The traits studied are all of economic importance and included morphometric traits , edibility traits , and disease traits (Polydora sp. and Marteilioides chungmuensis). A total of 18,849 single nucleotide polymorphisms were obtained from genotyping by sequencing and used to estimate genetic parameters (heritability and genetic correlation) and the prediction accuracy of genomic selection for these traits. Multi-locus mixed model analysis indicated high estimates of heritability for edibility traits; 0.44 for moisture content, 0.59 for taste, and 0.72 for tenderness. The morphometric traits, shell length, shell width, shell depth and shell weight had estimated genomic heritabilities ranging from 0.28 to 0.55. The genomic heritabilities were relatively low for the disease related traits: Polydora sp. prevalence (0.11) and M. chungmuensis (0.10). Genomic correlations between whole weight and other morphometric traits were from moderate to high and positive (0.58–0.90). However, unfavourably positive genomic correlations were observed between whole weight and the disease traits (0.35–0.37). The genomic best linear unbiased prediction method (GBLUP) showed slightly higher accuracy for the traits studied (0.240–0.794) compared with both BayesA and BayesCπ methods but these differences were not significant. In addition, there is a large potential for using low-density SNP markers for genomic selection in this population at a number of 3000 SNPs. Therefore, there is the prospect to improve morphometric, edibility and disease related traits using genomic information in this species.Genomic selection has been widely used in terrestrial animals but has had limited application in aquaculture due to relatively high genotyping costs. Genomic information has an important role in improving the prediction accuracy of breeding values, especially for traits that are difficult or expensive to measure. The purposes of this study were to  Crassostrea angulata) is an important aquaculture species around the world, especially in Asian and European countries [6 metric tonnes in 2014 [Marteilioides chungmuensis causes spots on the soft tissue of the Portuguese oyster; while polychaete “mud-worms” of the genus Polydora cause black blisters on the inner surfaces of the shell [Polydora sp. may compete with oysters for food [The Portuguese oyster  was assessed by the presence of dark spots  on the inner valves of the oysters [M. chungmuensis, was inferred from spots (marks) present on the oyster gonad at harvest. Disease traits were recorded as either 1 for the presence or 0 for the absence of the particular parasite. Shell length, shell width and shell depth were measured with calipers and an electronic scale was used for shell weight and wet weight of the entire oyster. Tenderness was assessed by pushing one finger on meat muscle and recorded as 1 for soft meat and 0 for firm meat. Taste was evaluated by the sensory testing panel to determine whether the soft tissue tasted salty (1) or mild (0) . The sof oysters . The preGenomic DNA was extracted and purified by Diversity Array Technology Pty Limited  with a DAfter PCR equimolar amounts of amplification products from each sample of the 96-well microtiter plate were bulked and applied to c-Bot (Illumina) bridge PCR followed by sequencing on Hiseq2500 . The sequencing (single read) was run for 77 cycles. Sequences generated from each lane were processed using proprietary DArT analytical pipelines. In the primary pipeline the fastq files were first processed to filter away poor quality sequences, applying more stringent selection criteria to the barcode region compared to the rest of the sequence. In that way the assignments of the sequences to specific samples carried in the “barcode split” step were very reliable. Filtering was performed on the raw sequences using the following parameters: filter with filter parameters, barcode region with Min Phred pass score 30 and Min pass percentage 75, whole read with Min Phred pass score 10 and Min pass percentage 50.Approximately 2,500,000 sequences per barcode/sample were identified and used in marker calling. Finally, identical sequences were collapsed into “fastqcoll files”, which were “groomed” using DArT PL’s proprietary algorithm to correct for low quality bases. The “groomed” fastqcoll files were used in the secondary pipeline for DArT PL’s proprietary SNP and SilicoDArT (presence/absence of restriction fragments in representation) calling analysis algorithms DArTsoft14, which clustered all tags from all libraries using DArT PL’s C++ algorithm at the threshold distance of 3 for SNP calling. Technical parameters, especially the balance of read counts for the allelic pairs, were used to parse into separate SNP loci. Additional selection criteria included analysis of approximately 1000 controlled cross populations. Testing for Mendelian distribution of alleles in these populations facilitated selection of technical parameters discriminating well true allelic variants from paralogous sequences. In addition, multiple samples were processed from DNA to allelic calls as technical replicates and scoring consistency was used as the main selection criteria for high quality/low error rate markers. Calling quality was assured by high average read depth per locus . A total of 18,849 SNPs were produced after quality control under DArTseq standard, and 11,766 SNPs passed quality filtering with call rates of 50% for markers and samples that used for the downstream analyses.Polydora sp. and Marteilioides chungmuensis) were analysed on the observed binary scale [Genetic parameters were estimated using a genomic best linear unbiased prediction (GBLUP) approach. Univariate linear mixed models  were used to estimate the variance components and trait heritabilities. A total of 11,492 SNPs was used to estimate genetic parameters in this Portuguese oyster population:ry scale .Genetic correlations between traits using SNP marker information were estimated with bivariate models. The bivariate models were an extension of the univariate models as described in Mrode . The fixy = Xb + ijgj + eA (Model 2); where y is the phenotypic vector; Xb is the fixed term, where b is a vector containing fixed effects: generations, sex, and ijgjA is the estimated genomic breeding value (GEBV) of individual i with A is the marker matrix and g are the marker effects. All effects of markers, phenotypic and residual variance, and GEBVs estimated by BayesA and BayesCπ were carried out using the R package BGLR [Our study evaluated 3 methods for genomic prediction: BayesA , BayesCπage BGLR . The MCMage BGLR .Five-fold cross-validation was used to evaluate the prediction accuracy . Phenotypes from the validation population were masked and predicted from the training population. Prediction accuracy was calculated as the correlation between the estimated breeding values (EBVs) of the validation set and the actual phenotypes divided by the square root of the estimated heritability of total population. Mean prediction accuracy values obtained from the different sets were computed and compared between pedigree and genomic approaches.Two strategies to assess the effects of marker density on the accuracies of genomic prediction were used gBLUP method. First, the density of the SNP panel used to build the genomic relationship matrix was changed by progressively increasing the minor allele frequency (MAF) threshold from 0.01  to 0.475 , resulting in progressively fewer markers being used. For the second strategy, the SNP panel members were randomly selected from the full dataset with three replicates for each panel size.Polydora sp. and 0.13 ± 0.02 for M. chungmuensis (2 = 0.28–0.72) and not unexpectedly, were substantially higher than those obtained for disease related traits, which were 0.11 for Polydora sp. prevalence and 0.10 for M. chungmuensis (2 = 0.50–0.55) were considerably higher than those reported for the Pacific oyster, Crassostrea gigas (h2 = 0.23–0.26) [Chlamys farreri (h2 = 0.39–0.54) [The mean and standard errors for morphometric traits were: 7.42 ± 0.05 (cm) for shell length, 4.16 ± 0.03 (cm) for shell width, 2.76 ± 0.02 (cm) for shell depth, 38.60 ± 0.60 (g) for shell weight. Those for edibility traits were 12.55 ± 0.28 (%) for moisture content, 0.05 ± 0.01 for taste, 0.91 ± 0.02 for tenderness. For disease traits they were: 0.26 ± 0.02 for gmuensis . Heritabgmuensis . Our est23–0.26)  but were39–0.54) . The difPolydora sp. prevalence and 0.10 for M. chungmuensis, suggesting that there is limited scope for selection against these disease traits in this population using genomic information. Our heritability estimates for disease related traits were significantly lower than those obtained for Ostreid herpesvirus disease trait in the Pacific oyster [Crassostrea gigas [C. gigas [Bonamia ostreae in flat oysters, Ostrea edulis [Marteilia sydneyi in Sydney rock oyster, Saccostrea glomerata [B. roughleyi and M. sydneyi in S. glomerata [H. nelsoni and Perkinsus marinus in the eastern oyster, Crassostrea virginica [Finally, the additive variance component estimates in disease related traits were small, with heritability estimates of 0.11 for c oyster . Howeverc oyster . Interesea gigas ; summer C. gigas ; Bonamiaa edulis ; Marteillomerata ; B. rouglomerata ; and H. irginica . In genegr = 0.35–0.37) that were in line with those previously reported in this population using a pedigree relationship matrix [Polydora infestation. Lastly, the favourably positive and significant genetic correlation between whole weight and moisture content (gr = 0.60) suggests that whole weight is driven by water not viscera in this Portuguese oyster population. All phenotypic correlations