated the Montana Tropical to be composed of several progenitor breeds without an indication of a dominant breed. Local ancestry analysis showed the Montana animals to have an average of 24% (standard deviation of ±5.41) Zebu ancestry. The total taurine ancestry was 62%, consisting of 19.6% (±6.96) from tropical adapted taurine, 20.1% (±5.99) from British taurine, 21.92% (±7.79) from continental taurine and 14% (±5.26) of the genome was undetermined. Based on the pedigree, these animals would have 20.8% ± 8.5% Zebu, 50.5% ± 14.4% tropical adapted taurine, 21.1% ± 13.5% British taurine and 7.6% ± 5.1% of continental European taurine in their composition. The genomic regions in the composite originating from each biological type highlight the trait complementarity each genetic group contributes. For example, the genomic region of high tropical adapted taurine ancestry was shown to harbour the slick hair locus, and the regions of high indicine ancestry are associated with high length of productive life. This study unravels the complex genetic ancestry of the Montana Tropical composite, highlighting the effective blend of ancestral gene pools that enhance key production and adaptation traits.

The meat market has enormous importance for the world economy, and the quality of the product offered to the consumer is fundamental for the success of the sector. In this study, we analyzed a database which contained information on 2470 animals from a commercial farm in the state of São Paulo, Brazil. Of this total, 2181 animals were genotyped, using 777,962 single-nucleotide polymorphisms (SNPs). After quality control analysis, 468,321 SNPs provided information on the number of genotyped animals. Genome-wide association analyses (GWAS) were performed for the characteristics of the rib eye area (REA), subcutaneous fat thickness (SFT), shear force at 7 days’ ageing (SF7), and intramuscular fat (IMF), with the aid of the single-step genomic best linear unbiased prediction (ssGBLUP) method, with the purpose of identifying possible genomic windows (~1 Mb) responsible for explaining at least 0.5% of the genetic variance of the traits under analysis (≥0.5%). These genomic regions were used in a gene search and enrichment analyses using MeSH terms. The distributed heritability coefficients were 0.14, 0.20, 0.18, and 0.21 for REA, SFT, SF7, and IMF, respectively. The GWAS results indicated significant genomic windows for the traits of interest in a total of 17 chromosomes. Enrichment analyses showed the following significant terms (FDR ≤ 0.05) associated with the characteristics under study: for the REA, heat stress disorders and life cycle stages; for SFT, insulin and nonesterified fatty acids; for SF7, apoptosis and heat shock proteins (HSP27); and for IMF, metalloproteinase 2. In addition, KEGG (Kyoto encyclopedia of genes and genomes) enrichment analysis allowed us to highlight important metabolic pathways related to the studied phenotypes, such as the growth hormone synthesis, insulin-signaling, fatty acid metabolism, and ABC transporter pathways. The results obtained provide a better understanding of the molecular processes involved in the expression of the studied characteristics and may contribute to the design of selection strategies and future studies aimed at improving the productivity of Nellore cattle.

Genomic regions related to tropical adaptability are of paramount importance for animal breeding nowadays, especially in the context of global climate change. Moreover, understanding the genomic architecture of these regions may be very relevant for aiding breeding programs in choosing the best selection scheme for tropical adaptation and/or implementing a crossbreeding scheme. The composite MONTANA TROPICAL® population was developed by crossing cattle of four different biological types to improve production in harsh environments. Pedigree and genotype data (51962 SNPs) from 3215 MONTANA TROPICAL® cattle were used to i) characterize the population structure; ii) identify signatures of selection with complementary approaches, i.e. Integrated Haplotype Score (iHS) and Runs of Homozygosity (ROH); and iii) understand genes and traits related to each selected region. The population structure based on principal components had a weak relationship with the genetic contribution of the different biological types. Clustering analyses (ADMIXTURE) showed different clusters according to the number of generations within the composite population. Considering results of both selection signatures approaches, we identified only one consensus region on chromosome 20 (35399405-40329703 bp). Genes in this region are related to immune function, regulation of epithelial cell differentiation, and cell response to ionizing radiation. This region harbors the slick locus which is related to slick hair and epidermis anatomy, both of which are related to heat stress adaptation. Also, QTLs in this region were related to feed intake, milk yield, mastitis, reproduction, and slick hair coat. The signatures of selection detected here arose in a few generations after crossbreeding between contrasting breeds. Therefore, it shows how important this genomic region may be for these animals to thrive in tropical conditions. Further investigations on sequencing this region can identify candidate genes for animal breeding and/or gene editing to tackle the challenges of climate change.

