Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines  Sorghum bicolor L. Moench) is a cereal crop that can play an important role for sustainable farming, as it is particularly resilient to stress conditions caused by drought and erratic rainfall  can increase the accuracy of genetic evaluations relative to single-trait (ST) analysis by exploiting information from correlated characters . The potGenome-based MT analysis has been applied for breeding in hybrid crops of other major cereals like maize, rice and wheat e.g., . In sorgThe aims of this study were to investigate if multi-trait analysis improves across-environment genomic predictions for grain yield and stay-green in sorghum, and to identify the combinations of traits that optimize results in different prediction scenarios. To attain these objectives, the optimal combination of traits was empirically determined for each scenario by evaluating the gain in prediction quality of alternative MT models relative to the ST model. In addition, we explored if the performance of best-predictive MT genomic models can be further enhanced by incorporating pedigree information.The dataset used in this study is part of the sorghum breeding program of female parental lines conducted by the University of Queensland and the Department of Agriculture and Fisheries in Queensland, Australia. The phenotypic records consisted of 26 testcross performance trials where a total of 646 female lines were tested in hybrid combinations across 12 locations between 2008 and 2014. Phenotypes of 2645 testcross hybrids were used to assess female lines performance across a target population of environments (TPE) covering the main sorghum cropping region of Australia. The series of trials belongs to an advanced stage of yield testing (AYT), where measurements are taken from relatively large plots. More details on field layout and structure of the dataset are given in 1. The sequence data generated were then aligned to the most recent version (v3.1.1) of the sorghum reference genome sequence  methodology involving complexity reduction of the genomic DNA to remove repetitive sequences using methylation sensitive restriction enzymes prior to sequencing on Next Generation sequencing platforms (DArT)sequence  to identsequence  in R  of the i-th female line crossed with the j-th male tester in the k-th environment, which was fitted by a random line effect (Li), a fixed male tester effect (Mj), a fixed environmental effect (Ek), and all possible interactions between these factors. Since line effects were considered random, all the interactions involving Li were random, while MEjk was fixed. Note that LMEijk includes the error of genotype mean. All random effects were assumed independent homoscedastic and normally distributed with zero mean.where i. Moreover, using BLUP(Li) as response variable in the genomic prediction model is problematic because it would result in double shrinkage of predicted breeding values. Therefore, to eliminate shrinkage in line BLUPs before the genomic prediction stage, we applied the deregression procedure of i), as obtained by inverting the coefficient matrix of the mixed model equation  to genomic effects; g is the vector of additive genomic effects with distribution g ∼ N (where the vector BLUP(Li) .h2) of line means were obtained as: Given the variance components from the ST prediction model, the narrow-sense heritabilities  and (2) were extended using multivariate mixed models. We present a general formulation for any combination among traits T = . The multi-trait case of model (1) can be represented in vector notation as:yijk is a vector collecting spatially adjusted BLUEs of multiple traits T from separate univariate analyses in stage one; and Li, LMij, LEik, LMEijk are the vectors of multi-trait random effects with respective assumed distributions Li ∼ MVN, LMij ∼ MVN, LEik ∼ MVN, LMEijk ∼ MVN, where Σq, for q = L, LM, LE, LME, is a covariance matrix among traits and ⊗ is the Kronecker product operator. For all random effects, the matrix Σq was modeled as unstructured, allowing for unequal variances across traits and specific covariances for each pair of traits.where in this case The multi-trait G-BLUP model was defined as:yT is now a multi-trait vector of deregressed BLUP(Li), ordered as lines within traits, obtained from joint multivariate analysis using model (3); gT is the vector of multi-trait additive genomic effects with distribution gT ∼ MVN; and the multivariate residual effects were assumed eT ∼ MVN, where matrix DT has diagonal elements containing weights based on individual reliabilities of deregressed BLUP(Li) for each trait, as obtained from the second stage of multi-trait analysis (model 3). The covariance matrices among traits for additive genomic effects (Σg) and residuals effects (Σe) were assumed unstructured.where the subscript refers to traits T; K = wA + (1 − w)sG, where A is the numerator relationship matrix among lines computed from the full pedigree data and sG is a scaled matrix G that is compatible with A in reference to the base breeding population , and collects additive genomic effects as well as residual polygenic effects. Under our approach, the optimal w is empirically determined in order to maximize predictive ability after evaluating a sequence of candidate weights within the range 0 < w < 1. To assess the benefits of including additional pedigree information, the best-predictive multi-trait model using conventional G-BLUP  was compared to the equivalent optimized multi-trait K-BLUP model.We also considered multi-trait prediction models fitting a kinship matrix that combines pedigree and genomic information. For this, we extended the BLUP method advocated by pulation . The wei2. Uni- and multivariate models in the second and the genomic prediction stages were fitted by REML using the average information algorithm as implemented in ASReml-R  and other auxiliary characters are used to potentially improve predictions of the target trait. Predictive performance of MT models was evaluated through fivefold cross-validation, where 80% of lines were randomly assigned to the training lines set (TLS) and the remaining 20% formed the validation lines set (VLS). We explored two potential scenarios for MT prediction . One sceThe first MT prediction scenario was studied for two cases: when GY or SG were the traits of interest. However, the Aux@TL + VL scenario was considered only for SG as target trait. Given that one of the main purposes of the trial series used in this study is to obtain representative measurements of GY productivity, the fact that lines are field-tested for other traits, but not for GY, would imply atypical circumstances seriously affecting GY during an experiment. Moreover, extremely reduced or aborted grain production affects normal canopy development; hence, data of traits such as PH or SG should be discarded or considered with extreme caution. For these reasons, the Aux@TL + VL scenario was not considered when GY was the target of prediction, as it would represent an unrealistic or exceptional situation in the context of an advanced stage of sorghum testing.yT and y were used to validate rPA) between yT (y). Unbiasedness of genomic predictions was measured by the regression coefficient (b) of yT (y) on b = 1 indicates an empirically unbiased predictor. Accuracy of predictions was assessed by computing the mean squared error of prediction (MSEP) from the linear regression. The evaluation of prediction models was based on average values over 20 replicates of each cross-validation scenario using the same random seed for all models.In each prediction scenario, the performances of all possible MT models combining two, three, and the four traits were compared to that of the ST model, with the latter taken as benchmark. The response variables used for validation of genomic predictions were consistent with the models applied for analysis: h2. The heritability of SG was higher than that of GY, but lower than those of PH and FT. Additive genetic correlations were significant only for GY with SG and PH, while FT was statistically uncorrelated with the other traits. Significant residual correlations were estimated between GY and the rest of the traits as well as between PH and FT.The narrow-sense heritability estimates using ST G-BLUP are given in b) for all models were below 1 for GY, which indicates overestimation of genomic predictions. The average empirical bias of GY predictions from the ST model was slightly reduced only by the MT model including PH alone as auxiliary trait. The relative decrease (%) in MSEP by MT models was consistent with their increases in predictive ability; MT G-BLUP exploiting PH information had a greater