Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?

Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending on the number of genotyped animals. The algorithm for proven and young (APY) can be used to create a sparse inverse of G (GAPY~-1) with close to linear memory and computing requirements. In ssGREML, the inverse of the realized relationship matrix (H-1) also includes the inverse of the pedigree relationship matrix, which can be dense with a long pedigree, but sparser with short. The main purpose of this study was to investigate whether costs of ssGREML can be reduced using APY with truncated pedigree and phenotypes. We also investigated the impact of truncation on variance components estimation when different numbers of core animals are used in APY. Simulations included 150K animals from 10 generations, with selection. Phenotypes (h2 = 0.3) were available for all animals in generations 1-9. A total of 30K animals in generations 8 and 9, and 15K validation animals in generation 10 were genotyped for 52,890 SNP. Average information REML and ssGREML with G-1 and GAPY~-1 using 1K, 5K, 9K, and 14K core animals were compared. Variance components are impacted when the core group in APY represents the number of eigenvalues explaining a small fraction of the total variation in G. The most time-consuming operation was the inversion of G, with more than 50% of the total time. Next, numerical factorization consumed nearly 30% of the total computing time. On average, a 7% decrease in the computing time for ordering was observed by removing each generation of data. APY can be successfully applied to create the inverse of the genomic relationship matrix used in ssGREML for estimating variance components. To ensure reliable variance component estimation, it is important to use a core size that corresponds to the number of largest eigenvalues explaining around 98% of total variation in G. When APY is used, pedigrees can be truncated to increase the sparsity of H and slightly reduce computing time for ordering and symbolic factorization, with no impact on the estimates.

The estimation of variance components is computationally expensive under large-scale genetic evaluations due to several inversions of the coefficient matrix. Variance components are used as parameters for estimating breeding values in mixed model equations (MME). However, resulting breeding values are not Best Linear Unbiased Predictions (BLUP) unless the variance components approach the true parameters. The increasing availability of genomic data requires the development of new methods for improving the efficiency of variance component estimations. Therefore, this study aimed to reduce the costs of single-step genomic REML (ssGREML) with the Algorithm for Proven and Young (APY) for estimating variance components with truncated pedigree and phenotypes using simulated data. In addition, we investigated the influence of truncation on variance components and genetic parameter estimates. Under APY, the size of the core group influences the similarity of breeding values and their reliability compared to the full genomic matrix. In this study, we found that to ensure reliable variance component estimation, it is required to consider a core size that corresponds to the number of largest eigenvalues explaining around 98% of the total variation in G to avoid biased parameters. In terms of costs, the use of APY slightly decreased the time for ordering and symbolic factorization with no impact on estimations.

Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires.

Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k single nucleotide polymorphism (SNP) panels. After imputation and quality control, 61,666 SNPs were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent single nucleotide polymorphisms (SNPs) across all chromosomes were 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help us to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

There was a desire to implement genomic selection for Angus cattle in Brazil since the technology has been proved to increase genetic gain in animal breeding programs. Single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously combines pedigree and genomic information, was used to estimate individuals' genomic breeding values (GEBV) or genetic merit. Genomic selection can accelerate genetic progress by increasing accuracy, especially in young animals without progeny. The accuracy of GEBV can also be improved by combing data from other countries to increase the reference population (i.e., genotyped and phenotyped animals) in small, genotyped populations. Thus, the main objective of this study was to evaluate the accuracy of GEBV for young Brazilian Angus (BA) bulls and heifers with ssGBLUP, including or not the genotypes from American Angus sires. The accuracies with ssGBLUP were higher than those from traditional BLUP (EBV calculated from pedigree), improving accuracies by, on average, 16% for young bulls and heifers. Including genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

Multiple Country and Breed Genomic Prediction of Tick Resistance in Beef Cattle.

