At 24 h post-IMI, genes responsive to IMI in the mammary gland were preferentially enriched for genetic variants associated with mastitis resistance rather than milk production. Response genes in the liver were mainly enriched for variants associated with mastitis resistance at an early time point (3 h) post-IMI, whereas responsive genes at later stages were enriched for associated variants with milk production. The up- and down-regulated genes were enriched for associated variants with mastitis resistance and milk production, respectively. The patterns were consistent across breeds, indicating that different breeds shared similarities in the genetic basis of these traits. Our approaches provide a framework for integrating multiple layers of data to understand the genetic architecture underlying complex traits.Connecting genome-wide association study (GWAS) to biological mechanisms underlying complex traits is a major challenge. Mastitis resistance and milk production are complex traits of economic importance in the dairy sector and are associated with intra-mammary infection (IMI). Here, we integrated IMI-relevant RNA-Seq data from Holstein cattle and sequence-based GWAS data from three dairy cattle breeds ( These studies have revealed that many genes with significantly affected expression levels are involved in both inflammatory responses and overall metabolism2425262Mastitis is most often caused by invading pathogens and frequently occurs in all lactating mammals and is a significant health problem in both human and veterinary medicineE. coli and E. coli endotoxin (LPS)). We assumed that the gene expression patterns induced by IMI were similar across breeds. The major objectives of this study were (1) to investigate the distributions of association signals of mastitis resistance and milk production traits in gene regions responsive to IMI, (2) to gain novel immuno-biological insights into the genetic basis of mastitis resistance and milk production, and (3) to provide a general framework for extending GWAS results to biological mechanistic hypotheses of variation in complex traits and diseases by integrative analysis with biological information from small-scale independent experimental populations.In the present study, mastitis resistance and three milk production traits  of Nordic Holstein (HOL), Nordic red cattle (RDC) and Jersey (JER) were analysed for their associations with imputed sequence-level genotype variants. The genotype data consisted of approximately 13–15 million single nucleotide polymorphisms (SNPs) from 10,597 animals. The RNA-Seq data were generated from IMI experiments of nine HOL animals involving two tissues (liver and mammary gland) and two pathogenic factors  was conducted for mastitis resistance, milk, fat, and protein yields in HOL, RDC, and JER separately. The –loglots see . The genDGAT1 gene on chromosome 14 and explained 18.3% and 13.9% (HOL), 6.3% and 7.2% (RDC), and 3.1% and 2.8% (JER) of the genomic variance of fat and milk yields, respectively. By contrast, no large-effect SNPs were observed for mastitis resistance or protein yield in any of the three breeds. Notably, the top SNPs on the significantly associated chromosomes jointly explained 9.7%, 17.4%, 22.3%, and 23.9% of the variance for mastitis resistance and protein, fat, and milk yields, respectively, in HOL; 6.8%, 8.4%, 12.6%, and 13.82% in RDC; and 0%, 0%, 3.1%, and 3.9% in JER. Hence, although the GWAS results demonstrated the importance of a small number of loci with moderate to large effects, they collectively explained only a small fraction of the total genomic variance. Loci with small effects remained undetectable by GWAS due to limited sample size  and very stringent genome-wide significance thresholds.The SNPs with the largest effect on fat and milk yields in the three breeds were in very close proximity to the well-known fat/milk-associated i.e., 3, 6, 9, 12, and 48 h) post-IMI with LPS compared with a time point before IMI  in the liver are available in i.e., 12 and 24 h) post-IMI with E. coli compared with a time point before IMI  in the liver and that of infected mammary quarters compared with controls at 24 h post-IMI are available in i.e., the sets of response genes) were constructed using six FDR cut-offs  in each experimental comparison. Ultimately, a total of 48 genomic features containing 11,446 unique genes were included for the following post-GWAS analyses  in a subset of genes responsive to IMI, and the averaged Pearson correlation of –log10P-values between HOL and RDC was 0.67 across the four traits with high significance (P < 0.01) . The –log < 0.01) . A simil < 0.01) . These fE. coli compared with a time point before IMI  and the mammary gland data from the same animals at 24 h post-IMI compared with the control were analysed. P < 0.05) in both HOL and RDC, indicating that IMI mainly influenced the immune response in the mammary gland. A similar pattern was also observed in JER (see P < 0.01)  , suggestAt a very early time point (3 h) post-IMI with LPS, response genes in the liver were mainly enriched for association signals in mastitis resistance rather than in milk production, whereas at 9 h post-IMI, the response genes were enriched for association signals in both mastitis resistance and milk production , except 2(fold-change) values . The detailed statistical information of the post-GWAS analysis for these genomic features in the three breeds is also summarized in 2(fold-change) > 2) compared with those of milk production with high significance (P < 0.01), whereas more association signals of milk production were enriched in the highly down-regulated genes (log2(fold-change) < −2) compared with those of mastitis resistance with high significance (2(fold-change) > 1 (<−1)  see . These oP-value from the post-GWAS analysis in HOL was identified as the top feature for each trait. A genomic feature linear mixed model (GFLM) was applied to estimate the explained genomic variance by each of the top features (details in the Methods section).The genomic feature with the smallest H 2) for mastitis resistance in HOL, RDC, and JER, respectively, approximately 5% of the variance for three milk production traits in HOL and RDC, but less than 1% of the variance for milk production traits in JER  > 1) was identified in the liver at 6 h post-IMI with LPS, containing 1790 up-regulated genes with approximately 1% of SNPs over the whole genome. This feature explained 7.53%, 10.89%, and 18.88% of the genomic variance , 14.95% (19.32%), and 8.79% (9.49%) of the genomic variance for milk (fat) yield in HOL, RDC, and JER, respectively, and approximately 6% of the variance for protein yield and less than 0.01% of the variance for mastitis resistance in the three breeds  < −1), which was identified in the liver at 12 h post-IMI with e breeds . A functe breeds . The detThe top genomic feature for protein yield  > 2) was identified in the liver at 48 h post-IMI with LPS, containing 48 highly up-regulated genes with less than 0.01% of SNPs over the whole genome. This feature explained 2.67%, 3.31%, and 5.33% of the genomic variance for protein yield in HOL, RDC and JER, respectively, 1.09%, 1.89%, and 1.34% of the variance for mastitis resistance, respectively, but less than 1% of the variance for milk and fat yield in the three breeds . A functTo the best of our knowledge, this study is the first to integrate sequence-based GWAS and IMI-relevant transcriptome data to exploit the genetic basis underpinning mastitis resistance and milk production in dairy cattle. We provide genomic evidence that genes in the mammary gland responding to IMI were more associated with mastitis resistance than milk production. Moreover, responsive genes in the liver played roles not only in the regulation of the immune response but also in the dysregulation of overall metabolism, providing novel immuno-biological insights into the genetic mechanisms underlying the unfavourable correlation between mastitis and milk production. The patterns were consistent across breeds, revealing that different breeds could share similarities in genetic architecture underlying mastitis resistance and milk production. This finding is in line with previous observations that the innate immune response to IMI remains highly conserved among breeds29E. coli and LPS as determined by microarray analyses have been previously reported using the same samples as those in the current study2427et al.e.g., increased production of acute phase proteins) and then decreases its overall metabolism, particularly of lipids and cholesterol37The global gene expression alterations in the mammary gland and liver during IMI with Single-marker GWAS has limitations for deciphering the genetic and biological mechanisms underlying complex traits, therefore many studies using different strategies have been conducted to investigate the distributions of causal genomic variants contributing to complex phenotypes along the genome11328i.e., post-GWAS) based on prior biological knowledge have been suggested as a computationally simple way to extract additional information from genome-wide marker data414244i.e., t2) within the region to an empirically derived distribution under a competitive null hypothesis. This approach is more likely to match the genetic architecture underlying complex phenotypes, whereby genetic variation is governed by many loci with small effects. Our previous studies44e.g., count or score-based) in most scenarios, and the number of false positives could be effectively controlled when the following criteria are met: 1) the average number of markers in each gene is approximately the same among the genomic features, and 2) the average linkage disequilibrium (LD) between markers in different genes is approximately the same44Secondary analyses of GWAS results  in small-scale samples to assist in the understanding of the genetic architecture and biological mechanisms underlying complex traits at the population level. However, because gene expression patterns are highly time- and tissue-dependent, some trait-associated genes might not show differential expression in some tissues at a certain time. Therefore, incorporating more biological information  related to the studied complex traits could be important for understanding the flow of biological information underpinning complex traits, which will help us identify the appropriate genomic features that are highly enriched for causal variants. Our current genomic feature modelling approaches provide a general framework to investigate and integrate multiple layers of omics data from high-throughput technologies or existing pathway annotation databases, potentially leading to a better understanding of the genetic and biological basis underlying complex traits and diseases.In principle, many genomic features can be constructed using prior information from different sources, such as single genes, haplotypes, chromosomes, sequence ontologies, biological pathways, and experimental studies. The gain in knowledge generated by their use relies heavily on the quality of the genomic feature classification strategies on which the marker sets are based. As trait-associated genomic markers are not uniformly, or necessarily physically, clustered in the genome14All experimental procedures involving animals were approved by the Danish Animal Experiments Inspectorate and complied