The aim of this paper is to describe in detail the development, over 30 years, of a large-size composite cattle population, destined for meat production in challenging environments in the tropical and sub-tropical regions of South America. Montana cattle were developed using crosses between Bos taurus and Bos indicus animals, and here we present details regarding the evolution of the breed, both numerically and in terms of the racial composition of the animals, and we assess the levels of heterosis and recombination and the genetic structure of the breed. Overall, given the continuous introduction of external germplasm, Montana Composite cattle have retained high levels of heterosis while maintaining inbreeding at very low levels.

There may be an increased risk of metabolic disorders, such as rumen acidosis, in cattle fed high-concentrate diets, particularly those from Bos taurus indicus genotypes, which have shown to be more sensitive to ruminal acidification. Therefore, this study aimed to estimate (co)variance components and identify genomic regions and pathways associated with ruminal acidosis in feedlot Nellore cattle fed high-concentrate diets. It was utilized a dataset containing a total of 642 Nellore bulls that were genotyped from seven feedlot nutrition studies. The GGP Indicus 35k panel was used with the single step genome-wide association study methodology in which the effects of the markers were obtained from the genomic values estimated by the GBLUP model. A bivariate model to estimate genetic correlations between the economically important traits and indicator traits for acidosis was used. The traits evaluated in this study that were nutritionally related to rumen acidosis included average daily gain (ADG), final body weight, time spent eating (TSE), time spent ruminating, rumenitis score (RUM), rumen absorptive surface area (ASA), rumen keratinized layer thickness (KER) and hot carcass weight (HCW). The identified candidate genes were mainly involved in the negative or non-regulation of the apoptotic process, salivary secretion, and transmembrane transport. The genetic correlation between HCW and ASA was low positive (0.27 ± 0.23), and between ADG and ASA was high moderate (0.58 ± 0.59). A positive genetic correlation between RUM and all performance traits was observed, and TSE correlated negatively with HCW (-0.33 ± 0.21), ASA (-0.75 ± 0.48), and KER (-0.40 ± 0.27). The genetic association between economically important traits and indicator traits for acidosis suggested that Nellore cattle may be more sensitive to acidosis in feedlot systems.

This study aimed to estimate (co)variance components and genetic parameters for calving ease (CE) and their genetic correlations with growth, reproductive, carcass, and feed efficiency traits in Nellore cattle. Phenotypes for CE are scored in two categories: normal calving and assisted calving. The traits considered were probability of precocious calving, age at first calving, stayability, adjusted scrotal circumference at 365 days of age, accumulated cow productivity, age at puberty of males, gestation length, birth weight, adjusted weights at 210 and 450 days of age, adult cow weight, frame score, hip height, rib eye area, subcutaneous backfat thickness, rump fat thickness, intramuscular fat percentage, residual feed intake and dry matter intake. The estimation of genetic parameters was performed using a two-trait threshold-linear animal model, except for CE, stayability, and probability of precocious calving, which were evaluated through a two-trait threshold animal model. The direct (0.27) and maternal (0.19) heritability estimates for CE in heifers primiparous Nellore indicated that selecting for this trait is feasible. The selection to improve the female sexual precocity should consider CE during the selection and mating decisions to reduce calving problems. Genetic correlation estimates between CE and BW suggest that selecting low birth weight to reduce calving problems is not an appropriate strategy to improve calving ease in heifers Nellore. Therefore, adopting a multi-trait selection model with CE and BW in the Nellore breed would reduce calving difficulties, particularly in sexually precocious heifers, without impairing the growth, reproductive, feed efficiency conversion, and carcass indicator traits.

