Genome-wide association study (GWAS) is a powerful tool to identify candidate genes and genomic regions underlying key biological mechanisms associated with economically important traits. In this context, the aim of this study was to identify genomic regions and metabolic pathways associated with backfat thickness (BFT) and rump fat thickness (RFT) in Nellore cattle, raised in pasture-based systems. Ultrasound-based measurements of BFT and RFT (adjusted to 18 months of age) were collected in 11,750 animals, with 39,903 animals in the pedigree file.Additionally, 1,440 animals were genotyped using the GGP-indicus 35K SNP chip, containing 33,623 SNPs after the quality control. The single-step GWAS analyses were performed using the BLUPF90 family programs. Candidate genes were identified through the Ensembl database incorporated in the BioMart tool, while PANTHER and REVIGO were used to identify the key metabolic pathways and gene networks. A total of 18 genomic regions located on 10 different chromosomes and harbouring 23 candidate genes were identified for BFT. For RFT, 22 genomic regions were found on 14 chromosomes, with a total of 29 candidate genes identified. The results of the pathway analyses showed important genes for BFT, including TBL1XR1, AHCYL2, SLC4A7, AADAT, VPS53, IDH2 and ETS1, which are involved in lipid metabolism, synthesis of cellular amino acids, transport of solutes, transport between Golgi Complex membranes, cell differentiation and cellular development.The main genes identified for RFT were GSK3β, LRP1B, EXT1, GRB2, SORCS1 and SLMAP, which are involved in metabolic pathways such as glycogen synthesis, lipid transport and homeostasis, polysaccharide and carbohydrate metabolism. Polymorphisms located in these candidate genes can be incorporated in commercial genotyping platforms to improve the accuracy of imputation and genomic evaluations for carcass fatness. In addition to uncovering biological mechanisms associated with carcass quality, the key gene pathways identified can also be incorporated in biology-driven genomic prediction methods.

Summary Identifying genes or genomic regions influencing carcass‐quality traits such as fatness (FTN) is essential to optimize the genetic selection processes in beef cattle. The aim of this study was to identify genomic regions associated with FTN in Nellore cattle as well as to elucidate the metabolic pathways related to the phenotypic expression. Ultrasound‐based measurements of FTN were collected in 11 750 animals, with 39 903 animals in the pedigree file. Additionally, 1440 animals were genotyped using the GGP‐indicus 35K SNP panel, which contained 33 623 SNPs after quality control. Twenty genes related to FTN were found on 11 chromosomes, explaining 12.96% of the total additive genetic variance. Gene ontology revealed seven genes: NR1L2, PKD2, GSK3β, EXT1, RAD51B, SORCS1 and DPH6, associated with important processes related to FTN. In addition, novel candidate genes (MAATS1, LYPD1, CDK5RAP2, RAD51B, c13H2Oorf96 and TRAPPC11) were detected and could provide further knowledge to uncover genetic regions associated to carcass fatness in beef cattle.

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Body conformation traits assessed based on visual scores are widely used in Zebu cattle breeding programs. The aim of this study was to identify genomic regions and biological pathways associated with body conformation (CONF), finishing precocity (PREC), and muscling (MUSC) in Nellore cattle. The measurements based on visual scores were collected in 20,807 animals raised in pasture-based systems in Brazil. In addition, 2775 animals were genotyped using a 35 K SNP chip, which contained 31,737 single nucleotide polymorphisms after quality control. Single-step GWAS was performed using the BLUPF90 software while candidate genes were identified based on the Ensembl Genes 69. PANTHER and REVIGO platforms were used to identify key biological pathways and STRING to create gene networks. Novel candidate genes were revealed associated with CONF, including ALDH9A1, RXRG, RAB2A, and CYP7A1, involved in lipid metabolism. The genes associated with PREC were ELOVL5, PID1, DNER, TRIP12, and PLCB4, which are related to the synthesis of long-chain fatty acids, lipid metabolism, and muscle differentiation. For MUSC, the most important genes associated with muscle development were SEMA6A, TIAM2, UNC5A, and UIMC1. The polymorphisms identified in this study can be incorporated in commercial genotyping panels to improve the accuracy of genomic evaluations for visual scores in beef cattle.

