Identification of genetic markers associated with hyperketonemia patterns in early lactation Holstein cows.

Ketosis, evidenced by hyperketonemia with elevated blood ß-hydroxybutyrate (BHB) levels, is a significant metabolic disorder of dairy cattle, typically diagnosed within the first 6 weeks post-calving when high energy levels are essential to milk production. Our study aimed to identify genetic markers linked to hyperketonemia (HYK) patterns in Holstein cows during early lactation and compare these to HYK-negative cows. We screened 964 cows for HYK using a threshold of BHB ≥1.2 mmol/L during the first 2 weeks postpartum (screening period, SP). Cows that tested negative initially were retested the following week. Cows were deemed HYK-negative (CON group) if BHB levels were below 1.2 mmol/L in both tests, while those with BHB levels exceeding this threshold at any test were treated and classified as HYK-positive (HYK+). Post-treatment, HYK+ cows were monitored for two-week follow-up period (FP) and classified based on their recovery: cured (CUR; consistently low BHB), recurrent (REC; fluctuating BHB levels), severe (SEV; high initial BHB that decreased), or chronic (CHR; persistently high BHB). Using 489 cows that were genotyped, a GWAS was conducted using GCTA software, revealing significant associations of several SNPs across different HYK patterns when compared to the CON group. These SNPs were primarily linked to genes affecting milk traits and were enriched in biological pathways relevant to protein glycosylation, inflammatory response, glucose homeostasis, and fatty acid synthesis. Our findings highlight genomic regions, potential candidate genes, and biological pathways related to ketosis, underscoring potential targets for improving health management in dairy cattle. These insights could lead to better strategies for managing ketosis through genetic selection, ultimately enhancing dairy cattle welfare and productivity. Further research with a larger number of cows is recommended to validate these findings and help confirm the implicated SNPs and genes.

Genomic integration to identify molecular biomarkers associated with indicator traits of gastrointestinal nematode resistance in sheep.

This study aimed to integrate GWAS and structural variants to propose possible molecular biomarkers related to gastrointestinal nematode resistance traits in Santa Inês sheep. The phenotypic records FAMACHA, haematocrit, white blood cell count, red blood cell count, haemoglobin, platelets and egg counts per gram of faeces were collected from 700 naturally infected animals, belonging to four Brazilian flocks. A total of 576 animals were genotyped using the Ovine SNP12k BeadChip and were imputed using a reference population with Ovine SNP50 BeadChip. The GWAS approaches were based on SNPs, haplotypes, CNVs and ROH. The overlapping between the significant genomic regions detected from all approaches was investigated, and the results were integrated using a network analysis. Genes related to the immune system were found, such as ABCB1, IL6, WNT5A and IRF5. Genomic regions containing candidate genes and metabolic pathways involved in immune responses, inflammatory processes and immune cells affecting parasite resistance traits were identified. The genomic regions, biological processes and candidate genes uncovered could lead to biomarkers for selecting more resilient sheep and improving herd welfare and productivity. The results obtained are the start point to identify molecular biomarkers related to indicator traits of gastrointestinal nematode resistance in Santa Inês sheep.

Different selection practices affect the environmental sensitivity of beef cattle.

The objective of the present study was to evaluate the effects of different selection practices on the environmental sensitivity of reproductive and growth traits in males and females of three Nellore selection lines [control (NeC), selection (NeS), and traditional (NeT) lines]. Moreover, genetic trends for the intercept and slope were estimated for each line, and the possible reranking of sires was examined. A total of 8,757 records of selection weight (SW), 3,331 records of scrotal circumference (SC), and 2,311 records of days to first calving (DFC) from Nellore cattle born between 1981 and 2017 were analyzed. (Co)variance components and genetic parameters of all traits were estimated using a reaction norm model with Gibbs sampler. In all cattle lines, the mean heritability of the studied traits ranged from 0.39 to 0.75 for SW in both males and females, from 0.46 to 0.68 for SC, and from 0.06 to 0.57 for DFC along with the environmental descriptor. In all cattle lines, the genetic correlation coefficients between the intercept and slope ranged from 0.03 to 0.81 for SW, from -0.14 to 0.39 for SC, and from -0.87 to -0.42 for DFC. Genetic trends for the slope and proportion of plastic genotypes indicated that the NeS line was more responsive to environmental changes, whereas the NeC and NeT lines tended to respond more modestly. Reranking of sires was observed for all traits, specifically in the NeC and NeT lines, because of the weak correlation between the opposite extreme environments. In the NeS line, reranking of sires was observed for DFC alone. Our results indicate that the effects of genotype-environment interaction are important and should be considered in genetic evaluations of Nellore cattle. Moreover, different selection practices affected the environmental sensitivity of the Nellore selection lines tested in this study.