This study aimed to estimate variance components (VCs) for growth and reproductive traits in Nellore cattle using two relationship matrices (pedigree relationship A matrix and pedigree plus genomic relationship H matrix), and records collected before and after genomic selection (GS) implementation. The study also evaluated how genomic breeding values (GEBV) are affected by variance components and discarding old records. The analysed traits were weight at 120 days (W120), weight and scrotal circumference at 450 days (W450 and SC450, respectively). Three datasets were used to estimate VCs, including all phenotypic information (All) or records for animals born before or after GS implementation (Before or After datasets, respectively). Both relationship matrices were considered for VC estimation, the A matrix was used in all three datasets and VC from each combination were named as A_Before, A_After, and A_All). The H was used in two datasets: H_All and H_After. Different VCs were used for GEBV prediction through ssGBLUP. This step used two possible Datasets, using all available phenotypic data (Dataset 1) or just records collected since GS implementation (Dataset 2). Validation was conducted using accuracy, bias and dispersion according to the LR method and prediction accuracy from corrected phenotypes. The heritability of all traits increased from A_Before to A_After, while estimates for A_All were intermediary. In the previous order, the estimates were 0.16, 0.17, and 0.15 for W120; 0.31, 0.39, and 0.35 for W450; 0.35, 0.47, and 0.41 for SC. For W450 and SC, using the H matrix reduced the heritability (0.33 and 0.32 for W450; 0.41 and 0.38 for SC for H_After and H_All, respectively). For W120, Dataset1 and VCs from A_After showed the highest accuracy for direct and maternal GEBV (0.953 and 0.868). For W450, Dataset 1 and VC from H_After allowed the highest accuracy (0.854) but use Dataset 2 and same VC source yield similar value (0.846). For SC, Dataset 2 with VC from H_After showed the highest accuracy (0.925). To use Dataset 2 does not cause important changes in bias or dispersion with respect to Dataset 1. The VC and genetic parameters changed for W120, W450, and SC450, using records before or after the GS implementation. For W450 and SC450, genetic variance and heritability estimates increased with the use of GS. For W120, genomic predictions were more accurate using A for VC estimation. Accuracy gains were observed for W450 and SC450 using H in VC estimation and/or discarding records before GS. It is possible to discard phenotypic records before GS implementation without generating bias or dispersion in the GEBV of young candidates.

The objective of this study was to obtain (co)variance components, heritability, and genetic and phenotypic correlation estimates for feed efficiency and feed behaviour-related indicator traits. Further, it aimed to predict the direct and correlated responses for feed efficiency traits when selection was applied for feeding behaviour-related traits in Nelore cattle. Phenotypic records (n = 4840) from 125 feed efficiency tests (RFI: Residual feed intake and DMI: Dry matter intake) carried out between 2011 and 2018 were considered in this study. Animals belonged to five farms located in two Brazilian geographical regions (Midwest and Southeast). Animals under similar management and environmental conditions in the feedlot were evaluated when they attained an average of 13.5 ± 4.15 months of age. Feed behaviour-related traits were also obtained, including meal criteria (MC), meal frequency (MF), average meal duration (AMD), meal duration (MD), average consumption per meal (ACM), and consumption rate (CR) through the GrowSafe System® electronic bunk system. The contemporary groups for all traits were composed of farm, management group, feed efficiency test, sex, and birth year. The (co)variance components were estimated using the restricted maximum likelihood method considering a multi-trait (n = 8) animal model. The heritability estimates for RFI (0.23 ± 0.02), DMI (0.31 ± 0.02), MF (0.65 ± 0.02), AMD (0.29 ± 0.02), ACM (0.24 ± 0.02), MD (0.41 ± 0.02), MC (0.48 ± 0.02), and CR (0.42 ± 0.02) were moderate to high. The highest genetic correlation was obtained between CR and MD (-0.91 ± 0.04), MD and AMD (0.73 ± 0.03), CR and AMD (-0.68 ± 0.04), and RFI and DMI (0.81 ± 0.02). The highest phenotypic correlation was between ACM and AMD (0.76 ± 0.02), DMI and MD (0.77 ± 0.02), and DMI and RFI (0.77 ± 0.02). Genetic improvement for feed efficiency and feeding behaviour-related traits is feasible and the results obtained herein provided valuable information regarding the genetic background of Nelore feeding behaviour-related traits. The genetic association between feeding behaviour and feed efficiency-related traits suggested that animals spending less time feeding at a low feeding rate also had lower DMI and higher feed efficiency (RFI), and likely had lower energy maintenance requirements. The relative efficiency of selection showed that feeding behaviour-related traits were not adequate indicator traits to improve RFI and DMI. The DMI might be an effective selection criterion to improve RFI and reduce the herd's maintenance requirements.