with Danish Ministry of Justice laws concerning animal experimentation and care of experimental animals. The animal experiments were conducted in strict accordance with regulations and guidelines established by these committees. An inspection was carried out by members of these committees during the animal infection experiments.E. coli LPS (0111:B4)  dissolved in 10 ml of a 0.9% NaCl solution, whereas the left front quarter received 10 ml of the 0.9% NaCl solution as a control. The following clinical findings verified the induction of mastitis by LPS, such as fever and high SCCs in the milk of the infected quarters. Liver biopsies were collected at −22, 3, 6, 9, 12, and 48 h relative to LPS treatment in all the studied cows. The detailed information of the liver biopsy samples from the three Holstein cows post-IMI with LPS has been previously described by Jiang et al.In total, three and six healthy Holstein animals at the early stage of their first lactation were used in the following two IMI experiments, respectively. For the first IMI experiment, the udder health of the three Holstein cows was evaluated based on bacteriological examinations and somatic cell count (SCC) before LPS treatment. All cows were free of mastitis-causing pathogens and had SCCs < 138,000 cells/ml in all the quarters. At the start of the trial, the right front quarter of all the cows was inoculated with 200 μg of E. coli treatment, all six Holstein animals were evaluated and had normal body temperature and white blood cell count, a negative glutaraldehyde test, and low Californian Mastitis Test ) scores ranging from 1 to 5. They were found to be free from mastitis-causing pathogens and had SCCs in milk <27,000 cells/ml in all the quarters. One of the front quarters from each of the six animals was infused with 10 ml of a 0.9% NaCl solution containing ~20–40 CFU of live E. coli, whereas another quarter serving as a control received 10 ml of the 0.9% NaCl solution. The details of the liver biopsy samples from the six cows at −144, 12 and 24 h post-IMI with E. coli have been previously described by Jorgensen et al.et al.For the second IMI experiment, prior to the E. coli , and mammary gland tissues collected from udder quarters at 24 h post-IMI with and without E. coli infection . The RNA extraction was performed as described previouslyi.e., each library) was then sequenced using a 100 bp paired-end method with Illumina HiSeq 2000 sequencing technology by AROS Applied Biotechnology .RNA-Seq libraries were prepared from liver samples collected at −22, 3, 6, 9, 12, and 48 h post-IMI with LPS , liver tissue samples from −144, 12, and 24 h post-IMI with i.e., different time-points post-IMI) was considered as the only effect for the liver samples, whereas infection status  was included in the model for the mammary gland samples. The statistical tests for each analysis were adjusted for multiple testing using the FDR methodThe approaches applied for RNA-Seq bioinformatics analyses have been previously describedi.e., de-regressed breeding values) of 5,056 HOL, 4,310 RDC, and 1,231 JER cattle from routine genetic evaluation by Nordic Cattle Genetic Evaluation (http://www.nordicebv.info/). The average reliabilities of the de-regressed proofs for milk, fat, and protein yields and mastitis resistance were 0.95, 0.95, 0.95, and 0.85, respectively, in HOL; 0.95, 0.95, 0.95, and 0.83, respectively, in RDC; and 0.92, 0.92, 0.92, and 0.76, respectively, in JER. The details of the imputation from 50 K and High-Density  genotypes of these cattle to whole-genome sequence data have been described previouslyrun4 of the 1,000 Bull Genome project The definitions of milk production traits  and mastitis resistance were standardized among the Nordic countries. The phenotypes used for the single-marker association analysis were de-regressed proofs ; 1 is a vector of ones; μ is the overall mean; Z is a design matrix relating phenotypes to random polygenic effects; a is a vector of random polygenic effects , where a~Ne is the vector of residuals, where e~N, b is the allele substitution effect, and η is a vector of random residual deviates with (co)variance structure −9, 3.3 × 10−9, and 3.7 × 10−9. Manhattan plots were created using qqman v.0.1.2 in the R packagei.e., 0.454). The variance explained by an individual SNP was calculated as follows:where p is the allele frequency, and β is the SNP effect estimated from GWAS.where Considering the genes detected in RNA-Seq as genomic features, a post-GWAS analysis was performed based on the GWAS results, where test statistics were obtained for the association of each individual SNP. The imputed sequence SNPs were mapped to the bovine reference genome (UMD3.1). A genetic marker was considered as relevant to a gene if the chromosome position of the marker was between the start and end positions of the genemf is the number of markers located in a genomic feature, and t2 is the squared of t . The approach applied to test the association between a phenotype and a genomic feature has been described previously44e.g., t2) was first ranked based on the chromosome position of the markers, and a test statistic was then randomly chosen from this vector. All test statistics were moved to the new positions, with the remaining markers maintaining the original order, whereby the chosen statistic became the first. This uncoupled any associations between markers and genomic features while maintaining the correlation structure among the test statistics. Afterward, a new summary statistic of a genomic feature was calculated based on the original position of the feature. The permutation was repeated 1,000 times for each studied genomic feature, and an empirical P-value was then calculated based on one-tailed tests of the proportion of randomly sampled summary statistics larger than that observed. Here, we used response genes detected in RNA-Seq to define genomic features. Genes were thus used as the sampling units in the permutation procedure. Our previous simulation studies demonstrated that this post-GWAS method performs better than or equal to other commonly used methods  in most situations, whereby the genetic variations of the traits are controlled by a large number of loci with small effects44where By grouping markers into two sets (the genomic feature and the remaining genome), a genomic feature linear mixed model (GFLM) was used to assess the joint contribution of a set of markers in a genomic feature to a phenotype. The model isy is the vector of phenotype , 1 is a vector of ones, μ is the overall mean, gf is the vector of genomic values captured by markers in the genomic feature, gr is the vector of genomic values captured by markers in the remaining genome, and e is the vector of residuals. The assumptions for all the random effects are given bywhere Gf and Gr are genomic relationship matrices, built based on the markers located in the genomic feature and the remaining genome, respectively. These G matrices were built using the second method described by VanRaden (2008)D is a diagonal matrix with diagonal elements equal to r2 is the reliability of the de-regressed proof, and where The proportion of genomic variance explained by a genomic feature was calculated asP-value detected in HOL for each of the studied traits was considered to be of interest, and these features were subjected to functional enrichment analyses using a web-based tool, KOBAS2.0http://kobas.cbi.pku.edu.cn/home.do). A hypergeometric gene set enrichment test, based on a gene ontology (GO) database, was applied, and the P-values for each cluster were corrected using the FDR method. The semantic similarities among the enriched GO terms (FDR < 0.05) were calculated using the SimRel approachhttp://revigo.irb.hr/). The detailed information for assigning x and y coordinates to each GO term to ensure more semantically similar terms are close in the scatterplots has been previously describedThe genomic feature with the smallest ftp://ftp.ensembl.org/pub/release-84/gtf/bos_taurus). The whole-genome sequencing data from the 1000 Bull Genomes Project are publicly available as sequence data at NCBI with SRA no. SRP039339 (http://www.ncbi.nlm.nih.gov/bioproject/PRJNA238491) and variations in dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/). The phenotype and imputed sequence genotype data are available only upon agreement with the commercial breeding organization (http://www.vikinggenetics.com/) and should be requested directly from the authors or the breeding organization.The RNA-Seq data were submitted to NCBI with the accession number GSE75379. All genomic annotation data defining gene regions are publicly available in Ensembl .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Zea mays L.). Inferences on genomic and residual trait connections were depicted separately as directed acyclic graphs. These graphs provide information beyond mere pairwise genetic or residual associations between traits, illustrating for example conditional independencies and hinting at potential causal links among traits. Network analysis suggested some genetic correlations as potentially spurious. Genomic and residual networks were compared between Dent and Flint.Relationships among traits were investigated on the genomic and residual levels using novel methodology. This included inference on these relationships via Bayesian networks and an assessment of the networks with structural equation models. The methodology employed three steps. First, a Bayesian multiple-trait Gaussian model was fitted to the data to decompose phenotypic values into their genomic and residual components. Second, genomic and residual network structures among traits were learned from estimates of these two components. Network learning was performed using six different algorithmic settings for comparison, of which two were score-based and four were constraint-based approaches. Third, structural equation model analyses ranked the networks in terms of goodness of fit and predictive ability, and compared them with the standard multiple-trait fully recursive network. The methodology was applied to experimental data representing the European heterotic maize pools Dent and Flint ( In breeding of animals and plants, it is crucial to distinguish between genetic (heritable) contributions and environmental factors influencing traits, and the decomposition of phenotypic variances and covariances between traits into their genetic and environmental components has been under investigation for decades e.g., . Identife.g., by a structure matrix a priori. For a given structure e.g., body composition and bone density in mouse intercross populations , can describe systems of phenotypes connected via feedback or recursive relationships . SEM areulations , or genee.g., traits) in terms of their conditional independencies. There are two main types of algorithms for learning a BN: constraint-based and score-based algorithms. The former uses a sequence of conditional independence tests to learn the network among variables, while the latter compares the fit of many  possible networks to the empirical data using a score. BN have been used for many purposes in quantitative genetics, for example, to predict individual total egg production of European quails using earlier expressed phenotypic traits  learning methods can be used. BN are models representing the joint distribution