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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 explained1%) in different treatments. These molecular findings indicate that there is genotype-environment interaction among the different prenatal treatments and that the FP treatment showed greater major sperm defects compared to the NP treatment.

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.

Made available in DSpace on 2020-12-12T01:30:23Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01 The aim was to conduct a weighted single-step genome-wide association study to detect genomic regions and putative candidate genes related to residual feed intake, dry matter intake, feed efficiency (FE), feed conversion ratio, residual body weight gain, residual intake and weight gain in Nellore cattle. Several protein-coding genes were identified within the genomic regions that explain more than 0.5% of the additive genetic variance for these traits. These genes were associated with insulin, leptin, glucose, protein and lipid metabolisms; energy balance; heat and oxidative stress; bile secretion; satiety; feed behaviour; salivation; digestion; and nutrient absorption. Enrichment analysis revealed functional pathways (p-value

Made available in DSpace on 2020-12-12T01:51:04Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-09-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The aim of this study was to evaluate the genomic predictions using the single-step genomic best linear unbiased predictor (ssGBLUP) method based on SNPs and haplotype markers associated with beef fatty acids (FAs) profile in Nelore cattle. The data set contained records from 963 Nelore bulls finished in feedlot (±90 days) and slaughtered with approximately 24 months of age. Meat samples from the Longissimus dorsi muscle were taken for FAs profile measurement. FAs were quantified by gas chromatography using a SP-2560 capillary column. Animals were genotyped with the high-density SNP panel (BovineHD BeadChip assay) containing 777,962 markers. SNPs with a minor allele frequency and a call rate lower than 0.05 and 0.90, respectively, monomorphic, located on sex chromosomes, and with unknown position were removed from the data set. After genomic quality control, a total of 469,981 SNPs and 892 samples were available for subsequent analyses. Missing genotypes were imputed and phased using the FImpute software. Haplotype blocks were defined based on linkage disequilibrium using the Haploview software. The model to estimate variance components and genetic parameters and to predict the genomic values included the random genetic additive effects, fixed effects of the contemporary group and the age at slaughter as a linear covariate. Accuracies using the haplotype-based approach ranged from 0.07 to 0.31, and those SNP-based ranged from 0.06 to 0.33. Regression coefficients ranged from 0.07 to 0.74 and from 0.08 to 1.45 using the haplotype- and SNP-based approaches, respectively. Despite the low to moderate accuracies for the genomic values, it is possible to obtain genetic progress trough selection using genomic information based either on SNPs or haplotype markers. The SNP-based approach allows less biased genomic evaluations, and it is more feasible when taking into account the computational and operational cost underlying the haplotypes inference. Faculdade de Ciências Agrárias e Veterinárias Departamento de Zootecnia Universidade Estadual Paulista Júlio de Mesquita Filho UNESP Faculdade de Zootecnia e Engenharia de Alimentos Departamento de Nutrição e Produção Animal Universidade de São Paulo Zoetis Department of Animal Sciences Purdue University Faculdade de Zootecnia e Engenharia de Alimentos Departamento de Medicina Veterinária Universidade de São Paulo Faculdade de Ciências Agrárias e Veterinárias Departamento de Zootecnia Universidade Estadual Paulista Júlio de Mesquita Filho UNESP FAPESP: 2009/16118-5 FAPESP: 2015/25304-8 FAPESP: 2016/24085-3

Made available in DSpace on 2022-04-29T08:39:28Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-01-01 This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection. Departamento de Zootecnia Faculdade de Ciências Agrarias e Veterinárias Universidade Estadual Paulista (Unesp) Departamento de Medicina Veterinária Faculdade de Zootecnia e Engenharia de Alimentos Universidade de São Paulo (Usp) Departamento de Genética y Mejoramiento Animal Facultad de Veterinaria Universidad de La República Instituto Nacional de Investigación Agropecuaria (INIA) Associação Nacional de Criadores e Pesquisadores (ANCP) Departamento de Zootecnia Faculdade de Ciências Agrarias e Veterinárias Universidade Estadual Paulista (Unesp)

The aim of this study was to evaluate the relationship between temperament in Nellore bulls with carcass and meat quality traits. In total, 1,400 bulls were studied, and temperament was assessed using two measurements: movement score (MOV) and flight speed test (FS). Both MOV and FS were measured at two time points, with background (MOVb and FSb) temperament measured at yearling age, ~550 d after birth, and the preslaughter (MOVps and FSps) temperament measured at the end of the feedlot period. The change of temperament resulting in an increase or decrease in reactivity was also used to measure meat quality. The traits used to define carcass and meat quality included carcass bruises (BRU), hot carcass weight (HCW, kg), ribeye area (REA, cm2), backfat thickness (BFT, cm), marbling score (MS), meat pH after thawing (pH), presence or absence of dark cutters, color parameters of luminosity (L*), redness (a*) and yellowness (b*), cooking loss (CL, %), and Warner–Bratzler shear force (WBSF, kg). A principal component (PC) analysis was initially applied to the carcass and meat quality traits, followed by logistic regression models and linear mixed models to evaluate the effects of temperament on carcass and meat quality. The risks of carcass bruises and dark cutters did not differ as a function of any temperament trait (P > 0.05). In turn, animals classified as high MOVb (reactive) had lower PC3 values (P = 0.05), CL (P = 0.02), and tended to have lower MS (P = 0.08). In addition, animals classified as high FSb (faster and reactive cattle) produced carcasses with smaller REA (P < 0.01), higher meat pH (P < 0.01), lower color gradients (L*, P = 0.04; b*, P < 0.01), and lower PC1 and PC4 scores (P < 0.01) when compared with the low FSb class. For preslaughter temperament, high MOVps was related to lower color a* (P = 0.04), whereas high FSps was related to lower HCW, MS, and PC2 (P < 0.01) than the calmer ones (low FSps). The reduction in MOV was related to more tender meat, and the reduction in FS to heavier carcass and brighter meat. We conclude that excitable temperament in Nellore cattle may have negative effects in some of the carcass and meat quality attributes assessed, mainly those related to muscle deposition on carcass and color gradients. Measurement of temperament before the cattle entered the feedlot was a better predictor of carcass and meat quality traits, compared with temperament assessment at the end of the feeding period.

Made available in DSpace on 2019-10-06T16:51:46Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-02-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The objective of this study was to present heritability estimates and accuracy of genomic prediction using different methods for meat quality traits in Nelore cattle. Approximately 5000 animals with phenotypes and genotypes of 412,000 SNPs, were divided into two groups: (1) training population: animals born from 2008 to 2013 and (2) validation population: animals born in 2014. A single-trait animal model was used to estimate heritability and to adjust the phenotype. The methods of GBLUP, Improved Bayesian Lasso and Bayes Cπ were performed to estimate the SNP effects. Accuracy of genomic prediction was calculated using Pearson's correlations between direct genomic values and adjusted phenotypes, divided by the square root of heritability of each trait (0.03–0.19). The accuracies varied from 0.23 to 0.73, with the lowest accuracies estimated for traits associated with fat content and the greatest accuracies observed for traits of meat color and tenderness. There were small differences in genomic prediction accuracy between methods. São Paulo State University (Unesp) School of Agricultural and Veterinarian Sciences Centre for Genetic Improvement of Livestock University of Guelph BRF Company Federal University of Pelotas (UFPel) São Paulo State University (Unesp) College of Veterinary and Animal Science São Paulo State University (Unesp) School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp) College of Veterinary and Animal Science

Made available in DSpace on 2021-06-25T10:50:16Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-03-01 The objective of this study was to identify genomic regions associated with beef fatty acid (FA) profile in Nellore cattle finished in feedlot, using haplotype based analyzes. Also, the metabolic pathway enrichment analysis was performed to seek for genes with known functions, close to the haplotype blocks, associated with the evaluated traits, providing subsidies for a better understanding of how the genes affect this trait. A total of 963 Nellore bulls with phenotype for FA, were genotyped using the Illumina BovineHD BeadChip. Samples from the Longissimus thoracis muscle were taken for FAs profile measurement by gas chromatography using a SP-2560 capillary column. Haplotype blocks were defined based on the linkage disequilibrium (LD), where pairs of SNPs are in strong LD if the confidence interval is equal to or greater than 95% with D’ equal to 0.98 and the lower limit is above 0.70. The model used to estimate the haplotypes effects for the sum of beef fatty acid groups included the fixed effects of contemporary group, animal age at slaughter (as linear covariable), haplotypes (as linear regression over the number of haplotype copies: 0, 1 e 2), and the additive genetic effect as random (polygenic effect). Genome-wide association analyzes were performed for each haplotype block (single marker association) by a Restricted Maximum Likelihood model (REML) using ASREML v. 4.3 software. A total of 15, 15, 4, 3, 6, 5, and 10 haplotypes were significantly associated (FDR< 0.10) with monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), total amount of saturated fatty acids (SFA), PUFA:SFA ratio, omega 3 (n3), omega 6 (n6), and n6:n3 ratio, respectively. The statistically significant haplotypes harbored 38 genes, among which we highlighted those involved in lipid biosynthesis and metabolism, low-density lipoprotein oxidation, transport of fatty acids and cholesterol, adipocyte functions, biosynthesis of glycoprotein hormones, regulation of serum triglyceride and high-density lipoprotein-cholesterol levels, and adipogenesis. In addition, several novel regions were identified, including a putative quantitative trait locus for lipid metabolism and energy homeostasis. The enrichment analysis by DAVID tool revealed 14 significant (P< 0.05) terms related to fatty acids such as cellular response to retinoic acid, glycerol transport, regulation of Notch signaling pathway, and Ras signaling pathway. These results would contribute to the improvement of nutritional and health value of beef lipids in indicine cattle by including information from causal mutations in genetic evaluation through weighted their effects differently in genomic predictions. Departamento de Zootecnia Faculdade de Ciências Agrárias e Veterinárias (FCAV) Universidade Estadual Paulista Julio de Mesquita Filho (UNESP) Departamento de Nutrição e Produção Animal Faculdade de Medicina Veterinária e Zootecnia Universidade de São Paulo (USP) Department of Animal Biosciences Centre for Genetic Improvement of Livestock University of Guelph Department of Animal and Poultry Science Centre for Genetic Improvement of Livestock University of Guelph Departament of Animal Science University of Purdue Centro de Pesquisa em Bovinos de Corte Instituto de Zootecnia (IZ), Rodovia Carlos Tonanni km 94 Departamento de Zootecnia Faculdade de Ciências Agrárias e Veterinárias (FCAV) Universidade Estadual Paulista Julio de Mesquita Filho (UNESP)