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.

Impact of including information from bulls and their daughters in the training population of multiple-step genomic evaluations in dairy cattle: A simulation study.

The objective of this study was to investigate the impact of accounting for parent average (PA) and genotyped daughters' average (GDA) on the estimation of deregressed estimated breeding values (dEBVs) used as pseudo-phenotypes in multiple-step genomic evaluations. Genomic estimated breeding values (GEBVs) were predicted, in eight different simulated scenarios, using dEBVs calculated based on four methods. These methods included PA and GDA in the dEBV (VR) or only GDA (VRpa) and excluded both PA and GDA from the dEBV with either all information or only information from PA and GDA (JA and NEW, respectively). In general, VR and NEW showed the lowest and highest GEBV reliabilities across scenarios, respectively. Among all deregression methods, VRpa and NEW provided the most consistent bias estimates across the majority of scenarios, and they significantly yielded the least biased GEBVs. Our results indicate that removing PA and GDA information from dEBVs used in multiple-step genomic evaluations can increase the reliability of GEBVs, when both bulls and their daughters are included in the training population.

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.

Inbreeding and runs of homozygosity before and after genomic selection in North American Holstein cattle.

BACKGROUND: While autozygosity as a consequence of selection is well understood, there is limited information on the ability of different methods to measure true inbreeding. In the present study, a gene dropping simulation was performed and inbreeding estimates based on runs of homozygosity (ROH), pedigree, and the genomic relationship matrix were compared to true inbreeding. Inbreeding based on ROH was estimated using SNP1101, PLINK, and BCFtools software with different threshold parameters. The effects of different selection methods on ROH patterns were also compared. Furthermore, inbreeding coefficients were estimated in a sample of genotyped North American Holstein animals born from 1990 to 2016 using 50 k chip data and ROH patterns were assessed before and after genomic selection. RESULTS: Using ROH with a minimum window size of 20 to 50 using SNP1101 provided the closest estimates to true inbreeding in simulation study. Pedigree inbreeding tended to underestimate true inbreeding, and results for genomic inbreeding varied depending on assumptions about base allele frequencies. Using an ROH approach also made it possible to assess the effect of population structure and selection on distribution of runs of autozygosity across the genome. In the simulation, the longest individual ROH and the largest average length of ROH were observed when selection was based on best linear unbiased prediction (BLUP), whereas genomic selection showed the largest number of small ROH compared to BLUP estimated breeding values (BLUP-EBV). In North American Holsteins, the average number of ROH segments of 1 Mb or more per individual increased from 57 in 1990 to 82 in 2016. The rate of increase in the last 5 years was almost double that of previous 5 year periods. Genomic selection results in less autozygosity per generation, but more per year given the reduced generation interval. CONCLUSIONS: This study shows that existing software based on the measurement of ROH can accurately identify autozygosity across the genome, provided appropriate threshold parameters are used. Our results show how different selection strategies affect the distribution of ROH, and how the distribution of ROH has changed in the North American dairy cattle population over the last 25 years.

Candidate genes for male and female reproductive traits in Canchim beef cattle.

BACKGROUND: Beef cattle breeding programs in Brazil have placed greater emphasis on the genomic study of reproductive traits of males and females due to their economic importance. In this study, genome-wide associations were assessed for scrotal circumference at 210 d of age, scrotal circumference at 420 d of age, age at first calving, and age at second calving, in Canchim beef cattle. Data quality control was conducted resulting in 672,778 SNPs and 392 animals. RESULTS: Associated SNPs were observed for scrotal circumference at 420 d of age (435 SNPs), followed by scrotal circumference at 210 d of age (12 SNPs), age at first calving (six SNPs), and age at second calving (four SNPs). We investigated whether significant SNPs were within genic or surrounding regions. Biological processes of genes were associated with immune system, multicellular organismal process, response to stimulus, apoptotic process, cellular component organization or biogenesis, biological adhesion, and reproduction. CONCLUSIONS: Few associations were observed for scrotal circumference at 210 d of age, age at first calving, and age at second calving, reinforcing their polygenic inheritance and the complexity of understanding the genetic architecture of reproductive traits. Finding many associations for scrotal circumference at 420 d of age in various regions of the Canchim genome also reveals the difficulty of targeting specific candidate genes that could act on fertility; nonetheless, the high linkage disequilibrium between loci herein estimated could aid to overcome this issue. Therefore, all relevant information about genomic regions influencing reproductive traits may contribute to target candidate genes for further investigation of causal mutations and aid in future genomic studies in Canchim cattle to improve the breeding program.