among these traits had slightly smaller magnitudes than the genetic correlations using genomic information , suggesting that these traits can co-select via selective breeding. Selection for one trait will result in desirable changes in the remaining traits. The high genetic correlation indicates that pleiotropic effects, or physical linkage and linkage disequilibrium could exist , and a sp matrix . This caormation .Marteilioides chungmuensis (0.12 PBLUP to 0.24 GBLUP) and for Polydora sp. (0.15 PBLUP to 0.30 GBLUP). Our study is in agreement with previous reports that indicate breeding value prediction accuracy to be higher using genomic information than pedigree relationships in aquaculture species [The prediction accuracy using the genomic information (G-matrix) for the nine traits studied were higher than that obtained using the Best Linear Unbiased Prediction using pedigree information  , Table 3 species ,54,55,56To find a cost-effective genotyping strategy, we investigated the effects of SNP density on prediction accuracy of breeding values. The first series used subsets of SNPs defined by increasing the minor allele frequency (MAF) threshold leading to a decrease of SNP panel size. The second series involved randomly selecting the given number of SNPs to include in the panel with three replicates for each panel. The number of SNP markers had little impact on prediction accuracy until marker densities decreased below about 3000 SNPs. With the MAF approach, the genomic prediction accuracies obtained from lower density SNP panels ranged from 0.75  to 0.67  . Using tPolydora sp. prevalence (0.11) and M. chungmuensis (0.10). Moderate to high and positive genetic correlations among whole weight and other morphometric traits (0.58–0.90); meanwhile, unfavourably positive genetic correlations were obtained between whole weight and the disease traits (0.35–0.37). There are no significant differences in prediction accuracy among three methods . Finally, there is large potential for using low-density SNP markers for genomic selection in this population.The moderate to high estimates of genomic heritability for edibility and growth traits; 0.44 for moisture content, 0.59 for taste, 0.72 for tenderness, and from 0.28 to 0.55 for shell length, shell width, shell depth and shell weight. Whereas, the genomic heritabilities were relatively low for the disease traits:


Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype–phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect remain unnoticed in many GWAS approaches. Here, we propose a two-step procedure to address this problem. In a first step, cubic splines are fitted to the test statistic values and genomic regions with spline-peaks that are higher than expected by chance are considered as quantitative trait loci (QTL). Then the SNPs in these QTLs are prioritized with respect to the strength of their association with the phenotype using a Random Forests approach. As a case study, we apply our procedure to real data sets and find trustworthy numbers of, partially novel, genomic variants and genes involved in various egg quality traits. The importance of genotype-phenotype association studies to understand the genetic basis of traits, either qualitative or quantitative, is well established . Single-A further challenge in analyzing quantitative traits is to discover loci having moderate to small phenotypic effects. The SNPs present either inside or in the vicinity of these quantitative trait loci (QTLs) display association strengths which are too small to exceed the statistical significance threshold value. Consequently, only a small part of the overall variance is captured in a typical GWAS analysis . HaplotyAll the above mentioned methodologies have their advantages and challenges. Among other factors, the success of different association methods is heavily influenced by the genetic architectures of the trait of interest ,34. GiveIn this study, we propose a framework that mainly focuses on the identification of robust genotype-phenotype association signals by combining the important SNPs obtained in different association analyses. For this purpose, we first perform a signal detection strategy using the test statistic values of single-SNP based GWAS analysis for the detection of QTLs. Second, using a Random Forests based feature selection technique, we assess the relative importance of SNPs regarding their association level with the phenotype. Unlike the previous two stage studies ,28,46, wIn order to demonstrate the functionality of our framework, we have analysed two different GWAS (genotype-phenoype) datasets in this study. The first dataset contains the eggshell strength (ESS) measured at two different time points during the productive life of chicken and the second dataset is related to egg weight (EW) in chicken. Our results show that, using our framework, we are able to identify important novel markers/genes which could provide new insights into the genetic architecture of these traits.In this study, we have analysed two chicken datasets to detect genotype-phenotype associations underlying economically important egg quality traits, namely eggshell strength (ESS) and egg weight (EW).Dataset 1: The first dataset contains eggshell strength recorded at two time points during the lifetime of the birds. We have previously used this dataset in Reference [® 600 K Chicken Genotyping Array. The corresponding phenotypic data contain de-regressed breeding values of eggshell breaking strength from individual birds at two different stages of production. The eggshell strength was measured at the poles of an egg and represents the force in Newton needed to break the egg. For the first time point, ESS was recorded at the age of 42, 45, and 48 weeks, while for the second time point, recordings were made at the age of 64 and 68 weeks. Average values of the recorded breaking strengths at time point 1 (ESS1) and time point 2 (ESS2) were then used in an animal model for the breeding value estimation. In this analysis, we also used pedigree data consisting of 40,545 individuals from six generations, in total. The estimated breeding values were then de-regressed following Garrick et al. [p-value < 1 × 10eference  to identk et al.  to obtaiDataset 2: The second dataset pertains to egg weight recorded in 36 weeks old adult birds. The dataset has been previously analysed to perform GWAS of age dependent egg weights (EW) in chicken [® 600 K Chicken Genotyping Array. From the seven age levels analysed in the original study, we re-analysed only EW at 36 weeks of age as the most significant associations were reported for this trait. The genotypic data were filtered for SNP call rates, minor allele frequencies and Hardy Weinberg equilibrium using the same threshold values as given for the first dataset. After filtering, we used 294,705 SNPs and 1036 birds in our analysis. chicken . The datOur proposed analysis framework consists of six phases to detect important SNPs associated with phenotypes under study.Phase 1: Following the study of Liu et al. [u et al. , we perfu et al.  softwaresoftware .(1)y=WαsimpleM approach [In Equation , y is theference , the adjapproach  to evaluPhase 2: For the elaboration of association signals embedded in the Wald test statistics, we apply a cubic smoothing spline on these values. The cubic smoothing spline is a piece-wise defined cubic function and is based on the same principle as the normal cubic regression. The assumption implicit in this approach is that the individual association values are observed with noise and that these values can be considered as estimations of some underlying function g. Given the marker positions in the genome g is estimated by minimizing the following expressiong corresponding to SNP i at chromosomal position x. The assumption of g being continuous and twice differentiable leads to its approximability via a cubic smoothing spline [In Equation , the firth curve . g″ reprg spline . Thus, aPhase 3: For delineation of the obtained association signals in the form of peaks, we determined the inflection points based on the smoothed values. As the smoothing curve represents a function Phase 4: In order to separate the peak regions having association signals higher than those arose by chance, we have created a null distribution by permutating the phenotypic data. For the construction of the null distribution, Phases 1, 2 and 3 have been applied to each permutated dataset and the maximum peak values were recorded. In our analysis, we permutated the dataset 1000 times. In the real dataset, we defined a peak region as a QTL if the corresponding peak height exceeds the 95th percentile of the null distribution.Phase 5: Adopting the strategy from our previous study [us study , the Ranus study  which isPhase 6: Finally, to prioritize the SNPs which are in the QTLs detected in Phase 4, we use the important SNPs from Phase 5 and define the SNPs discovered in both Phases as robust SNPs in our analysis.We scan the genome to identify the genes corresponding to the robust SNPs using BioMart . Only thIn our study, we suggest an analysis framework to improve the power of commonly implemented GWAS. The overall framework comprises the following steps. First, a linear mixed model (LMM) based single-SNP GWAS is performed to obtain test statistics representing the strength of association between each SNP and the phenotype. Second, performing the signal detection strategy by fitting a cubic smoothing spline on the test statistic values, we identify QTLs. Third, we apply the RF classifier using the Boruta algorithm to assess the relative importance of SNPs regarding the level of their association with the phenotype. Finally, the important