Made available in DSpace on 2021-06-25T10:22:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-02-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) There is a growing interest to improve feed efficiency (FE) traits in cattle. The genomic selection was proposed to improve these traits since they are difficult and expensive to measure. Up to date, there are scarce studies about the implementation of genomic selection for FE traits in indicine cattle under different scenarios of pseudo-phenotypes, models, and validation strategies on a commercial large scale. Thus, the aim was to evaluate the feasibility of genomic selection implementation for FE traits in Nelore cattle applying different models and pseudo-phenotypes under validation strategies. Phenotypic and genotypic information from 4 329 and 3 467 animals were used, respectively, which were tested for residual feed intake, DM intake, feed efficiency, feed conversion ratio, residual BW gain, and residual intake and BW gain. Six prediction methods were used: single-step genomic best linear unbiased prediction, Bayes A, Bayes B, Bayes Cπ, Bayesian least absolute shrinkage and selection operator (BLASSO), and Bayes R. Phenotypes adjusted for fixed effects (Y*), estimated breeding value (EBV), and EBV deregressed (DEBV) were used as pseudo-phenotypes. The validation approaches used were: (1) random: the data was randomly divided into ten subsets and the validation was done in each subset at a time; (2) age: the partition into training and testing sets was based on year of birth and testing animals were born after 2016; and (3) EBV accuracy: the data was split into two groups, being animals with accuracy above 0.45 the training set; and below 0.45 the validation set. In the analyses that used the Y* as pseudo-phenotype, prediction ability (PA) was obtained by dividing the correlation between pseudo-phenotype and genomic EBV (GEBV) by the square root of the heritability of the trait. When EBV and DEBV were used as the pseudo-phenotype, the simple correlation of this quantity with the GEBV was considered as PA. The prediction methods show similar results for PA and bias. The random cross-validation presented higher PA (0.17) than EBV accuracy (0.14) and age (0.13). The PA was higher for Y* than for EBV and DEBV (30.0 and 34.3%, respectively). Random validation presented the highest PA, being indicated for use in populations composed mainly of young animals and traits with few generations of data recording. For high heritability traits, the validation can be done by age, enabling the prediction of the next-generation genetic merit. These results would support breeders to identify genomic approaches that are more viable for genomic prediction for FE-related traits. Animal Science Department Goiás Federal University Embrapa Rice and Beans, GO-462, km 12 Animal Science Department São Paulo State University – Júlio de Mesquita Filho (UNESP), Prof. Paulo Donato Castelane Cobb-Vantress Inc. National Association of Breeders and Researchers Department of Veterinary Medicine Faculty of Animal Science and Food Engineering University of Sao Paulo Embrapa Cerrados, BR-020, 18 Sobradinho Animal Science Department São Paulo State University – Júlio de Mesquita Filho (UNESP), Prof. Paulo Donato Castelane CAPES: #88887.162699/2018-00

Made available in DSpace on 2021-06-25T11:02:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-02-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) This study aimed to assess the predictive ability of different machine learning (ML) methods for genomic prediction of reproductive traits in Nellore cattle. The studied traits were age at first calving (AFC), scrotal circumference (SC), early pregnancy (EP) and stayability (STAY). The numbers of genotyped animals and SNP markers available were 2342 and 321 419 (AFC), 4671 and 309 486 (SC), 2681 and 319 619 (STAY) and 3356 and 319 108 (EP). Predictive ability of support vector regression (SVR), Bayesian regularized artificial neural network (BRANN) and random forest (RF) were compared with results obtained using parametric models (genomic best linear unbiased predictor, GBLUP, and Bayesian least absolute shrinkage and selection operator, BLASSO). A 5-fold cross-validation strategy was performed and the average prediction accuracy (ACC) and mean squared errors (MSE) were computed. The ACC was defined as the linear correlation between predicted and observed breeding values for categorical traits (EP and STAY) and as the correlation between predicted and observed adjusted phenotypes divided by the square root of the estimated heritability for continuous traits (AFC and SC). The average ACC varied from low to moderate depending on the trait and model under consideration, ranging between 0.56 and 0.63 (AFC), 0.27 and 0.36 (SC), 0.57 and 0.67 (EP), and 0.52 and 0.62 (STAY). SVR provided slightly better accuracies than the parametric models for all traits, increasing the prediction accuracy for AFC to around 6.3 and 4.8% compared with GBLUP and BLASSO respectively. Likewise, there was an increase of 8.3% for SC, 4.5% for EP and 4.8% for STAY, comparing SVR with both GBLUP and BLASSO. In contrast, the RF and BRANN did not present competitive predictive ability compared with the parametric models. The results indicate that SVR is a suitable method for genome-enabled prediction of reproductive traits in Nellore cattle. Further, the optimal kernel bandwidth parameter in the SVR model was trait-dependent, thus, a fine-tuning for this hyper-parameter in the training phase is crucial. Department of Animal Science School of Agricultural and Veterinary Sciences Sao Paulo State University (UNESP) Department of Exact Sciences School of Agricultural and Veterinary Sciences Sao Paulo State University (UNESP) Department of Animal Nutrition and Production School of Veterinary Medicine and Animal Science University of Sao Paulo (USP) National Council of Technological and Scientific Development (CNPq) Department of Animal Science School of Agricultural and Veterinary Sciences Sao Paulo State University (UNESP) Department of Exact Sciences School of Agricultural and Veterinary Sciences Sao Paulo State University (UNESP)

Made available in DSpace on 2022-04-29T08:31:48Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-09-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The multiple sire system (MSS) is a common mating scheme in extensive beef production systems. However, MSS does not allow paternity identification and lead to inaccurate genetic predictions. The objective of this study was to investigate the implementation of single-step genomic BLUP (ssGBLUP) in different scenarios of uncertain paternity in the evaluation for 450-day adjusted liveweight (W450) and age at first calving (AFC) in a Nellore cattle population. To estimate the variance components using BLUP and ssGBLUP, the relationship matrix (A) with different proportions of animals with missing sires (MS) (scenarios 0, 25, 50, 75, and 100% of MS) was created. The genotyped animals with MS were randomly chosen, and ten replicates were performed for each scenario and trait. Five groups of animals were evaluated in each scenario: PHE, all animals with phenotypic records in the population; SIR, proven sires; GEN, genotyped animals; YNG, young animals without phenotypes and progeny; and YNGEN, young genotyped animals. The additive genetic variance decreased for both traits as the proportion of MS increased in the population when using the regular REML. When using the ssGBLUP, accuracies ranged from 0.13 to 0.47 for W450 and from 0.10 to 0.25 for AFC. For both traits, the prediction ability of the direct genomic value (DGV) decreased as the percentage of MS increased. These results emphasize that indirect prediction via DGV of young animals is more accurate when the SNP effects are derived from ssGBLUP with a reference population with known sires. The ssGBLUP could be applied in situations of uncertain paternity, especially when selecting young animals. This methodology is shown to be accurate, mainly in scenarios with a high percentage of MS. Grupo de Melhoramento Animal Faculdade de Ciências Agrárias E Veterinárias Universidade Estadual Paulista Júlio de Mesquita Filho Zoetis Faculdade de Medicina Veterinária E Zootecnia Universidade de São Paulo Associação Nacional de Criadores E Pesquisadores (ANCP) Instituto Nacional de Pesquisa Agropecuária (INIA) University of Georgia Grupo de Melhoramento Animal Faculdade de Ciências Agrárias E Veterinárias Universidade Estadual Paulista Júlio de Mesquita Filho FAPESP: 2013/25910-0

