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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 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 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 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, 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.Lay SummaryThe 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. 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.Teaser TextEstimation of variance components is becoming computationally challenging due to the increasing size of genomic information. We investigated the impacts of using the algorithm for proven and young (APY) in genetic evaluations. The use of APY has no impact on variance components and genetic parameters estimation.

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.

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. Keywords: unknown parent group, founder, genomic selection, missing pedigree, breeding values

O objetivo deste trabalho foi avaliar os critérios para formação de grupos de contemporâneos (GCs) na avaliação genética de peso ao desmame em bovinos Nelore. Foram utilizados 713.474 registros de 3.066 rebanhos localizados no Centro-Oeste e no Norte do Brasil. Os dados foram provenientes do Serviço de Registro Genealógico das Raças Zebuínas da Associação Brasileira dos Criadores de Zebu. As estruturas de dados foram definidas com base no número de desvios-padrão (DPs) para remoção de outliers (±2,0, ±2,5, ±3,0 e ±3,5) e no número mínimo de animais por GC (3, 7 e 15). A avaliação genética foi realizada por meio de modelo animal via inferência bayesiana. As estruturas de dados com ±3,5 DPs e GC com mínimo de 15 animais apresentaram a maior variância genética aditiva (82,65±2,93), e aquelas com ±2,0 DPs e GC com mínimo de 3 animais, a menor (60,23±1,96). A formação correta de GCs resulta em arquivos de dados mais consistentes, o que permite obter estimativas mais confiáveis dos parâmetros genéticos. As melhores respostas à seleção são obtidas ao se adotar os seguintes critérios para remoção de outliers: 2,5, 3,0 e 3,5 desvios-padrão e mínimo de 15 animais por grupo de contemporâneos. The objective of this work was to evaluate the criteria for the formation of contemporary groups (CGs) in the genetic evaluation of body weight at weaning in Nellore cattle. A total of 713,474 records from 3,066 herds located in Midwestern and Northern Brazil were used. Data were obtained from the genealogical registry of zebu breeds of the Brazilian association of zebu breeders. Data structures were defined based on the number of standard deviations (SDs) for outlier removal (±2.0, ±2.5, ±3.0, and ±3.5) and on the minimal number of animals per CG (3, 7, and 15). Genetic evaluation was performed with an animal model using Bayesian inference. Data structures with ±3.5 SDs and CG with at least 15 animals presented the highest additive genetic variance (82.65±2.93), and those with ±2.0 SDs and CG with at least 3 animals showed the lowest one (60.23±1.96). The proper formation of CGs results in better-quality data archives, allowing to obtain more trustable estimates for the genetic parameters. Better selection responses are obtained when the following criteria are adopted for the removal of outliers: 2.5, 3.0, and 3.5 standard deviations and a minimum of 15 animals per contemporary group.

We proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using Bayesian inference. A total of 3,856 MY, FP, and PP test-day records, measured between 2000 and 2014, from 535 first lactations of Saanen and Alpine goats, including their cross, were used in this study. The initial analyses were performed using the following single-trait random regression models (STRRM): third- and fifth-order Legendre polynomials (Leg3 and Leg5), linear B-splines with 3 and 5 knots, the Ali and Schaeffer function (Ali), and Wilmink function. Heterogeneity of residual variances was modeled considering 3 classes. After the selection of the best STRRM to describe each trait on the basis of the deviance information criterion (DIC) and posterior model probabilities (PMP), the functions were combined to compose the MTRRM. All combined MTRRM presented lower DIC values and higher PMP, showing the superiority of these models when compared to other MTRRM based only on the same function assumed for all traits. Among the combined MTRRM, those considering Ali to describe MY and PP and Leg5 to describe FP (Ali_Leg5_Ali model) presented the best fit. From the Ali_Leg5_Ali model, heritability estimates over time for MY, FP. and PP ranged from 0.25 to 0.54, 0.27 to 0.48, and 0.35 to 0.51, respectively. Genetic correlation between MY and FP, MY and PP, and FP and PP ranged from -0.58 to 0.03, -0.46 to 0.12, and 0.37 to 0.64, respectively. We concluded that combining different functions under a MTRRM approach can be a plausible alternative for joint genetic evaluation of milk yield and milk constituents in goats.