Study on the introgression of beef breeds in Canchim cattle using single nucleotide polymorphism markers.

The aim of this study was to evaluate the level of introgression of breeds in the Canchim (CA: 62.5% Charolais-37.5% Zebu) and MA genetic group (MA: 65.6% Charolais-34.4% Zebu) cattle using genomic information on Charolais (CH), Nelore (NE), and Indubrasil (IB) breeds. The number of animals used was 395 (CA and MA), 763 (NE), 338 (CH), and 37 (IB). The Bovine50SNP BeadChip from Illumina panel was used to estimate the levels of introgression of breeds considering the Maximum likelihood, Bayesian, and Single Regression method. After genotype quality control, 32,308 SNPs were considered in the analysis. Furthermore, three thresholds to prune out SNPs in linkage disequilibrium higher than 0.10, 0.05, and 0.01 were considered, resulting in 15,286, 7,652, and 1,582 SNPs, respectively. For k = 2, the proportion of taurine and indicine varied from the expected proportion based on pedigree for all methods studied. For k = 3, the Regression method was able to differentiate the animals in three main clusters assigned to each purebred breed, showing more reasonable according to its biological viewpoint. Analyzing the data considering k = 2 seems to be more appropriate for Canchim-MA animals due to its biological interpretation. The usage of 32,308 SNPs in the analyses resulted in similar findings between the estimated and expected breed proportions. Using the Regression approach, a contribution of Indubrasil was observed in Canchim-MA when k = 3 was considered. Genetic parameter estimation could account for this breed composition information as a source of variation in order to improve the accuracy of genetic models. Our findings may help assemble appropriate reference populations for genomic prediction for Canchim-MA in order to improve prediction accuracy. Using the information on the level of introgression in each individual could also be useful in breeding or crossing design to improve individual heterosis in crossbred cattle.

Association of Apolipoprotein B and Adiponectin Receptor 1 Genes with Carcass, Bone Integrity and Performance Traits in a Paternal Broiler Line.

Apolipoprotein B (APOB) and Adiponectin Receptor 1 (ADIPOR1) are related to the regulation of feed intake, fat metabolism and protein deposition and are candidate genes for genomic studies in birds. In this study, associations of two single nucleotide polymorphisms (SNPs) g.102A>T (APOB) and g.729C>T (ADIPOR1) with carcass, bone integrity and performance traits in broilers were investigated. Genotyping was performed on a paternal line of 1,454 broilers. The SNP detection was carried out by PCR-RFLP technique using the restriction enzymes HhaI for the SNP g.729C>T and MslI for the SNP g.102A>T. The association analyses of the two SNPs with 85 traits were performed using the restricted maximum likelihood (REML) and Generalized Quasi-Likelihood Score (GQLS) methods. For REML the model included the random additive genetic effect of animal and fixed effects of sex, hatch and SNP genotypes. In the GQLS method, a logistic regression was used to associate the genotypes with phenotypes adjusted for fixed effects of sex and hatch. The SNP g.729C>T in the ADIPOR1 gene was associated with thickness of the femur and breast skin yield. Thus, the ADIPOR1 gene seems implicated in the metabolism and/or fat deposition and bone integrity in broilers.

Effect of IGF1, GH, and PIT1 markers on the genetic parameters of growth and reproduction traits in Canchim cattle.