Made available in DSpace on 2022-04-28T19:41:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Context: Livestock feed costs have a higher impact on the profitability of beef production systems and are directly related to feed efficiency. However, these traits are hard and have high costs to measure, reducing the availability of phenotypic records and reliability of genetic evaluations. Thus, the use of genomic information can increase the robustness of genetic studies that address them. Aims: The aim of the present study was to estimate genetic parameters for feed efficiency, growth, reproductive and carcass traits in Nelore cattle and the correlated response among them, using genomic information. Methods: Residual feed intake (RFI), dry-matter intake, feed conversion ratio, feed efficiency (FE), residual average daily gain (RG), residual feed intake and average daily gain (RIG), birthweight, weight at 120, 240, 365 and 450 days of age, scrotal circumference at 365 and 450 days of age, rib-eye area, backfat thickness and rump fat thickness were evaluated. The genetic parameters were estimated using the single-step genomic best linear unbiased prediction approach. Key results: The FE-related traits showed low to moderate heritability ranging from 0.07 to 0.23. Feed efficiency-related traits showed low genetic correlations with reproductive (-0.24 to 0.27), carcass (-0.17 to 0.27) and growth (-0.19 to 0.24) traits, except for growth with dry-matter intake (0.32-0.56) and weight at 365 days of age with FE (-0.40). Conclusions: The selection to improve growth, reproductive and carcass traits would not change RFI, RG and RIG. The choice of the most adequate selection criterion depends on the production system, that is, RFI might be used for low-input beef cattle systems, and RIG would be used for more intensive and without-any-dietary-restrictions beef cattle systems. Implications: The estimates of heritability and genetic correlations suggest that genetic selection for feed efficiency using RFI, RG and RIG in Nellore cattle leads to higher genetic gain than does that using FE and feed conversion ratio without affecting other profitability traits. Department of Animal Science Federal University of Goiás (UFG) Embrapa Rice and Beans Department of Animal Science São Paulo State University (UNESP) Cobb-Vantress Inc. 72761 National Association of Breeders and Researchers (ANCP) Department of Veterinary Medicine Faculty of Animal Science and Food Engineering University of Sao Paulo Embrapa Cerrados, Planaltina Department of Animal Science São Paulo State University (UNESP)

The aim of the study was to evaluate prediction methods, validation approaches and pseudo-phenotypes for the prediction of the genomic breeding values of feed efficiency related traits in Nellore cattle. It used the phenotypic and genotypic information of 4,329 and 3,594 animals, respectively, which were tested for residual feed intake (RFI), dry matter intake (DMI), feed efficiency (FE), feed conversion ratio (FCR), residual body weight gain (RG), and residual intake and body weight gain (RIG). Six prediction methods were used: ssGBLUP, BayesA, BayesB, BayesCπ, BLASSO, and BayesR. Three validation approaches were used: 1) random: where the data was randomly divided into ten subsets and the validation was done in each subset at a time; 2) age: the division into the training (2010 to 2016) and validation population (2017) were based on the year of birth; 3) genetic breeding value (EBV) accuracy: the data was split in the training population being animals with accuracy above 0.45; and validation population those below 0.45. We checked the accuracy and bias of genomic value (GEBV). The results showed that the GEBV accuracy was the highest when the prediction is obtained with ssGBLUP (0.05 to 0.31) (Figure 1). The low heritability obtained, mainly for FE (0.07 ± 0.03) and FCR (0.09 ± 0.03), limited the GEBVs accuracy, which ranged from low to moderate. The regression coefficient estimates were close to 1, and similar between the prediction methods, validation approaches, and pseudo-phenotypes. The cross-validation presented the most accurate predictions ranging from 0.07 to 0.037. The prediction accuracy was higher for phenotype adjusted for fixed effects than for EBV and EBV deregressed (30.0 and 34.3%, respectively). Genomic prediction can provide a reliable estimate of genomic breeding values for RFI, DMI, RG and RGI, as to even say that those traits may have higher genetic gain than FE and FCR.

Made available in DSpace on 2021-06-25T10:34:20Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-11-01 The reduction of age at puberty in young bulls is an important issue to reduce the growing phase and promote substantial savings in production cost as well as to reduce the generation interval. This study aims to estimate genetic parameters for age at puberty in males (APM), growth, carcass, and reproductive-related traits in young Nelore bulls. Additionally, the direct and correlated response for growth, reproductive, and carcass traits were also estimated when a selection was applied for APM in such young bulls. APM was determined through evaluations carried out after weaning, using information from andrological clinical examination, ultrasound records, and scrotal circumference measurements. Records from 181,345 Nelore animals born between 2010 to 2017 were used. These animals were calves from 5,257 sires and 50,742 dams, and belonged to 18 farms located in the Midwest, Southeast, Northeast, and North regions of Brazil. The relationship matrix was built using information from 265,813 animals. The covariance components and the genetic parameters for APM, adjusted scrotal circumference at 365 (SC365) and 450 (SC450) days of age, adjusted weight at 210 (W210) and 450 (W450) days of age, age at first calving (AFC), ribeye area (REA), subcutaneous fat thickness (SFT), and rump fat thickness (RFT) were estimated using a linear animal model. A threshold animal model was used for Stayability (STAY) and early heifer pregnancy (PP30) traits. The heritabilities obtained were: 0.30 (APM), 0.33 (SC365), 0.37 (SC450), 0.14 (W210), 0.26 (W450), 0.08 (AFC), 0.29 (PP30), 0.19 (STAY), 0.32 (REA), 0.19 (SFT), and 0.28 (RFT). The genetic correlations estimates between APM and growth, reproductive, and carcass related traits were: -0.39 (SC365), -0.58 (SC450), -0.01 (W210), 0.08 (W450), 0.30 (AFC), -0.24 (PP30), 0.35 (STAY), 0.08 (REA), -0.05 (SFT), and -0.30 (RFT). The APM is a new approach to evaluate early sexual precocity in young bulls, and it would allow better identification of young candidates when compared to those selected based on scrotal circumference measures. Genetic progress is expected for APM in young bulls since it would favor the heifer's sexual precocity together with the dam's longevity without impacting growth and carcass traits. The results of this study encourage the use of male sexual precocity trait assisted by genomic selection to improve the genetic evaluation reliability of young bulls. Departamento de Zootecnia Faculdade de Ciências Agrárias e Veterinárias (FCAV) Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP) Laboratório de Reprodução Animal Faculdade de Medicina Veterinária e Zootecnia (FAMEZ) Universidade Federal de Mato Grosso do Sul (UFMS) Faculdade de Zootecnia e Engenharia de Alimentos Universidade de São Paulo Departamento de Medicina Veterinária PROCRIAR Assistência Veterinária Departamento de Produção Animal Universidade Federal de Goiás Associação Nacional dos Criadores e Pesquisadores (ANCP) Faculdade de Medicina Veterinária e Zootecnia Universidade de São Paulo Departamento de Nutrição e Produção Animal Departamento de Zootecnia Faculdade de Ciências Agrárias e Veterinárias (FCAV) Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP)

Made available in DSpace on 2020-12-12T02:45:17Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-06-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) The aim of this study was to compare the predictive performance of the Genomic Best Linear Unbiased Predictor (GBLUP) and machine learning methods (Random Forest, RF; Support Vector Machine, SVM; Artificial Neural Network, ANN) in simulated populations presenting different levels of dominance effects. Simulated genome comprised 50k SNP and 300 QTL, both biallelic and randomly distributed across 29 autosomes. A total of six traits were simulated considering different values for the narrow and broad-sense heritability. In the purely additive scenario with low heritability (h2 = 0.10), the predictive ability obtained using GBLUP was slightly higher than the other methods whereas ANN provided the highest accuracies for scenarios with moderate heritability (h2 = 0.30). The accuracies of dominance deviations predictions varied from 0.180 to 0.350 in GBLUP extended for dominance effects (GBLUP-D), from 0.06 to 0.185 in RF and they were null using the ANN and SVM methods. Although RF has presented higher accuracies for total genetic effect predictions, the mean-squared error values in such a model were worse than those observed for GBLUP-D in scenarios with large additive and dominance variances. When applied to prescreen important regions, the RF approach detected QTL with high additive and/or dominance effects. Among machine learning methods, only the RF was capable to cover implicitly dominance effects without increasing the number of covariates in the model, resulting in higher accuracies for the total genetic and phenotypic values as the dominance ratio increases. Nevertheless, whether the interest is to infer directly on dominance effects, GBLUP-D could be a more suitable method. Department of Animal Science Faculty of Agricultural and Veterinary Sciences Sao Paulo State University (UNESP) Department of Animal Sciences University of Wisconsin WI Department of Animal Science Faculty of Animal Science and Food Engineering University of Sao Paulo National Council of Technological and Scientific Development (CNPq) Department of Animal Science Faculty of Agricultural and Veterinary Sciences Sao Paulo State University (UNESP)