The aim of this work was to evaluate the impact of applying genomic information in pedigree uncertainty situations on genetic evaluations for growth- and cow productivity–related traits in Nelore commercial herds. Records for accumulated cow productivity (ACP) and adjusted weight at 450 days of age (W450) were used, as well as genotypes of registered and commercial herd animals, genotyped with the Clarifide Nelore 3.1 panel ( 29,000 SNPs). The genetic values for commercial and registered populations were estimated using different approaches that included (ssGBLUP) or did not include genomic information (BLUP), with different pedigree structures. Different scenarios were tested, varying the proportion of young animals with unknown sires (0, 25, 50, 75, and 100

Abstract Knowledge of population structure and breed composition is important for managing breed information within a breeding program. Composite Montana Tropical® was developed by crossing animals of four different biological types or racial groups. By using genomic data it is possible to conduct several studies of population structure and selection signatures. Pedigree and genotype data (51962 SNP) from 3215 Montana Tropical® animals were used to (1) predict the composition of biological groups in the pedigree and how this composition relates to generations within the composite breed; (2) characterize the distribution of runs of homozygosity (ROH) in relation to generations within the composite breed; (3) estimate and compare the inbreeding coefficient (F) based on information from genomic markers (FROH) and pedigree (FPED); (4) identify genomic regions of islands of ROH; and (5) identify related traits in each significant genomic region under selection. The population structure (PCA and ADMIXTURE) was not related to the racial composition of the pedigree and has a strong relationship with the number of generations within the breed. There was an increase in FROH as the number of equivalent generations increased. Estimates of FROH in the Montana Tropical® population also showed that from the equivalent generation five onwards, there was a reduced number of animals with FROH equal to zero. It is believed that using PCA results in a breed’s genetic improvement program to correct for population structure is an interesting alternative to replace the correction by NABC proportions based on pedigree. The population study showed selection signatures in regions of chromosomes 4, 6, 20 and 26. The frequency with which markers in homozygosity were observed increased with the generations within the breed, demonstrating that it is really a selection process that is occurring after the formation of the composite population. Most of these selection signature regions present carcass quality QTLs demonstrating that the objective of developing a breed of cattle for pasture production systems is being achieved at the genomic level over the generations of the breed.

Given the economic relevance of fertility and reproductive traits for the beef cattle industry, investigating their genetic background and developing effective breeding strategies are paramount. Considering their late and sex-dependent phenotypic expression, genomic information can contribute to speed up the rates of genetic progress per year. In this context, the main objectives of this study were to estimate variance components and genetic parameters, including heritability and genetic correlations, for fertility, female precocity, and semen production and quality (andrological attributes) traits in Nellore cattle incorporating genomic information. The heritability estimates of semen quality traits were low-to-moderate, while moderate-to-high estimates were observed for semen morphological traits. The heritability of semen defects ranged from low (0.04 for minor semen defects) to moderate (0.30 for total semen defects). For seminal aspect (SMN_ASPC) and bull reproductive fitness (BULL_FIT), low (0.19) and high (0.69) heritabilities were observed, respectively. The heritability estimates for female reproductive traits ranged from 0.16 to 0.39 for rebreeding of precocious females (REBA) and probability of pregnancy at 14 months (PP14), respectively. Semen quality traits were highly genetically correlated among themselves. Moderate-to-high genetic correlations were observed between the ability to remain productive in the herd until four years of age (stayability; STAY) and the other reproductive traits, indicating that selection for female reproductive performance will indirectly contribute to increasing fertility rates. High genetic correlations between BULL_FIT and female reproductive traits related to precocity (REBA and PP14) and STAY were observed. The genetic correlations between semen quality and spermatic morphology with female reproductive traits ranged from -0.22 (REBA and scrotal circumference) to 0.48 (REBA and sperm vigor). In addition, the genetic correlations between REBA with semen quality traits ranged from -0.23 to 0.48, and with the spermatic morphology traits it ranged from -0.22 to 0.19. All male and female fertility and reproduction traits evaluated are heritable and can be improved through direct genetic or genomic selection. Selection for better sperm quality will positively influence the fertility and precocity of Nellore females. The findings of this study will serve as background information for designing breeding programs for genetically improving semen production and quality and reproductive performance in Nellore cattle.

