Resistance to natural tick infestation varies with age and coat and hair traits in a tropically adapted beef cattle breed.

Rhipicephalus (Boophilus) microplus causes considerable livestock production losses. Knowledge of the traits that influence tick resistance contributes to the development of breeding strategies designed to improve herd productivity. Within this context, this study evaluated the resistance of Caracu, a tropically adapted cattle breed, to R. microplus. Tick count, hair length, coat thickness, and coat color were evaluated in 202 naturally tick-infested females (cows and heifers) over a period of 18 months. Blood samples were collected from all animals during the winter season for hematological analysis. Data were analyzed using Pearson correlations, generalized linear models, and principal component analysis. Correlation coefficients of tick count with coat color, coat thickness, and hair length were estimated within each season. Hematological parameters were only included in the winter season analysis and were analyzed by the restricted maximum likelihood method using log-transformed data. No differences in blood parameters were observed between animals with and without ticks. However, tick count was negatively correlated with erythrocytes (-0.29) and hematocrit (-0.24) and positively correlated with mean corpuscular hemoglobin (0.21) and mean corpuscular hemoglobin concentration (0.25). These findings suggest that higher tick counts lead to a decrease in erythrocytes but also to an increase in the amount of hemoglobin per erythrocyte, which could reduce the damage caused by low erythrocyte levels due to tick hematophagy, delaying or preventing anemia. Although tick infestation on pasture was demonstrated by the infestation of all staff members during herd management, none of the animals exhibited high tick counts, providing evidence of resistance of Caracu animals to R. microplus. Tick infestation was influenced by age class (cows > heifers), season (spring and summer > fall and winter), coat thickness (>1.5 mm > <1.5 mm), and hair length (>6 mm > <6 mm). Three components were extracted by principal component analysis, which accounted for 69.46% of data variance. The findings of this study will contribute to the development of efficient strategies aimed at reducing economic losses due to tick infestation and could be applied in animal breeding to select for tick resistance traits, reducing chemical control strategies and consequently improving sustainable livestock production.

Weighted single-step genome-wide association study and functional enrichment analyses for gastrointestinal nematode resistance traits in Santa Ines sheep.

This study aimed to identify genomic regions, pathways, and putative candidate genes associated with resistance to gastrointestinal nematode in Santa Ines sheep. The phenotypic information comprised 5529 records from 1703 naturally infected animals. After genomic data quality control, 37,511 SNPs from 589 animals were available. The weighted single-step approach for genome-wide association study was performed to estimate the SNP effects and variances accounted by 10-SNP sliding windows. Confirming the polygenic nature of the studied traits, 20, 22, 21, and 19 genomic windows that explained more than 0.5% of the additive genetic variance were identified for fecal egg counts (FEC), Famacha© (FAM), packed cell volume (PCV), and total plasma protein (TPP), respectively. A total of 81, 122, 106, and 101 protein-coding genes were found in windows associated with FEC, FAM, PCV, and TPP, respectively. Several protein-coding genes related to the immune system and inflammatory response functions were identified within those genomic regions, such as ADCY9, ADRB2, BRAF, CADM1, CCL20, CD70, CREBBP, FNBP1, HTR4, IL16, IL22, IL26, MAPK8, NDFIP1, NLRC3, PAK5, PLCB1, PLCB4, ROCK1, TEK, TNFRSF12A, and VAV1. Functional enrichment analysis by DAVID tool also revealed many significant (P < 0.05) pathways and Gene Ontology terms that could be related to resistance to gastrointestinal nematode in Santa Ines sheep, such as chemokine signaling pathway (oas04062), cAMP signaling pathway (oas04024), cGMP-PKG signaling pathway (Oas04022), platelet activation (Oas04611), Rap1 signaling pathway (oas04015), and oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen (GO:0016705). These results contribute to improving the knowledge of the genetic architecture of resistance to gastrointestinal nematode in Santa Ines sheep.

Genome-wide association study for age at puberty in young Nelore bulls.