Made available in DSpace on 2020-12-12T01:00:19Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01 This study aimed to identify genomic regions influencing growth traits in Nellore cattle and evaluate the predictive ability of each trait based on results obtained from single-step genome-wide association analyzes (ssGWAS) considering different single nucleotide polymorphims (SNP) densities of markers. The National Association of Breeders and Researchers provided the dataset, from eighteen Nellore herds participating of the Nellore Brazilian breeding program. The traits birth weight (BW), adjusted weight at 210 (W210) and at 450 (W450) days of age and adult cow weight (ACW) were considered. A total of 963 animals, genotyped using the Illumina BovineHD BeadChip, were used as a reference population to impute genotypes of 7,689 animals, genotyped in low-density panel. Genotype imputation was performed using the FImpute 2.2 software. The ssGWAS was used to identify genomic regions associated to growth traits. Several genes in enrichment analysis were related to muscle and adipose tissue development and metabolism, feed efficiency, milk composition and maternal behavior. The predictive ability varied from low (0.10) to moderate (0.68). The predictive ability and bias for both panels were similar for all traits. The results found in this study should improve the understanding of genetic and physiologic mechanism associated with growth traits. However, the association of these results with other approaches, like system biologic and other omics information should improve the identification of causative genetic variants in growth traits in indicine cattle. Departament of Veterinary Medicine College of Animal Science and Food Engineer University of São Paulo (USP), 225 Duque de Caxias Norte Avenue Departament of Animal Science College of Agricultural and Veterinarian Sciences São Paulo State University (UNESP), Access Road Professor Paulo Donato Castellane s/n Zoetis, 1240 Dr. Chucri Zaidan Street, Edifício Morumbi Corporate, Diamond Tower, 4th floor National Institute of Agricultural Research (INIA) Nacional Association of Breeders and Researchers (ANCP), 463 João Godoy Street Departament of Animal Science College of Agricultural and Veterinarian Sciences São Paulo State University (UNESP), Access Road Professor Paulo Donato Castellane s/n

Made available in DSpace on 2020-12-12T02:33:29Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-03-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Brazilian beef cattle are raised predominantly on pasture in a wide range of environments. In this scenario, genotype by environment (G×E) interaction is an important source of phenotypic variation in the reproductive traits. Hence, the evaluation of G×E interactions for heifer’s early pregnancy (HP) and scrotal circumference (SC) traits in Nellore cattle, belonging to three breeding programs, was carried out to determine the animal’s sensitivity to the environmental conditions (EC). The dataset consisted of 85 874 records for HP and 151 553 records for SC, from which 1800 heifers and 3343 young bulls were genotyped with the BovineHD BeadChip. Genotypic information for 826 sires was also used in the analyses. EC levels were based on the contemporary group solutions for yearling body weight. Linear reaction norm models (RNM), using pedigree information (RNM_A) or pedigree and genomic information (RNM_H), were used to infer G×E interactions. Two validation schemes were used to assess the predictive ability, with the following training populations: (a) forward scheme—dataset was split based on year of birth from 2008 for HP and from 2011 for SC; and (b) environment-specific scheme—low EC (−3.0 and −1.5) and high EC (1.5 and 3.0). The inclusion of the H matrix in RNM increased the genetic variance of the intercept and slope by 18.55 and 23.00% on average respectively, and provided genetic parameter estimates that were more accurate than those considering pedigree only. The same trend was observed for heritability estimates, which were 0.28–0.56 for SC and 0.26–0.49 for HP, using RNM_H, and 0.26–0.52 for SC and 0.22–0.45 for HP, using RNM_A. The lowest correlation observed between unfavorable (−3.0) and favorable (3.0) EC levels were 0.30 for HP and −0.12 for SC, indicating the presence of G×E interaction. The G×E interaction effect implied differences in animals’ genetic merit and re-ranking of animals on different environmental conditions. SNP marker–environment interaction was detected for Nellore sexual precocity indicator traits with changes in effect and variance across EC levels. The RNM_H captured G×E interaction effects better than RNM_A and improved the predictive ability by around 14.04% for SC and 21.31% for HP. Using the forward scheme increased the overall predictive ability for SC (20.55%) and HP (11.06%) compared with the environment-specific scheme. The results suggest that the inclusion of genomic information combined with the pedigree to assess the G×E interaction leads to more accurate variance components and genetic parameter estimates. School of Agricultural and Veterinarian Sciences São Paulo State University (UNESP), Via de Acesso Prof. Paulo Donato Castelane National Council for Science and Technological Development School of Agricultural and Veterinarian Sciences São Paulo State University (UNESP), Via de Acesso Prof. Paulo Donato Castelane FAPESP: 2009/16118-5 FAPESP: 2015/25356-8 FAPESP: 2017/02291-3 CNPq: 559631/2009-0

The objective of this study was to estimate genetic parameters for carcass and meat quality traits, as well as their genetic correlations using pedigree and genomic information. A total of 3,716; 3,702; 3,439; 3,705; and 3,714 records of 12th-13th rib LM area (LMA), backfat thickness (BF), HCW, marbling score (MARB), and Warner-Bratzler peak shear force (WBSF), respectively, were used. Animals were genotyped with BovineHD BeadChip and GeneSeek Genomic Profiler Indicus HD - GGP75Ki panel. The (co)variance components were estimated by Bayesian inference using a multitrait ssGBLUP analysis. The animal model included fixed effects of contemporary group (defined by the combination of farm and year of birth, and management group at yearling) and age of animal at slaughtering as a covariate (linear). Direct additive genetic and residual effects were fitted as random. The posterior means and SD of heritabilities for LMA, BF, HCW, MARB, and WBSF were 0.28 (0.03), 0.21 (0.04), 0.21 (0.04), 0.12 (0.04), and 0.11 (0.03), respectively. The posterior means for genetic correlations between LMA and meat quality were positive and moderate with MARB (0.38 ± 0.12) and negative with WBSF (-0.47 ± 0.12). Low genetic correlations were estimated between BF and WBSF (-0.03 ± 0.16) and between HCW and MARB (-0.04 ± 0.14), indicating that these traits are not controlled by the same set or linked genes. Carcass traits (LMA, BF, and HCW) presented moderate heritability providing quick response to the selection purpose. The estimates of heritability for meat quality traits (MARB and WBSF) were low and indicate that the rate of genetic improvement for these traits would be slow. Genetic correlations indicated that selection for carcass traits would not be strongly antagonistic for improving meat quality.

Made available in DSpace on 2019-10-06T17:03:28Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-02-20 Background: Genome-wide association studies (GWAS) are utilized in cattle to identify regions or genetic variants associated with phenotypes of interest, and thus, to identify design strategies that allow for the increase of the frequency of favorable alleles. Visual scores are important traits of cattle production in Brazil because they are utilized as selection criteria, helping to choose more harmonious animals. Despite its importance, there are still no studies on the genome association for these traits. This study aimed to identify genome regions associated with the traits of conformation, precocity and muscling, based on a visual score measured at weaning. Results: Bayesian approaches with BayesC and Bayesian LASSO were utilized with 2873 phenotypes of Nellore cattle for a GWAS. The animals were genotyped with Illumina BovineHD BeadChip, and a total of 309,865 SNPs were utilized after quality control. In the analyses, phenotype and deregressed breeding values were utilized as dependent variables; a threshold model was utilized for the former and a linear model for the latter. The association criterion was the percentage of genetic variance explained by SNPs found in 1 Mb-long windows. The Bayesian approach BayesC was better adjusted to the data because it could explain a larger phenotypic variance for both dependent variables. Conclusions: There were no large effects for the visual scores, indicating that they have a polygenic nature; however, regions in chromosomes 1, 3, 5, 7, 14, 15, 16, 19, 20 and 23 were identified and explained a large part of the genetic variance. Animal Science Department School of Agricultural and Veterinary Sciences São Paulo State University (Unesp) Animal Science Department São Paulo State University (Unesp), Dracena Animal Science Department School of Agricultural and Veterinary Sciences São Paulo State University (Unesp) Animal Science Department São Paulo State University (Unesp), Dracena

Made available in DSpace on 2019-10-06T17:00:29Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-14 Background: Traditional single nucleotide polymorphism (SNP) genome-wide association analysis (GWAA) can be inefficient because single SNPs provide limited genetic information about genomic regions. On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium (LD) between haplotypes and causal variants and may also potentially capture epistastic interactions between variants within a haplotyped locus, providing an increase in the power and robustness of the association studies. We performed GWAA (413,355 SNP markers) using haplotypes based on variable-sized sliding windows and compared the results to a single-SNP GWAA using Warner-Bratzler shear force measured in the longissimus thorasis muscle of 3161 Nelore bulls to ascertain the optimal window size for identifying the genomic regions that influence meat tenderness. Results: The GWAA using single SNPs identified eight variants influencing meat tenderness on BTA 3, 4, 9, 10 and 11. However, thirty-three putative meat tenderness QTL were detected on BTA 1, 3, 4, 5, 8, 9, 10, 11, 15, 17, 18, 24, 25, 26 and 29 using variable-sized sliding haplotype windows. Analyses using sliding window haplotypes of 3, 5, 7, 9 and 11 SNPs identified 57, 61, 42, 39, and 21% of all thirty-three putative QTL regions, respectively; however, the analyses using the 3 and 5 SNP haplotypes, cumulatively detected 88% of the putative QTL. The genes associated with variation in meat tenderness participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes. Conclusions: GWAA using haplotypes based on variable-sized sliding windows allowed the detection of more QTL than traditional single-SNP GWAA. Analyses using smaller haplotypes (3 and 5 SNPs) detected a higher proportion of the putative QTL. Animal Science Department São Paulo State University (Unesp) Division of Animal Sciences University of Missouri Animal Science Department São Paulo State University (Unesp)