Context In beef cattle populations, there is little evidence regarding the minimum number of genetic markers needed to obtain reliable genomic prediction and imputed genotypes. Aims This study aimed to evaluate the impact of single nucleotide polymorphism (SNP) marker density and minor allele frequency (MAF), on genomic predictions and imputation performance for high and low heritability traits using the single-step genomic Best Linear Unbiased Prediction methodology (ssGBLUP) in a simulated beef cattle population. Methods The simulated genomic and phenotypic data were obtained through QMsim software. 735 293 SNPs markers and 7000 quantitative trait loci (QTL) were randomly simulated. The mutation rate (10−5), QTL effects distribution (gamma distribution with shape parameter = 0.4) and minor allele frequency (MAF ≥ 0.02) of markers were used for quality control. A total of 335k SNPs (high density, HD) and 1000 QTLs were finally considered. Densities of 33 500 (35k), 16 750 (16k), 4186 (4k) and 2093 (2k) SNPs were customised through windows of 10, 20, 80 and 160 SNPs by chromosome, respectively. Three marker selection criteria were used within windows: (1) informative markers with MAF values close to 0.5 (HI); (2) less informative markers with the lowest MAF values (LI); (3) markers evenly distributed (ED). We evaluated the prediction of the high-density array and of 12 scenarios of customised SNP arrays, further the imputation performance of them. The genomic predictions and imputed genotypes were obtained with Blupf90 and FImpute software, respectively, and statistics parameters were applied to evaluate the accuracy of genotypes imputed. The Pearson’s correlation, the coefficient of regression, and the difference between genomic predictions and true breeding values were used to evaluate the prediction ability (PA), inflation (b), and bias (d), respectively. Key results Densities above 16k SNPs using HI and ED criteria displayed lower b, higher PA and higher imputation accuracy. Consequently, similar values of PA, b and d were observed with the use of imputed genotypes. The LI criterion with densities higher than 35k SNPs, showed higher PA and similar predictions using imputed genotypes, however lower b and quality of imputed genotypes were observed. Conclusion The results obtained showed that at least 5% of HI or ED SNPs available in the HD array are necessary to obtain reliable genomic predictions and imputed genotypes. Implications The development of low-density customised arrays based on criteria of MAF and even distribution of SNPs, might be a cost-effective and feasible approach to implement genomic selection in beef cattle.

This study investigated the effect of different prenatal nutrition approaches in 126 pregnant Nellore cows on reproductive and nutrigenetic traits of the male offspring during the finishing phase. For that purpose, three nutritional treatments were used in these cows during pregnancy: PP – protein-energy supplementation in the final third, FP – protein-energy supplementation during the entire pregnancy, and NP – (control) only mineral supplementation. The male progeny (63 bulls; 665 ± 28 days of age) were evaluated for scrotal circumference, seminal traits, number of Sertoli cells and testicular area. We performed a genomic association (700 K SNPs) for scrotal circumference at this age. In addition, a functional enrichment was performed in search of significant metabolic pathways (P < 0.05) with inclusion of genes that are expressed in these genomic windows by the MetaCore software. With the exception of major sperm defects (P < 0.1), the other phenotypes showed no difference between prenatal treatments. We found genes and metabolic pathways (P < 0.05) that are associated with genomic windows (genetic variance explained >1