Ticks cause substantial production losses for beef and dairy cattle. Cattle resistance to ticks is one of the most important factors affecting tick control, but largely neglected due to the challenge of phenotyping. In this study, we evaluate the pooling of tick resistance phenotyped reference populations from multi-country beef cattle breeds to assess the possibility of improving host resistance through multi-trait genomic selection. Data consisted of tick counts or scores assessing the number of female ticks at least 4.5 mm length and derived from seven populations, with breed, country, number of records and genotyped/phenotyped animals being respectively: Angus (AN), Brazil, 2,263, 921/1,156, Hereford (HH), Brazil, 6,615, 1,910/2,802, Brangus (BN), Brazil, 2,441, 851/851, Braford (BO), Brazil, 9,523, 3,062/4,095, Tropical Composite (TC), Australia, 229, 229/229, Brahman (BR), Australia, 675, 675/675, and Nguni (NG), South Africa, 490, 490/490. All populations were genotyped using medium density Illumina SNP BeadChips and imputed to a common high-density panel of 332,468 markers. The mean linkage disequilibrium (LD) between adjacent SNPs varied from 0.24 to 0.37 across populations and so was sufficient to allow genomic breeding values (GEBV) prediction. Correlations of LD phase between breeds were higher between composites and their founder breeds (0.81 to 0.95) and lower between NG and the other breeds (0.27 and 0.35). There was wide range of estimated heritability (0.05 and 0.42) and genetic correlation (-0.01 and 0.87) for tick resistance across the studied populations, with the largest genetic correlation observed between BN and BO. Predictive ability was improved under the old-young validation for three of the seven populations using a multi-trait approach compared to a single trait within-population prediction, while whole and partial data GEBV correlations increased in all cases, with relative improvements ranging from 3% for BO to 64% for TC. Moreover, the multi-trait analysis was useful to correct typical over-dispersion of the GEBV. Results from this study indicate that a joint genomic evaluation of AN, HH, BN, BO and BR can be readily implemented to improve tick resistance of these populations using selection on GEBV. For NG and TC additional phenotyping will be required to obtain accurate GEBV.

Genomic Study of Babesia bovis Infection Level and Its Association With Tick Count in Hereford and Braford Cattle.

Bovine babesiosis is a tick-borne disease caused by intraerythrocytic protozoa and leads to substantial economic losses for the livestock industry throughout the world. Babesia bovis is considered the most pathogenic species, which causes bovine babesiosis in Brazil. Genomic data could be used to evaluate the viability of improving resistance against B. bovis infection level (IB) through genomic selection, and, for that, knowledge of genetic parameters is needed. Furthermore, genome-wide association studies (GWAS) could be conducted to provide a better understanding of the genetic basis of the host response to B. bovis infection. No previous work in quantitative genetics of B. bovis infection was found. Thus, the objective of this study was to estimate the genetic correlation between IB and tick count (TC), evaluate predictive ability and applicability of genomic selection, and perform GWAS in Hereford and Braford cattle. The single-step genomic best linear unbiased prediction method was used, which allows the estimation of both breeding values and marker effects. Standard phenotyping was conducted for both traits. IB quantifications from the blood of 1,858 animals were carried using quantitative PCR assays. For TC, one to three subsequent tick counts were performed by manually counting adult female ticks on one side of each animal's body that was naturally exposed to ticks. Animals were genotyped using the Illumina BovineSNP50 panel. The posterior mean of IB heritability, estimated by the Bayesian animal model in a bivariate analysis, was low (0.10), and the estimations of genetic correlation between IB and TC were also low (0.15). The cross-validation genomic prediction accuracy for IB ranged from 0.18 to 0.35 and from 0.29 to 0.32 using k-means and random clustering, respectively, suggesting that genomic predictions could be used as a tool to improve genetics for IB, especially if a larger training population is developed. The top 10 single nucleotide polymorphisms from the GWAS explained 5.04% of total genetic variance for IB, which were located on chromosomes 1, 2, 5, 6, 12, 17, 18, 16, 24, and 26. Some candidate genes participate in immunity system pathways indicating that those genes are involved in resistance to B. bovis in cattle. Although the genetic correlation between IB and TC was weak, some candidate genes for IB were also reported in tick infestation studies, and they were also involved in biological resistance processes. This study contributes to improving genetic knowledge regarding infection by B. bovis in cattle.