impact on improving accuracy of genomic prediction for GY. When SG was the target of prediction, ST G-BLUP outperformed MT G-BLUP models in predictive ability. Even though prediction models incorporating GY data in the TLS reduced the absolute deviation of b from 1, these models tended to over-predict additive genetic values for SG (b < 1). The inclusion of auxiliary traits in TLS produced higher MSEP relative to using only SG data for prediction.Different results were found for SG predictions when MT G-BLUP models used auxiliary trait records on both TLS and VLS . Under tK for optimization of multi-trait genomic prediction. K using w = 0.25) for all evaluation criteria to a marginal extent. Under the prediction scenario for SG, bias and MSEP from multi-trait models were slightly higher when K (using w = 0.30) was used instead of G. However, an additional 10% gain in predictive ability was obtained by MT K-BLUP in this scenario.After evaluating predictive performance of G-BLUP method, we explored the use of a combined pedigree-genomic matrix per se. Multi-trait BLUP can be seen as a generalized (linear) selection index, with the additional abilities to account for unbalanced information from any set of relatives while properly adjusting for fixed effects in the data . This is in line with the idea that genetic (genomic) similarities between relatives are actually trait-specific (G are derived from posterior trait-specific (co)variance estimates of SNP effects, which are obtained from a previous Bayesian analysis. The authors found, based on simulations, that the benefits of their weighting method were generally not significant for the low-heritability trait that more closely fitted an infinitesimal model (using 500 QTLs). Alternatively, our multi-trait K-BLUP uses a more straightforward approach that assumes a common weight across the genome, while still relaxing the assumptions of conventional multi-trait G-BLUP by allowing the similarity matrix to vary across traits in order to optimize prediction of the target. Given the complexity of traits and the highly structured breeding population used for this study, genomic predictions are likely to rely more on familial relationships and less on information from specific SNPs in LD with QTL (Here, we used a different optimal weight specific , which cwith QTL . In thiswith QTL .While genotyping costs are being constantly reduced, the efficient use of phenotypic data becomes more relevant for plant breeding programs, since field phenotyping is still costly and labor-intensive. This work has shown for the first time how phenotypes of several traits routinely measured in grain sorghum breeding can be efficiently utilized through multi-trait analysis to assist genome-based selection of a target trait. We demonstrated empirically that genomic prediction of parental breeding values for GY benefits mainly from using PH as auxiliary trait. This trait is particularly promising in practice, since PH phenotype can be potentially measured in all trials, as opposed to SG, which can only be phenotyped under specific environmental conditions. In addition, high throughput field phenotyping technologies are expected to increase accuracy of PH measurement in sorghum , which cBased on findings from this study, when SG is the target of prediction, most advantage from multi-trait genomic analysis can be obtained only if predicted lines have been phenotyped for GY. This may be beneficial when the interest is to predict the genetic aptitude for drought-adaptation in lines that have been field-tested for GY performance but could not yet experience post-flowering water stress. Such scenario is compatible with selection for broad adaptation across the TPE, which is generally the best approach to deal with the largely unpredictable occurrence of drought in the Australian sorghum region . Expressper se since, in this case, direct information from SG-GY genetic correlations is exploited predictively through multi-trait analysis. This article also shows, for the first time in plant breeding, how a similarity matrix using trait-specific combinations of pedigree and marker-based relatedness can further enhance multi-trait genomic prediction. Collectively, our results imply useful properties of multi-trait BLUP to evaluate alternative prediction schemes for genetic improvement of crop productivity and drought adaptability in grain sorghum. Given that the traits considered in this study are commonly measured in cereal breeding programs, findings presented here can be also relevant for practical implementation in other major crops.This study demonstrates, based on extensive breeding data, that there is potential to improve genome-based predictions of grain yield and stay-green traits in grain sorghum by using multi-trait genomic analysis. Results suggest that better predictive abilities and accuracies for GY prediction are obtained when PH information on the training lines is included in multi-trait genomic models. When SG is the target, the quality of predictions is likely to improve only if GY performance data is available for both the training and the predicted lines The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.JV, DJ, MM, and FvE designed the research. JV performed the statistical analyses and wrote the manuscript. DJ and FvE edited the manuscript. DJ coordinated the experiments and data collection. CH and EM processed and prepared the dataset. All authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Bovine respiratory disease complex (BRDC) is one of the most important sources of loss within the beef cattle industry in the USA. Steps have been taken to reduce the incidence of BRDC through vaccination. Despite the effectiveness of vaccines, large proportions of cattle still experience morbidity and mortality. Identification of genomic regions that are associated with variation in response to vaccination would allow for the selection of individuals genetically predisposed to respond to vaccination based on specific markers, while heritability and accuracy estimates would help facilitate genomic selection. This in turn may lead to selection for beef cattle herds that may have lower incidence rate of BRDC after vaccination. This study utilizes an Angus herd of more than 2000 head of cattle to identify these regions of association.Genome wide association studies were performed for viral neutralization antibody level and response to vaccination traits against four different viruses associated with BRDC: bovine viral diarrhea virus 1 and 2 (BVDV1 and BVDV2), bovine respiratory syncytial virus (BRSV), and bovine herpesvirus (BHV1). A total of six 1-Mb windows were associated with greater than 1% of the genetic variance for the analyzed vaccination response traits. Heritabilities ranged from 0.08 to 0.21 and prediction accuracy ranged from 0.01 to 0.33 across 7 different vaccination traits.Although six 1-Mb windows were identified as associated with 1% or greater genetic variance for viral neutralization antibody level and response to vaccination traits, few genes around these windows could readily be considered candidates. This indicates the need for further functional genomic annotation, as these regions appear to be gene deserts. Traits ranged from lowly to moderately heritable, which indicated the potential for selection of individuals that are genetically pre-disposed to respond to vaccination. The relatively low amount of genetic variance accounted for by any 1-Mb window indicated that viral neutralization antibody level and response to vaccination traits are polygenic in nature. Selection for these traits is possible, but likely to be slow due to the low heritabilities and absence of markers with high genetic variation associated with them. Bovine respiratory disease complex (BRDC) is one of the most costly and pervasive disease conditions facing beef cattle producers. With more than $750 million dollars in losses each year due to morbidity, mortality, and performance loss, vaccines have been one method used in an attempt to reduce overall incidence rates and by extension economic losses –4. DespiIn order to improve efficacy of vaccination for BRDC, it is desired that all calves exhibit an adequate immune response after vaccination. If this is the case, calves would likely be better protected against future exposure to BRDC viruses after a multi-shot vaccination procedure and thereby result in less economic loss due to BRDC. One way to improve beef cattle response to vaccination may be through genomic selection. Previously, we reported on variables that influenced response to vaccination against bovine viral diarrhea virus 1 (BVDV1), bovine viral diarrhea virus 2 (BVDV2), bovine respiratory