Made available in DSpace on 2018-12-11T17:38:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-12-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The aims of this study were to identify haplotyped loci associated with meat tenderness (WBSF) in candidate genes and to search for new polymorphisms in these regions that influence such trait in Nelore cattle. Fifty-two genes that had previously been associated with WBSF or that have biological functions that may influence tenderness were chosen for the haplotype association analysis in 1657 animals. Of the 52 tested candidate genes, two haplotyped loci located in ASAP1 and CAPN1 were significantly associated with WBSF. The effect of the significant haplotype alleles varied from −0.44 to 0.80 and −1.03 to 1.52 kg in CAPN1 and ASAP1, respectively. Exonic regions near and within these loci were chosen for sequencing to search for new polymorphisms. Four SNPs were identified in ASAP1 and seventeen in CAPN1. All SNPs detected in the sequenced regions in ASAP1 showed no association with WBSF even though two of them (rs714391435 and rs109256712) were in strong linkage disequilibrium (LD) with most SNPs that composed the significant haplotyped locus. Four SNPs located in CAPN1 were found for the first time in this study. One of them (rs1121961662) showed significant association with WBSF and is also in strong LD with all SNPs within the significant haplotyped locus in CAPN1 gene. The SNP rs1121961662 could be used as a molecular marker for this QTL and may be included in low-density arrays to improve the selection of meat tenderness in Bos indicus animals. Animal Science Department São Paulo State University (UNESP) School of Agricultural and Veterinarian Science Division of Animal Sciences University of Missouri Animal Science Department São Paulo State University (UNESP) School of Agricultural and Veterinarian Science CNPq: 2009/16118-5 FAPESP: 2013/00035-9 FAPESP: 2014/23013-3 CNPq: 480369/2009-7

Made available in DSpace on 2021-06-25T10:22:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-03-01 The aim of this study was to compare the genomic prediction ability for carcass composition indicator traits in Nellore cattle using the Best Linear Unbiased Prediction (BLUP), Genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), Bayesian methods (BayesA, BayesB, BayesC and BayesianLASSO) and an approach combining the pedigree matrix of genotyped animals with both the genomic matrix and Bayesian methods. Phenotypic and genotypic information on about 66,000 and 21,000 animals, respectively, evaluated by National Association of Breeders and Researchers (ANCP) were available for body structure (BS), finishing precocity (FP), musculature (MS), Longissimus muscle area (LMA), back fat thickness (BF) and rump fat thickness (RF). The genotypes were obtained based on the low-density panel Zoetis CLARIFIDE® Nellore version 3.1 containing 30.754 markers. To obtain the prediction ability, the dataset was split into training (genotyped sires and dams with progenies) and validation (genotyped young animals without progeny records and without phenotypes) subsets. For genomic models, the predictive ability was assessed through the correlation between the deregressed expected progeny differences and DGVs. For BLUP model, the prediction ability was evaluated through the correlation between estimated breeding value (EBV) and deregressed expected progeny differences (dEPD). To evaluate the extent of prediction bias the linear regression coefficients between the response variable (dEPD) and DGVs (or EBVs for BLUP model) considering only the animals in the validation set, were calculated. In terms of prediction ability and bias, Bayesian approaches were superior for visual scores traits and the ssGBLUP for carcass traits obtained by ultrasonography, however, more biased results were obtained for BF and RF using the ssGBLUP. The ssGBLUP model showed less biased prediction for low heritability traits, such as LMA, and also it has lower computational demand and it is a straightforward method for implementing genomic selection in beef cattle. Therefore, earlier reliable genetic evaluation of unproven sires trough genomic selection is appealing in order to increase the genetic response for carcass traits in the Nellore (Bos taurus indicus) beef cattle. Departament of Veterinary Medicine College of Animal Science and Food Engineer University of Sao Paulo (USP), 225 Duque de Caxias Norte Avenue Departament of Animal Science College of Agricultural and Veterinarian Sciences Sao Paulo State University (UNESP), Via de Acesso Professor Paulo Donato Castellane s/n National Association of Breeders and Researchers (ANCP) Jardim America, 463 Joao Godoy Street Brazilian Agricultural Research Corporation (EMBRAPA) Distrito Federal Departament of Animal Science College of Agricultural and Veterinarian Sciences Sao Paulo State University (UNESP), Via de Acesso Professor Paulo Donato Castellane s/n

The objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A22 matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A22) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the population increased. The EBV accuracy reduction was higher for GEN and YOUNG groups. By analyzing the scenarios for YOUNG (from 0 to 100% of MS), the decrease was 87.8 and 86% for AFC and W550, respectively. When applying the ssGBLUP model, the accuracies of genetic evaluation also decreased as the MS in the pedigree for both traits increased. However, the accuracy reduction was less than those observed for BLUP model. Using the same comparison (scenario 0 to 100% of MS), the accuracies reductions were 38 and 44.6% for AFC and W550, respectively. There were no differences between the strategies for scaling the G matrix for ALL, BULL, and FEM groups under the different scenarios with missing pedigree. These results pointed out that the uninformative part of the A22 matrix and genotyped animals with paternity uncertainty did not influence the scaling of G matrix. On the basis of the results, it is important to have a G matrix in the same scale of the A22 matrix, especially for the evaluation of young animals in situations with missing pedigree information. In these situations, the ssGBLUP model is an appropriate alternative to obtain a more reliable and less biased estimate of breeding values, especially for young animals with few or no phenotypic records. For accurate and unbiased genomic predictions with ssGBLUP, it is necessary to assure that the G matrix is compatible with the A22 matrix, even in situations with paternity uncertainty. Made available in DSpace on 2017-12-21T23:28:05Z (GMT). No. of bitstreams: 1 ApplicationofsinglestepgenomicBLUPunderdifferentuncertainpaternityscenariosusingsimulateddata..pdf: 782402 bytes, checksum: ec6960b2579b83b77b9aca6119ae98c8 (MD5) Previous issue date: 2017-12-21

Carcass traits measured after slaughter are economically relevant traits in beef cattle. In general, the slaughter house payment system is based on HCW. Ribeye area (REA) is associated with the amount of the meat in the carcass, and a minimum of backfat thickness (BFT) is necessary to protect the carcass during cooling. The aim of this study was to identify potential genomic regions harboring candidate genes affecting those traits in Nellore cattle. The data set used in the present study consisted of 1,756 Nellore males with phenotype records. A subset of 1,604 animals had both genotypic and phenotypic information. Genotypes were generated based on a panel with 777,962 SNPs from the Illumina Bovine HD chip. The SNP effects were calculated based on the genomic breeding values obtained by using the single-step GBLUP approach and a genomic matrix re-weighting procedure. The proportion of the variance explained by moving windows of 100 consecutive SNPs was used to assess potential genomic regions harboring genes with major effects on each trait. The top 10 non-overlapping SNP-windows explained 8.72%, 11.38%, and 9.31% of the genetic variance for REA, BFT, and HCW, respectively. These windows are located on chromosomes 5, 7, 8, 10, 12, 20, and 29 for REA; chromosomes 6, 8, 10, 13, 16, 17, 18, and 24 for BFT; and chromosomes 4, 6, 7, 8, 14, 16, 17, and 21 for HCW. For REA, there were identified genes ( and ) involved in the cell cycle biological process which affects many aspects of animal growth and development. The and genes, both from AA transporter family, was also associated with REA. The AA transporters are essential for cell growth and proliferation, acting as carriers of tissue nutrient supplies. Various genes identified for BFT (, , , , , and ) have been associated with lipid metabolism in different mammal species. One of the most promising genes identified for HCW was the . There is evidence, in the literature, that this gene is located in putative QTL affecting carcass weight in beef cattle. Our results showed several genomic regions containing plausible candidate genes that may be associated with carcass traits in Nellore cattle. Besides contributing to a better understanding of the genetic control of carcass traits, the identified genes can also be helpful for further functional genomic studies.

The objective of this study was to determine whether visual scores used as selection criteria in Nellore breeding programs are effective indicators of carcass traits measured after slaughter. Additionally, this study evaluated the effect of different structures of the relationship matrix ( and ) on the estimation of genetic parameters and on the prediction accuracy of breeding values. There were 13,524 animals for visual scores of conformation (CS), finishing precocity (FP), and muscling (MS) and 1,753, 1,747, and 1,564 for LM area (LMA), backfat thickness (BF), and HCW, respectively. Of these, 1,566 animals were genotyped using a high-density panel containing 777,962 SNP. Six analyses were performed using multitrait animal models, each including the 3 visual scores and 1 carcass trait. For the visual scores, the model included direct additive genetic and residual random effects and the fixed effects of contemporary group (defined by year of birth, management group at yearling, and farm) and the linear effect of age of animal at yearling. The same model was used for the carcass traits, replacing the effect of age of animal at yearling with the linear effect of age of animal at slaughter. The variance and covariance components were estimated by the REML method in analyses using the numerator relationship matrix () or combining the genomic and the numerator relationship matrices (). The heritability estimates for the visual scores obtained with the 2 methods were similar and of moderate magnitude (0.23-0.34), indicating that these traits should response to direct selection. The heritabilities for LMA, BF, and HCW were 0.13, 0.07, and 0.17, respectively, using matrix and 0.29, 0.16, and 0.23, respectively, using matrix . The genetic correlations between the visual scores and carcass traits were positive, and higher correlations were generally obtained when matrix was used. Considering the difficulties and cost of measuring carcass traits postmortem, visual scores of CS, FP, and MS could be used as selection criteria to improve HCW, BF, and LMA. The use of genomic information permitted the detection of greater additive genetic variability for LMA and BF. For HCW, the high magnitude of the genetic correlations with visual scores was probably sufficient to recover genetic variability. The methods provided similar breeding value accuracies, especially for the visual scores.