SNPs are prioritized within those QTLs to discover a robust set of SNPs associated with the phenotype. Two different GWAS (genotype and phenoype) datasets related to eggshell strength (ESS) and egg weight (EW) have been analysed using this framework to demonstrate its functionality.SimpleM method [In order to demonstrate the limited power of conventional single-SNP based GWAS analysis, we first analysed both datasets using a LMM as suggested for the analysis of egg weight (EW) in the study of Liu et al. . In the M method  for multToday it is well known that quantitative traits are influenced by a large number of genes mostly having small effects. But as shown in To identify genes showing weak association signals that remain undetected in the typical GWAS analysis, we applied our analysis framework to both datasets.The analysis of the ESS datasets reveals eight QTLs for ESS1 and five QTLs for ESS2 based on the signal detection approach. The details of these QTLs are given in Of particular interest here is the LD analysis that we performed based on the robust SNPs to further elaborate their makeup in the identified QTLs. The LD analysis reveals, as expected, that the robust SNPs inside the QTLs have a remarkably higher level of LD than the surrounding SNPs see A. To thiThe extraction of the genes corresponding to the robust SNPs reveals 14 and 3 genes for ESS1 and ESS2, respectively  and BNC1 (Basonuclin 1) have functions related to the homeostasis of ions in the cell. On the other hand, the CDH4 (Cadherin-4) gene that was found for both ESS1 and ESS2 encodes for R-cadherin/cadherin-4 which are single-chain integral membrane glycoproteins and mediate calcium-dependent cell—cell adhesion. Reduced levels of these cell adhesion molecules lead to the age-related decline in tissue homeostasis . Along wThe analysis of the EW dataset resulted in the detection of eleven QTLs including the one revealed on chromosome 1 (GGA1) in the original study . The addThe extraction of the genes associated with the robust SNPs related to EW results in the determination of 16 genes , can lead to the discovery of genes which despite having association to the phenotypes, remain undetected in the typical GWAS. Especially, the combined usage of both methods  not only identify the QTLs having small effects but also helps to identify the SNPs in those QTLs that had their association value higher than expected by chance. see A. Moreov2+) and bicarbonate (HCO3−) which are prerequisites for eggshell mineralization in the uterus part of the oviduct. These ions are supplied in large amounts via trans-epithelial transport in the uterus, for which ion channels, ion pumps and ion exchangers are required [+) from the uterine cell of chicken to plasma. In this regard, two paralogs  of the ATP6V0A2 gene found in our study have been previously reported to transfer hydrogen ion from chicken uterine cells to blood plasma during the process of egg calcification [Although both of the traits analysed in this study were related to egg quality, the identified genes are distinct for ESS and EW in this study. This distinction was expected as the chickens genotyped in the two datasets have different genetic backgrounds. Remarkably, however some of these genes are involved in the same biological function related to transmembrane transportation of elements including minerals and organic compounds. Further, the majority of the ESS1 related genes are responsible for the availability of calcium (Carequired . This furequired . Similarrequired . The hydfication ,75. Whenfication . Transmefication . Similarfication .The other important functional category that many of the genes related to ESS could be linked to is cell morphogenesis. Previous studies presenting the transcriptome profile of different segments of the chicken oviduct have also reported a large number of genes annotated for functions related to morphogenesis ,78,79. I


The genetic underpinnings of calf mortality can be partly polygenic and partly due to deleterious effects of recessive lethal alleles. Prediction of the genetic merits of selection candidates should thus take into account both genetic components contributing to calf mortality. However, simultaneously modeling polygenic risk and recessive lethal allele effects in genomic prediction is challenging due to effects that behave differently. In this study, we present a novel approach where mortality risk probabilities from polygenic and lethal allele components are predicted separately to compute the total risk probability of an individual for its future offspring as a basis for selection. We present methods for transforming genomic estimated breeding values of polygenic effect into risk probabilities using normal density and cumulative distribution functions and show computations of risk probability from recessive lethal alleles given sire genotypes and population recessive allele frequencies. Simulated data were used to test the novel approach as implemented in probit, logit, and linear models. In the simulation study, the accuracy of predicted risk probabilities was computed as the correlation between predicted mortality probabilities and observed calf mortality for validation sires. The results indicate that our novel approach can greatly increase the accuracy of selection for mortality traits compared with the accuracy of predictions obtained without distinguishing polygenic and lethal gene effects. Important fractions of deleterious mutations segregating in diploid organisms are recessive alleles that cause fatal effects when present in a homozygous state. In dairy cattle breeds, intensive use of a limited number of elite breeding sires through artificial insemination has led to the spread of recessive lethal alleles in populations  into polygenic risk probabilities using normal density and cumulative distribution functions. Simulated data are used to test the advantages of the presented approach by comparing its prediction accuracies with those of an alternative approach where risk probabilities are predicted without distinguishing polygenic and recessive lethal allele effects.In this section, we present an approach that allows the prediction of genetic risk posed to the survival of future offspring by both the recessive lethal locus and polygenic components of a breeding animal as the basis for selection to improve mortality traits.i with recessive lethal allele a and wild-type allele A, the probability of the offspring’s mortality due to the lethal allele (hereafter referred to as plethi) is the probability of the offspring having an aa genotype. This can be computed given the sire’s genotype and the frequency of the recessive allele in the population (pa), which accounts for the probability of receiving a copy of the lethal allele from a dam under the assumption of random mating:The computation of risk probabilities from recessive lethal alleles is straightforward when using the carrier status of an animal as inferred from its genotype and the recessive allele frequencies in the population. Given the genotype of a breeding animal, e.g., a sire, for a particular recessive lethal locus, an offspring’s risk probability of succumbing to a lethal gene is the probability that it receives two copies of the recessive lethal allele. Assuming a locus i for an offspring can be given as:Assuming complete penetrance and given three possible outcomes of the sire’s genotype, i.e., AA, Aa, or aa, the lethal risk probability at locus In cases of recessive lethal alleles with a penetrance other than 100%, the risk probability in Eq.  and logistic regression (logit) models. Despite their violations of normality assumptions, linear models (LM) have also been employed in many studies for genetic analysis of categorical traits , in relation to a fixed threshold (τ). In the context of mortality traits, the categories of response are usually “survival,” denoted as event 0, or “death,” denoted as event 1, i.e., a binary response, during a given monitored period. Accordingly, the observed categorical outcomes (y) are linked to the underlying liability (l) such that:In genetic modeling of categorical traits, one of the most commonly used approaches is the threshold model Wright . In the η) is assumed to be a function of the predictors such that:η is the vector of all expectations ηi, β is the vector of fixed effects, a is the vector of random additive genetic effects, and X and Z are the design matrices for the fixed and random effects, respectively. Thus, the true underlying liability (l) will be the expectation plus the residuals such that:l is the vector of all li and e is the vector of random residuals. The underlying liability for each individual as a linear function of the linear predictors can thus be rewritten as:Thus, the expected liability (Combining Eqs.  and 6)::6):7\docηi estimated from the data and an assumed value for the fixed threshold τ, the observed outcomes are conditional on the residuals as follows:Given ηi and τ can then be estimated as:Φe(.) is the cumulative density function with τ is commonly set to 0 as a convenient origin. Since the liability cannot be observed, the variation in the liability is scaled to be ei ~ N.The probability of observing event 1  given Xib = 1μ, the probability of observing event 1 can be estimated as:i.Considering the simplest case, where the population mean is the only fixed effect in the threshold model, i.e., πi) is a function of the linear predictors:Alternatively, a logistic distribution can be assigned to the residuals, resulting in a model known as the logit model. In this model, it is assumed that the logit of an underlying probability  can then be estimated by the inverse logit