syncytial virus (BRSV), and bovine herpesvirus (BHV1) –15. A ge2) estimates, which were the proportion of additive genetic variance out of the total phenotypic variance, ranged from 0.08 to 0.22. The greatest spread of heritabilities was found within BVDV2 with a minimum estimated heritability of 0.08 and a maximum of 0.22. In contrast, BRSV had the smallest spread with a minimum heritability of 0.12 and a maximum of 0.18. Maternal decay and maternal antibody titer traits were only available for BVDV1 and BVDV2. Total genetic and residual posterior variances were relatively similar across all viral neutralization antibody level and response to vaccination traits. Maternal decay of BVDV2 titer level had the lowest posterior genetic variance (0.005) and residual variance (0.02). In contrast, no single antibody titer level or response to vaccination trait had both the highest genetic and residual posterior variance.Estimates of heritabilities and variances were obtained for each viral antibody titer levels and response to vaccination traits Table . HeritabThe amount of genetic variance that every 1-Mb window across the bovine genome could account for was estimated based on the model for a viral neutralization antibody level or response to vaccination trait. Furthermore, the posterior probability of inclusion for each SNP and 1-Mb window was calculated as well. There were no 1-Mb regions across all viral neutralization antibody level and response to vaccination traits for all four viruses that exceeded a 0.9 posterior probability of inclusion. Six 1-Mb windows across the genome across traits could account for greater than 1% of the trait genetic variance Table . The perAll genes within these windows were evaluated as potential candidate genes. Unfortunately, none of the genes within these windows were annotated with immune function Gene Ontology terms. However, higher order gene families names, e.g. TRIML, may have some function related to immune system response . AdditioTraits were also analyzed categorically, with phenotypes being set as their integer value. This marginally improved the heritability of the response to vaccination and viral neutralization antibody levels Table . HoweverIndividuals were separated into five groups that exhibited high genetic diversity between groups while maintaining genetic similarity within groups. The number of individuals in each of the five groups can be seen in Table In this study, we set out to identify regions of the genome that were associated with viral neutralization antibody level and response to vaccination traits for four viruses associated with BRDC. To do so, a genome wide association study was performed to characterize regions based on the proportion of genetic variance they could account for within a trait. Heritability estimates for viral neutralization antibody level and response to vaccination traits were lowly to moderately heritable. Ranging from 0.08 to 0.22, we determined that the heritability of viral neutralization antibody level and response to vaccination traits varied the most in BVDV1 and BVDV2, and the least in BRSV. However, when only traits measured on all four viruses are compared, BVDV1 and BVDV2 have a much more concentrated range in heritabilities than the other two. Additionally, as described in Kramer et al. 2017, BRSV was the singular virus out of the four studied that did not contain individual calves that failed to seroconvert, potentially due to the ubiquitous nature of BRSV , 19. ThiLow correlations were calculated between direct genetic value and the true phenotype, with a range of − 0.10 to 0.09. Accuracies also ranged from an absolute value of 0.002 to 0.31. These accuracies are a direct result of low heritabilities as accuracy was calculated as correlation divided by the square root of heritability. While the correlation between true and predicted phenotype is low, and accuracies are on average 0.11, some traits such as BHV1 overall vaccination response and BVDV1 final titer may have a level of accuracy such that they could be utilized in a selection index. These determined correlations and accuracies underscore the point that responses to vaccination traits remain complicated even after fitting management practices and other environmental and physiological factors in a model. With a greater number of individuals, it may be possible to improve the accuracy closer to its maximum, although it will be capped by the heritability estimates of response to vaccination traits (0.28–0.45 range for maximums). If accuracies of genomic predictions can be increased towards these maximums, then the rate of genetic progress for response to vaccination and viral neutralization antibody level traits will increase.These traits appear to be highly polygenic, as no singular region was able to account for a large proportion of genetic variation and only six regions were estimated to account for more than 1% of that variation Figs. , 3, 4, 5Bos taurus genome along with the help of the Functional Annotation of Animal Genomes (FAANG) initiative. All of this together provides a solid foundation for genetic variation within response to vaccination in Angus calves, and a basis for selection procedures.This study aimed to examine response to vaccination against viruses associated with BRDC in Angus calves using genome wide association studies. Vaccination is one of the most widely used tools by producers to reduce and/or prevent BRDC incidence in herds. Efficacy remains lower than desired; therefore selection of cattle for response to vaccination could increase the effectiveness of vaccine use. This would potentially reduce losses associated with BRDC and improve animal welfare. Using serum samples across a 6–9 week period we were able to identify genomic regions associated with multiple response to vaccination traits, and determine the predictive accuracy of response to vaccination. Windows of size 1-Mb were found to account for up to 1.93% of genetic variation, indicating the polygenic nature of response to vaccination. With low to moderate heritabilities across response to vaccination traits, there appears to be room for selection of animals with improved vaccination response. Genes within the associated regions had little functional annotation associated with them, which may be resolved in future builds of the n = 2518) from a purebred American Angus herd located at Iowa State University Ames, IA, were used in this study. Not all individuals were utilized for all response to vaccination traits due to limited availability of recorded data at specific time points and non-genotyped calves. Therefore, the number of calves analyzed for each response to vaccination trait was less than the total number of genotyped calves. Calves were born in either a spring season or fall season classification, and across multiple years .Calves  and FImpute [The BovineSNP50 BeadChip  and BovineHD BeadChip  were used by Neogen GeneSeek Operations  to perform SNP genotyping. Individuals were imputed by using the SNPipeline package  rather than rounding to integer values. All other fixed effects, covariates, and the matrix of random marker effects remained the same.Heritability estimates for each trait were obtained through BayesC with a π value of 0. This was chosen due to BayesB shrinking small effects to allow for the detection of relatively larger effects, and therefore biasing heritability estimates , 39. HerPrediction accuracies were estimated through cross validation for the direct genetic values that were predicted for each individual in BayesC. Genotyped individuals were initially classified into 9 unequal groups through K-means clustering  after crBos taurus assembly of the genome.The 1-Mb genomic regions accounting for more than 1% OF genetic variation for response to vaccination traits were identified for candidate gene investigation. A region extending half a mega base on either side of the identified 1-Mb regions, for a total of 2 Mb in length, was analyzed by looking at the biological function of every gene currently annotated in the UMD 3.1