of random variables . Instead of using the IC algorithm to analyze MCMC samples of covariance matrices, we use predicted (fitted) genomic values and residuals from a multiple-trait model as input variables for structure-learning algorithms. This approach allows using available software for learning BN, is computationally easy and fast, and use of bootstrapping to assess uncertainty regarding the edges is straightforward. Using this strategy, we study the behavior of various BN algorithms on experimental data. Afterward, we compare and assess the BN algorithms by fitting an SEM to the structures learned from them.The methodology is illustrated using a maize data set. Biological objectives of this case study are the investigation of connections among maize traits and the comparison of networks with respect to two underlying sources of information  and two heterotic groups (Dent and Flint). Five well-characterized complex traits were studied: biomass yield, plant height, maturity, and male and female flowering time. With these, well-known connections can be reconstructed and new insights into potentially spurious trait connections are obtained. After illustrating trait connections with respect to their genomic and residual sources and identifying spurious connections, a clearer picture on which traits should be included in a multiple-trait prediction model or in an indirect selection process may be gained.The data contain a sample of phenotypes and genotypes representing two important European heterotic pools, Dent and Flint  (decitons per hectare), biomass dry matter content (DMC) (percentage), plant height (PH) (centimeters), days to tasseling (DtTAS) (days), and days to silking (DtSILK) (days). Phenotypic values are available as adjusted means across locations and replications. The adjusted means were mean-centered in each DH population, as we focused on allelic rather than on population effects. All traits were standardized to unit variance based on their sample variance.All DH lines of both panels were genotyped with the Illumina MaizeSNP50 BeadChip containing 56,110 SNP markers  fitting a Bayesian multiple-trait Gaussian model (MTM) to all five traits to obtain posterior means of genomic values and of model residuals for each panel, and transforming the predicted genomic values to meet assumptions on sample independence required by a BN analysis; (2) applying the BN analysis to the residual and transformed genomic trait components; and (3) assessing the quality of the inferred structures by a structure-comparative SEM analysis.nd × 1)-dimensional column vectors of scaled adjusted means, genomic values, and residuals sorted by trait and genotype within trait, respectively; d × 1)-dimensional intercept vector n × 1)-dimensional vector of ones. Since phenotypes We fitted a Bayesian multivariate Gaussian model to the n × n)-dimensional realized-kinship matrix estimated using simple matching coefficients (d × d)-dimensional covariance matrices containing the genomic and residual variance and covariance components, and the (n × n)-dimensional identity matrix The joint distribution of ficients . G and Ra priori independent inverse-Wishart distributions with specific degrees of freedom i.e.,BGLR and the MTM function of its multiple-trait extension.Flat priors were assigned to the elements of the intercept vector An MCMC approach based on Gibbs sampling was used to explore posterior distributions. A burn-in of 30,000 MCMC samples was followed by an additional 300,000 MCMC samples. The MCMC samples were thinned with a factor of two, resulting in 150,000 MCMC samples for inference. Posterior means were then calculated for e.g., nonadditive genetic effects and nonaccounted-for (micro-)environmental effects, such as nongenetic physiological and morphological trait dependencies. Random genomic values fitted with model (1) represent, e.g., genetic connections among traits, induced inter alia by pleiotropy and linkage disequilibrium. Here, we take the fitted genomic values and residuals and investigate them with BN.Subsequent BN analyses concentrated on the data . As a coPredictive ability from a univariate Bayesian prediction model was derived for each trait with 10 replicates of a fivefold cross-validation to compare single-trait prediction with multiple-trait prediction. Prior assumptions were analogous to those of the MTM, that is, scaled-inverse i.e., elements of BN learning algorithms assume independent observations. In the MTM described above, independence of residuals between genotypes was assumed. In contrast, the genomic components in n × n)-dimensional lower triangular matrix, and define a (dn × dn)-dimensional matrix For this purpose, we decomposed the kinship matrix For For a standard Cholesky decomposition of vailable .n × d)-dimensional matrices formed from the vectors In subsequent BN analysis, A BN is a model that describes connections among random variables. It consists of two components: a statement about the joint distribution of these random variables, and its graphical representation as a directed acyclic graph (DAG)   as an ext-test (GS 1) and with a Monte Carlo permutation test (GS 2)  . Furthert (GS 4) . MultiplInter alia, BGE assigns the same scores to equivalent network structures   (TABU 1)ructures . Equivalbnlearn  different learning settings were applied to the genomic and residual components of model (1), respectively; all algorithms assuming a Gaussian BN. Regarding the constraint-based approaches, the number of permutations used for each of the permutation tests was 450 . Each of the six settings was run with 500 bootstrap samples and the significance level was set to 0.01 (as mentioned above). After structure learning from the bootstrap samples, we averaged over the 500 resulting networks as described in the R package bnlearn .d × d)-dimensional matrix a priori. The resulting structure matrices are lower triangular matrices, since loops are not allowed in a BN structure. The lower triangular form might not be produced by an arbitrary order of traits but can always be formed by rearranging the order of the traits by columns and rows; see Supplemental Material, Figure S1, for an example of how a DAG is transformed into a structure matrix. If a learned structure was an element of an equivalent class of several structures, only one representative of this equivalent class was translated into a structure matrix The structure learned by the BN needed to be translated into a structure matrix for SEM analysis. Thus, a -dimensional genomic and residual structure matrices and In the following paragraph, we present how the structures found in the BN analysis translate into SEM; doing so also clarifies how MTM and SEM are related. Genomic or residual trait dependence can be described by the recursive linear regression modelsi.e., the model is homoscedastic. Note that i andi. Since the true structure matrices For genotype Then, the MTM can be reformulated as the following SEM in its reduced form . Analyses were performed with the statistical programming tool R (BLR function (bnlearn (BGLR package (https://github.com/QuantGen/MTM).Phenotypic data are available from file S1 of g tool R . Single-function . StructuPhenotypic, genomic, and residual correlations between the five traits were estimated with the MTM for the Flint and Dent panels separately . In bothAs the genotypic values in the MTM were correlated between genotypes due to kinship, estimates were transformed to meet the model assumptions of the BN algorithms. The transformation modified the relationship between estimated genotypic values as expected . The inferred networks varied in up to two edges and/or three directions. In general, score-based algorithms tended to choose more edges than constraint-based algorithms, and the networks of residual components were sparser than those of genomic components. Within the constraint-based or score-based approaches, inference was more consistent than between them.BN inference was sensitive to the choice of algorithm and test . Connections that appeared in both panels were those between the flowering traits, from DtSILK to PH, from DMY to DMC, and between DMY and PH. Specific to the Flint panel was the edge from DtTAS to DMC, while the Dent panel showed a corresponding connection from DtSILK to DMC. An additional edge in the Dent panel not detected in the Flint panel was between DMC and PH. One network in the Flint panel showed an edge from PH to DtTAS, but it was only supported by 56% of the bootstrap samples in the respective network algorithm.BN inference of residual components was very similar in the Dent and Flint panels, irrespective of the algorithmic settings  was never inferred, neither on the genomic nor on the residual components.BN inference of genomic components was more variable than that of residual components within and across panels. In the Flint panel, algorithms recognized edges from DtSILK to DtTAS, from DtTAS to PH, from DtSILK to DMC; and between DMY and DMC, between DMY and DtTAS, between DMY and PH, and between DMC and DtTAS. In the Dent panel, only the connection between DMC and DMY was never inferred by any algorithm. A fully recursive network . When two or more BN algorithms inferred the same network for the same component , SEM analysis was only performed once for this structure. In the SEM, genomic and residual networks were assessed separately Table S1) and jointly Table S2).For assessing the structures inferred by the different BN algorithms, we integrated them into SEM, and compared them with MTM using goodness-of-fit criteria and predictive ability  . logL evTable S1 and Table S2). Averaged over traits and for each trait individually, predictive abilities were similar for all models  and differences between models were <1 SD and not significant. The magnitude of predictive-ability estimates in the single-structure SEM was consistent with predictive-ability estimates in the double-structure SEM in both panels. Since all SEM with a genomic structure in the Flint panel had higher DIC (Table S1) than the MTM, genomic structures might not have been identified correctly, which might also have affected the double-structure SEM.Predictive abilities and their SDs were derived from 10 replicates of a fivefold cross-validation with random sampling of training and test sets within each panel , this topic warrants further research.Genomic correlations, networks, and model performance criteria varied between the Dent and Flint panels, and incorporating genomic networks into prediction models improved goodness of fit more in Dent than in Flint. As the DH lines in the Dent panel were genetically more diverse than in the Flint panel . Based oIn both panels, the marginal logL was higher for many SEM than for MTM, even though the network structure on the covariance matrices implied variable selection. This shows that restrictions on the covariance structures among traits were generally supported by the data. Following the principle of parsimony, a fully recursive structure might not be the best representation of connections among traits.e.g., i.e., model complexity varies over networks. As DIC reflects model complexity, it is a natural companion to the marginal likelihood for SEM ranking. While goodness-of-fit criteria evaluate the quality of data description by a network, predictive ability reflects the generalization