Genetic differences among Moraxella bovis and Moraxella bovoculi isolates from infectious bovine keratoconjunctivitis (IBK) outbreaks in southern Brazil.

The objective of this study was to evaluate the genetic diversity of Moraxella bovis and Moraxella bovoculi bacteria isolated from infectious bovine keratoconjunctivitis (IBK) outbreaks in the state of Rio Grande do Sul, Brazil. The genetic diversity among Moraxella spp. was evaluated by RAPD-PCR, JWP1-JWOPA07-PCR, ERIC-PCR and by sequencing the 16S-23S intergenic regions. Based on the dendrogram, two genetically differentiated clades were observed; 14 isolates were classified as M. bovis and 17 as M. bovoculi. Genetic distances between the M. bovis samples ranged from 0.0379 to 0.4285, while for M. bovoculi the dissimilarities ranged from zero to 0.7297. Alternatively, based on sequencing analyses of the 16S-23S intergenic region, M. bovis and M. bovoculi isolates were grouped into the same two different clades, but it was not possible to differentiate between isolates within clades. PCR techniques were demonstrated to be a satisfactory tool to unravel the genetic variability among Moraxella spp., while sequencing of the 16S-23S intergenic region was only able to differentiate two species of the Moraxella genus. Despite sampling geographically close regions, we demonstrate considerable genetic diversity in M. bovis and M. bovoculi strains and genetically distinct M. bovis strains co-infecting the same animal.

Applying the Metafounders Approach for Genomic Evaluation in a Multibreed Beef Cattle Population.

Pedigree information is incomplete by nature and commonly not well-established because many of the genetic ties are not known a priori or can be wrong. The genomic era brought new opportunities to assess relationships between individuals. However, when pedigree and genomic information are used simultaneously, which is the case of single-step genomic BLUP (ssGBLUP), defining the genetic base is still a challenge. One alternative to overcome this challenge is to use metafounders, which are pseudo-individuals that describe the genetic relationship between the base population individuals. The purpose of this study was to evaluate the impact of metafounders on the estimation of breeding values for tick resistance under ssGBLUP for a multibreed population composed by Hereford, Braford, and Zebu animals. Three different scenarios were studied: pedigree-based model (BLUP), ssGBLUP, and ssGBLUP with metafounders (ssGBLUPm). In ssGBLUPm, a total of four different metafounders based on breed of origin (i.e., Hereford, Braford, Zebu, and unknown) were included for the animals with missing parents. The relationship coefficient between metafounders was in average 0.54 (ranging from 0.34 to 0.96) suggesting an overlap between ancestor populations. The estimates of metafounder relationships indicate that Hereford and Zebu breeds have a possible common ancestral relationship. Inbreeding coefficients calculated following the metafounder approach had less negative values, suggesting that ancestral populations were large enough and that gametes inherited from the historical population were not identical. Variance components were estimated based on ssGBLUPm, ssGBLUP, and BLUP, but the values from ssGBLUPm were scaled to provide a fair comparison with estimates from the other two models. In general, additive, residual, and phenotypic variance components in the Hereford population were smaller than in Braford across different models. The addition of genomic information increased heritability for Hereford, possibly because of improved genetic relationships. As expected, genomic models had greater predictive ability, with an additional gain for ssGBLUPm over ssGBLUP. The increase in predictive ability was greater for Herefords. Our results show the potential of using metafounders to increase accuracy of GEBV, and therefore, the rate of genetic gain in beef cattle populations with partial levels of missing pedigree information.

Tag-SNP selection using Bayesian genomewide association study for growth traits in Hereford and Braford cattle.