transformation:The underlying probability (τ) must be estimated. Here, we estimate an approximate τ based on the cumulative distribution function of the normal distribution.In addition to the probit and logistic regression models, it is a common practice in routine genetic evaluation to fit categorical trait data using LM, treating the traits as normally distributed. It has been shown that the loss of power when using a linear Gaussian model for categorical traits is negligible compared with implementing logit or probit regression, despite the violations of assumptions of normality , the proportion of an event (yi = 1) (hereafter π) is the proportion of the underlying liability (l) above the threshold, i.e.:Φ(τ) is the cumulative probability of the normal distribution N. τ is unknown, but π can be estimated as the proportion of observed yi = 1 events in the data. Thus, the unknown threshold (τ) can be estimated by inverse probability transformation as:Given a particular threshold , bu, bu11), Finally, since we are interested in the polygenic risk transmitted from a breeding animal, e.g., a sire, which on average passes half of its breeding value to its future offspring, the polygenic risk probability transmitted is half the probability of the sire estimated using the models presented above. Thus:ptotal) can be given as:pleth is the risk probability from the lethal component and ppoly is the risk probability from the polygenic component.The risk probabilities computed separately from the recessive lethal alleles and the polygenic effect can finally be combined to give the total risk probability of an individual’s future offspring. Assuming that the probabilities of survival from the lethal allele and polygenic components are independent, the total risk probability . Thus, heritability at the underlying scale was 0.075.Simulation of the TBVs was performed at the liability scale, with a target heritability of 0.02 at the observed scale, according to heritability estimates reported in the literature for the Holstein breed  used for prediction. Recessive lethal loci were assigned by randomly sampling from SNPs with minor allele frequencies (MAFs) between 0.04 and 0.05 on 20 randomly selected chromosomes. Each lethal locus was located on a different chromosome, and the loci were thus assumed to be independent of one another.Four different scenarios were simulated with regard to the penetrance of recessive lethal alleles. These included three scenarios where all 20 lethal alleles were assumed to have an equal penetrance of 60, 80, or 100%. The fourth scenario considered a mixture of four penetrance groups : 60, 70, 80, and 100% penetrance.e ~ N of 6.8% from a normal distribution with mean 0 and variance Liability for death was generated by adding a residual effect to TBV. The residual effect was sampled from Breeding values and risk probabilities were estimated using the above approach. To predict risks from polygenic and recessive lethal allele effects separately, the data used to predict polygenic effects excluded records of death caused by recessive lethal alleles (Data_poly). Similarly, the GRM used for the prediction of GEBVs was constructed without recessive lethal alleles. All GRMs used for the different scenarios were calculated using the first method presented by VanRaden , and SNPTo test the proposed approach’s superiority over the conventional approach, breeding values and risk probabilities were also estimated using a conventional approach that did not distinguish the polygenic and lethal allele effects. Thus, the conventional approach predicted total breeding values using phenotypic data without excluding observations of death due to recessive lethal alleles  and GRMs that did not include recessive lethal genotypes. Subsequently, the predicted GEBVs were transformed into total risk probabilities.In addition, we also assessed the accuracy of the polygenic GEBVs predicted without distinguishing the two effects, i.e., with the data that included death due to recessive lethal alleles, but taking into account the effects of the recessive lethal alleles, either by including recessive lethal alleles in the construction of the GRM or using models that included fixed regression on recessive allelic genotype code. Below, we present the methods used to predict the GEBVs that were subsequently transformed into risk probabilities for each of the models .These approaches were implemented using three statistical models, namely, generalized linear mixed models with logit and probit link functions and a linear mixed model, using DMU software (Madsen and Jensen i of η is ηi = Φ−1(πi), μ is the overall mean, g is the random additive genetic effects with distribution G is the GRM constructed using only markers, i.e., excluding the recessive lethal loci. Both Data_poly and Data_all were analyzed using this model.Three probit models were used to estimate breeding values and risk probabilities due to polygenic effects. The first probit model was:d is the fixed regression coefficient for lethal genotype score and x is the vector of recessive lethal statuses. Since a homozygous recessive genotype at a given locus can cause mortality regardless of the genotype at another recessive lethal locus, the element of x is 1 as long as the recessive lethal allele is in a homozygous state at any locus and “0” otherwise. The G matrix in this model also excluded genotypes of recessive lethal loci, and this model was used to analyze Data_all.The second probit model includes fixed regression on lethal genotype:g* are the vectors of random additive genetic effects with distribution G* is the GRM based on the markers including lethal alleles and The third probit model was implemented to test the impact of including recessive lethal alleles in the GRM on genomic prediction:i of η is The logit models used in the analysis were:n Models –22)..23\documy is the vector of observations  and e is the vector of residuals with distribution I is an identity matrix and The implemented LM included three genomic best linear unbiased prediction models:nents in –22)..26\documTo assess the accuracy of predicted risk probabilities, the predicted total transmitted risk probabilities of the sires of the G4 animals were compared with the observed proportion of deaths among their G4 offspring, whose phenotypes were masked during the prediction. This is to be consistent with the situation where the candidate animals do not have offspring at the time of selection. For the approach that distinguished polygenic and recessive lethal allele effects, the predicted total risk probability was calculated using Eq.  to combiIn addition, the advantages of incorporating recessive lethal alleles, by either including them in the GRM  or considering lethal genotype as a fixed effect in regression  to predict polygenic breeding values, were assessed by the accuracy of GEBVs, which was measured as the correlation between GEBVs and simulated polygenic TBVs.t-test across the replicates.The statistical significance of differences in prediction accuracies between scenarios (approaches) and models, i.e., probit, logit, and linear, was tested using a pairwise P < 0.001) than those obtained with the conventional approach. The difference in prediction accuracy between the two approaches ranged between 20 and 29.1 percentage points, depending on the penetrance scenario assumed.Figure In all three statistical models, i.e., LM1, Probit1, and Logit1, the highest accuracy was observed when all lethal alleles had 100% penetrance (Pen100), while the lowest accuracy was observed when all alleles had the lowest penetrance (Pen60). Among the three models, Probit1 resulted in the highest prediction accuracies, followed by Logit1, in both approaches. However, these differences in prediction accuracy between the three models were not statistically significant.Table P < 0.01) and PenGRP (P < 0.05). Among the three approaches, approach (3) produced the lowest GEBV prediction accuracies. The prediction accuracies obtained using this approach were significantly lower (P < 0.001) than those obtained using approaches (1) and (2).Figure Genomic prediction for traits with mixed “major” genes and polygenic inheritance has been shown to benefit from models that account for differences in marker effects compared with models with “infinitesimal” assumptions  and a mixture of penetrance levels (PenGRP).In our simulation study, the accuracy of the predicted GEBVs was generally low across the compared models. This is expected given the low heritability considered in the simulation. In dairy cattle breeding, definitions of different calf and young stock mortality traits are dependent on monitoring period. In general, studies across several dairy cattle breeds have shown very low heritability estimates for calf and young stock mortality traits , which can be used for selection decisions. Therefore, an overall weight for the trait of interest can be used to integrate the breeding values, which account for both the polygenic and recessive lethal allele components, into a selection index with no need to assign weights for each recessive lethal allele.This study proposed an approach for predicting the probability of mortality of future offspring by predicting the risk probabilities from polygenic and recessive lethal components separately. The approach was tested using simulated data and found to be superior to approaches that do not distinguish polygenic and lethal allele effects. No statistically significant differences in prediction accuracy were observed between the probit model, logit model, and LM, suggesting that the novel approach can be implemented using different models, with comparable power.