of structure estimation from one subset of the data (training set) to another subset (test set). However, true structures of traits remain unknown and validating inferred connections in experiments with a broad range of genetic material after hypothesis generation with BN and SEM is crucial.Our results suggest that the choice of method for network learning is crucial, and that a thorough assessment of network structures is necessary when dealing with real-life data. Resulting networks might not explain or fit the data better than a full network as seen here . Assessment of the different network structures was done by considering several model quality criteria  together, and by evaluating if these criteria ranked networks consistently. Marginal likelihoods are used routinely to evaluate the plausibility of prior assumptions  was generally higher in the constraint-based networks than in the score-based networks.SEM analysis suggested score-based approaches are better for learning the structure of the genomic networks, and constraint-based approaches are better for the residual networks. This might have resulted from constraint-based algorithms having been more restrictive (given the chosen significance level) and from residual networks being sparser than genomic networks. Evidence for the significance of edges .Influence of the significance level in the constraint-based algorithms warrants further investigation. However, it might be expected that a larger cf. part A of If variables A and B are directly associated with each other, and B and C are alike, then an association between A and C would be a logical consequence, even if there is no direct association between A and C  depended on traits measured during the vegetation period (DtTAS and DtSILK). Additionally, the inference of direction between DtTAS and DtSILK in the residual networks was relatively uncertain with 52 and 55% of bootstrap samples supporting the direction. This might be because both flowering traits are determined by the time of transition from vegetative to reproductive growth (TVR) and no direct dependence between them truly exists, as both traits depend on TVR. The connection between DtTAS and DtSILK would then be an example of an induced connection by an unobserved confounder in a network.A conclusion of our study is that by illustrating trait connections with respect to their genomic and residual nature separately and identifying spurious connections, a clearer picture on traits can be gained. This can be useful for multiple-trait prediction and indirect selection.www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.044263/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


Eucalyptus breeding population involving 90 E.grandis and 78 E.urophylla parents and their 949 F1 hybrids to develop genomic prediction models for eight phenotypic traits - basic density and pulp yield, circumference at breast height and height and tree volume scored at age three and six years. We assessed the impact of different genomic prediction methods, the composition and size of the training and validation set and the number and genomic location of SNPs on the predictive ability (PA).Genomic prediction is a genomics assisted breeding methodology that can increase genetic gains by accelerating the breeding cycle and potentially improving the accuracy of breeding values. In this study, we use 41,304 informative SNPs genotyped in a 1 hybrids, indicating that model training has to be carried out in hybrid populations if one is to predict in hybrid selection candidates. The different genomic prediction methods provided similar results for all traits, therefore either GBLUP or rrBLUP represents better compromises between computational time and prediction efficiency. Only slight improvement was observed in PA when more than 5000 SNPs were used for all traits. Using SNPs in intergenic regions provided slightly better PA than using SNPs sampled exclusively in genic regions.Heritabilities estimated using the realized genomic relationship matrix (GRM) were considerably higher than estimates based on the expected pedigree, mainly due to inconsistencies in the expected pedigree that were readily corrected by the GRM. Moreover, the GRM more precisely capture Mendelian sampling among related individuals, such that the genetic covariance was based on the true proportion of the genome shared between individuals. PA improved considerably when increasing the size of the training set and by enhancing relatedness to the validation set. Prediction models trained on pure species parents could not predict well in FEucalyptus breeding programs by the selection of interspecific hybrids.The size and composition of the training set and number of SNPs used are the two most important factors for model prediction, compared to the statistical methods and the genomic location of SNPs. Furthermore, training the prediction model based on pure parental species only provide limited ability to predict traits in interspecific hybrids. Our results provide additional promising perspectives for the implementation of genomic prediction in The online version of this article (doi:10.1186/s12870-017-1059-6) contains supplementary material, which is available to authorized users. Eucalyptus species and their hybrids are the most widely planted hardwoods in tropical, subtropical and temperate regions, due to their fast growth, short rotation times, wide environmental adaptability and suitability for commercial pulp and paper production [E.grandis and E.urophylla, in particular, are generally superior to their parents in growth, wood quality and biotic and abiotic stresses resistance, by inheriting both the fast growth and good rooting abilities of E.grandis while by maintaining disease tolerance and wide adaptability of E.urophylla [oduction , 2. Interophylla . A converophylla , 4. To aGenomic prediction or genomic selection (GS) is one of the most recent developments in genomics-assisted methods that are aimed at improving breeding efficiency and genetic gains. Genomic prediction provides a genome-wide paradigm for marker-assisted selection (MAS) , 6. In Gith locus, Vgi, is assumed to themselves follow a prior distribution, p(Vgi). Therefore, the variance can vary across loci, and combining the information from the prior distribution with that of the data yields an estimate of Vgi [The accuracies of genomic prediction models vary depending on the statistical methods employed. Several methods have been developed for GS, including ridge-regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased prediction (GBLUP), BayesA, BayesB, Bayesian LASSO, BayesR and reproducing kernel Hilbert space (RKHS) regression , 13. The of Vgi , 15. For of Vgi . The RKH of Vgi . Recent  of Vgi , 18.Ne), increasing the size of reference population leads to improved accuracy of genomic predictions. Closer relationship between training population and selection candidates has also been reported to lead to a higher accuracy of genomic predictions, while enlarging the genetic diversity of the training population resulted in lower accuracy [Ne also increased [Ne populations provides little or no improvement on predictive accuracy [Beside statistical methods, other factors are known to influence the accuracy of genomic prediction models, such as the size of the training population, number of markers employed, and relatedness between the training and validation population and, by extension, to the future selection candidates. Hayes et al.  found thaccuracy . A numbencreased , 21–23. accuracy , 25.E. grandis, E. urophylla and their F1 hybrids to build prediction models using different subsets of parents and progeny for training and validation sets. A multi-species single-nucleotide polymorphism (SNP) chip containing 60,904 SNPs [Going one step further from previous studies in forest trees, where individuals of the same breeding generation were allocated to training and validation sets for the evaluation of genomic prediction models, in this study we used both the parental and progeny generations of 904 SNPs  were useE. urophylla and 95 E. grandis trees (G0 population) following a incomplete diallel mating design, resulting in 16,660 progeny individuals (G1 population) comprising 476 full-sib families with 35 individuals per family. In 2009, the progenies were deployed in a field trial under a randomized complete block design with single-tree plots and 35 replicates per family in Belmonte  at Veracel Celulose S.A. . Our experimental population consists of 168 parents (78 of E.urophylla and 90 of E.grandis) (G0), as not all parents were still alive at the time of the study, and 958 progeny individuals (G1) sampled across 338 full-sib families by avoiding low performing trees. The number of individuals in each full-sib family ranged from one to 13 with an average of 2.8 individuals per family.The breeding population in this study was obtained through controlled crossings of 86 E. grandis and at age five years for E. urophylla. Briefly, height was measured using a Suunto hypsometer/height meter (PM-5/1520 series) and CBH was measured with a centimetre tape at 130 cm above ground. Wood properties were estimated by employing near-infrared reflectance spectra of sawdust samples collected at breast height using a FOSS NIRSystem 5000-M and applying calibration models developed earlier by Veracel S.A..For the 958 G1 samples, height, volume, and circumference at breast height (CBH) were measured at age three and six years, respectively, and wood traits (basic density and pulp yield) were measured at age five years. For the 168 G0 parents, the same traits had been measured at age seven years for A mixed linear model was applied to minimize the impacts of environmental and age differences on each trait.Y is a vector of observations of a single trait; β is a vector of fixed effects, including overall mean, experimental sites and age differences; u is a vector of random additive genetic effect of individuals with a normal distribution, u ~ N.The 168 G0 and 958 G1 populations were genotyped using the Illumina Infinium EuCHIP60K  that con2) for SNPs located on the same chromosome. Following Remington et al. [After quality control, missing genotypes of the remaining individuals were filled in by imputation. We first tested the accuracy of imputation methods across a range of missing data 2% - 30%) by artificial removing SNPs from a fraction of our genotypes. Among the available family-based and population-based methods we assessed the following programs for imputation accuracy: BEAGLE , fastPHA% by artin et al. , the dec2 < 0.2) calculated in PLINK [We further estimated population structure and pairwise genomic relationships among the 1117 individuals by performing principal components analysis (PCA)  and by cin PLINK . Pedigregenomic best linear unbiased predictor (GBLUP) [ridge regression BLUP (rrBLUP) [Bayesian LASSO (BL) [reproducing kernel Hilbert space (RKHS) regression [Four statistical methods were assessed for their ability to estimate the parameters in eq.  and for  (GBLUP) , ridge r(rrBLUP) , BayesiaSSO (BL) , and repgression . These mgression .The GEBVs were estimated using the following mixed linear model:y is the vector of adjusted phenotypes of single trait, β is the vector of overall mean fitted as a fixed effect, a is the vector of random effects, and e is the vector of random residual effects. 