GC). Kinship was found to play a major role in correcting population confounding effects and results advocate a ‘trait-specific’ fit of different GWAS models. A survey of genome-wide linkage disequilibrium (LD), one of the critical factors affecting GWAS, is also presented and our findings are compared to other recent studies in potato. The genetic material used here, and the outputs of this study represent a novel resource for genetic analysis in potato.Genome-wide association studies (GWAS) have become a powerful tool for analyzing complex traits in crop plants. The current study evaluates the efficacy of various GWAS models and methods for elucidating population structure in potato. The presence of significant population structure can lead to detection of spurious marker-trait associations, as well as mask true ones. While appropriate statistical models are needed to detect true marker-trait associations, in most published potato GWAS, a ‘one model fits all traits’ approach has been adopted. We have examined various GWAS models on a large association panel comprising diverse tetraploid potato cultivars and breeding lines, genotyped with single nucleotide polymorphism (SNP) markers. Phenotypic data were generated for 20 quantitative traits assessed in different environments. Best Linear Unbiased Estimates (BLUEs) for these traits were obtained for use in assessing GWAS models. Goodness of fit of GWAS models, derived using different combinations of kinship and population structure for all traits, was evaluated using Quantile-Quantile (Q-Q) plots and genomic control inflation factors (λ Solanum tuberosum Group tuberosum is a highly heterozygous, autotetraploid crop. Due to the many genetic complexities posed by autopolyploidy, most potato genetic studies have been performed at the diploid level using bi-parental populations. Significant recent progress has been made in the development of improved algorithms and software for linkage and QTL analysis in autopolyploid crop species  and simple sequence repeats (SSRs)  in potato.th edition . These trait BLUEs were used in GWAS as well as all other analyses involving phenotypic values.Field trials were conducted in 2012 and 2013 at two different sites . At each site replicated trials (two replicates) with two nitrogen levels (100 and 200 kg/ha) were conducted according to an alpha design, thereby leading to a total of eight ‘environments’. Twenty quantitative traits were measured, but not all were phenotyped in every environment (Table S1). The traits analyzed were plant height, yield, days to emergence (assessed as days to reach 50% emergence), stolon attachment , tuber uniformity , foliage , total number of tubers, tuber dry matter (percentage dry matter based on air-dried and water-immersed weights), eye depth , tuber shape (length to breadth ratio), tuber skin brightness , mean number of stems per plant , tubers per stem, tuber flesh color , after cooking blackening , enzymatic browning , tuber fry quality (percentage glucose in tubers stored at 6° with CIPC (Chlorpropham) for ∼3 months after harvest), dormancy break , ethylene treated tuber sprouting, and maturity. To generate phenotypic values for each trait, genotype was modeled as a fixed effect while all other effects were treated as random, and the Best Linear Unbiased Estimates (BLUEs) for all traits were calculated using REML implemented in Genstat 15https://www.sasa.gov.uk/) for further validation against the varietal SSR genotypic database held at SASA. Correspondence of phenotypic data between replicates, sites and years was also assessed for any gross inconsistencies. For all traits, residuals plots from REML analysis were examined for presence of any outliers and adherence to a normal distribution.Field trials were rogued by potato field staff for any discrepancies in the observed phenotypes and misidentification/mislabeling of clones. Genotypes which showed inconsistent phenotypes were sent for DNA fingerprinting at Science and Advice for Scottish Agriculture  and multiplied in 2011, the year preceding the first field trial. In addition, we included seven diverse diploid potato genotypes in the genotyping panel to act as ‘outgroups’ and to aid validation of population structure analysis. The diploid material included the sequenced doubled monoploid 44 clone , hereaftGenomic DNA was extracted from young leaf tissue from individual field grown plants using the Qiagen DNeasy Plant Maxi Kit (Qiagen), quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit , and normalized to a concentration of 30 ng/μL. The panel was genotyped at GenProbe  for the Infinium 8k Potato SNP Array  values which should follow a uniform distribution under the null hypothesis. Models were ranked using genomic control inflation factor (λGC) metric calculated using R package ‘GenABEL’  plots of the observed GenABEL’ . Bonferr2) was used to calculate correlations between marker-pairs using SNP dosage scores (0 to 4). LD was calculated based on marker pairs located within (a) chromosomal short arm, (b) chromosomal long arm, (c) euchromatin , (d) heterochromatin, and (e) whole chromosomal region for all 12 chromosomes. Extent of LD decay was estimated by implementing Quantile regression  and where r2 equals 0.1 on the 90th percentile, respectively. For whole chromosomal regions, extent of LD decay was also estimated using non-linear regression of r2 against the physical map distance according to Pearson correlation coefficient (rThe measure of polymorphic information content (PIC) for each SNP was calculated according to https://doi.org/10.25387/g3.6716033.Supplemental Material is available at figshare and brief captions for the uploaded material are provided here, as follows: Table S1 (traits phenotyped); Table S2 (germplasm included in the study); Table S3 (List of SNPs mapping to multiple positions); Table S4 ; Table S5 ; Table S6 (Size of pericentromeric heterochromatin); Table S7 (Pairwise measures of population differentiation); Table S8 (Significant marker-trait associations); Table S9 (Annotation of SolCAP SNPs used in the study); Table S10 (Genotype and phenotype data); Figure S1 ; Figure S2 (PCA performed on the genomic relationship matrix of the association panel clones); Figure S3 (Traits Pairs plot); Figure S4 and S5 ; Figure S6 (nMDS Screeplot); Figure S7 (population structure present in the association panel); Figure S8 .The germplasm panel displays a broad range of genotypic and phenotypic diversity, reflecting the diverse geographic origins, release dates, and market classes of the selected potato varieties and breeding clones. Distribution of minor allele frequencies (MAF) across all tetraploid genotypes is shown in The association panel was phenotyped for 20 quantitative traits in up to eight environments . A ‘pairs’ plot showing a scatterplot of the relationship between traits, the distribution of individual traits and the correlation between traits is presented in Figure S3. The residuals for all traits were normally distributed (data not shown). 2 statistic using pairwise combinations of SNPs present across all 12 chromosomes. Previously LD in potato has been assessed using a varying level of structural units ranging from a few fragments per chromosome  was within twofold limits of that observed for the euchromatin (2.73 Mb). Chromosome-scale LD blocks and decay patterns for all 12 chromosomes are shown in 1/10,90 at the whole chromosomal-region level, ranging from 2.54 Mb (Chromosome 9) to 4.68 Mb (Chromosome 3), whereas chromosome 8 displayed extremely conserved LD1/10,90 of 20.04 Mb. 1/10,90 = 8 Mb) in their autotetraploid Andigena population (N = 652). The r2 equalling 0.1 within a distance of 5 cM and 10 cM, respectively. In our study, overall LD1/10,90 for euchromatin and whole chromosomal regions were 2.73 Mb and 3.27 Mb, respectively. Relating physical size of the assembled potato genome  as implemented by 1/10 was 1.09 Mb which despite being derived from average r2 fitted using non-linear regression is still within reasonable LD decay limits expected for potato but much slower than that reported by Genotyping was carried out for 351 tetraploid and 7 diploid potato genotypes. Initial data quality checks revealed ten pairs of tetraploid genotypes showing more than 99% similarity. These genotypes also contained those clones which during the roguing of field trials had shown morphological features incompatible with those expected from previous knowledge. The identical pairs were DNA fingerprinted with SSRs and only the individuals, one per pair, for which phenotypic assessments and fingerprinting patterns matched with the potato database were retained, thereby, reducing the net number of genotypes in the tetraploid panel from 351 to 341.The individual relatedness between genotypes in the revised panel was estimated using a genomic relationship matrix. Kinship analysis assigned more closely related lines such as those belonging to individual breeding programs and/or possessing resistance to specific pest or disease  into ten smaller clusters (hereafter referred to as ‘Kinship groups’). Although the categorization of clones into distinct groups is subjective, the clustering of the panel into ten kinship groups is supported by the hierarchical clustering derived dendrogram . KinshipFst, Table S7) which further supports the absence of any strong population