Our goal was to define a breeding objective for Brangus beef cattle in Brazil. Bioeconomic models were produced and used to estimate economic values (EVs). The scenarios simulated were typical full-cycle beef production systems that are used in tropical and subtropical regions. The breeding objective contained pregnancy rate (PR), warm carcass weight (WCW), mature cow weight (MCW), number of nematode eggs per gram of faeces (EPG) and tick count (TICK). Two models were used in series to estimate the EV. A deterministic model was used to simulate effects of PR, WCW and MCW on profitability with a constant parasite load. Subsequently, stochastic models were used to estimate economic values for TICK and EPG as consequences of their environmental effects on weight gains, mortality and health costs. The EV of PR, WCW, MCW, EPG and TICK, was US$1.59, US$2.11, -US$0.24, -US$5.35 and -US$20.88, respectively. Results indicate positive emphasis should be placed on PR (12.49%) and WCW (65.07%) with negative emphasis on MCW (13.92%), EPG (2.77%) and TICK (5.75%). In comparison with the indexes usually used, these results suggest a reformulation in the selection indexes of the beef production system in tropical and subtropical regions in order to obtain greater profitability.

The objectives of this research were to estimate genetic parameters using a threshold animal model by traditional (pedigree and phenotype) and single-step genomic BLUP methodologies, and assess breeding value accuracy gains with the inclusion of genomic information for eye pigmentation (EP), hair coat at weaning (HW) and yearling (HY) and breed standard (BS) in Hereford and Braford cattle. The relationship of these traits with tick resistance was also evaluated. The data used in this study were obtained from Conexão Delta G breeding program and belong to animals born between 1991 and 2014, totaling 169,839 individuals in the pedigree data, 73,615 phenotypes for EP, 81,043 for HW, 41,390 for HY, 28,186 for BS and 16,621 for tick count (TC). The genotypic data, after quality control, retained 41,011 SNP markers and 3,954 samples, including 2841 Braford and 909 Hereford animals with phenotypic records for at least one of the considered traits. Estimated heritabilities through traditional and genomic methods ranged from high to medium magnitude, being, respectively 0.46 ± 0.02 and 0.46 ± 0.02 for EP, 0.44 ± 0.03 and 0.44 ± 0.03 for HW, 0.42 ± 0.02 and 0.41 ± 0.02 for HY and 0.33 ± 0.02 and 0.32 ± 0.02 for BS. Genetic correlations among all traits were of low magnitude (-0.23

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 aim of this study was to evaluate different methods used in genomic selection, and to verify those that select a higher proportion of individuals with superior genotypes. Thus, F2 populations of different sizes were simulated (100, 200, 500, and 1000 individuals) with 10 replications each. These consisted of 10 linkage groups (LG) of 100 cM each, containing 100 equally spaced markers per linkage group, of which 200 controlled the characteristics, defined as the 20 initials of each LG. Genetic and phenotypic values were simulated assuming binomial distribution of effects for each LG, and the absence of dominance. For phenotypic values, heritabilities of 20, 50, and 80% were considered. To compare methodologies, the analysis processing time, coefficient of coincidence (selection of 5, 10, and 20% of superior individuals), and Spearman correlation between true genetic values, and the genomic values predicted by each methodology were determined. Considering the processing time, the three methodologies were statistically different, rrBLUP was the fastest, and Bayesian LASSO was the slowest. Spearman correlation revealed that the rrBLUP and GBLUP methodologies were equivalent, and Bayesian LASSO provided the lowest correlation values. Similar results were obtained in coincidence variables among the individuals selected, in which Bayesian LASSO differed statistically and presented a lower value than the other methodologies. Therefore, for the scenarios evaluated, rrBLUP is the best methodology for the selection of genetically superior individuals.