The aim of this study was to perform a Bayesian genomewide association study (GWAS) to identify genomic regions associated with growth traits in Hereford and Braford cattle, and to select Tag-SNPs to represent these regions in low-density panels useful for genomic predictions. In addition, we propose candidate genes through functional enrichment analysis associated with growth traits using Medical Subject Headings (MeSH). Phenotypic data from 126,290 animals and genotypes for 131 sires and 3,545 animals were used. The Tag-SNPs were selected with BayesB (π = 0.995) method to compose low-density panels. The number of Tag-single nucleotide polymorphism (SNP) ranged between 79 and 103 SNP for the growth traits at weaning and between 78 and 100 SNP for the yearling growth traits. The average proportion of variance explained by Tag-SNP with BayesA was 0.29, 0.23, 0.32 and 0.19 for birthweight (BW), weaning weight (WW205), yearling weight (YW550) and postweaning gain (PWG345), respectively. For Tag-SNP with BayesA method accuracy values ranged from 0.13 to 0.30 for k-means and from 0.30 to 0.65 for random clustering of animals to compose reference and validation groups. Although genomic prediction accuracies were higher with the full marker panel, predictions with low-density panels retained on average 76% of the accuracy obtained with BayesB with full markers for growth traits. The MeSH analysis was able to translate genomic information providing biological meanings of more specific gene products related to the growth traits. The proposed Tag-SNP panels may be useful for future fine mapping studies and for lower-cost commercial genomic prediction applications.

Economic selection index in small rural dairy farms.

This study aimed to calculate economic values (EVs) and economic selection indices for milk production systems in small rural properties. The traits 305-d milk yield in kg (MY), fat (FP) and protein (PP) percentage, daily fat (FY) and protein (PY) yield, cow live weight in kg (LW), calving interval (CI), and logarithm of daily somatic cell count (SCC) in milk were considered the goals and selection criteria. The production systems were identified from 29 commercial properties based on the inventory of revenues and costs and of zootechnical field data. Later, bioeconomic models were developed to calculate the productive performance, revenues, and costs concerning milk production to estimate EVs, which were calculated as the difference in annual profit with dairy production resulting from a change in one unit of the trait while keeping the others constant and dividing the value by the number of cows. After the EVs were known, ten economic selection indices were estimated for each system so they could be compared by modifying the selection criteria and calculating the relative importance of each selection criteria, the accuracy of the economic selection index, and response expected to the selection in USD, among other parameters. One of the systems detected was called less intensive (LS) and was characterized by having ten cows in lactation that produced 13·5 l/d and consumed 1·8 kg of concentrate/d. The second system detected was called more intensive (IS) and had 22 cows in lactation that produced 17·5 l/d and consumed 3·4 kg of concentrate/d. Monthly profits per cows in lactation of USD 2·60 and USD 68·77 were recorded for LS and IS, respectively. The EVs of the traits MY, FP, and PP were all positive, while for the other traits they were all negative in all situations. The best economic selection indices were those featuring selection criteria MY, LW, and CI, while the trait LW had the greatest importance in both systems. These results indicate that animal frame must be controlled in order to maximize the system's profit.

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle.

The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling, and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variance components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into 4 or 5 groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyses using the historical pedigree and phenotypes contributed additional information to calculate the GEBV, and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in accuracy obtained with genomic blending methods, mainly ssGBLUP in bivariate analyses, indicate that genomic predictions should be used as a tool to improve genetic gains in relation to the traditional PBLUP selection.

The use of infrared images to detect ticks in cattle and proposal of an algorithm for quantifying the infestation.

This paper presents a study on the use of low resolution infrared images to detect ticks in cattle. Emphasis is given to the main factors that influence the quality of the captured images, as well as to the actions that can increase the amount of information conveyed by these images. In addition, a new automatic method for analyzing the images and counting the ticks is introduced. The proposed algorithm relies only on color transformations and simple mathematical morphology operations, thus being easy to implement and computationally light. Tests were carried out using a large database containing images of the neck and hind end of the animals. It was observed that the proposed algorithm is very effective in detecting ticks visible in the images, even if the contrast with the background is not high. On the other hand, due to both intrinsic and extrinsic factors, the thermographic images used in this study did not always succeed in creating enough contrast between ticks and cattle's hair coat. Although these problems can be mitigated by following some directives, currently only rough estimates for tick counts can be achieved using infrared images with low spatial resolution.