structure in the panel used here. These findings are in accordance with the observations made in previous studies  with a criterion of representing the full data set in the smallest possible number of dimensions. At the chosen nMDS solution, there was no clear visual separation of clones into distinct subpopulations indicating a lack of strong population structure within the panel . However, nMDS analysis was still able to detect subtle relationships among the clones, possibly resulting from the weak underlying stratification among genotypes, as depicted in the nMDS-based dendrogram  showing e.g., ‘GL’ series) formed distinct clusters and others were interspersed among the cultivar groups. The four diploid wild genotypes clustered into a separate group, which surprisingly included one tetraploid clone, 3053-18, a ‘late blight R-gene differential’ for the resistance gene R5. This result is likely due to close genetic relationship of 3053-18 to the late blight resistant wild species in this cluster, close relatives of which were used to introgress late blight resistance (S. tuberosum group Phureja cv. MayanGold and S. stenotomum) were clustered with one of the tetraploid clusters predominantly containing older  cultivars and advanced breeding lines.The correspondence of kinship groups to the nMDS-derived subpopulations was very high with six kinship groups nesting into their respective subpopulations as single large blocks. Members of the remaining kinship groups did show their assignment to separate subpopulations but still the allocation was in cohesive blocks of at least three genotypes. This allocation of members of individual kinship groups to more than one subpopulation could be attributed to their shared lineage. The details of individual members belonging to these kinship groups and subpopulations are present in Table S2a. The germplasm was also analyzed using phylogenetic analysis  by inclusistance . The remPopulation structure was also assessed using PCA  and STRUCTURE . Population structure obtained using PCA and nMDS was compared to visualize patterns of variation in terms of ‘country of origin’ and ‘year of release’. The assessment using ‘year of release’ failed to reveal any specific patterns for both PCA as well as nMDS (data not shown). This observation contrasts with that of Visualization of nMDS-based population structure in terms of ‘country of origin’ of each genotype yielded a more harmonious representation of the germplasm as compared to the PCA. The subpopulations were not clearly defined but a country-specific pattern was apparent . One subpopulation  comprised modern UK cultivars while another subpopulation  contained older UK varieties. Two interspersed subpopulations at the center (cyan and brown) primarily contained ‘mainland’ European germplasm; and the remaining one  included mainly international non-European material. The analysis suggests the majority of the UK potato cultivars represent more diverse genetic pools than rest of the European and non-European cultivated potato germplasm with limited admixture. Similarly in other studies, population stratification within a region has been reported. For example, two subpopulations of European winter wheat from multiple national programs  were prevs. observed –log10(p) values and genomic control inflation factors (λGC) achieved for each ‘trait x model’ combination. The success (ranking) of GWAS models in controlling the overall genome-wide type I error rate , summarized over 20 traits, is shown in All GWAS analyses described here as well as in the subsequent sections were performed on 290 tetraploid clones. This reduction in the association panel was due to clone identity issues, as described in the preceding section, as well as loss of some genotypes due to lack of enough seed tubers for successive field trials. Seed tubers for these genotypes were not supplemented from other sources to avoid inducing any bias arising due to variation in seed tuber generation as the seed material for the reported study was generated from a designated seed production site in the preceding year. Regarding markers, a total of 5,157 SNPs were retained for GWAS after filtering for missing data (>= 20%) and minor allele frequency (MAF=<0.05) thresholds. Performance of GWAS models was evaluated using Q-Q plots of the expected viz. STRUCTURE, principal component analysis and non-metric multidimensional scaling. GC estimates for Q and QK GWAS models involving Q matrices from above three methods and their overall rankings are provided in p-value inflation than nMDS and PCA while the latter two performed better for 6 traits each. Evaluation of these models as QK models  controlled the p-value inflation observed in Q models with different methods for population structure estimation and brought λGC close to 1. This further implies that with the inclusion of correction for kinship effects, the method used for population structure estimation is not very critical for performing GWAS in potato. Figure S13 and Figure S14 illustrate Q-Q plots obtained from Q and QK models tested using three different Q-matrices, respectively. Overall the results indicate that no single method was able to capture population confounding effects better than the others for all 20 traits. Nevertheless, when overall model performance was taken into consideration, nMDS seemed to provide slight advantage over PCA and STRUCTURE (Various methods have been used for controlling the confounding effects of population structure for GWAS. Here we examined GWAS models with population structure estimates (Q matrices) from three different methods TRUCTURE  and was GC values and rankings for these four models achieved for all 20 traits are presented in p-values close to the expected (λGC∼1), were comparable where K model was most effective for 7 traits and QK for 13 traits. Furthermore, comparison of the individual components of the QK model revealed that correction for Kinship was more efficient in overcoming the germplasm confounding effects than adjusting for population structure. As compared to K Model, Q Model never ranked first or second for any of the 20 traits and performed close to K and QK models for only one trait  to the K model. p-value inflation further confirms lack of significant population structure in potato.Four principal GWAS models  were investigated for all 20 traits. The λA careful assessment of kinship and population structure in GWAS studies is required to avoid spurious associations arising from systematic differences in allele frequencies due to the difference in sample ancestries. However, the results described above indicate that this may only be critical when the trait in question is also confounded with the kinship and/or population structure present in the germplasm. Overcompensation of mixed models for population structure and relatedness can lead to false negatives (type II error) as also reported by viz. Naïve, K, Q and QK for performing GWAS in potato and validate our findings using previously published reports. i.e., (a) from models where p-value inflation was close to the expected normal value (λGC∼1) and (b) which passed the set Bonferroni correction p-value (α = 0.05) threshold are included for reporting GWAS results. Effectiveness of K and QK models over Q and Naïve models in controlling type I error is clearly evident in the Manhattan plots  gene  lower and thus more significant than those reported by the QK model  with large effect QTL will have greater success with 8k SNP array than with the polygenic traits  with complex architecture as observed in the current and previous studies (The current study analyses LD in various subsets of the genome and reports a moderate level of LD decay in potato. The primary aim of GWAS is to filter real marker-trait associations that arise from physical linkage of loci from spurious effects that result as a consequence of potential confounders such as kinship and population structure. The germplasm examined here shows multiple levels of relatedness with a prominent kinship and weak population structure, despite which the nMDS analysis was able to reveal the underlying patterns of subpopulations in the panel. These subpopulations showed an apparent structuring according to country of origin and further provide novel insights into the breeding strategies adopted in Europe. Evaluation of various GWAS models for 20 traits further demonstrated the importance of applying trait-specific models and exploiting different population structure methods for performing association mapping in potato. The main finding reveals kinship, rather than population structure, as the major factor controlling the level of spurious associations in cultivated potato.