Ipomoea batatas) populations to detect loci associated with agronomic traits. Our strategy could constitute a cost-effective approach for preliminary experiments done performed to large-scale studies.Next-generation sequencing (NGS)-based genotyping methods can generate numerous genetic markers in a single experiment and have contributed to plant genetic mapping. However, for high precision genetic analysis, the complicated genetic segregation mode in polyploid organisms requires high-coverage NGS data and elaborate analytical algorithms. In the present study, we propose a simple strategy for the genetic mapping of polyploids using low-coverage NGS data. The validity of the strategy was investigated using simulated data. Previous studies indicated that accurate allele dosage estimation from low-coverage NGS data (read depth < 40) is difficult. Therefore, we used allele dosage probabilities calculated from read counts in association analyses to detect loci associated with phenotypic variations. The allele dosage probabilities showed significant detection power, although higher allele dosage estimation accuracy resulted in higher detection power. On the contrary, differences in the segregation patterns between the marker and causal genes resulted in a drastic decrease in detection power even if the marker and casual genes were in complete linkage and the allele dosage estimation was accurate. These results indicated that the use of a larger number of markers is advantageous, even if the accuracy of allele dosage estimation is low. Finally, we applied the strategy for the genetic mapping of autohexaploid sweet potato ( Recent advances in next-generation sequencing (NGS) technology have revolutionized genomics-assisted breeding. NGS-based genotyping by sequencing GBS;  and genePolyploidy is the presence of multiple sets of chromosomes in a single organism and is a common occurrence in the plant kingdom. Polyploid plant species are often valuable as crops, as their genome multiplication results in comparatively higher yields . In addiTo use multiple-dose markers, the allele dosage must be determined. Several techniques can be used to estimate allele dosage in polyploids . We propose a simple genetic mapping strategy for autopolyploids using low-coverage NGS data, and evaluate its validity using simulated and real data from two genetic mapping populations in sweet potato (Ipomoea batatas (L.) Lam). Sweet potato is a hexaploid species with 90 chromosomes (2n = 6x = 90). In our proposed method, the allele dosage probability for each single-nucleotide polymorphism (SNP) marker site is calculated on the basis of read depth information from low-coverage double digest (dd) RAD-seq genotyping data. We did not attempt to determine allele dosage where the read depths were too small. Alternatively, allele dosage probabilities can be used in subsequent genetic mappings. This idea is similar to a previous study that used continuous genotype values from the signal intensities of the SNP-chips for genetic mapping  (K123–11); hereafter, this population is referred to as KX-F1. The other population originated from self-pollinated (S1) Xushu 18 (n = 248) used in a previous study (Two populations of autohexaploid sweet potato (2n = 6x = 90) were used. One was the Fus study . KX-F1 wPstI and MspI. DNA fragments of 300–900 bp in length were fractionated using BluePippin . The nucleotide sequences of the libraries were determined on the HiSeq 2000 and HiSeq 4000 platforms  in paired-end mode (93 base or 101 base). The ddRAD-seq reads for the X18-S1 populations were obtained from the DNA Data Bank of Japan (DDBJ) sequence archive, under the accession numbers DRA004836, DRA004837, and DRA004838. Data were processed as described in http://hannonlab.cshl.edu/fastx_toolkit). The filtered reads were mapped onto the I. trifida “Mx23Hm” (ITR_r1.0) genome sequence . The ddRAD-seq analyses were performed as described in sequence . The parce  of dosage id was calculated using the binomial distribution function:id was calculated as follows:M was obtained for each SNP marker, with individuals as row elements. The column elements were the relative probabilities of the reference type allele dosages calculated by equation (2). Calculation with the binomial distribution function was performed in ‘dbinom’ in R  (Continuous). This method was analogous to the one used in x in the equations (3) and (4) (Dogmat). This approach was analogous to the ‘general’ option, from a feature of the R package GWASpoly  usinglm’ in R . The auglm’ in R  with devevd , Duplex × Duplex (DD) or Triplex × Triplex (TT). The simulated genotype and NGS data were generated using the ‘rgeno’ and ‘rflexdog’ functions from the R package updog, respectively  strongly affected the power to detect the gene. In the present study, we simulated genes with PVE values of 0.1, 0.2, and 0.3. We used the ‘optimize’ function of R to adjust the phenotypes to the PVE.i.e., the expected value of the random variable) was set as the length of the genetic map distance (in cM) between the marker and the gene. The number of crossovers for each simulation experiment was drawn from the ‘rpois’ function in R.To investigate the relationship between the power to detect and the genetic distance between the marker and the target gene, the number of crossovers between the marker and the gene was determined using a random variable drawn from a Poisson distribution. The lambda parameter of the Poisson distribution . This difference in the segregation pattern between the marker and the target gene resulted in a drastic decrease in detection power  value in the continuous genotype than in the dosage matrix genotype (For the internode length of KX-F1, a significant peak was detected on homologous group 12 . A compagenotype .We genotyped populations using ddRAD-seq. In general, polyploid species have large genomes and multiple allele dosages. Therefore, numerous markers must be developed to capture genetic variation in the entire genomic region. NGS-based genotyping, such as RAD-seq and GBS, are powerful because they generate hundreds of thousands of markers per experiment. High read counts per marker are necessary for the accurate allele dosage estimation . Hence, In order to evaluate the accuracy of allele dosage estimation in low-coverage NGS data, we performed simulation experiments, however, the determination of allele dosage with low-coverage data, especially in markers with complicated segregation patterns (DD and TT in Interval mapping that uses estimated genotype information between markers is a powerful approach to detect genetic loci associated with phenotypic variation . InteresFinally, we confirmed the applicability of the proposed strategy on real genotype and phenotype data from autohexaploid sweet potato . Unlike


Eucalyptus pellita. We used two progeny trials consisting of 5742 trees from 244 OP families to estimate genetic parameters and to test genomic prediction accuracies of three growth traits  and kraft pulp yield (KPY). From 5742 trees, 468 trees from 28 families were genotyped with 2023 pre-selected markers from candidate genes. We used the pedigree-based additive best linear unbiased prediction (ABLUP) model and two marker-based models (single-step genomic BLUP – ssGBLUP and genomic BLUP – GBLUP) to estimate the genetic parameters and compare the prediction accuracies. Analyses with the two genomic models revealed large dominant effects influencing the growth traits but not KPY. Theoretical breeding value accuracies were higher with the dominance effect in ssGBLUP model for the three growth traits. Accuracies of cross-validation with random folding in the genotyped trees have ranged from 0.60 to 0.82 in different models. Accuracies of ABLUP were lower than the genomic models. Accuracies ranging from 0.50 to 0.76 were observed for within family cross-validation predictions with low relationships between training and validation populations indicating part of the functional variation is captured by the markers through short-range linkage disequilibrium (LD). Within-family phenotype predictive abilities and prediction accuracies of genetic values with dominance effects are higher than the additive models for growth traits indicating the importance of dominance effects in predicting phenotypes and genetic values. This study demonstrates the importance of genomic approaches in OP families to study nonadditive effects. To capture the LD between markers and the quantitative trait loci (QTL) it may be important to use informative markers from candidate genes.Most of the genomic studies in plants and animals have used additive models for studying genetic parameters and prediction accuracies. In this study, we used genomic models with additive and nonadditive effects to analyze the genetic architecture of growth and wood traits in an open-pollinated (OP) population of  Eucalyptus pellita F. Muell, or red mahogany, is a medium-size to tall tree with good form that can grow up to 40 m or more in height and 1 m in diameter  (Ceratocystis spp. (stem canker) fungal pathogens , we developed custom marker panels for routine genotyping in several species of Eucalyptus including E. pellita. These marker panels were developed from candidate genes of growth and wood traits selected from our previous RNA-seq studies  methods using genomic relationship matrices can provide high accuracies of the estimated genetic parameters. This is mainly because markers capture the Mendelian segregation term families . The MenHowever, the main advantage of using markers in OP families is to study the nonadditive effects, something that is not possible with traditional methods. Dominance plays a significant role in the genetic control of growth traits compared to wood traits. Studying dominance and epistatic effects usually requires clonally replicated full-sib families (A matrix) is combined with a marker derived genomic relationship matrix (G matrix) to generate a combined