Made available in DSpace on 2020-12-12T01:34:00Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-10-01 The aim of this study was to estimate genetic parameters and to identify genomic regions associated with the calving ease (CE) in precocious Nellore heifers. A total of 1,277 CE phenotypes were collected and scored into two categories: i- non assisted calving, categorized as success (1) and ii- assisted calving where heifers required any form of assistance or intervention to give birth, categorized as failure (2). A pedigree structure containing the identification of the animal's sire and dam was used, with the relationship matrix comprising a total of 6,511 animals. Genotypic data from 1,201 animals were obtained using low-density panel (Clarifide Nelore 3.1) encompassing over 29,001 single nucleotide polymorphisms (SNP) markers. A threshold sire-maternal grandsire model (S-MGS) was used to estimate the genetic parameters, which included sire, maternal grandsire and residual effects as random effects and the fixed effects of contemporary groups (farm and year of calving, sex) and birth weight of the calf as covariable (linear effect). Genomic breeding values were estimated using an animal model with the direct and maternal genetic variances which were previously obtained by means of a S-MGS threshold model. The direct and maternal heritability estimates for CE were obtained considering the covariance of direct and maternal effects fixed as zero. Regions that accounted for more than 0.5% of the additive genetic variance were used. The direct and maternal heritability estimates for CE were low (0.18) and moderate (0.39) respectively, indicating that genetic progress for this trait is feasible, and so, it would respond favorably to direct selection. Genes identified within the significant windows, such as CA8, FAM110B, TOX, ARID1A, RBM15, HSF1 and PLAG1 were found to be related with maternal and direct effects on CE. Gene enrichment analysis revealed processes that might directly influence fetal processes involved in female pregnancy and stress response. These results should help to better understand the genetic and physiological mechanisms regulating placenta development and fetal development, and this information might be useful for future genomic studies in Nellore cattle. Department of Veterinary Medicine College of Animal Science and Food Engineer University of São Paulo (USP), 225 Duque de Caxias Norte Avenue, Jardim Elite Department of Animal Science College of Agricultural and Veterinarian Sciences São Paulo State University (UNESP), Via de Acesso Professor Paulo Donato Castellane s/n Nacional Association of Breeders and Researchers (ANCP), 463 João Godoy Street, Jardim América Beef and Wool Program National Research Institute for Agriculture (INIA), Ruta, 50 Km 11 Department of Animal Science Minas Gerais Federal University (UFMG) Escola de Veterinária da UFMG, Caixa Postal 567, Belo Horizonte/MG Department of Animal Science College of Agricultural and Veterinarian Sciences São Paulo State University (UNESP), Via de Acesso Professor Paulo Donato Castellane s/n

Made available in DSpace on 2019-10-06T16:51:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-11-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The aim of the present study was to compare the predictive ability of SNP-BLUP model using different pseudo-phenotypes such as phenotype adjusted for fixed effects, estimated breeding value, and genomic estimated breeding value, using simulated and real data for beef FA profile of Nelore cattle finished in feedlot. A pedigree with phenotypes and genotypes of 10,000 animals were simulated, considering 50% of multiple sires in the pedigree. Regarding to phenotypes, two traits were simulated, one with high heritability (0.58), another with low heritability (0.13). Ten replicates were performed for each trait and results were averaged among replicates. A historical population was created from generation zero to 2020, with a constant size of 2000 animals (from generation zero to 1000) to produce different levels of linkage disequilibrium (LD). Therefore, there was a gradual reduction in the number of animals (from 2000 to 600), producing a “bottleneck effect” and consequently, genetic drift and LD starting in the generation 1001 to 2020. A total of 335,000 markers (with MAF greater or equal to 0.02) and 1000 QTL were randomly selected from the last generation of the historical population to generate genotypic data for the test population. The phenotypes were computed as the sum of the QTL effects and an error term sampled from a normal distribution with zero mean and variance equal to 0.88. For simulated data, 4000 animals of the generations 7, 8, and 9 (with genotype and phenotype) were used as training population, and 1000 animals of the last generation (10) were used as validation population. A total of 937 Nelore bulls with phenotype for fatty acid profiles (Sum of saturated, monounsaturated, omega 3, omega 6, ratio of polyunsaturated and saturated and polyunsaturated fatty acid profile) were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. To compare the accuracy and bias of direct genomic value (DGV) for different pseudo-phenotypes, the correlation between true breeding value (TBV) or DGV with pseudo-phenotypes and linear regression coefficient of the pseudo-phenotypes on TBV for simulated data or DGV for real data, respectively. For simulated data, the correlations between DGV and TBV for high heritability traits were higher than obtained with low heritability traits. For simulated and real data, the prediction ability was higher for GEBV than for Yc and EBV. For simulated data, the regression coefficient estimates (b (Yc,DGV) ), were on average lower than 1 for high and low heritability traits, being inflated. The results were more biased for Yc and EBV than for GEBV. For real data, the GEBV displayed less biased results compared to Yc and EBV for SFA, MUFA, n-3, n-6, and PUFA/SFA. Despite the less biased results for PUFA using the EBV as pseudo-phenotype, the b (Yi,DGV estimates obtained for the different pseudo-phenotypes (Yc, EBV and GEBV) were very close. Genomic information can assist in improving beef fatty acid profile in Zebu cattle, since the use of genomic information yielded genomic values for fatty acid profile with accuracies ranging from low to moderate. Considering both simulated and real data, the ssGBLUP model is an appropriate alternative to obtain more reliable and less biased GEBVs as pseudo-phenotype in situations of missing pedigree, due to high proportion of multiple sires, being more adequate than EBV and Yc to predict direct genomic value for beef fatty acid profile. Faculdade de Ciências Agrárias e Veterinárias UNESP Faculdade de Medicina Veterinária e Zootecnia USP Faculdade de Zootecnia e Engenharia de Alimentos USP Instituto Nacional de Investigación Agropecuaria Faculdade de Ciências Agrárias e Veterinárias UNESP FAPESP: 2009/16118-5 FAPESP: 2011/2141-0

Made available in DSpace on 2018-11-26T17:44:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-01-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) The aim of the present study was to estimate covariance components and genetic parameters for beef fatty acid (FA) composition of intramuscular fat in the longissimus thoracis muscle in Nelore bulls finished in feedlot. Twenty-two FAs were selected. The heritability estimates for individual FAs ranged from 0.01 to 0.35. The heritability estimates for myristic (0.25 +/- 0.09), palmitic (0.18 +/- 0.07), oleic (0.28 +/- 0.09), linoleic (0.16 +/- 0.06) and alpha-linolenic (0.35 +/- 0.10) FAs were moderate. Stearic, elaidic, palmitoleic, vaccenic, conjugated linoleic acid, docosahexanoic, eicosatrienoic and arachidonic FAs had heritability estimates below 0.15. The genetic-correlation estimates between the individual saturated FAs (SFAs) were low and negative between myristic and stearic FAs (-0.22 +/- 0.84), moderate between palmitic and myristic FAs (0.58 +/- 0.56) and negative between palmitic and stearic FAs (-0.69 +/- 0.45). The genetic correlations between the individual long-chain polyunsaturated FAs (PUFAs) were positive and moderate (>0.30). However, the genetic-correlation estimates between long-chain PUFAs and alpha-linolenic acid were low (

The aim of this study was to estimate genetic and phenotypic associations of growth traits with carcass and meat traits in Nellore cattle. Data from male and female animals were used for weaning weight (WW; N = 241,416), yearling weight (YW, N = 126,596), weight gain from weaning to yearling (GWY, N = 78,687), and yearling hip height (YHH, N = 90,720), respectively; 877 male animals were used for hot carcass weight (HCW) and 884 for longissimus muscle area (LMA), backfat thickness (BT), marbling score (MS), and shear force (SF). The variance components were estimated by the restricted maximum likelihood method using three-trait animal models that included WW. The model for WW included direct and maternal additive genetic, maternal permanent environmental, and residual effects as random effects; contemporary group as fixed effects; and age of dam at calving and age of animal as covariates (linear and quadratic effects). For the other traits, maternal effects and the effect of age of dam at calving were excluded from the model. Heritability ranged from 0.10 ± 0.12 (LMA) to 0.44 ± 0.007 (YW). Genetic correlations ranged from -0.40 ± 0.38 (WW x LMA) to 0.55 ± 0.10 (HCW x YW). Growth, carcass, and meat traits have sufficient genetic variability to be included as selection criteria in animal breeding programs.