The heritability of log-transformed FWEC amounted to 0.09 ± 0.02, with no contribution from the animal permanent environmental variance to the between animal variation across seasons. Three-trait heritability estimates for log-transformed FWEC amounted to 0.07 ± 0.05 in autumn, 0.13 ± 0.05 in winter and 0.19 ± 0.05 in spring. These results suggest sufficient genetic variation in FWEC to support selection for lower log-transformed FWEC. However, the best time to record data for selection is after the break of the season in winter and in spring, when sheep are stimulated by a greater intake of infective larvae from the pasture after the first rains. Genetic correlations among FWEC in the respective seasons were moderate to high, ranging from 0.55 to 0.89. Phenotypic correlations, on the other hand, were significant but lower in magnitude, ranging from 0.09 to 0.16. These results provide useful information for developing strategies for the genetic improvement of ovine resistance to gastrointestinal nematodes under Mediterranean conditions in South Africa by using FWEC as an indicator trait.Gastrointestinal nematodes result in severe economic and production losses to the sheep industry. An increase in resistance of the nematodes to chemicals used for control, as well as a demand of consumers for meat products free from chemicals, has fostered research on alternative control strategies. Breeding for resistance to nematodes offers an alternative to control parasitism but its effectiveness depends on genetic variation in faecal worm egg count (FWEC), an indirect measure of parasite resistance. A historic dataset of FWEC from four Merino lines subjected to natural parasite challenge was used to estimate genetic parameters for FWEC in three seasons  using a repeated records animal model, followed by a three-trait animal model analysis treating FWEC in different seasons as separate traits. The effects of selection line, birth year, sex, the sex x birth year interaction, season and the season x year interaction were significant when using 4994 records recorded from 1997 to 2000 ( The profitability of sheep production relies, in part, on the efficient application of proper strategies to reduce nematode infestations under challenge conditions. At present, the major control strategy for nematodes is the use of chemical drenches to treat and prevent production and economic losses associated with infestation. However, development of parasite resistance to chemicals is common  was first assessed in a single-trait repeatability model. Subsequently, FWEC in the three seasons were analysed in a three-trait model. This model was used to determine whether FWEC in different seasons was genetically the same trait and also for deriving genetic and phenotypic correlations among FWEC records obtained in the different seasons.Medicago sativa) and medics (M. truncatula). Animals also grazed oat (Avena sativa) fodder crops during winter and spring as well as wheaten stubble in summer. They occasionally received oat grain as a supplement when grazing stubble lands. Standard drenching and husbandry programmes for the region were followed.The data used in this study came from four lines of Merino sheep that were selected for an increased clean fleece weight with a limit on fibre diameter  variances respectively. X, Z1 and Z2 represented the corresponding incidence matrices relating the respective effects to y and e was the randomly distributed vector of residuals. It was assumed that:In these analyses, The later analysis assumed equal genetic variances for log-transformed FWEC across seasons, as well as unity genetic correlations among expressions of log-transformed FWEC in each season. To test these assumptions, the analyses were followed by within-season single-trait analyses for records obtained during autumn, winter and spring respectively. To obtain the best operational model for these analyses, the fixed effects of selection line (as reported previously), birth year (1997–2000), birth type (multiple or single), sex (ram or ewe) and age of dam (2–7+ years) were fitted within seasons. In addition, all two-factor interactions were initially included in the model but only the birth year x sex interaction was retained in the operational model along with the other significant fixed effects for subsequent analyses. Random effects were then added to the operational model, resulting in the following models for analyses in matrix notation:Co variance  = 0][Co variance  = Aσam][1, Z2 and Z3 represented the corresponding incidence matrices relating the respective effects to y and e was the randomly distributed vector of residuals. A was the numerator relationship matrix and σam the covariance between direct and maternal additive genetic effects.In these analyses, y was a vector of observations for log-transformed FWEC within seasons, and b, a, m and c were vectors of fixed effects, direct genetic variances, maternal genetic variances and maternal PE variances respectively. X, ZIt was assumed that:Ratios corresponding to additive genetic and permanent environmental variances were computed from these repeatability and single-trait within-season analyses. These variances were expressed relative to the total phenotypic variance. Likelihood Ratio tests (LRT) were used to test the contribution of each random term to improvements in the operational model for significance. The single-trait within-season analyses were followed by a three-trait analysis treating FWEC in different seasons as separate traits. This analysis also allowed the calculation of season-specific heritability estimates, as well as genetic and phenotypic correlations among FWEC expressions in different seasons. The full pedigree file, including 4650 animals born between 1989 and 2000 as the progeny of 357 sires and 1447 dams, was used in all analyses.p < 0.001). Sex x birth year interactions are commonly found in similar literature and will not be reported. Seasonal means for FWEC varied quite markedly across years . The addition of animal permanent environment as another random effect did not result in a further improvement of the Log-likelihood value, which stayed at -1904.87. Log-likelihood values for the subsequent analyses on within-season FWEC accordingly suggested a significant improvement from the model with fixed effects only to the model also including direct additive genetic variation for log-transformed FWEC only in winter and spring . The adding of additional random effects failed to result in further improvements to the goodness of fit of the models fitted in winter and spring.Log-likelihood values for the repeatability model analysis on log-transformed FWEC amounted to -1932.02 for the fixed effect operational model prior to the addition of the additive genetic effect of animal. When animal genetic effects were added to the analysis, the Log-likelihood value improved to -1904.87 .Extreme individual variation of FWEC data is commonly reported in literature, ranging from 0 to 50 000 epg  heritability estimates for FWEC in winter and spring coincided with the growth of pastures after the first rains in the Mediterranean environment and the hatching of parasite eggs during the wet season. The better nutritional conditions during this time of the year seem to have improved the ability of resistant hosts to mount an immune response at the genetic level, which in turn affected the expression of parasite resistance, leading to higher heritability estimates. Studies by Nieuwoudt, Theron and Kruger (h2 = 0.00–0.03), moderate during winter to late spring (h2 = 0.21–0.25) and reached a high of 0.51 during mid-winter. Khusro et al. (h2 = 0.22), while a higher heritability estimate of 0.38 was observed in hoggets. In the latter study, the data were collected from commercial farms with limited information on the season of sampling. However, the current study, along with studies by Greeff et al. (The across-season heritability derived for this study from the repeatability model was low at 0.09. However, the three-trait analysis suggested that the magnitude of the genetic variance component for log-transformed FWEC was increased by 87% from 0.046 in autumn to 0.086 in winter. The genetic