Background Cattle resistance to ticks is known to be under genetic control with a complex biological mechanism within and among breeds. Our aim was to identify genomic segments and tag single nucleotide polymorphisms (SNPs) associated with tick-resistance in Hereford and Braford cattle. The predictive performance of a very low-density tag SNP panel was estimated and compared with results obtained with a 50 K SNP dataset. Results BayesB (π = 0.99) was initially applied in a genome-wide association study (GWAS) for this complex trait by using deregressed estimated breeding values for tick counts and 41,045 SNP genotypes from 3455 animals raised in southern Brazil. To estimate the combined effect of a genomic region that is potentially associated with quantitative trait loci (QTL), 2519 non-overlapping 1-Mb windows that varied in SNP number were defined, with the top 48 windows including 914 SNPs and explaining more than 20% of the estimated genetic variance for tick resistance. Subsequently, the most informative SNPs were selected based on Bayesian parameters (model frequency and t-like statistics), linkage disequilibrium and minor allele frequency to propose a very low-density 58-SNP panel. Some of these tag SNPs mapped close to or within genes and pseudogenes that are functionally related to tick resistance. Prediction ability of this SNP panel was investigated by cross-validation using K-means and random clustering and a BayesA model to predict direct genomic values. Accuracies from these cross-validations were 0.27 ± 0.09 and 0.30 ± 0.09 for the K-means and random clustering groups, respectively, compared to respective values of 0.37 ± 0.08 and 0.43 ± 0.08 when using all 41,045 SNPs and BayesB with π = 0.99, or of 0.28 ± 0.07 and 0.40 ± 0.08 with π = 0.999. Conclusions Bayesian GWAS model parameters can be used to select tag SNPs for a very low-density panel, which will include SNPs that are potentially linked to functional genes. It can be useful for cost-effective genomic selection tools, when one or a few key complex traits are of interest. Electronic supplementary material The online version of this article (doi:10.1186/s12711-017-0325-2) contains supplementary material, which is available to authorized users.

Summary The selection of genetically superior individuals is conditional upon accurate breeding value predictions which, in turn, are highly depend on how precisely relationship is represented by pedigree. For that purpose, the numerator relationship matrix is essential as a priori information in mixed model equations. The presence of pedigree errors and/or the lack of relationship information affect the genetic gain because it reduces the correlation between the true and estimated breeding values. Thus, this study aimed to evaluate the effects of correcting the pedigree relationships using single-nucleotide polymorphism (SNP) markers on genetic evaluation accuracies for resistance of beef cattle to ticks. Tick count data from Hereford and Braford cattle breeds were used as phenotype. Genotyping was carried out using a high-density panel (BovineHD - Illumina® bead chip with 777 962 SNPs) for sires and the Illumina BovineSNP50 panel (54 609 SNPs) for their progenies. The relationship between the parents and progenies of genotyped animals was evaluated, and mismatches were based on the Mendelian conflicts counts. Variance components and genetic parameters estimates were obtained using a Bayesian approach via Gibbs sampling, and the breeding values were predicted assuming a repeatability model. A total of 460 corrections in relationship definitions were made (Table 1) corresponding to 1018 (9.5%) tick count records. Among these changes, 97.17% (447) were related to the sire's information, and 2.8% (13) were related to the dam's information. We observed 27.2% (236/868) of Mendelian conflicts for sire–progeny genotyped pairs and 14.3% (13/91) for dam–progeny genotyped pairs. We performed 2174 new definitions of half-siblings according to the correlation coefficient between the coancestry and molecular coancestry matrices. It was observed that higher-quality genetic relationships did not result in significant differences of variance components estimates; however, they resulted in more accurate breeding values predictions. Using SNPs to assess conflicts between parents and progenies increases certainty in relationships and consequently the accuracy of breeding value predictions of candidate animals for selection. Thus, higher genetic gains are expected when compared to the traditional non-corrected relationship matrix.

The biggest challenge for jatropha breeding is to identify superior genotypes that present high seed yield and seed oil content with reduced toxicity levels. Therefore, the objective of this study was to estimate genetic parameters for three important traits (weight of 100 seed, oil seed content, and phorbol ester concentration), and to select superior genotypes to be used as progenitors in jatropha breeding. Additionally, the genotypic values and the genetic parameters estimated under the Bayesian multi-trait approach were used to evaluate different selection indices scenarios of 179 half-sib families. Three different scenarios and economic weights were considered. It was possible to simultaneously reduce toxicity and increase seed oil content and weight of 100 seed by using index selection based on genotypic value estimated by the Bayesian multi-trait approach. Indeed, we identified two families that present these characteristics by evaluating genetic diversity using the Ward clustering method, which suggested nine homogenous clusters. Future researches must integrate the Bayesian multi-trait methods with realized relationship matrix, aiming to build accurate selection indices models.

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. Keywords: unknown parent group, founder, genomic selection, missing pedigree, breeding values Made available in DSpace on 2020-12-08T09:05:22Z (GMT). No. of bitstreams: 1 fgene.pdf: 750695 bytes, checksum: feed0a2c3f357eed6e731aa7bc936a64 (MD5) Previous issue date: 2020 Article 556399.