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This study was undertaken to compare different non-linear models for fitting growth curves of Polled Nellore animals as well as to estimate genetic parameters for the components of the growth curve. The study involved body weight-age data of 6,717 Polled Nellore cattle from birth to 650 days of age, which belonged to the Brazilian Association of Zebu Breeders (ABCZ), corresponding to the period from 1980 to 2011. Four non-linear models (Brody, Bertalanffy, Logistic, and Gompertz) were fitted and compared by the adjusted coefficient of determination (R2adj), mean absolute deviation of residuals (MAD), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC). To estimate the genetic parameters and genetic values of asymptotic weight (A), integration constant (B), and maturation rate (K), the Bayesian inference method was adopted. The Brody model showed the lowest values of MAD, RMSE, AIC, and BIC and the highest R2adj. Heritability estimates for parameters A, B, and K were 0.11, 0.16, and 0.30, respectively, whereas genetic correlations were 0.01 (A-B), -0.91 (A-K), and 0.24 (B-K). The Brody model provided the best fit. The K parameter shows enough genetic variability for selection in the herd. Heavier animals in adulthood tend to exhibit lower growth rates. Despite the low heritability estimate of parameter A, there were genetic gains, indicating that selection is being efficient on asymptotic weight.

Visual scoring traits have been proposed as an alternative to evaluate body composition of Zebu cattle near the slaughter season when phenotyping technologies are not available. Considering the increased demand for high-quality animal protein in developing countries, there is a need to genetically improve body muscle (MUSC) in Zebu cattle (Bos taurus indicus), especially in animals raised in pasture-based systems. Therefore, our main objectives were to estimate genetic parameters, perform a genome-wide association study based on the single-step GBLUP approach (ssGWAS), and identify candidate genes and metabolic pathways related to MUSC in Nellore cattle. A total of 20,808 Nellore animals born between 2009 and 2018 were visually score at 18 months of age and 2,775 of these animals were also genotyped using the GGP-Indicus 35K SNP panel (33,247 SNPs after quality control). Heritability was estimated based on the REML approach and the model included the effects of age at measurement as covariable and the contemporary group (farm, birth season, management group and sex). The ssGWAS was performed using the BLUPF90 family programs. The identification of candidate genes was performed through the Ensembl database incorporated in the BioMart tool. MUSC is heritable (0.38) and can be improved through selection. Nineteen genomic regions (explaining 38.12% of the total additive genetic variance) located on BTA1, BTA7, BTA9, BTA16, and BTA21 and harboring 19 candidate genes were identified. The main genes identified were SEMA6A, TIAM2, UNC5A, and UIMC1, which are related to the metabolism of energy, growth, homeostasis and axonogenesis, and therefore, muscle development. These findings contribute to a better understanding of the molecular mechanisms over the gene expression of muscle visual score in Nellore cattle, and the polymorphisms located in these genes can be incorporated in commercial genotyping platforms to improve the accuracy of imputation and genomic evaluations for body and carcass traits.

Inbreeding and selection are forces that affect heritability, important to selection in livestock species. So, this study aims to relate those forces in a Holstein population under selection for superior milk production. Inbreeding coefficients was calculated considering 3 generations, after the individual inbreeding coefficient was calculated, inbred (7.92±2.72) and non-inbred group (0.46±0.38) were formed. The genetic variance was higher for the inbred group and for the residual variance was lower. As result, those variance changes resulted in higher heritability for all traits analyzed for the inbred group. For the levels of inbreeding observed in this study, the selection for higher productions possibly masked the effects of inbreeding.