1 and Z are incident matrix of β and a, respectively.where a) in Eq.  in Eq.  is assummentclass2pt{minimwhere entclass1pt{minimaA in Eq. (G) that is calculated from genotypic data using M is the matrix of samples with SNPs encoded as 0, 1, 2 , P is the matrix of allele frequencies with the j-th column given by 2(pj− 0.5), where pj is the observed allele frequency of the samples [a) in the Eq. . The residual variance The Bayesian LASSO (BL) method is the Bayesian treatment of LASSO regression as proposed by Legarra et al. . In BL tequation , is assudfe , Se , r , δ , and λ2) were defined following the guidelines proposed by de los Campos et al. [We implemented the BL method using the “BLR” function from the BLR package in R . Here a s et al. . The chaK is computed by means of a Gaussian kernel that is given by Kij = exp(−hdij) [h is a semi-parameter that controls how fast the prior covariance function declines as genetic distance increase and dij is the genetic distance between two samples computed as xik and xjkare kth SNPs  for the ith and jth samples, respectively. We implemented the RKHS method through the “BGLR” function from the BGLR package in R [h values were defined as dij. The Gibbs chain length was 20,000 iterations with the first 2000 iterations discarded as burn-in and a thinning interval set to 100.RKHS assumes that the random additive effects in Eq.  are \doc(−hdij) . h is a age in R , which uby Perez , where tWe estimated the pedigree-based narrow-sense heritability (tails in ). The rewhere 1) assignment of individuals to either TS or VS was random. For the second scenario (CV2) all G0 parents were assigned to the TS and complemented with a random selection of G1 individuals up to the required number in the set, while the VS was composed exclusively of the remaining G1 individuals. The third (CV3) and fourth (CV4) scenarios were built based on minimizing and maximizing relatedness between TS and VS. The relatedness-based assignment of individuals was determined using the procedure described in Spindel et al. [3 was then built by assigning individuals to TS and VS based on dissimilarity, such that individuals from the same cluster were not allowed to be both in the same TS or VS. For CV4 individuals from same cluster were forced to be either in the TS or VS to increase relatedness within TS and VS [We simultaneously assessed the impact of the size and genetic participation of G0 and G1 individuals in the training set (TS) and validation set (VS) of the genomic prediction models. Regarding relative TS/VS sizes, we divided all 1117 (G0 and G1) individuals into five different size groups with a TS:VS ratio of 1:1, 2:1, 3:1, 4:1 or 9:1. The corresponding sizes of the TS/VS were respectively 558/559, 743/374, 836/281, 892/225 and 1003/114 individuals. Within these pre-established size compositions, four scenarios were employed where the participation of G0 and G1 individuals were evaluated to assess the impact of varying the degrees of relationship and diversity between TS and VS. In the first scenario , five TS/VS sizes and four TS/VS composition scenarios  on the predictive ability (PA) of genomic prediction. For each of the 100 models, 200 replicate runs were carried out for each trait and the performance of the models were evaluated in terms of their PA  on the PA of genomic prediction models. 12 subsets with different numbers of SNPs were generated by randomly selecting 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10,000, 20,000 and 41,304 SNPs from all the available SNPs. For SNP location, SNPs subsets located in different regions of the genome were established by including SNPs located in four different regions: (i) coding sequences (CDS) only ; (ii) entire genic regions including CDS, UTRs, introns, and sequences 2 kb up and downstream of the gene ; (iii) intergenic regions , and (iv) all 41,304 SNPs. The location and classification of each SNP was obtained by mapping SNPs onto the g SnpEff . Genomicr = 0.92 and 0.95 respectively), height at age three was only weakly correlated with height at age 6 (r = 0.36). For wood properties traits, basic density was negatively correlated with pulp yield, although only weakly so (r = −0.28). Growth traits showed no correlations with wood traits (r = − 0.1 to 0.1).Growth  and wood properties (basic density and pulp yield) were measured for all 168 G0 and 949 G1 individuals. The raw phenotypic data were adjusted using a mixed linear model to minimize the impacts of environment and age differences. The pairwise correlations between the adjusted traits were described by calculating Pearson correlation coefficients . The first two PCs explained 6.07% and 3.8% of the total genetic variance    showed the highest PAs for all traits except pulp yield, whereas models based on CV3 (minimum relatedness between TS and VS) gave the worst predictions. The models based on CV1 (random assignment) and CV4 (maximum relatedness between TS and VS) showed no significant differences in PA , and that the number of SNPs has a larger impact than their genomic location  went from 0.47 to 0.24 (a 50% decrease) when the number of SNPs dropped from 40,000 to 10, whereas CBH of age three (h2 = 0.113) decreased from 0.128 to 0.03 (a 77% decrease). Overall, slight significant differences were seen in PAs by using SNP sets located in different genomic regions  among SNPs in the four regions tested, the extent of LD differed among them, with LD showing the most rapid decay in coding regions and the slowest decay in intergenic regions  [Picea glauca) [Picea engelmannii × glauca) [Pinus pinaster) [All four genomic prediction methods performed significantly better than the pedigree-based evaluations for all complex traits assessed Fig. . While scalyptus , loblolls taeda) , white s glauca) , 46, int glauca) , 48 and inaster) . Genomicinaster) , or an oinaster) . Our resOverall, the different genomic prediction methods provided similar results for the all traits evaluated, with only a slight advantage for RKHS which showed better PAs for the low-heritability growth traits Fig. . HoweverEucalyptus breeding scenarios had in fact shown that with up to N = 1000 individuals in the TS, the accuracy would rapidly increase, and additional gains were seen up to N = 2000 individuals for traits with low heritabilities, for larger numbers of QTLs involved in traits and for larger effective population size (Ne). After N = 2000 the predictive accuracy would tend to plateau irrespective of the Ne and genotyping density [Our results show that the size and compositions of training and validation sets had the largest impact on the PA, irrespective of the analytical method used Fig. . The ave density . Simulat density , 56 and  density  in crop 3 (minimized relatedness between TS and VS) resulted in significantly worse predictions than in scenario CV4 when relatedness was maximized. Our results are in line with previous reports in forest trees, such as white spruce [Eucalyptus [As expected, relatedness between TS and VS had a large impact on PAs for all traits. Prediction models built under scenario CVe spruce , 46 and calyptus . Increas2). This result could be due to two reasons. On one hand, by keeping all G0 parents in TS, we ensure that a large genetic diversity is available for model training, which could explain the positive impact of G0 inclusion on predictions. On the other hand, it is possible that by allocating all G0 individuals to the TS the positive effect we observe is strictly not due to increased predictive power but rather because we avoid the potentially negative impact of having pure species parents in the validation set in combination with G1 progeny that were largely F1 hybrids. In order to evaluate this, we estimated PA of genomic prediction models by using GBLUP and RKHS, having only the 168 G0 parents for TS and randomly selected 168 G1 individuals in VS. To control for the effect of the strongly reduced TS size, we compared this setup with random assignment of individuals to TS or VS but keeping the size of each at N = 168. The results showed considerably lower PAs (even zero or negative) when using only pure species parents to predict G1 hybrid progeny phenotypes  were also generally lower than CV2, suggest that the higher PAs we observe for scenario CV2 is mostly due to avoiding the negative effect of having pure species parents in the VS.PAs were considerably higher when all the G0 parents were kept in the TS  and their F1 progeny. We show that models trained on pure species parents do not allow for accurate prediction in F1 hybrids, likely due to the strong genetic divergence between the two species and lack of consistent patterns of LD between the two species and their hybrids.Our experimental results provide further promising perspectives for the implementation of genomic prediction in e length , the useAdditional file 1:Average accuracy of SNP imputation methods with increasing proportions of missing data. SNPs on chromosomes 6 and 8 were randomly removed from the dataset to generate specific missing data proportions. Accuracy between imputed and true SNP genotypes were subsequently calculated with the different methods. (DOCX 1714 kb)Additional file 2:Predictive abilities on genomic selection model that comprises of statistical methods, genetic compositions and relative sizes of Training Set/Validation Set for each trait. (XLSX 16 kb)Additional file 3:ANOVA analysis of sources of variation affecting the predictive ability. (DOCX 48 kb)Additional file 4:Mean and standard deviation of predictive ability with the five prediction methods for the eight traits. (DOCX 96 kb)Additional file 5:Mean and standard deviation of predictive ability estimated with the four Training Set/Validation Set compositions. (DOCX 84 kb)Additional file 6:Mean and standard deviation of predictive ability estimated with the five relative sizes of Training Set/Validation Set expressed in proportions and numbers of individuals. (DOCX 89 kb)Additional file 7:Mean and standard deviation of predictive ability across increasing numbers of SNPs, statistical methods (RKHS and GBLUP), four Training Set/Validation Set compositions for each of eight traits. (XLSX 61 kb)Additional file 8:Mean and standard deviation of predictive ability estimated with SNPs in four genomic locations, with two statistical methods (RKHS and GBLUP), four Training Set/Validation Set compositions for each of eight traits. (XLSX 58 kb)Additional file 9:ANOVA of predictive ability with SNP genomic location and SNP number as sources of variation. (DOCX 62 kb)Additional file 10:Average predictive ability estimated with different numbers of SNPs fitted into the model. (DOCX 136 kb)Additional file 11:Average predictive abilities estimated using SNP sets located in different genomic regions. (DOCX 82 kb)Additional file 12:Decay of linkage disequilibrium (LD) with physical distance estimated with SNPs in different genomic locations. (a) A comparison of the decay of LD with physical distance in four classes of SNPs located with coding, genic, intergenic and all regions, respectively. Dots of pairwise LD versus physical distance and the LD decay for SNPs located in all regions (b), coding region (c), genic region (d) and intergenic region (e), respectively. (DOCX 1375 kb)Additional file 13:1 hybrids. Predictive ability estimated under three training/validation sets (TS/VS) scenarios with two methods (GBLUP and RKHS) for each trait. PO168 (red boxes): all 168 E. grandis and E. urophylla pure species G0 parents used for training and 168 G1 random selected hybrid progeny for validation; random168 (green): randomly selected 168 individuals from all 1117 for TS and 168 randomly also for VS; random558 (blue): randomly divided all 1117 individuals into TS and VS of same size (558/558). Outlier estimates are indicated by black dots. (DOCX 174 kb)Predictive abilities by training in pure species eucalypt parents and predicting in their F