Genotype × environment interactions in reproductive traits of Nellore cattle in northeastern Brazil.

We evaluate genotype × environment (G × E) interactions for age at first calving (AFC) and calving interval (CI) of Nellore cattle in northeastern Brazil using four hierarchical reaction norm models (HRNMs). The best-fit model for the traits was the one step heteroscedastic hierarchical reaction norm model. Heritability was close to zero in the worst environments and increased as the environments improved (from 0.06 to 0.12 for AFC and from 0.01 to 0.03 for CI). The correlations between the intercept and the slope of the reaction norms for CI and AFC were from medium to high magnitude (0.75 ± 0.10 and 0.90 ± 0.04, respectively), indicating that animals with higher average breeding values had the greatest responses to the improvement of environmental conditions. The variation in heritability indicates different response to selection according to the environment in which the animals of the population are evaluated. The G × E was evident in bulls with more female offspring. In conclusion, our data demonstrate that selection for AFC in medium- and high-level environments leads to higher genetic gains.

Multi-trait linear reaction norm model to describe the pattern of phenotypic expression of some economic traits in beef cattle across a range of environments.

The multi-trait reaction norm (MTRN) model was extended to beef cattle reared under tropical conditions with the following objectives: to compare multi-trait (MT) and MTRN models regarding the genetic parameters obtained; and to characterize G × E, the pattern of phenotypic expression, and the environmental sensitivity of animals for postweaning weight gain (PWG), scrotal circumference (SC), and annual average productivity of the cow (PRODAM). There was divergence in the estimates between the MT and MTRN models when the posterior probability intervals of additive genetic variances and heritability coefficients of PWG and PRODAM were analyzed. The MTRN model indicated an increase in heritability for PWG and PRODAM with improvement of the environmental conditions. For SC, heritability was practically the same, irrespective of the environmental conditions. The genetic correlations between the traits studied were low but varied over environments by the MTRN model. Considering genetic correlations obtained by the MTRN model for the same trait, lower estimates were obtained between extreme favorable and unfavorable environments. This finding suggest re-ranking of breeding values in different environments mainly for PWG and PRODAM. Thus, G × E is more important for PWG and PRODAM than for SC and should be included in the genetic evaluation of these traits. The traits PWG and PRODAM can be considered plastic traits, whereas SC is poorly plastic. The genetic trends in individual animal slopes indicate that the population is moving towards greater plasticity. This could be a matter of concern for breeders since greater plasticity seems to limit heritability and, consequently, the responses to selection.

Genome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach.

BACKGROUND: Meat quality involves many traits, such as marbling, tenderness, juiciness, and backfat thickness, all of which require attention from livestock producers. Backfat thickness improvement by means of traditional selection techniques in Canchim beef cattle has been challenging due to its low heritability, and it is measured late in an animal's life. Therefore, the implementation of new methodologies for identification of single nucleotide polymorphisms (SNPs) linked to backfat thickness are an important strategy for genetic improvement of carcass and meat quality. RESULTS: The set of SNPs identified by the random forest approach explained as much as 50% of the deregressed estimated breeding value (dEBV) variance associated with backfat thickness, and a small set of 5 SNPs were able to explain 34% of the dEBV for backfat thickness. Several quantitative trait loci (QTL) for fat-related traits were found in the surrounding areas of the SNPs, as well as many genes with roles in lipid metabolism. CONCLUSIONS: These results provided a better understanding of the backfat deposition and regulation pathways, and can be considered a starting point for future implementation of a genomic selection program for backfat thickness in Canchim beef cattle.