Made available in DSpace on 2018-12-11T17:31:30Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-06-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The objective of this study was to compare SNP-BLUP, BayesCπ, BayesC and Bayesian Lasso methodologies to predict the direct genomic value for saturated, monounsaturated, and polyunsaturated fatty acid profile, omega 3 and 6 in the Longissimus thoracis muscle of Nellore cattle finished in feedlot. A total of 963 Nellore bulls with phenotype for fatty acid profiles, were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. The predictive ability was evaluated using cross validation. To compare the methodologies, the correlation between DGV and pseudo-phenotypes was calculated. The accuracy varied from − 0.40 to 0.62. Our results indicate that none of the methods excelled in terms of accuracy, however, the SNP-BLUP method allows obtaining less biased genomic evaluations, thereby; this method is more feasible when taking into account the analyses' operating cost. Despite the lowest bias observed for EBV, the adjusted phenotype is the preferred pseudophenotype considering the genomic prediction accuracies regarding the context of the present study. Faculdade de Ciências Agrárias e Veterinárias UNESP Jaboticabal Faculdade de Medicina Veterinária e Zootecnia USP Pirassununga Faculdade de Zootecnia e Engenharia de Alimentos USP Pirassununga Faculdade de Ciências Agrárias e Veterinárias UNESP Jaboticabal FAPESP: #2009/16118-5 FAPESP: (#2011/2141-0

Made available in DSpace on 2018-12-11T17:30:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-01-01 Subcutaneous fat deposition measured as backfat thickness (BFT) increases protection for the bovine carcass during cooling, conferring to BFT an important characteristic for the meat industry. To study the influence of BFT on meat quality traits of Nellore bulls (Bos indicus), data from 1652 animals aged 20-24 months in feedlot finishing were used. The principal component analysis (PCA) was performed to characterise meat quality variables in longissimus thoracis muscle. Measurements comprised the rib eye area, BFT, marbling, shear force, myofibril fragmentation index, cooking losses, intramuscular lipid content and colour (lightness, yellowness, redness, chromaticity and hue). Considering BFT as a separation criterion, the K-means cluster analysis was applied to classify beef samples. The first four PC explained roughly 66% of total variability and meat colour (yellowness and chromaticity) was more effective to define the first PC. Tenderness or toughness (shear force and cooking losses) and fatness (BFT and intramuscular lipid content) were more effective to define the second and third PC, respectively. Three BFT groups were formed and projected in the gradient defined by PC 2 and PC 3. BFT means in the clusters were 10.82 ± 3.19 (I), 5.03 ± 1.01 (II) and 2.54 ± 0.63 (III) mm with 185, 947 and 520 animals in each group, respectively. The projection of I, II and III in the gradient allowed to distinguish fatness between beef samples and tenderness between I and III. Additionally, 57.32% of animals (Group II) were placed between the two previous groups. Beef samples with higher values of shear force and cooking losses (tough meat) showed lower BFT and myofibril fragmentation index values, possibly due to fibre shortening. PCA and K-means cluster analysis presented as interesting multivariate techniques to identify Nellore bulls regarding meat quality as some of the traits used in the study are difficult to measure. The three-cluster solution represented the main biological type of Nellore bulls finished on feedlot in Brazil showing that only 11.2% of beef samples (Cluster I) can be considered tender. This information can be useful for breeding programs of Nellore bulls. In this study, Cluster I shows optimal beef quality (SF ≤ 4.52 ± 1.17 kg) with better marbling level and less cooking losses. Nellore cattle producers should target BFT at least 5.00 mm to prevent fibre shortening. However, the only condition which provides optimal beef tenderness (i.e. shear force values lower than 4.9 kg) was found in Cluster I. The BFT does not seem to be a suitable characteristic for the selection of animals to improve tenderness due the weak relationship between BFT and shear force. Animal Nutrition and Growth Laboratory Department of Animal Science Luiz de Queiroz' College of Agriculture University of São Paulo (ESALQ-USP) Department of Animal Breeding and Nutrition College of Veterinary and Animal Science São Paulo State University (UNESP) Department of Animal Science College of Agriculture and Veterinary Science São Paulo State University (UNESP) Access Route Paulo Donato Castellane Statistical and Agronomic Experimentation Luiz de Queiroz' College of Agriculture University of São Paulo (ESALQ-USP) Institute of Biosciences São Paulo State University (UNESP) Rubião Junior District Department of Animal Breeding and Nutrition College of Veterinary and Animal Science São Paulo State University (UNESP) Department of Animal Science College of Agriculture and Veterinary Science São Paulo State University (UNESP) Access Route Paulo Donato Castellane Institute of Biosciences São Paulo State University (UNESP) Rubião Junior District

Made available in DSpace on 2018-11-28T03:37:23Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-02-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The objective of this study was to estimate the genetic-quantitative relationships between the beef fatty acid profile with the carcass and meat traits of Nellore cattle. A total of 1826 bulls finished in feedlot conditions and slaughtered at 24 months of age on average were used. The following carcass and meat traits were analysed: subcutaneous fat thickness (BF), shear force (SF) and total intramuscular fat (IMF). The fatty acid (FA) profile of the Longissimus thoracis samples was determined. Twenty-five FAs (18 individuals and seven groups of FAs) were selected due to their importance for human health. The animals were genotyped with the BovineHD BeadChip and, after quality control for single nucleotide polymorphisms (SNPs), only 470,007 SNPs from 1556 samples remained. The model included the random genetic additive direct effect, the fixed effect of the contemporary group and the animal's slaughter age as a covariable. The (co)variances and genetic parameters were estimated using the REML method, considering an animal model (single-step GBLUP). A total of 25 multi-trait analyses, with four traits, were performed considering SF, BF and IMF plus each individual FA. The heritability estimates for individual saturated fatty acids (SFA) varied from 0.06 to 0.65, for monounsaturated fatty acids (MUFA) it varied from 0.02 to 0.14 and for polyunsaturated fatty acids (PUFA) it ranged from 0.05 to 0.68. The heritability estimates for Omega 3, Omega 6, SFA, MUFA and PUFA sum were low to moderate, varying from 0.09 to 0.20. The carcass and meat traits, SF (0.06) and IMF (0.07), had low heritability estimates, while BF (0.17) was moderate. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with BF were 0.04, 0.64 and -0.41, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with SF were 0.29, -0.06 and -0.04, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with IMF were 0.24, 0.90 and -0.67, respectively. The selection to improve meat tenderness in Nellore cattle should not change the fatty acid composition in beef, so it is possible to improve this attribute without affecting the nutritional beef quality in zebu breeds. However, selection for increased deposition of subcutaneous fat thickness and especially the percentage of intramuscular fat should lead to changes in the fat composition, highlighting a genetic antagonism between meat nutritional value and acceptability by the consumer. Sao Paulo State Univ, Fac Agr & Vet Sci, Dept Anim Sci, Jaboticabal, SP, Brazil Univ Sao Paulo, Sch Vet Med & Anim Sci, Dept Anim Sci, Pirassununga, SP, Brazil Univ Sao Paulo, Fac Anim Sci & Food Engn, Dept Anim Sci, Pirassununga, SP, Brazil Univ Sao Paulo, Fac Anim Sci & Food Engn, Dept Food Engn, Pirassununga, SP, Brazil CNPq, Natl Counsel Technol & Sci Dev, Brasilia, DF, Brazil INIA, Natl Agr Res Inst, Las Brujas 90200, Uruguay Sao Paulo State Univ, Fac Agr & Vet Sci, Dept Anim Sci, Jaboticabal, SP, Brazil FAPESP: 2009/16118-5 FAPESP: 2011/21241-0 FAPESP: 2012/23979-0

Made available in DSpace on 2018-12-11T17:27:01Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-01-29 Background: The objective of this study was to evaluate the accuracy of genomic predictions for rib eye area (REA), backfat thickness (BFT), and hot carcass weight (HCW) in Nellore beef cattle from Brazilian commercial herds using different prediction models. Methods: Phenotypic data from 1756 Nellore steers from ten commercial herds in Brazil were used. Animals were offspring of 294 sires and 1546 dams, reared on pasture, feedlot finished, and slaughtered at approximately 2 years of age. All animals were genotyped using a 777k Illumina Bovine HD SNP chip. Accuracy of genomic predictions of breeding values was evaluated by using a 5-fold cross-validation scheme and considering three models: Bayesian ridge regression (BRR), Bayes C (BC) and Bayesian Lasso (BL), and two types of response variables: traditional estimated breeding value (EBV), and phenotype adjusted for fixed effects (Y∗). Results: The prediction accuracies achieved with the BRR model were equal to 0.25 (BFT), 0.33 (HCW) and 0.36 (REA) when EBV was used as response variable, and 0.21 (BFT), 0.37 (HCW) and 0.46 (REA) when using Y∗. Results obtained with the BC and BL models were similar. Accuracies increased for traits with a higher heritability, and using Y∗instead of EBV as response variable resulted in higher accuracy when heritability was higher. Conclusions: Our results indicate that the accuracy of genomic prediction of carcass traits in Nellore cattle is moderate to high. Prediction of genomic breeding values from adjusted phenotypes Y∗was more accurate than from EBV, especially for highly heritable traits. The three models considered (BRR, BC and BL) led to similar predictive abilities and, thus, either one could be used to implement genomic prediction for carcass traits in Nellore cattle. Faculdade de Ciências Agrárias e Veterinárias UNESP Department of Animal Sciences University of Wisconsin-Madison Faculdade de Medicina Veterinária e Zootecnia UNESP Faculdade de Ciências Agrárias e Veterinárias UNESP Faculdade de Medicina Veterinária e Zootecnia UNESP