variance component of log-transformed FWEC in spring was increased by an order of magnitude exceeding 3.5 in comparison to FWEC in autumn. Phenotypic variance components were not affected to the same extent, ranging from 0.64 to 0.88 . These rd Kruger , Cloete d Kruger  and Mated Kruger  conducted Kruger , using dd Kruger . Our resd Kruger  when esto et al.  also repf et al. , Rahman f et al. , Rinaldif et al.  and Kumbf et al.  on goats2 estimates for FWEC of respectively 0.24 in Muzaffarnagari sheep and 0.22 in 22-week-old Perendale sheep. Comparable results were reported by Clarke (Overall, the heritability estimates of FWEC in our study under South African conditions, although moderate, were below the mean value of 0.27 that was derived from 16 literature sources documented in a major review article by Safari, Fogarty and Gilmour . In a sty Clarke  in Meriny Clarke . A studyy Clarke . Hence, y Clarke . SimilarH. contortus larvae in sheep. FWEC were counted with a sensitivity of 50 epg wet faeces in this case.The lower magnitude of heritability estimates for FWEC obtained in our study could be attributed to a greater variation of FWEC between months within seasons and between seasons within years (Greeff et al. Genetic correlations among log-transformed FWEC in different seasons were moderate to high and positive. Log-transformed FWEC thus seems to be governed by mostly the same genes, irrespective of the season. Although genetic correlations among seasonal log-transformed FWEC measures were not significantly different from each other as indicated by their standard errors, it is notable that the genetic correlation between FWEC in autumn and in spring amounted to only 0.55. The large standard errors accompanying genetic correlations involving FWEC in autumn are possibly because of the smaller additive variance component derived in autumn. Moderate to high positive correlations among FWEC measures in autumn, winter and spring observed in this study seem to suggest that individual free-ranging animals susceptible to nematode infestation in one season are likely to be at risk in other seasons as well. Animals that consume higher volumes of the infected pastures are likely to host greater parasite loads Zajac . A studyFinally, it is relevant to consider whether FWEC and other traits involving host resistance should form part of formal sheep recording in South Africa. Cloete et al.  suggesteGenetic variation for FWEC exists. Selection for lower parasite loads is likely to pay dividends in South Africa, as in overseas countries. However, this study suggested that the best time to challenge sheep for deriving breeding values to assist with selection for parasite resistance is sometime after the break of season in winter and/or spring, when greater numbers of infective larvae after the first rains provide an adequate natural challenge under Mediterranean conditions. This study has an implication for the timing of data collection to allow breeding for parasite resistance under South African Mediterranean conditions.


Porcine reproductive and respiratory syndrome (PRRS) is one of the most infectious swine diseases in the world, resulting in over 600 million dollars of economic loss in the USA alone. More recently, the USA swine industry has been having additional major economic losses due to the spread of porcine epidemic diarrhea (PED). However, information regarding the amount of genetic variation for response to diseases in reproductive sows is still very limited. The objectives of this study were to identify periods of infection with of PRRS virus (PRRSV) and/or PED virus (PEDV), and to estimate the impact their impact on the phenotypic and genetic reproductive performance of commercial sows.P < 0.05) for all traits analyzed except for total piglets born. Heritability estimates for traits during Clean (without any disease), PRRS, and PED ranged from 0.01  to 0.41 . Genetic correlations between traits within disease statuses ranged from −0.99  to 0.99 . Within trait, between disease statuses, estimates ranged from − 0.17 (number weaned between PRRS and PED) to 0.99 (abortion between Clean and PRRS).Disease (PRRS or PED) was significant (Results indicate that selection for improved performance during PRRS and PED in commercial sows is possible and would not negatively impact performance in Clean environments. Porcine reproductive and respiratory syndrome (PRRS) is one of the most infectious swine diseases in the world. Animals infected with the PRRS virus (PRRSV) show respiratory symptoms and impaired performance, such as slower growth rates in newborn and growing pigs and reproductive failure in pregnant sows . This maMore recently, another disease that has been causing severe economic impacts in the USA swine industry is porcine epidemic diarrhea (PED). Pigs of all ages infected with the PED virus (PEDV) show diarrhea and vomiting, with affected piglets experiencing nearly 100% mortality within 2 to 3 d of birth .Vaccination and biosecurity have been the main prevention strategies to control PRRS. Although these strategies have shown to limit the impact of this disease at some degree, additional strategies should be evaluated to help further decrease the impact of PRRS. Recent studies have suggested that selection for improved performance in PRRSV-infected sows is possible –8. TheseThe objectives of this study were: 1) to identify periods of infection of PRRSV and/or PEDV, 2) to estimate the impact of diseases (PRRS and/or PED) on reproductive performance of commercial sows, and 3) to estimate genetic parameters within and between challenged and non-challenged environments.Performance data and a five-generation pedigree were available from 10 commercial farms in North Carolina, USA. Data included 21,160 farrowing records from 5,352 Large White × Landrace crossbred multiparous sows farrowing from April 2013 to January 2016. All sows used in this study were first parity gilts when collection started, and no new animals were added to the dataset. At the start of data collection, all the sows used were PRRS- and PED-negative for these viruses. The sows used were fully pedigreed and were progeny of 100 sires and 1,595 dams. Progeny of sires were well distributed across farms, with only 8 sires present in 3 or fewer farms. On average, sires had 5.96 progeny sows per farm. Traits analyzed included abortion , total number of piglets born , number of piglets born alive , number of stillborn piglets , number of mummified piglets , number of piglets born dead , proportion of piglets born dead , and number weaned . Traits with a large number of zeros  were analyzed as the natural log of the phenotype + 1 in order to create a more normal and narrow distribution for those traits  showed that this strategy was successful in splitting data into Clean and Diseased (i.e. PRRS) statuses and capture the effects of the disease. A similar strategy has been used by others, which further validated the approach by Lewis et al. , 7, 8. FHeritabilities for reproductive traits are generally low, which is what was observed in this study. In general, heritabilities were similar to those previously reported in reproductive sows in Clean environments, which have been largely discussed in several studies , 8, 10, To our knowledge, there are no studies that reported genetic parameters for reproduction traits in sows infected with PEDV. The heritability estimates during PED were comparable to those found in the Clean status, with the exception of AB, TB, and NW, which were higher during PED than during Clean. This overall similarity with the Clean status was expected; infection with PEDV should not have an impact on reproductive performance in sows, as the disease does not infect piglets in utero, so there should be no decrease in TB or increase in the born dead traits with PEDV infection. Since there should be no impact of PED on TB, it was surprising to find that the heritability of TB during PED was estimated to be 0.26 ± 0.05, which was higher than what was found in Clean (0.11 ± 0.02). The moderate heritability estimate for AB 0.41 ± 0.06) was also surprising, since PED is only known for high mortality in piglets. However, this heritability indicates