relationship matrix (H matrix). The combined H matrix is then used in BLUP analysis to estimate the genetic parameters and breeding values. Thus, in the ssGBLUP, information from both genotyped as well as non-genotyped individuals is used which leads to higher precision and accuracy of the genetic parameters estimated. The genomic relationships of the G matrix are transferred to non-genotyped samples through the A matrix leading to high accuracies of genetic parameters. Thus, the advantage of ssGBLUP is that all the available information is used optimally  to estimate genetic parameters with genomic approaches. We used ssGBLUP and GBLUP to study additive and nonadditive genetic variances influencing growth traits and pulp yield. The main objectives of this study were: 1) to estimate genetic parameters using ssGBLUP and GBLUP methods with additive and dominance effects; and, 2) to compare the prediction accuracies from different models.In this study, we used 244 OP families of E. pellita trials used in this study were both located in southern Guangxi Province, China. One was at Dongmen Forest Farm at approximate latitude 22°23’ N, longitude 107°52’ E and an elevation of 120 m above sea level. The other trial was planted on land belonging to Yulin Forestry Research Institute at around latitude of 22°39′N, longitude 110°09′ E and altitude of 400 m above sea level. The Dongmen trial comprised 6 complete replicates with each family represented by a 4-tree-row plot within each replicate and with randomization of the layout of family plots within each replicate. The Yulin trial was laid out with 30 complete replicates of single tree plots, also with randomization of the layout of family plots within each replicate. At both sites, the spacing was 2.0 m (between trees) × 3.5 m (between rows).The st generation E. pellita provenance family trials established during the 1990s to early 2000s in southern China. The trial at Dongmen included 229 OP families that were a subset of those included in the Yulin trial. Details of these seedlots (OP families) are provided in The trial at Yulin included 244 open-pollinated families from plus-trees selected for growth, stem form and typhoon tolerance in three 1The trial at Dongmen was planted in July 2011 and that at Yulin was planted in June 2011. Both trials were assessed for survival and growth at age of approximately 41/2 years (around 54 months); all surviving trees were measured for total height (Ht) and for diameter over bark at a height of 1.3 m (Diameter at Breast Height - DBH).3, The measurements of diameter and total height were used to calculate conical tree volumes (over bark) for all trees using the following formula:At approximately age 5 years (around 61 months), wood and DNA samples were taken from a subset of trees in each trial that included 28 families in common between the trials, ∼8 trees sampled per family from each site. This provided 232 and 236 trees for sampling from Dongmen and Yulin respectively, making a total of 468 trees sampled across the two sites. The reason for just 28 families being sampled was a combination of: i) resources available for this study, primarily costs of DNA genotyping and KPY determinations; and, ii) the number of families available that had ≥8 trees surviving (and not stunted or wind damaged) at each site . None of the 28 parents of the families that were genotyped, were themselves sampled for genotyping or KPY determinations.E. pellita marker panel. The E. pellita marker panel consisted of single nucleotide polymorphism (SNP) markers and small biallelic insertion deletions (INDEL) markers from 2,000 candidate genes. The markers included in the marker panel were distributed across all the 11 chromosomes. Markers were preselected for potential association with various traits. Preselection of the SNPs was based on allele frequency differences from sequencing pooled samples representing the core germplasm of E. pellita breeding used in SE Asia. After applying different filters  > 5%, SNP call rate of > 90%), in total 2,023 markers were genotyped in 423 trees which were then used in all subsequent genomic analyses (Table S2).The DNA samples were obtained as stem cambium scrapings. These were obtained by removing a window of bark (size of around 3 × 3 cm) at 1.3 m height on the stem facing the row direction, then scrapping cambial tissue using a sharp wood chisel into a labeled 2 ml centrifuge tube containing standard CTAB buffer. The wood samples were taken in the form of drill fras. This was obtained using an 11 mm drill bit that was drilled into the tree stem to a depth of approximately 6 cm in the place where cambial scrapping had been taken and hence was free of bark. The drill fras samples were air-dried and then shipped to Forest Quality Pty Ltd in Tasmania, Australia, for oven drying, grinding and then the prediction of kraft pulp-yield (KPY) individually for each sample using Near Infra-Red spectral analyses. DNA from cambial scrapes were extracted and genotyped at Gondwana Genomics Pty Ltd, Canberra, using Prior to analyses, the phenotypic trait data for DBH, total height, tree volume (over bark) and KPY were adjusted to account for site differences. For this, data from each trait from Yulin were adjusted using PROC STANDARD procedure of SAS software to have the same mean and standard deviation as the same trait at Dongmen. Phenotypic data were adjusted for trial design effects by considering family by rep as a random effect. Adjusted phenotypes were used in all subsequent analyses.Three models, traditional ABLUP, and two genomic models ssGBLUP and GBLUP were used to estimate the breeding values. For ssGBLUP models, trait data of all trees was used, for GBLUP trait data of only genotyped trees was used. For ABLUP trait data of all trees as well as just the genotyped trees was used in two different analyses.Where The GBLUP model is the same as the ABLUP except that the Where The molecular genetic values (MGV) are then estimated as A matrix is replaced by H matrix from combing pedigree and genotype information to estimate genomic estimated breeding values (GEBVs).In ssGBLUP, similar to the GBLUP, the th row and th column. th marker.Additive genomic relationship matrix is based on the VanRaden method  and derith row and jth column.Dominance genomic relationships matrix is based on  package of R software to develop the H matrix. The combined H-matrix for ssGBLUP was developed using the following equation.We used HIBLUP  with HIBLUP (https://hiblup.github.io/) (The combined HD matrix with dominance (hub.io/)  and breehub.io/)  packages2) was estimated as 2) was estimated as Narrow-sense heritability  is estimated within the genotyped trees as the Pearson correlation between the adjusted phenotypes and the predicted breeding values/genetic values from cross-validation.Cross-validation tests were performed to test the accuracies and predictive abilities of different models. Three types of cross-validations were performed; random folding, balanced family folding and family folding. Random folding tests were performed in all samples and in genotyped samples separately. The balanced family folding and the family folding tests were performed only in the genotyped samples. In random folding, trees were randomly split into training and validation populations. A 10-fold cross-validation was performed in random folding. In the balanced family folding, a minimum of 20% of trees from each family were used in the validation population. To have a minimum of 20% trees/family, a fivefold cross-validation was performed in balanced family folding. In family folding, entire families were removed from the training population to remove genetic relatedness between training and validation populations. Thus, for family folding cross-validation a 24-fold test was performed. Even though the genotyped trees are from 28 families, only 24 families were tested in validation as individuals from four families were less than eight. In family folding, the ABLUP model was not tested as predicted breeding values would have been equal to the mean of the training model for each predicted family.https://doi.org/10.25387/g3.12782567.The pedigree and trait data used in this study are in Table S1. The marker genotype data used in this study are in Table S2. Supplemental material available at figshare: Pedigree-based ABLUP and marker and pedigree-based single-step GBLUP (ssGBLUP) were used to estimate genetic parameters of growth traits  for which trait data were available from 5,742 trees. Narrow-sense heritability estimates for all three models  are similar among each of the three traits . While tInclusion of the dominance effect in the model (ssGBLUP_AD) resulted in the reduction of error variance while maintaining the additive genetic variance similar to that of ABLUP and ssGBLUP_A models. Inclusion of genomic data also improved model fit over pedigree model as indicated by goodness-of-fit statistics (Akaike Information Criterion - AIC). Among the two genomic models (ssGBLUP_A and ssGBLUP_AD) inclusion of dominance in the model further improved the model fit for all three traits .A matrix) with H-matrix used in ssGBLUP for 28 parents which had genotyped progeny; but the parents themselves were not genotyped . To demonstrate this, we compared the additive relationship matrix from pedigree (enotyped . This coH-matrix (H-matrix (H-matrix (data not shown). This illustrates that H-matrix can be used to detect deep genetic relationships among the parents even when they are not genotyped.According to the pedigree file (Table S1), among the 28 families, there are seven parents derived from three families (three grandmothers). Three parents 137, 339 and 347 are from one family, therefore, are half-sibs; 165 and 185 are from a second family and 344 and 353 are from a third family. Except for one parent (137), all other parental relationships are confirmed with H-matrix . This maH-matrix . ExaminiTheoretical breeding