Piscirickettsia salmonis is one of the main infectious diseases affecting coho salmon (Oncorhynchus kisutch) farming, and current treatments have been ineffective for the control of this disease. Genetic improvement for P. salmonis resistance has been proposed as a feasible alternative for the control of this infectious disease in farmed fish. Genotyping by sequencing (GBS) strategies allow genotyping of hundreds of individuals with thousands of single nucleotide polymorphisms (SNPs), which can be used to perform genome wide association studies (GWAS) and predict genetic values using genome-wide information. We used double-digest restriction-site associated DNA (ddRAD) sequencing to dissect the genetic architecture of resistance against P. salmonis in a farmed coho salmon population and to identify molecular markers associated with the trait. We also evaluated genomic selection (GS) models in order to determine the potential to accelerate the genetic improvement of this trait by means of using genome-wide molecular information. A total of 764 individuals from 33 full-sib families (17 highly resistant and 16 highly susceptible) were experimentally challenged against P. salmonis and their genotypes were assayed using ddRAD sequencing. A total of 9,389 SNPs markers were identified in the population. These markers were used to test genomic selection models and compare different GWAS methodologies for resistance measured as day of death (DD) and binary survival (BIN). Genomic selection models showed higher accuracies than the traditional pedigree-based best linear unbiased prediction (PBLUP) method, for both DD and BIN. The models showed an improvement of up to 95% and 155% respectively over PBLUP. One SNP related with B-cell development was identified as a potential functional candidate associated with resistance to P. salmonis defined as DD. Oncorhynchus kisutch) globally, reaching about 160,000 tons in 2014, representing more than 90% of total production  (h2 = 0.11 to 0.41) (2 = 0.16)  and the total additive genetic variance explained by genetic markers is high (Marker assisted selection (MAS) can improve production traits in cases where the phenotypes are difficult to measure in the selected candidates ( is high . This meGenotyping by sequencing (GBS) is an alternative for genotyping in cases when SNP panels are not available. This approach reduces the complexity of the genome, and can be used to identify thousands of markers without prior marker discovery efforts or a reference genome. Currently, several approaches of GBS have been developed, significantly reducing the cost and labor (Double-digest restriction-site associated DNA (ddRAD) reduces DNA complexity by digesting DNA with two restriction enzymes (REs) simultaneously, without random shearing . Further details about this breeding population, in terms of reproductive management, rearing conditions, fish tagging and breeding objectives are described by P. salmonis was performed as it is described in detail by P. salmonis. Prior to the experiment, each fish was tagged with a passive integrated transponder (PIT-tag), placed in the abdominal cavity for genealogy traceability during the challenge test. The P. salmonis challenge was performed at Aquainnovo´s research station, located in Lenca River, X Region, Chile.The experimental challenge against 50) calculation, a random sample of 80 fish were selected from the population. Four different dilutions from the P. salmonis inoculum were evaluated . Twenty fish were challenged at each dilution. The dilutions were intraperitoneally (IP) injected with a volume of 0.2ml/fish. Daily mortality was recorded. This preliminary test spanned 26 days and a dilution of 1:680 was estimated as the LD50.For the lethal dose 50 (LD3) with a salt water concentration of 31 ppt. An average of eight individuals (ranging from 1 to 18) from each of the 107 families were distributed into each tank. The experimental challenge was performed through an intraperitoneal (IP) injection with 0.2ml/fish of the LD50 inoculum. The average weight of the fish at the inoculation was 279g (SD = 138g). To ensure that these fish were free from other pathogens, qRT-PCR was previously performed in order to control for the presence of Infectious Salmon Anemia Virus (ISAV), IPNV and Flavobacterium spp.For the main challenge, fish were distributed into three tanks  were measured and controlled. Fish were removed from the tanks after death, and a sample of the anterior kidney was taken and stored at -80° in RNALater. A necropsy assay was performed in conjunction with qRT-PCR to confirm the cause of death and the presence of P. salmonis. This was also done to control for the presence of other pathogens, such as Vibrio ordalii, Renibacterium salmoninarum and IPNV.The SbfI (specific for the CCTGCA|GG motif)) and MseI (specific for the T|TAA motif) in a 12 µl reaction volume, including 1 µl of SbfI and MseI adapter (8.3 pM), for 90 min at 37°. The ligation reaction was carried out by adding 1 µl of T4 ligase (NEB) diluted 1:100 in T4 buffer and incubating for 150 min at 37° and subsequently at 16° overnight.Ten ddRAD libraries were produced by multiplexing 828 individuals following the protocol described by Each ligation mix was diluted with 189 µl of dilute TE buffer (1:10). Kodak DNA Polymerase (ABM), a high-fidelity polymerase, was used to amplify DNA fragments with the correct adapters. PCR reactions (20 µl) were prepared containing 10 µl of PCR mix 2x, 1 µl of primer mix (10 µM each), 6 µl of diluted ligation mix and 3 µl of nuclease-free water. Each sample was PCR amplified using the following conditions: 95° for 2 min, followed by 17 cycles of 95° for 20s, 66° for 30s and 68° for 40s. After PCR, amplicon quality was checked by loading 5 µl on a 2% agarose gel. Subsequently, samples were pooled, so that the final concentration was similar among them within each library. Each library was concentrated through an evaporation step for 80 min in a Centrivap Mobile Console Centrifugal Evaporator (Labconco). This step was conducted until 300 µl of the generated library was obtained. Final volume of each library was loaded on a 1% agarose gel. Size of the bands selected for sequencing ranged from 750 and 1,500 bp and between 1,800 and 2,500 bp. DNA was purified through the QIAquick gel extraction kit (Qiagen) following manufacturer’s instructions. Finally, libraries were sequenced on an Illumina Hiseq2500 platform, using 150 base single-end.Raw sequence reads obtained from Illumina sequencing were analyzed using STACKS v. 1.41 . This soSample reads were trimmed to 134 bp for all subsequent analyses, demultiplexed and filtered using process_radtags. Rad-tags which passed the quality filter were aligned to the Oncorhynchus kisutch reference genome (GenBank: MPKV00000000.1) using BWA v. 0.7.12 . The refP < 1x10-6 and genotyping call rate < 0.80. Individuals were removed from the analyses if their genotyping call rates were below 0.70.A catalog of loci was constructed using the cstacks program using only the parents’ loci from pstacks. To build the catalog, the maximum number of mismatches allowed between sample tags was set to three (-n 3), and the matching was based on genomic location (g). After catalog construction, the sstacks program was used in order to match rad-tags against the catalog based again on genomic location (g), followed by the populations software, using defaults parameters. Loci were considered as valid if they were present in at least 75% of the individuals of the population. As a quality control step, the following parameters were used to filter low-confidence SNPs: Minor Allele Frequency (MAF) ≤ 0.05, Hardy-Weinberg Equilibrium (HWE) P. salmonis was defined as the day of death (DD) with values ranging from 1 to 50 depending on the time of death. Additionally, resistance was also evaluated as a binary (BIN) trait, either dead or alive at the end of the challenge. Values for this trait were 1 in cases where the fish died during the challenge, or 0 if the fish survived until the end of the challenge. Initial Body Weight (IW) for each fish, was measured prior to the IP injection.Resistance against All challenged individuals  were used for the pedigree-based approach, PBLUP, as a control for the performance evaluation of genomic predictions. A linear univariate animal model was used to estimate variance components and predict Estimated Breeding Values (EBVs) for DD, while for BIN a univariate threshold animal model was fitted . The modresidual . Both moHeritabilities for PBLUP models were computed as follows:g is a vector of random additive genetic polygenic effects with a distribution ∼N, similar to the PBLUP model described above, and implemented in the BLUPF90 software. The GBLUP is a modification of the PBLUP method, where ribed by . Only geThe single-step GBLUP (ssGBLUP) and weighted single-step GBLUP (wssGBLUP) models were similar to the PBLUP model except for a combined genomic and pedigree relationship. The kinship matrix used was H  . The scaThe different models were compared using a fivefold cross validation scheme. To reduce stochastic effects of sampling, the cross-validation analysis was replicated ten times. Briefly, all challenged individuals , were randomly separated into five validations sets. For each set, predictions were made by masking the animals’ phenotypes and using the remaining fish as a training set to estimate the marker effects. Thus, for each cross-validation run, the dataset was split into a training set (80%) and a validation set (20%). Accuracy was used to assess the performance of each model and was estimated as follows:P. salmonis resistance, as DD or BIN, four genome-wide association methodologies were performed using the BLUPF90 set of programs (In order to identify associations between genetic markers and https://doi.org/10.5061/dryad.b273q6p), Table S2 contains phenotypic data, and Table S3 contains the pedigree information. Table S4 contains the full list of genes located within the top ten 1-Mbp windows proximate to each SNP associated with P. salmonis resistance for DD and BIN identified through wssGBLUP. Table S5 contains information of the top ten markers which explain the highest percentage of the genetic variance for each method and trait. Table S6 contains results from the 10 replicated CV.Table S1 contains genotypic data , per base quality (Phred score) was evaluated. The average quality score ranged from 36 to 38 among libraries, indicating high quality of data. Illumina sequencing, including parents, yielded an average of 156,058,078 (± 16 million) of raw sequences per library. After initial QC, which included the removal of low quality sequences and reads with either missing or ambiguous barcodes, an average of 31,660,024 of the reads were