Selection for bulls that would reach puberty early reduces the generation interval and increases fertility and herd productivity. Despite its economic importance, there are few QTL associated with age at puberty described in the literature. In this study, a weighted single-step genome-wide association study was performed to detect genomic regions and putative candidate genes related to age at puberty in young Nelore bulls. Several protein-coding genes related to spermatogenesis functions were identified within the genomic regions that explain more than 0.5% of the additive genetic variance for age at puberty in Nelore bulls, such as ADAM11, BRCA1, CSNK2A, CREBBP, MEIOC, NDRG2, NECTIN3, PARP2, PARP9, PRSS21, RAD51C, RNASE4, SLX4, SPA17, TEX14, TIMP2 and TRIP13 gene. Enrichment analysis by DAVID also revealed several GO terms related to spermatogenesis such as DNA replication (GO:0006260), male meiosis I (GO:0007141), double-strand break repair (GO:0006302), base excision repair (GO:0006284), apoptotic process (GO:0006915), cell-cell adhesion (GO: 0098609) and focal adhesion (GO:0005925). The heritability for age at puberty shows that this trait can be improved based on traditional EBV selection. Adding genomic information to the system helps to elucidate genes and molecular mechanisms controlling the sexual precocity and could help to predict sexual precocity in Nelore bulls with greater accuracy at younger age, which would speed up the breeding programme for this breed.

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.

A genome-wide single nucleotide polymorphism and copy number variation analysis for number of piglets born alive.

BACKGROUND: In this study we integrated the CNV (copy number variation) and WssGWAS (weighted single-step approach for genome-wide association) analyses to increase the knowledge about number of piglets born alive, an economically important reproductive trait with significant impact on production efficiency of pigs. RESULTS: A total of 3892 samples were genotyped with the Porcine SNP80 BeadChip. After quality control, a total of 57,962 high-quality SNPs from 3520 Duroc pigs were retained. The PennCNV algorithm identified 46,118 CNVs, which were aggregated by overlapping in 425 CNV regions (CNVRs) ranging from 2.5 Kb to 9718.4 Kb and covering 197 Mb (~ 7.01%) of the pig autosomal genome. The WssGWAS identified 16 genomic regions explaining more than 1% of the additive genetic variance for number of piglets born alive. The overlap between CNVR and WssGWAS analyses identified common regions on SSC2 (4.2-5.2 Mb), SSC3 (3.9-4.9 Mb), SSC12 (56.6-57.6 Mb), and SSC17 (17.3-18.3 Mb). Those regions are known for harboring important causative variants for pig reproductive traits based on their crucial functions in fertilization, development of gametes and embryos. Functional analysis by the Panther software identified 13 gene ontology biological processes significantly represented in this study such as reproduction, developmental process, cellular component organization or biogenesis, and immune system process, which plays relevant roles in swine reproductive traits. CONCLUSION: Our research helps to improve the understanding of the genetic architecture of number of piglets born alive, given that the combination of GWAS and CNV analyses allows for a more efficient identification of the genomic regions and biological processes associated with this trait in Duroc pigs. Pig breeding programs could potentially benefit from a more accurate discovery of important genomic regions.

Genomic selection for meat quality traits in Nelore cattle.

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.

Genome-wide association study provides insights into genes related with horn development in Nelore beef cattle.

The causal mutation for polledness in Nelore (Bos taurus indicus) breed seems to have appeared first in Brazil in 1957. The expression of the polled trait is known to be ruled by a few groups of alleles in taurine breeds; however, the genetic basis of this trait in indicine cattle is still unclear. The aim of this study was to identify genomic regions associated with the hornless trait in a commercial Nelore population. A total of 107,294 animals had phenotypes recorded and 2,238 were genotyped/imputed for 777k SNP. The weighted single-step approach for genome-wide association study (WssGWAS) was used to estimate the SNP effects and variances accounted for by 1 Mb sliding SNP windows. A centromeric region of chromosome 1 with 3.11 Mb size (BTA1: 878,631-3,987,104 bp) was found to be associated with hornless in the studied population. A total of 28 protein-coding genes are mapped in this region, including the taurine Polled locus and the IFNAR1, IFNAR2, IFNGR2, KRTAP11-1, MIS18A, OLIG1, OLIG2, and SOD1 genes, which expression can be related to the horn formation as described in literature. The functional enrichment analysis by DAVID tool revealed cytokine-cytokine receptor interaction, JAK-STAT signaling, natural killer cell mediated cytotoxicity, and osteoclast differentiation pathways as significant (P < 0.05). In addition, a runs of homozygosity (ROH) analysis identified a ROH island in polled animals with 2.47 Mb inside the region identified by WssGWAS. Polledness in Nelore cattle is associated with one region in the genome with 3.1 Mb size in chromosome 1. Several genes are harbored in this region, and they may act together in the determination of the polled/horned phenotype. Fine mapping the locus responsible for polled trait in Nelore breed and the identification of the molecular mechanisms regulating the horn growth deserve further investigation.

Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data.

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.

Genomic prediction for beef fatty acid profile in Nellore cattle.

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.

Genomic Regions Associated with Feed Efficiency Indicator Traits in an Experimental Nellore Cattle Population.

The objective of this study was to identify genomic regions and metabolic pathways associated with dry matter intake, average daily gain, feed efficiency and residual feed intake in an experimental Nellore cattle population. The high-density SNP chip (Illumina High-Density Bovine BeadChip, 777k) was used to genotype the animals. The SNP markers effects and their variances were estimated using the single-step genome wide association method. The (co)variance components were estimated by Bayesian inference. The chromosome segments that are responsible for more than 1.0% of additive genetic variance were selected to explore and determine possible quantitative trait loci. The bovine genome Map Viewer was used to identify genes. In total, 51 genomic regions were identified for all analyzed traits. The heritability estimated for feed efficiency was low magnitude (0.13±0.06). For average daily gain, dry matter intake and residual feed intake, heritability was moderate to high (0.43±0.05; 0.47±0.05, 0.18±0.05, respectively). A total of 8, 17, 14 and 12 windows that are responsible for more than 1% of the additive genetic variance for dry matter intake, average daily gain, feed efficiency and residual feed intake, respectively, were identified. Candidate genes GOLIM4, RFX6, CACNG7, CACNG6, CAPN8, CAPN2, AKT2, GPRC6A, and GPR45 were associated with feed efficiency traits. It was expected that the response to selection would be higher for residual feed intake than for feed efficiency. Genomic regions harboring possible QTL for feed efficiency indicator traits were identified. Candidate genes identified are involved in energy use, metabolism protein, ion transport, transmembrane transport, the olfactory system, the immune system, secretion and cellular activity. The identification of these regions and their respective candidate genes should contribute to the formation of a genetic basis in Nellore cattle for feed efficiency indicator traits, and these results would support the selection for these traits.

Genome-Wide Association Study for Indicator Traits of Sexual Precocity in Nellore Cattle.

The objective of this study was to perform a genome-wide association study (GWAS) to detect chromosome regions associated with indicator traits of sexual precocity in Nellore cattle. Data from Nellore animals belonging to farms which participate in the DeltaGen® and Paint® animal breeding programs, were used. The traits used in this study were the occurrence of early pregnancy (EP) and scrotal circumference (SC). Data from 72,675 females and 83,911 males with phenotypes were used; of these, 1,770 females and 1,680 males were genotyped. The SNP effects were estimated with a single-step procedure (WssGBLUP) and the observed phenotypes were used as dependent variables. All animals with available genotypes and phenotypes, in addition to those with only phenotypic information, were used. A single-trait animal model was applied to predict breeding values and the solutions of SNP effects were obtained from these breeding values. The results of GWAS are reported as the proportion of variance explained by windows with 150 adjacent SNPs. The 10 windows that explained the highest proportion of variance were identified. The results of this study indicate the polygenic nature of EP and SC, demonstrating that the indicator traits of sexual precocity studied here are probably controlled by many genes, including some of moderate effect. The 10 windows with large effects obtained for EP are located on chromosomes 5, 6, 7, 14, 18, 21 and 27, and together explained 7.91% of the total genetic variance. For SC, these windows are located on chromosomes 4, 8, 11, 13, 14, 19, 22 and 23, explaining 6.78% of total variance. GWAS permitted to identify chromosome regions associated with EP and SC. The identification of these regions contributes to a better understanding and evaluation of these traits, and permits to indicate candidate genes for future investigation of causal mutations.

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.