value accuracies were estimated for ABLUP and ssGBLUP models using all trees. Theoretical breeding value accuracies are generally higher with the two ssGBLUP models (ssGBLUP-additive and ssGBLUP-dominance) compared to pedigree-based ABLUP model for all three growth traits. Among the two ssGBLUP models, breeding values accuracies are generally higher for the model with the dominance effects . AccuracAccuracies of predicted breeding values from ABLUP were also assessed by correlating EBVs estimated using all samples  with EBVs from cross-validation (EBV_CV). Similarly, accuracies of GEBVs from ssGBLUP were assessed by correlating GEBVs estimated using all samples  with the GEBVs from cross-validation (GEBV_CV). Cross-validation breeding values from the two methods (GEBV_CV and EBV_CV) were correlated with EBVs_all to compare the accuracies of GEBVs and EBVs . AccuracAs less than 8% of the trees  with trait data used in ssGBLUP were genotyped, estimates of genetic parameters and breeding values were mainly influenced by non-genotyped trees resulting in these estimates being similar between ABLUP and ssGBLUP . To gain2) for growth traits (2) were higher for DBH and Vol compared to Ht and the error associated with d2 was higher for Ht. For DBH, h2 with ABLUP (P_A) was close to zero. For the other three traits  h2 estimates with ABLUP were higher than those of GBLUP.Similar to the results of ssGBLUP, significant dominance effects were observed for all three growth traits but not for KPY resulting in moderate to high broad-sense heritability  and the total genetic values (additive and non-additive effects) estimated with GBLUP are indicated as molecular genetic values (MGVs). Three types of cross-validation tests, random folding, balanced family folding and family folding (in which the entire family was left out from training samples) are used for testing predictive abilities and prediction accuracies.Cross-validation results from random folding and balanced family folding are similar between each other compared to those from family folding. Predictive abilities of MGVs with random folding and balanced family folding  are higher for growth traits than the MBVs and EBVs . For KPYPredictive abilities of MBVs  were zero or close to zero for all traits with family folding . PredictE. pellita breeding population. Most of the previous studies in forest trees which used ssGBLUP have included only the additive effects in the model , GBLUP and ABLUP models to estimate genetic parameters and to test the prediction accuracies of different traits in an he model  and for he model . The larH-matrix. The dominance effects of ssGBLUP in this study are therefore mainly derived from the samples that have been genotyped which are transmitted to non-genotyped samples through pedigree relationships. However, the number of genotyped trees are a small fraction (< 10%) of the total number of trees used in this study. This could be one of the reasons for observing similar accuracies between ssGBLUP and pedigree-based ABLUP models with cross-validation  reflect the stability of predictions and the amount by which the breeding values will change when more information is available . In addilidation . Increasvs. 0.13, vs. 0.10, E. pellita by To better understand the effects of dominance in genomic models, we performed genomic analyses using only the genotyped trees. Genetic parameters and heritability estimated in the genotyped trees are in general lower than those using all trees reflecting the sampling bias due to small sample size and the small number of families in the genotyped trees. However, high heritability estimates were observed for Ht  have observed that additive-dominance prediction models will be better than additive models, only when high dominance ratios are detected. In this study, the high dominance ratios observed for the three growth traits (Predictive abilities with GBLUP_AD model are higher (>100% over additive models) for DBH and Vol with both random folding and family folding methods of cross-validation . This ish traits  and 4 maE. pellita was reduced by more than half (from 0.35 to 0.15) when the relationships between training and validation samples are minimized. However, in their study E. pellita.Results of cross-validation with random folding, balanced family folding and family folding revealed different patterns. Predictive abilities and prediction accuracies are higher in random folding and balanced family folding than in family folding . HoweverOne of the main reasons for the differences in accuracies between the three methods of cross-validation may be due to the relationships between training and validation samples used. Accuracy of genomic selection is influenced by genomic relationships as well as marker-trait associations captured by the markers  study compared to those in their 2016 study  we have used pre-selected markers from candidate genes which may explain the relatively high accuracies in family folding compared to the results of Eucalyptus globulus (Eucalyptus hybrids. These results indicate the importance of using SNP markers from candidate genes to capture the short-range LD between markers and QTL which is an important component of genomic selection.Accuracies of within-family predictions are mainly influenced by the marker-trait associations captured by the markers as differences in genomic relationships between the individuals within a family are small. For forward selections among sibs, markers that capture LD or marker-trait associations are essential . SimilarE. pellita. High theoretical breeding value accuracies were observed for the three growth traits which are reflected in the high accuracies observed in cross-validation tests using all trees. As the number of trees genotyped is a small fraction of the total number of trees in this study, results from cross-validation were similar between ABLUP and ssGBLUP models with all the trees. Cross-validation tests within the genotyped trees also revealed high accuracies. Inclusion of dominance in prediction models improved the predictive abilities and prediction accuracies of DBH and Vol which exhibited high dominance ratios. High within-family accuracies with family folding indicate that the markers may be capturing the short-range LD between the markers and QTL as the relationships between training and validation populations are low. Markers from candidate genes may be important to capture the short-range LD. This study demonstrates the potential of genomic studies to unravel non-additive effects underlying complex traits in OP families which is not possible with the traditional methods.In this study, we used ssGBLUP and GBLUP analyses to identify significant dominance effects underlying the growth traits in an OP population of


Lolium arundinaceum (Schreb.) Darbysh] in the Southeastern United States contains an endophyte that causes fescue toxicosis (FT) in grazing animals, a serious disease that causes approximately $1 billion in economic losses to the animal industries in the United States. Recently, a genetic test called T-Snip , was developed with the objective of identifying animals with genetic variation for FT tolerance. The aim of this study was to validate the use of this genetic test in mature, pregnant cows. Over 13 wk, weekly phenotypic data, including body weight, rectal temperatures, hair coat scores, hair shedding scores, and body condition scores, were collected on 148 pregnant purebred Angus cows at 2 locations in NC where infected fescue was the primary source of feed. Birth weights (cBW) and 205-d adjusted weaning weights (adjWW) from these cow’s calves were recorded. All cows were genotyped for T-Snip. At the end of the trial, each phenotypic trait was calculated as the slope of the linear regression of performance on weeks. The effect of T-Snip rating genotypes (4 levels) on slope traits was tested using a linear model also including the fixed-effects of location, parity, and the initial measurement for each trait (covariate). For cBW and adjWW, the model also included the sex of the calf and the month of birth as categorical effects. Associations of T-Snip genotypes were observed for body weight gain (aBWd) of pregnant cows , change in body condition score , and adjWW . For aBWd and adjWW, associations were found just within one location , which was the location with higher endophyte infection rate. For all associations, the direction of the T-Snip genotypes was the same and as expected: the greater the genotype score, the better performance. No associations were found for the other traits (P > 0.10). These results indicate that the T-Snip test may be predictive of cow performance  in an endophyte-infected tall fescue environment.Most tall fescue [ Lolium arundinaceum (Schreb.) Darbysh] is the predominant forage available in the southeastern United States, and most of it contains a fungal endophyte, Epichloë coenophiala, which produces ergot alkaloid compounds causing fescue toxicosis (FT) in grazing animals  has been developed with the objective of identifying animals with different genetic potential for FT response in cattle. P = 0.05) between this genetic test and average daily gain of feed calves, out of all traits evaluated. Recently, a genetic test called T-Snip .n = 127) were used. Approximately half of the animals  were located at the Upper Piedmont Research Station , while the remaining animals  were located at the Butner Beef Cattle Field Laboratory . To ensure cow performance was not impacted by lactational stress, all calves were weaned from cows prior to the start of the grazing period.A total of 150 multiparous (parities 2 to 4) pregnant purebred Black Angus cows and their calves . A subset of forage sample was dried, ground and sent to the University of Missouri Veterinary Medical Diagnostic Laboratory  for analysis of ergot alkaloid and ergopeptine concentrations present using HPLC as described by Over 13 wk, from April 2016 until July 2016, which is the