Identification of genomic regions and pathways associated with traits related to rumen acidosis in feedlot Nellore cattle.

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

Genetic correlation estimates between calving ease in primiparous cows and economically important traits in Nellore cattle.

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

Genetic parameters for various semen production and quality traits and indicators of male and female reproductive performance in Nellore cattle.

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

Estimates of genetic and phenotypic parameters for feeding behaviour and feed efficiency-related traits in Nelore cattle.

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

Genomic evaluation of commercial herds with different pedigree structures using the single-step genomic BLUP in Nelore cattle.

The aim of this work was to evaluate the impact of applying genomic information in pedigree uncertainty situations on genetic evaluations for growth- and cow productivity-related traits in Nelore commercial herds. Records for accumulated cow productivity (ACP) and adjusted weight at 450 days of age (W450) were used, as well as genotypes of registered and commercial herd animals, genotyped with the Clarifide Nelore 3.1 panel (~29,000 SNPs). The genetic values for commercial and registered populations were estimated using different approaches that included (ssGBLUP) or did not include genomic information (BLUP), with different pedigree structures. Different scenarios were tested, varying the proportion of young animals with unknown sires (0, 25, 50, 75, and 100%), and unknown maternal grandsires (0, 25, 50, 75, and 100%). The prediction accuracies and abilities were calculated. The estimated breeding value accuracies decreased as the proportion of unknown sires and maternal grandsires increased. The genomic estimated breeding value accuracy using the ssGBLUP was higher in scenarios with a lower proportion of known pedigree when compared to the BLUP methodology. The results obtained with the ssGBLUP showed that it is possible to obtain reliable direct and indirect predictions for young animals from commercial herds without pedigree structure.

Weighted single-step genome-wide association study and pathway analyses for feed efficiency traits in Nellore cattle.

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 < .05) such as neuropeptide signalling (GO:0007218), negative regulation of canonical Wingless/Int-1 (Wnt) signalling (GO:0090090), bitter taste receptor activity (GO:0033038), neuropeptide hormone activity (GO:0005184), bile secretion (bta04976), taste transduction (bta0742) and glucagon signalling pathway (bta04922). The identification of these genes, pathways and their respective functions should contribute to a better understanding of the genetic and physiological mechanisms regulating Nellore FE-related traits.

Accuracy of genomic breeding values and predictive ability for postweaning liveweight and age at first calving in a Nellore cattle population with missing sire information.

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.

Comparison between haplotype-based and individual snp-based genomic predictions for beef fatty acid profile in Nelore cattle.

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.

Genome Association Study for Visual Scores in Nellore Cattle Measured at Weaning.

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.

Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle.

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.

Genome-wide association between single nucleotide polymorphisms with beef fatty acid profile in Nellore cattle using the single step procedure.

BACKGROUND: Saturated fatty acids can be detrimental to human health and have received considerable attention in recent years. Several studies using taurine breeds showed the existence of genetic variability and thus the possibility of genetic improvement of the fatty acid profile in beef. This study identified the regions of the genome associated with saturated, mono- and polyunsaturated fatty acids, and n-6 to n-3 ratios in the Longissimus thoracis of Nellore finished in feedlot, using the single-step method. RESULTS: The results showed that 115 windows explain more than 1 % of the additive genetic variance for the 22 studied fatty acids. Thirty-one genomic regions that explain more than 1 % of the additive genetic variance were observed for total saturated fatty acids, C12:0, C14:0, C16:0 and C18:0. Nineteen genomic regions, distributed in sixteen different chromosomes accounted for more than 1 % of the additive genetic variance for the monounsaturated fatty acids, such as the sum of monounsaturated fatty acids, C14:1 cis-9, C18:1 trans-11, C18:1 cis-9, and C18:1 trans-9. Forty genomic regions explained more than 1 % of the additive variance for the polyunsaturated fatty acids group, which are related to the total polyunsaturated fatty acids, C20:4 n-6, C18:2 cis-9 cis12 n-6, C18:3 n-3, C18:3 n-6, C22:6 n-3 and C20:3 n-6 cis-8 cis-11 cis-14. Twenty-one genomic regions accounted for more than 1 % of the genetic variance for the group of omega-3, omega-6 and the n-6:n-3 ratio. CONCLUSIONS: The identification of such regions and the respective candidate genes, such as ELOVL5, ESSRG, PCYT1A and genes of the ABC group (ABC5, ABC6 and ABC10), should contribute to form a genetic basis of the fatty acid profile of Nellore (Bos indicus) beef, contributing to better selection of the traits associated with improving human health.

Gene expression profile of intramuscular muscle in Nellore cattle with extreme values of fatty acid.

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 α-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 ω3, 627 for ω6, and 22 for ω6/ω3 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.

Genomic prediction of breeding values for carcass traits in Nellore cattle.

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.

Selective signatures in composite MONTANA TROPICAL beef cattle reveal potential genomic regions for tropical adaptation.

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

Study of whole genome linkage disequilibrium in Nellore cattle.

BACKGROUND: Knowledge of the linkage disequilibrium (LD) between markers is important to establish the number of markers necessary for association studies and genomic selection. The objective of this study was to evaluate the extent of LD in Nellore cattle using a high density SNP panel and 795 genotyped steers. RESULTS: After data editing, 446,986 SNPs were used for the estimation of LD, comprising 2508.4 Mb of the genome. The mean distance between adjacent markers was 4.90 ± 2.89 kb. The minor allele frequency (MAF) was less than 0.20 in a considerable proportion of SNPs. The overall mean LD between marker pairs measured by r(2) and |D'| was 0.17 and 0.52, respectively. The LD (r(2)) decreased with increasing physical distance between markers from 0.34 (1 kb) to 0.11 (100 kb). In contrast to this clear decrease of LD measured by r(2), the changes in |D'| indicated a less pronounced decline of LD. Chromosomes BTA1, BTA27, BTA28 and BTA29 showed lower levels of LD at any distance between markers. Except for these four chromosomes, the level of LD (r(2)) was higher than 0.20 for markers separated by less than 20 kb. At distances < 3 kb, the level of LD was higher than 0.30. The LD (r(2)) between markers was higher when the MAF threshold was high (0.15), especially when the distance between markers was short. CONCLUSIONS: The level of LD estimated for markers separated by less than 30 kb indicates that the High Density Bovine SNP BeadChip will likely be a suitable tool for prediction of genomic breeding values in Nellore cattle.

Different prenatal supplementation strategies and its impacts on reproductive and nutrigenetics assessments of bulls in finishing phase.

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

Genome-Wide Association (GWAS) Applied to Carcass and Meat Traits of Nellore Cattle.

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

Prediction ability for growth and maternal traits using SNP arrays based on different marker densities in Nellore cattle using the ssGBLUP.

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.

Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.

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.

Relationships between temperament, meat quality, and carcass traits in Nellore cattle1.

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.