Made available in DSpace on 2018-12-11T17:21:13Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-12-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) This study was designed to test the impact of quality control, density and allele frequency of single nucleotide polymorphisms (SNP) markers on the accuracy of genomic predictions, using three traits with different heritabilities and two methods of prediction in a Nellore cattle population genotyped with the Illumina Bovine HD Assay. A total of 1756∼ 3150 and 3119 records of age at first calving (AFC)∼ weaning weight (WW) and yearling weight (YW), respectively, were used. Three scenarios with different exclusion thresholds for minor allele frequency (MAF), deviation from Hardy-Weinberg equilibrium (HWE) and correlation between SNP pairs (r2) were constructed for all traits: (1) high rigor (S1): call rate 0.999∼ (2) Moderate rigor (S2): call rate

Submitted by Guilherme Lemeszenski (guilherme@nead.unesp.br) on 2013-08-22T19:00:28Z No. of bitstreams: 1 S0103-84782013000300023.pdf: 408995 bytes, checksum: 711bee069b90428b5b28908f2c466488 (MD5) Made available in DSpace on 2013-08-22T19:00:28Z (GMT). No. of bitstreams: 1 S0103-84782013000300023.pdf: 408995 bytes, checksum: 711bee069b90428b5b28908f2c466488 (MD5) Previous issue date: 2013-03-01 Made available in DSpace on 2013-09-30T19:59:07Z (GMT). No. of bitstreams: 2 S0103-84782013000300023.pdf: 408995 bytes, checksum: 711bee069b90428b5b28908f2c466488 (MD5) S0103-84782013000300023.pdf.txt: 24695 bytes, checksum: ddb6a30328d4fca82ddff29c1e8ba228 (MD5) Previous issue date: 2013-03-01 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:18:28Z No. of bitstreams: 2 S0103-84782013000300023.pdf: 408995 bytes, checksum: 711bee069b90428b5b28908f2c466488 (MD5) S0103-84782013000300023.pdf.txt: 24695 bytes, checksum: ddb6a30328d4fca82ddff29c1e8ba228 (MD5) Made available in DSpace on 2014-05-20T13:18:29Z (GMT). No. of bitstreams: 2 S0103-84782013000300023.pdf: 408995 bytes, checksum: 711bee069b90428b5b28908f2c466488 (MD5) S0103-84782013000300023.pdf.txt: 24695 bytes, checksum: ddb6a30328d4fca82ddff29c1e8ba228 (MD5) Previous issue date: 2013-03-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Este trabalho foi desenvolvido com o objetivo de estudar a evolução das características de crescimento e carcaça em função da idade em bovinos da raça Hereford, utilizando modelos não-lineares. Foram utilizados dados de 240 bovinos machos (castrados) da raça Hereford, pertencentes à fazenda Experimental La Estanzuela do INIA, Colonia, Uruguai. As características começaram a ser mensuradas a partir da desmama de animais com aproximadamente seis meses de idade, até o abate (dois anos de idade). As medições de peso foram realizadas a cada 15±3 dias durante todo o período e sem jejum prévio. As mensurações de área de olho de lombo, obtidas por ultrassom, e altura dos animais foram obtidas a cada 90 dias, sendo que as medidas de ultrassom foram tomadas entre a 12ª e 13ª costelas. Os modelos não-lineares utilizados foram os modelos de Brody, Gompertz, Logístico e Von Bertalanffy. Para as características peso, altura e área de olho de lombo, todos os modelos avaliados atingiram a convergência. Para descrever a evolução dos dados de peso e área de olho de lombo em função da idade, o modelo de Von Bertalanffy foi o mais indicado, e os modelos de Brody e Logístico mostraram o pior ajuste, respectivamente. A evolução da altura dos animais desde o desmame até os dois anos de idade pode ser modelada através de qualquer um dos modelos não-lineares considerados neste estudo. The objective of this research was to study the evolution of growth and carcass traits as a function of age in Hereford cattle using nonlinear models. Records from 240 Hereford steers (castrated), belonging to the experimental station La Estanzuela of INIA Uruguay, were utilized. The animals were measured from weaning, approximately at 6 months of age, to slaughter (two years of age). The animals were weighed, without fasting, every 15±3 days throughout the period. The measurements of ribeye loin area (between the 12th and 13th rib) were obtained by ultrasound every 90 days. The height of the animals was obtained every 90 days. The Brody, Gompertz, Logistic and Von Bertalanffy nonlinear models were applied to describe the trajectory of weight, height and ribeye loin area along the age. For height, weight and ribeye loin area, all the models reached the convergence. To describe the evolution of weight and ribeye loin area, the Von Bertalanffy nonlinear model was the most suitable. For weight and ribeye loin area, the Brody and Logistic nonlinear models have shown the worst fit, respectively. The height trajectory from weaning until two years of age can be modeled by any of the nonlinear models considered in this research. Universidade Estadual Paulista (UNESP) Faculdade de Ciências Agrárias e Veterinárias (FCAV) Departamento de Zootecnia Universidade de São Paulo (USP) Faculdade de Medicina Veterinária e Zootecnia (FMVZ) Departamento de Nutrição e Produção Animal Estación Experimental La Estanzuela Instituto Nacional de Investigación Agropecuária (INIA) Universidade Estadual Paulista (UNESP) Faculdade de Ciências Agrárias e Veterinárias (FCAV) Departamento de Zootecnia

Made available in DSpace on 2018-11-26T17:12:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-11-25 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Background: Fatty acid type in beef can be detrimental to human health and has received considerable attention in recent years. The aim of this study was to identify differentially expressed genes in longissimus thoracis muscle of 48 Nellore young bulls with extreme phenotypes for fatty acid composition of intramuscular fat by RNA-seq technique. Results: Differential expression analyses between animals with extreme phenotype for fatty acid composition showed a total of 13 differentially expressed genes for myristic (C14:0), 35 for palmitic (C16:0), 187 for stearic (C18:0), 371 for oleic (C18:1, cis-9), 24 for conjugated linoleic (C18:2 cis-9, trans11, CLA), 89 for linoleic (C18:2 cis-9,12 n6), and 110 genes for a-linolenic (C18:3 n3) fatty acids. For the respective sums of the individual fatty acids, 51 differentially expressed genes for saturated fatty acids (SFA), 336 for monounsaturated (MUFA), 131 for polyunsaturated (PUFA), 92 for PUFA/SFA ratio, 55 for omega 3, 627 for omega 6, and 22 for omega 6/omega 3 ratio were identified. Functional annotation analyses identified several genes associated with fatty acid metabolism, such as those involved in intra and extra-cellular transport of fatty acid synthesis precursors in intramuscular fat of longissimus thoracis muscle. Some of them must be highlighted, such as: ACSM3 and ACSS1 genes, which work as a precursor in fatty acid synthesis; DGAT2 gene that acts in the deposition of saturated fat in the adipose tissue; GPP and LPL genes that support the synthesis of insulin, stimulating both the glucose synthesis and the amino acids entry into the cells; and the BDH1 gene, which is responsible for the synthesis and degradation of ketone bodies used in the synthesis of ATP. Conclusion: Several genes related to lipid metabolism and fatty acid composition were identified. These findings must contribute to the elucidation of the genetic basis to improve Nellore meat quality traits, with emphasis on human health. Additionally, it can also contribute to improve the knowledge of fatty acid biosynthesis and the selection of animals with better nutritional quality. Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, BR-14884900 Jaboticabal, SP, Brazil Conselho Nacl Desenvolvimento Cient & Tecnol, BR-71605001 Lago Sul, DF, Brazil Univ Sao Paulo, Fac Med Vet & Zootecnia, Dept Nutr & Prod Anim, BR-13635900 Pirassununga, SP, Brazil Univ Sao Paulo, Dept Zootecnia, BR-13418900 Piracicaba, SP, Brazil Univ Sao Paulo, Fac Zootecnia & Engn Alimentos, Dept Zootecnia, BR-13635900 Pirassununga, SP, Brazil Clemson Univ, Anim & Vet Sci Dept, Clemson, SC 29634 USA Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, BR-14884900 Jaboticabal, SP, Brazil FAPESP: 2011/2141-0 FAPESP: 2009/16118-5 FAPESP: 2012/15098-3

Made available in DSpace on 2022-04-28T19:03:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-06-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) The objective of this study was to identify genomic regions that are associated with meat quality traits in the Nellore breed. Nellore steers were finished in feedlots and slaughtered at a commercial slaughterhouse. This analysis included 1,822 phenotypic records of tenderness and 1,873 marbling records. After quality control, 1,630 animals genotyped for tenderness, 1,633 animals genotyped for marbling, and 369,722 SNPs remained. The results are reported as the proportion of variance explained by windows of 150 adjacent SNPs. Only windows with largest effects were considered. The genomic regions were located on chromosomes 5, 15, 16 and 25 for marbling and on chromosomes 5, 7, 10, 14 and 21 for tenderness. These windows explained 3,89% and 3,80% of the additive genetic variance for marbling and tenderness, respectively. The genes associated with the traits are related to growth, muscle development and lipid metabolism. The study of these genes in Nellore cattle is the first step in the identification of causal mutations that will contribute to the genetic evaluation of the breed. Departamento de Zootecnia Faculdade de Ciências Agrarias E Veterinárias Conselho Nacional de Desenvolvimento Científico E Tecnológico - CNPq Departamento de Melhoramento E Nutrição Animal Faculdade de Medicina Veterinária E Zootecnia Universidade Tecnológica do Paraná Dois Vizinhos Departamento de Medicina Veterinária Preventiva E Produção Animal Universidade Federal DaBahia