removed. In parallel with QC, reads were trimmed to 134 bp. Thus, 79% of the raw reads were retained for subsequent analysis. To create a set of all possible alleles in the population, data sets of the parental samples were used to create a STACKS catalog. This catalog consisted of 106,309 unique ddRAD loci from which 20,068 markers from 757 individuals were identified. Quality filtering reduced this to 9,389 putative bi allelic SNPs (see Table S1) with an average sequencing depth of 38x ranging from 11x to 501x. These markers were identified segregating along the genome of 580 individuals (see Table S1).P. salmonis resistance was located on chromosome 11  gene; a gene related with innate host defense through B-cells development , made it possible to identify genes near this marker. Within ∼55 Kbp of this marker is the In the case of BIN, two molecular markers, explaining most of the genetic variance, were identified in all of the four models . One of P. salmonis infection. The 14 KDa Phosphohistidine phosphatase-like (PHPT1), gelsolin-like (GSN), and glutamine synthase-like (GS) genes are located on chromosome 29 and near associated markers. Claudin-10 was found on scaffold04124. These genes have been previously identified as being up-regulated in Salmo salar individuals with low susceptibility to P. salmonis . Moreover, the retinoic acid receptor RXR-alpha-A-like (RXRA) gene, located on chromosome 29, has previously been identified as a molecular biomarker for P. salmonis infections, and has been found to be down-regulated in macrophages during infection .A full list of genes that are located within a 1-Mbp window proximate to the suggestive markers associated with Significant additive genetic variation was estimated for both DD and BIN when using all the data from challenged individuals from the 107 maternal, full-sib families . Using tBased on a fivefold cross validation, the accuracy of the PBLUP model was slightly lower for DD (0.271) than for BIN (0.316) . When geInterestingly, the accuracies obtained with GBLUP were higher than ssGBLUP and wssGBLUP for both traits. These accuracy values reached 0.529, 0.299 and 0.417 for DD and 0.807, 0.314 and 0.3797 for BIN. For both traits, the models with better performance were, GBLUP > Bayes C > wssGBLUP > ssGBLUP.P. salmonis resistance was detected in the present study. Moderate heritabilities were estimated using different trait definitions, either for DD or BIN. Estimated heritabilities were higher for resistance as a binary trait when compared to DD.Significant genetic variation for P. salmonis, defined as day of death  through a bivariate linear model. Our study estimated a similar heritability value (0.14) in the same population. Differences in the estimations are likely due to the univariate model we used instead of a bivariate model. We also estimated heritability for P. salmonis resistance using a threshold model for the binary trait, which was higher (0.27) than the value reported for DD. These results are consistent with previous findings for resistance against P. salmonis in Atlantic salmon using pedigree information . When using a threshold model to analyze resistance as a binary trait, a heritability of 0.24 (0.04) was calculated . The genPhotobacterium damselae subsp. has a polygenic architecture in Gilthead Sea Bream (Sparus aurata). Using a 50K SNP genotyping array, it was possible to elucidate a moderately polygenic architecture of P. salmonis resistance in Atlantic salmon  ), a gene that is related with B-cell development . The phagocytic ability of B-cells was three times higher than those observed in neutrophils in head kidney, while in PB no differences were observed .We hypothesize that B-cell development could help in the immune response against Piscirickettsia salmonis in coho salmon through different algorithms (to our knowledge). We used a genomic model that assumes that genetic variances are controlled by an infinite number of markers with minimum effects on the trait . The GBLUP method calculates genomic relationship (G matrix) using all the genotyped markers for this reason. An extension of this method, which combines genomic (G) and pedigree-based (A) relationship information into the H relationship matrix  segregating in the current population . These values are consistent with the results obtained for resistance against the bacterial disease pasteurellosis in The results from our genomic predictions are in agreement with previous studies that showed higher estimated accuracies using GS than with PBLUP in the same half/full-sib family structure in salmon breeding programs (Oncorhynchus mykiss). The authors reported a reduction in the predictive ability (PA) of 20 and 26% using wssGBLUP, either with a chip array (40K) or through RAD sequencing (10K) respectively. The authors attributed this pronounced reduction to stochastic fluctuations due to a small training group of their study (n = 583). However, when the number of individuals was increased, this model outperformed the pedigree-based model, reaching a relative increase in accuracy up to 108%, for the same trait definition . However, there was an estimated relative increase in accuracy ranging from 20 to 155% when comparing PBLUP to the others genomic models. In this regard, Bayes C and GBLUP showed an increase in accuracy of 140% and 155%, respectively. These high values are similar to the improvements seen with resistance against BCWD defined as a binary trait, which reached a relative increase up to 97% , and not all the challenged individuals. In the current study we evaluated a wide range of different models of GS for their potential implementation in aquaculture. The performance of each implemented model varied according to the underlying genetic architecture of the trait  found within this farmed coho salmon population. Additional studies are needed to elucidate the minimum number of markers necessary for GS.Our results, using ddRAD sequencing, are in agreement with other genomic studies, which utilize GBS techniques with aquaculture species. Using RAD sequencing, some authors have previously performed genomic-wide association studies in rainbow trout looking for associations with disease resistance. From a total of 4K identified SNPs, 31 markers were significantly associated with either BCWD or Infectious Hematopoietic Necrosis Virus (IHNV) resistance as a binary trait  evaluation of genomic selection methods; and ii) allowing the identification of molecular markers associated with the trait by means of GWAS. The aim was maximizing the phenotypic variance within the sample while keeping a balanced representation of fish per family. Thus, genotyping strategy was not totally random, but specifically focused on the most extreme families; 17 resistant and 16 susceptible families. We aimed at genotyping all the fish belonging to each selected family. Thus, each family was represented within the sample with an average of 23 (ranging from 11 to 43) fish/family.P. salmonis resistance and other complex traits in salmonids will be facilitated by the international initiative on the Functional Annotation of All Salmonid Genomes, FAASG , has a substantial advantage in terms of accuracy when compared to pedigree-based model for either DD or BIN. The improvement was up to 95 and 155% respectively in the current population. The association analyses were used to identify a gene related with B-cell development, which could also be involved in resistance against P. salmonis. To our knowledge, this is the first study aimed at dissecting the genetic architecture of P. salmonis resistance in a coho salmon population.Moderate significant genetic variation was estimated for resistance against www.g3journal.org/lookup/suppl/doi:10.1534/g3.118.200053/-/DC1Supplemental Material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


Genomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method uses a relationship matrix computed from marker and pedigree information, in which missing genotypes are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models using explicitly imputed genotypes for non-genotyped individuals.Carcass records included 988 genotyped Hanwoo steers with 35,882 SNPs and 1438 non-genotyped steers that were measured for back-fat thickness (BFT), carcass weight (CWT), eye-muscle area, and marbling score (MAR). Single-trait pedigree-based BLUP, Bayesian methods using only genotyped individuals, SSGBLUP and SSBR methods were compared using cross-validation.π. Gains in accuracy with the single-step methods ranged from +0.06 to +0.09 for BFT and from +0.05 to +0.07 for MAR. For CWT, SSBR always outperformed the corresponding Bayesian methods that used only genotyped individuals. However, although SSGBLUP incorporated information from non-genotyped individuals, prediction accuracies were lower with SSGBLUP than with BayesC (π = 0.9999) and BayesB (π = 0.98) for CWT because, for this particular trait, there was a benefit from the mixture priors of the effects of the single nucleotide polymorphisms.Methods using genomic information always outperformed pedigree-based BLUP when the same phenotypic data were modeled from either genotyped individuals only or both genotyped and non-genotyped individuals. For BFT and MAR, accuracies were higher with single-step methods than with BayesB, BayesC and BayesCSingle-step methods are the preferred approaches for prediction combining genotyped and non-genotyped animals. Alternative priors allow SSBR to outperform SSGBLUP in some cases. BayesC is another widely-used Bayesian mixture model, in which a common variance is used for all SNPs instead of locus-specific variances [π treats π as an unknown parameter with a uniform prior distribution [Since breeding technologies using genome-wide single nucleotide polymorphism (SNP) panels became available, genomic selection was rapidly adopted for improvement of livestock and has replaced the traditionally used pedigree-based best linear unbiased prediction (PBLUP). The BayesA and BayesB hierarchical Bayesian models with locus-specific variances were proposed by Meuwissen et al. . BayesB π , 3. Whenariances , and a mribution .