optimal time to observe the impact of both heat stress on FT, phenotypic data were collected on all cows. The average temperatures during the time of collection ranged from 13.1 °C in May and 27.8 °C in late July. Temperatures steadily increased throughout the 13 wk  and therWeekly phenotypic data collected on each cow included body weight (BW), body condition score (BCS), hair shedding score (HSS), hair coat score (HCS), and rectal temperature (RTemp). In order to reduce bias, two trained evaluators assigned the BCS, HSS, and HCS, and the average score was used for subsequent statistical analyses. Hair shedding scores, ranging from 1 to 5, with 1 being a completely shed out, short summer coat and 5 being a full nonshed winter coat , whereasBlood cards were shipped to GeneSeek  for T-Snip genotyping. T-Snip assigns two scores to each animal based on their genotypic variation for tolerance to FT: the tolerance rating, ranging from 0 to 5, and the tolerance index, ranging from 0 to 50, with 0 being least tolerant in both cases. The T-Snip index is generated by analyzing multiple genetic markers; these results are then used to calculate the T-Snip rating. The number of markers used, as well as the specific genes associated with those remain proprietary to AgBotanica, LLC . Analyses were performed using both the rating and the index, however, results presented here focus only on associations with the T-Snip ratings. The distribution of the T-Snip ratings are shown in Individual cow phenotypic measurements recorded across time  were summarized as the slope from the regression analyses of phenotypes on weeks. Slopes were estimated with the objective of assessing the change in individual phenotypic performance as a function of time. A linear regression was used for BW as described by The effect of T-Snip rating genotypes on slope traits was tested using the following linear model:yijk is the estimated slope; µ is the overall mean; TSnipi is the fixed-effect of the ith T-Snip rating genotype (l = 1 to 4); Lj is the fixed-effect of the jth location; (TSnip*L)ij denotes the interaction between TSnipi and Lj; Pk is the fixed-effect of the kth parity; Gl is the fixed-effect of the lth gestation status ; covm is the covariate of initial measurement of the mth animal ; and eijklm is the random error associated with yijklm, with I is the identity matrix. Analysis of aRTd included the effect of temperature heat index (THI) as an additional covariate, where this was calculated as the area under the curve of the weekly THI for the period analyzed. The distribution of THI over all 13 wk was normal (data not shown). The effect of week temperature on all other traits evaluated was tested but it was not significant (P > 0.05), and thus, removed from final analyses (data not shown). In addition to the effects of TSnip, L, TSnip*L, and P, the statistical analysis of cBW included the fixed-effects of calf sex and calving month, and no covariates, whereas the statistical analysis of adjWW included the fixed-effects of calf sex and calving month, and cBW as the covariate. Normality of the Studentized residuals was evaluated using Shapiro–Wilk’s test and considered normal at P > 0.01, and when Studentized residual fell within ±3 standard deviations. Homogeneity of the residuals was assessed visually. Least-squares means of significant effects (P < 0.10) were separated using Fisher’s Least Significant Difference test. Trends were considered when P < 0.15. A less stringent significance threshold was used in place of the traditional alpha of 0.05 because of the limited number of animals used in this trial , the distribution of genotypes  and a significant interaction for adjWW (P = 0.06). The relationship between T-Snip rating genotypes and aBWd was opposite between locations. In addition, the interaction between the linear effect of T-Snip and location was significant  aBWd from genotypes 1 (5.31 kg/w) to 4 (4.25 kg/w), whereas at UPRS, cows increased (P < 0.1) aBWd from genotype 1 (0.31 kg/w) to 4 (3.39 kg/w). These results are in accordance with the linear effect of T-Snip tested within each location (P = 0.599) at BBCFL and strong association (P = 0.017) at UPRS. For adjWW, there was an increase (P < 0.1) in adjWW at UPRS (T-Snip rating genotype 1 = 194.3 kg vs. 4 = 226.8 kg). Additionally, there was a significant interaction (P = 0.033) between the linear test for T-Snip and location, in which the linear effect of T-Snip was significant (P = 0.047) at UPRS but not (P = 0.276) at BBCFL. There were no other traits examined that demonstrated an effect of interaction between T-Snip rating genotype and location (P < 0.15).There was a tendency for the interaction between T-Snip rating genotypes and location for aBWd  and not at BBCFL (P = 0.482). In general, animals with genotype 4 (0.07) had greater (P ≤ 0.042) aBCSd than those with genotypes 1, 2, and 3 (0.02 for each). At UPRS, this association was stronger (P = 0.027), and similarly, genotype 4 (0.107) had greater aBCSd than all other genotypes. There was also a significant effect of T-Snip genotype  of T-Snip genotype. This was also the case when we evaluated the linear effect of the T-Snip , aBCSd (P < 0.001), aHSSd (P = 0.09), aRTd (P < 0.001), and cBW (P = 0.10). In general, animals at BBCFL had greater gain (aBWd), lower changes in body condition (aBCSd) and hair shedding (aHSSd), increased rectal temperature (aRTd), and greater cBW compared with cattle at UPRS.Although the focus of this study was not to compare locations, there was an effect of location for aBWd , whereas much stronger associations were found at the other location (UPRS), which had greater amount of endophyte-infected fescue. While a defined control group  was not included in the experimental design of this study, another study was conducted at the same time and location with mature multiparous Black Angus cows consuming novel or toxic endophyte-infected tall fescue , provide2) and residual (1.09 [kg/wk]2) variances during this period were greater than for the other periods, with 1.04 (kg/wk)2 and 1.94 (kg/wk)2, respectively, for w7_13, and 0.58 (kg/wk)2 and 1.09 (kg/wk)2, respectively, for 1_13, while having similar heritability estimate for these three time windows (~0.35). This is in accordance to reports showing that, under stress/disease conditions, traits show greater additive genetic and residual variances compared with the same traits under a “clean” environment  and negatively correlated with 205-d adjWW (r = −0.58). In this study, HCS and HSS decreased throughout the collection period as expected. However, there was no association between T-Snip rating genotypes and these traits. This lack of associations could indicate that this test is associated with growth traits (aBWd and aBCSd), which may not necessarily be translated/correlated to the other traits analyzed in this study. Lastly, the limited number of animals and difference in endophyte infection rate between locations may have limited the ability to detect associations.As for the other traits measured on the pregnant cows, elevated body temperature is a clear sign of disease and a common symptom of FT . In the Although no associations between cBW and T-Snip rating genotypes were found, there was a significant genotype-by-environment effect for adjWW, indicating that this genetic test is associated with different weaning performance. As for other traits analyzed, we observed a positive result in UPRS only. Under this environment, in which greater percentages of endophyte infection was observed, animals with greater T-Snip ratings had greater adjWW. This result was expected assuming that the T-Snip is a genetic marker for FT tolerance. This is also in accordance to what was observed originally by Further studies evaluating this genetic test on spring-born calves would be helpful in gaining a more thorough understanding of how FT effects on weaning weights interact with calving season and T-Snip. At UPRS, cows with greater rating genotypes weaned heavier calves. This result was further supported by our post hoc linear contrast test, which showed that this test, as for aBWd and aBCSd, is additive. This was not observed at BBCFL, further supporting the need of greater endophyte infection percentages in order to observe difference in performance based on T-Snip rating genotypes. However, a statistical association between this genetic test and cBW was not observed, although a numeric trend to the expected direction was observed within UPRS. Overall, these results further support that the T-Snip test may be predictive of calf weaning weight from cows exposed to high levels of toxic fescue during the summer.This study has evaluated the association between a commercial genetic test for FT with various production traits in pregnant Angus cows when risk of developing FT is greatest. We did not observe association between the T-Snip rating genotypes with most traits evaluated, regardless of the period of time evaluated. However, significant associations were found for major performance traits: cow BW during pregnancy and 205-d adjWW. These associations were observed within one of the two locations used in this study, suggesting that a greater percentage endophyte infection and/or greater concentrations of ergot alkaloids are critical to identify associations between T-Snip rating genotypes and traits of interest affected by FT. In addition, this genetic test was also significantly associated with BCSs of cows. In all these cases, the association between T-Snip genotypes and traits was on the expected direction  as was indicated by the developers of this genetic test. Finally, this study validated the use of T-Snip for growth traits in Angus cows and calves, and body condition of pregnant cows, while additional studies in different environments throughout the year would be beneficial in validating this commercial genetic test for FT.txaa181_suppl_Supplementary_MaterialsClick here for additional data file.