The availability of dense genomic information has increased genome-wide association studies for the bovine species; however research to assess the effect of single genes on production traits is still important to elucidate the genes functions. On this study the association of IGF1, GH, and PIT1 markers with growth and reproductive traits (birth weight, weaning weight, weight at 12 and 18 months of age, preweaning average daily weight gain, age and weight at first calving, and scrotal circumference at 12 and 18 months of age) were assessed by means of the variance component approach. The phenotypes were adjusted and then analyzed under two animal models, one which considered the polygenic and genotype (IGF1, GH or PIT1 markers) effects (Model 1), and the other which considers only the polygenic effect (Model 2). When the likelihood ratio test and the Bonferroni correction was applied at 5 % significance level, the genetic markers for the IGF1, GH, and PIT1 genes did not influence significantly the traits (p > 0.002). However, evidence of association of IGF1 with birth weight (p = 0.06) and GH with weight at first calving (p = 0.03) and with weight at 12 months of age (p = 0.08) was observed. In conclusion we could not confirm the associations between IGF1, GH, and PIT1 and growth traits that were previously reported in Canchim cattle, and no association was observed between these genes and reproductive traits. Future studies involving functional markers of IGF1, GH and PIT1 genes may help to clarify the role of these genes in growth and reproductive processes.

Linkage disequilibrium and haplotype block structure in a composite beef cattle breed.

BACKGROUND: The development of linkage disequilibrium (LD) maps and the characterization of haplotype block structure at the population level are useful parameters for guiding genome wide association (GWA) studies, and for understanding the nature of non-linear association between phenotypes and genes. The elucidation of haplotype block structure can reduce the information of several single nucleotide polymorphisms (SNP) into the information of a haplotype block, reducing the number of SNPs in a coherent way for consideration in GWA and genomic selection studies. RESULTS: The maximum average LD, measured by r2 varied between 0.33 to 0.40 at a distance of < 2.5 kb, and the minimum average values of r2 varied between 0.05 to 0.07 at distances ranging from 400 to 500 kb, clearly showing that the average r2 reduced with the increase in SNP pair distances. The persistence of LD phase showed higher values at shorter genomic distances, decreasing with the increase in physical distance, varying from 0.96 at a distance of < 2.5 kb to 0.66 at a distance from 400 to 500 kb. A total of 78% of all SNPs were clustered into haplotype blocks, covering 1,57 Mb of the total autosomal genome size. CONCLUSIONS: This study presented the first high density linkage disequilibrium map and haplotype block structure for a composite beef cattle population, and indicates that the high density SNP panel over 700 k can be used for genomic selection implementation and GWA studies for Canchim beef cattle.

Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection.

The aim of this study was to compare iterative and direct solvers for estimation of marker effects in genomic selection. One iterative and two direct methods were used: Gauss-Seidel with Residual Update, Cholesky Decomposition and Gentleman-Givens rotations. For resembling different scenarios with respect to number of markers and of genotyped animals, a simulated data set divided into 25 subsets was used. Number of markers ranged from 1,200 to 5,925 and number of animals ranged from 1,200 to 5,865. Methods were also applied to real data comprising 3081 individuals genotyped for 45181 SNPs. Results from simulated data showed that the iterative solver was substantially faster than direct methods for larger numbers of markers. Use of a direct solver may allow for computing (co)variances of SNP effects. When applied to real data, performance of the iterative method varied substantially, depending on the level of ill-conditioning of the coefficient matrix. From results with real data, Gentleman-Givens rotations would be the method of choice in this particular application as it provided an exact solution within a fairly reasonable time frame (less than two hours). It would indeed be the preferred method whenever computer resources allow its use.

Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection

The aim of this study was to compare iterative and direct solvers for estimation of marker effects in genomic selection. One iterative and two direct methods were used: Gauss-Seidel with Residual Update, Cholesky Decomposition and Gentleman-Givens rotations. For resembling different scenarios with respect to number of markers and of genotyped animals, a simulated data set divided into 25 subsets was used. Number of markers ranged from 1,200 to 5,925 and number of animals ranged from 1,200 to 5,865. Methods were also applied to real data comprising 3081 individuals genotyped for 45181 SNPs. Results from simulated data showed that the iterative solver was substantially faster than direct methods for larger numbers of markers. Use of a direct solver may allow for computing (co)variances of SNP effects. When applied to real data, performance of the iterative method varied substantially, depending on the level of ill-conditioning of the coefficient matrix. From results with real data, Gentleman-Givens rotations would be the method of choice in this particular application as it provided an exact solution within a fairly reasonable time frame (less than two hours). It would indeed be the preferred method whenever computer resources allow its use.