π. SSBR methods may promise higher prediction accuracies and provide computational benefits when many animals are genotyped. In SSGBLUP, the distribution of marker effects conditional on the variance of marker effects is assumed univariate normal, whereas in SSBR, the prior for marker effects can follow a t-distribution, a double exponential distribution or mixture distributions, which may be advantageous in some situations.In general, the number of individuals with genomic information is a small subset of the individuals represented in the population with pedigree and phenotypic information. “Single-step” methodologies were developed to take advantage of all pedigree, phenotypic and genomic information simultaneously , 7. The π were compared in terms of cross-validation accuracies.In this paper, prediction accuracies from PBLUP, BayesB, BayesC, BayesCπ, SSGBLUP and SSBR-B, SSBR-C, SSBR-CYoung Hanwoo bulls are routinely progeny-tested in batches at the Hanwoo Improvement Center . DNA samples were collected from steers that included the progeny-tested offspring from the 46th to 51st selection batches. SNP genotypes were determined using Illumina Bovine SNP50 v1 (50 k) or Bovine HD (778 k) beadchips .Carcass records were recorded at harvest at about 24 months of age. The carcass traits used in the analyses were back-fat thickness (BFT), carcass weight (CWT), eye-muscle area (EMA), and marbling score (MAR). Park et al.  reported−6) based on a Chi square test, or had a minor allele frequency (MAF) lower than 0.01, or a missing rate higher than 0.1 were excluded from further analysis. For the genotyped animals, SNPs with missing genotypes were imputed using Beagle 3.3 [Of the 44 k SNPs that are included on both the 50 and 778 k beadchips, only autosomal SNPs with known map location were used. For quality control, SNPs that departed from the Hardy–Weinberg equilibrium (p < 10agle 3.3 . After tThe numerator relationship matrix (NRM) based on pedigree information and the genomic relationship matrix (GRM) based on SNP genotypes were compared. Nineteen individuals, which showed unreasonable deviations between the NRM and GRM coefficients that were probably due to errors in the DNA sampling, were eliminated. Among these 19 individuals, five appeared to have been genotyped twice with different ID since their GRM relationship coefficients were near 1.0 while their NRM relationship coefficients were close to 0. For the other 14 individuals, either the GRM relationship coefficients were near 0 while those of the NRM were near 0.25 as would be the case for mistakenly recorded half-sib individuals, or the GRM relationship coefficients were near 0.25 while those of the NRM were near 0 as would be the case for half-sibs mistakenly recorded as unrelated. After elimination of these suspect individuals, the correlation coefficient between NRM and GRM increased from 0.856 to 0.866. Finally, 988 genotyped individuals remained for genomic prediction with a mean MAF of 0.243 and mean observed heterozygosity of 0.326.Additional carcass records for 1438 non-genotyped progeny-tested steers were collected from the 39th to the 51st selection batches for the single-step and PBLUP analyses. Ancestors of the 2426 individuals with carcass records contributed to an 11-generation pedigree file that included 9637 animals.Genotyped individuals were assigned to five mutually exclusive groups for cross-validation. K-means clustering based on pedigree relationship coefficients was used to minimize the relatedness between training and validation sets . The fivy is a vector of adjusted phenotypic records from ny animals, 1 is a vector of 1s, μ is the overall mean, Z is the design matrix allocating records to breeding values, u is the vector of breeding values, e is a random vector of residuals. It was assumed that A is the numerator relationship matrix and σg2 is the additive genetic variance. Residuals were assumed to be independently and identically distributed (iid) with null means and variance σe2. Pedigree-based BLUP with phenotypes either on all animals or only on genotyped animals were referred to as PBLUP  and PBLUP-G , respectively. Adjusted phenotypes were used to account for fixed effects in the validation set.In these analyses, the adjusted phenotypes were modeled as:y, 1 and e are ny × 1 vectors for ny = 988 minus genotyped validation animals, μ is as defined before, Mg is the ny × p matrix of SNP covariates at p loci, and α is a p × 1 random vector of allele substitution effects. A flat prior was used for μ. The prior for e was π methods [In these analyses, the adjusted phenotypes were modeled as:entclass1pt{minimay is the vector of adjusted phenotypes as before except that it includes both genotyped and non-genotyped individuals i.e. ny = 2426 minus validation animals, μ and e are as defined before, with residuals that are iid with null means and variance σe2, Z is the design matrix allocating records to breeding values, u is the vector of breeding values for both genotyped and non-genotyped individuals but now Agg is the 988 order partition of the numerator relationship matrix A that corresponds to genotyped animals, Ann is the 11,075 order partition of A that corresponds to non-genotyped animals, Ang or Agn are partitions of A corresponding to relationships between non-genotyped and genotyped animals or vice versa, and G is a GRM of order 988. We applied three methods to construct the GRM. The standard G was constructed as T being the centered matrix of SNP covariates  were performed using the BayesB method with the variance .π values are in Fig. π increased, and fewer SNPs were assumed to have non-zero effects. For CWT, we observed a different pattern with accuracies increasing as π increased and accuracies of BayesB being always higher than those of BayesC. These two results suggest that CWT is influenced by a few quantitative trait loci (QTL) that explain a large proportion of the genetic variance. The proportions of genetic variance explained by 1-Mb non-overlapping genomic windows are in Fig. Predictive accuracies for the four traits obtained with BayesB and BayesC for different π values that maximized the cross-validation accuracies in BayesB were 0.95, 0.98, 0.95, and 0.6 for BFT, CWT, EMA and MAR, respectively, and were used in SSBR-B. The corresponding π values in BayesC were 0.98, 0.9999, 0.98, and 0.6 for BFT, CWT, EMA and MAR, respectively, and were used in SSBR-C.The π value tend to capture the same few QTL with large effects, thus their similar prediction accuracies.Several windows showed distinctly larger effects than the rest of the genome for BFT and EMA, but the window with the largest effect explained only about 1% of the genetic variance. For MAR, the windows showed smaller effects than those for BFT and EMA with the most significant window explaining less than 0.3% of the genetic variance. These results show that, for BFT, EMA and MAR, many QTL each with a small effect are widely distributed across the whole genome, which is consistent with the infinitesimal model. In contrast, for CWT, one window on chromosome 4 and two windows on chromosome 14 explained together more than 15% of the genetic variance while the other windows showed small effects. Using single-SNP association tests, Lee et al.  found siπ = 0) without estimated variances were identical and equal to 0.351 for BFT, 0.415 for CWT, 0.413 for EMA and 0.377 for MAR as expected since these models with assumed variance parameters are equivalent in terms of prediction of breeding values [Prediction accuracies of models SSGBLUP-I and SSBR-C (π = 0), and BayesCπ, SSGBLUP-I and SSGBLUP-II and SSBR-B, SSBR-C, SSBR-C (π = 0), and SSBR-Cπ.To compare methods that use all individuals with those that use only genotyped individuals, prediction accuracies Fig.  were calπ than with PBLUP-G for all traits. When data from both genotyped and non-genotyped individuals were used, prediction accuracies of the single-step methods were higher than those of PBLUP for all traits.Methods using genomic information always outperformed PBLUP with the same phenotypic data. Using data from only genotyped animals, accuracies were higher with BayesB, BayesC and BayesCπ. Gains in accuracy with the single-step methods ranged from +0.06 to +0.09 for BFT and from +0.05 to +0.07 for MAR, whereas for EMA, there was no advantage and only a slight gain in accuracy was observed in PBLUP versus PBLUP-G. For CWT, SSBR always outperformed the corresponding Bayesian methods using only genotyped individuals and the gains in accuracy were +0.05 (SSBR-C (π = 0) vs. BayesC (π = 0)), +0.01 (SSBR-C (π = 0.9999) vs. BayesC (π = 0.9999)), +0.10 (SSBR-Cπ vs. BayesCπ) and +0.04 (SSBR-B (π = 0.98) vs. BayesB (π = 0.98)). However, although information from non-genotyped individuals was incorporated, for CWT prediction accuracy of SSGBLUP was lower than that of BayesC (π = 0.9999) and BayesB (π = 0.98) due to the benefits of mixture priors of the SNP effects for this particular trait.For BFT and MAR, prediction accuracies of the single-step methods were higher than those of BayesB, BayesC and BayesCThe differences in accuracies between single-step methods  and for genomic methods (SSBR-C compared to BayesC). Simultaneous use of all pedigree, phenotypic and genomic information in single-step methods improved prediction accuracy relative to methods that only use data from genotyped animals for all traits, except EMA. For EMA, there was similarly little benefit from including the extra data in the PBLUP analyses (compared to PBLUP-G).Both SSBR and SSGBLUP methods showed similar prediction accuracies when the genetic architecture appeared to approach the infinitesimal model as was the case for BFT, EMA, and MAR. However, for CWT, prediction accuracies of the SSBR methods were higher than those of SSGBLUP when there were only a few QTL with large effects. For that trait, the SSBR methods benefited from the use of the mixture priors.π was applied. However, it is computationally intensive to find this value of π through cross-validation. Methods for estimating π are beneficial, but they require large datasets. An appropriate π was more critical for the Bayesian methods that only used genotyped individuals than for the SSBR methods. For example, differences in prediction accuracies between BayesC (π = 0.9999) and BayesCπ reached values of 0.10 but only of 0.01 between SSBR-C (π = 0.9999) and SSBR-Cπ. Presumably, priors become less important in the single-step analyses where more data are used.The largest benefit of the SSBR methods was reached when an appropriate Three factors can result in increased accuracy. First, the inclusion of genomic information, which was revealed when genomic methods were compared to pedigree-based BLUP. Second, the use of additional phenotypic information from including non-genotyped individuals, which was shown by comparing Bayesian methods using only genotyped animals with their corresponding single-step methods. Third, the use of methods that exploit genomic regions with large effects, as was found for one of the four traits using either mixture priors or iterative weighted methods for computing GRM.SSGBLUP with iterative calculation of weighted genomic matrices had the disadvantage that it reduced prediction accuracy and increased bias for traits that were not associated with genomic regions with large effects, whereas the Bayesian models with mixture priors performed comparably regardless of the genomic architecture. SSGBLUP with iterative calculation of weighted genomic matrices shrinks small effects to zero, and more so with each additional iteration. There is no statistical basis to determine the optimal number of iterations except by trial and error, and neither one nor five iterations resulted in improvements in this dataset.In this study, which is based on a small population of Hanwoo cattle, prediction accuracy was higher for all genomic evaluations compared to pedigree-based BLUP. In such a situation, where the genomic reference population is relatively small, single-step methods, which can routinely account for genomic regions with large effects when they are present, are recommended for additional gains in accuracy.The “single-step” methodologies, which take advantage of all pedigree, phenotypic and genomic information simultaneously, give similar or higher prediction accuracies compared to methods using only genotyped individuals. Compared to SSGBLUP, the SSBR methods showed additional benefit for the CWT trait, which is associated with QTL with large effects. There is no disadvantage in using SSBR methods for all traits.