that there is opportunity to select for improved AB in PEDV-infected pigs, which is in accordance with the phenotypically lower AB during PED compared to Clean and PRRS sows. Less surprising was the heritability that was found for NW during PED (0.15 ± 0.05), which was higher than what was estimated during Clean (0.02 ± 0.01) or PRRS (0.11 ± 0.09). Similar to PRRS, there was an increase in both additive genetic and residual variance, with the increase in additive genetic variance being greater, which resulted in increased heritabilities in this study. It is also important to note that during disease, there would be a decrease in cross fostering to limit the spread of disease. When there is a lot of cross fostering and this information is not accurately recorded, genetic variation for of NW cannot be fully captured accurately because the sow and the piglets in her litter are not necessarily related, but more genetic variance can be captured with the increased relatedness of the litter when there is a decrease in cross fostering and thus an increase in heritability of NW can be seen. Although there was an increase in heritability for AB and NW from Clean to PED, the use of these traits for selection purpose during PEDV infection would be challenging. At both the nucleus and commercial levels, AB can be a challenging trait to collect accurately and there is added difficulty in analysis due its binary nature. At the commercial level, there is a high frequency of cross-fostering and limited records kept on these transfers, making genetic evaluations for NW a challenging task to be performed. An added challenge for identifying animals with variation in NW during PEDV infection is the nearly 100% piglet mortality , 4. None1 ± 0.06 Genetic and phenotypic correlations were estimated within each of the disease statuses. For the Clean status, most correlations were low, with high genetic and phenotypic correlations for NBD with SB and PROP, and for MUM with PROP, which makes sense since these traits all measure mortality. The low genetic correlations that were found between traits with NW in this study could be due to the lack of traceable cross-fostering information from these animals, which did not allow us to properly account for the foster dam information in the statistical analysis of the data.During PRRS, the genetic correlation estimates between traits were in general greater than for those in the Clean status. There were also much larger standard errors estimated during PRRS than in Clean. This must be due to the few records for the PRRS status as compared to the Clean status, as well as to the large variation seen during PRRS as compared to Clean for many of the traits. Some traits had opposing genetic correlations between Clean and PRRS, like NBA with SB and NBD which was negative in PRRS and positive during the Clean environment. This pattern indicates that the relationship between born dead traits with NBA is genetically favorable during PRRS as compared to their relationship during Clean. The relationship with NBA and NBD was also much higher than in Clean. Other traits, like NBD with SB and PROP, had genetic correlations that were similar to those that were estimated during Clean. These results indicate that selection for improved performance recorded during a PRRS outbreak in one trait would result stronger changes in other correlated traits, compared to the Clean status. Therefore, selection under Clean status would differ from that under PRRS status. To our knowledge, there are no reports available in the literature providing correlation estimates within PRRS status.Within the PED disease status, the genetic correlations between AB with NBD and NW, and between SB with NBD were similar in size and direction to their corresponding phenotypic correlations. The genetic correlations between MUM with SB and NW were larger than their corresponding phenotypic correlations, but were the same directionally. The standard errors for the genetic correlations for between MUM with SB, NBD, NBD, PROP, and NW and between PROP and NBA are extremely high. The high genetic correlation between AB and NBD could be due to the similarity in how these traits express performance (i.e. piglets born dead), although one accounts for the number of dead piglets (NBD) and the other does not (AB). This difference between the two may be reflected in their low phenotypic correlations. Compared to the Clean status, much of the genetic correlations were in opposite directions for PED. Comparisons between Clean and PED show that correlated response to selection in a Clean environment for these traits would be different that the response during PED. Also for PED, the standard errors were much larger than for Clean, probably due to the lower number of animals used for the PED analysis and greater residual variance.There were problems with convergence for models to estimate the relationship of MUM with PROP, NBD, and TB for PRRS, between PROP with NBD and TB with MUM for PED, and between SB and PROP for the Clean status. Within PRRS and PED, it is possible that these problems could be caused by the low number of animals that are within the disease statuses, and the limited number of animals represented in each farm. It is also problematic to estimate genetic correlations for traits where the heritability is not different than zero, like MUM for all disease statuses and PROP for PED, which could also be contributing to these convergence problems.Overall, the moderate to high positive correlations between Clean and PRRS statuses found in this study indicated that the underlying genetic mechanisms of these traits are similar between healthy and PRRSV-infected animals, suggesting that selection for improved performance under a PRRS disease status would not negatively affect performance during a Clean environment. In general, estimates found in this study were similar to those found in independent studies. For NBA, the genetic correlation between Clean and PRRS was 0.82 ± 0.12, which was comparable to the estimate by Rashidi et al. , but higThe expectation for genetic correlations between Clean and PED was that they would be high, since the reproductive performance was, in general, not significantly different between these statuses, with the exception of NW and AB. To our knowledge, there are currently no studies comparing genetic parameters between Clean and PED. Genetic correlations between Clean and PED disease statuses were positive moderate to high for most traits, with the exception of NBD, which had a low genetic correlation (0.11 ± 0.59). There was a significant difference in NBD between these two statuses, and although we may not understand why PED would show a lower NBD than in Clean, this low genetic correlation corroborates with this finding, indicating that, indeed, NBD between Cleaned and PED statuses are different. Nonetheless, the large SE associated with this estimate makes it hard to properly conclude on their genetic relationship. The high genetic correlations between Clean and PED statues for AB, NBA, and SB may be reasonable since PEDV infects the piglet only after birth, so they are born healthy and mortality is high post-infection , 4. WithThe genetic correlation estimates between PRRS and PED were more variable than for the previous comparisons. In addition, the standard errors of these estimates were much larger than for the other disease status comparisons, but this should be due to the low number of animals that had records during both statuses. This might also have contributed with the convergence problems we observed for MUM. An added possible practical problem with this trait could be that this trait may not be properly distinguished during recording of the data among the farms due to different staff and different procedures on the farms. Because of these convergence issues, we also estimated these correlations using a sire model (data not shown). The same convergence issues still occurred, and this analysis resulted in the same overall conclusions, but with estimates with much greater SE. Additionally, we used the sire model to investigate potential non-linear relationships between statuses within a trait (data not shown). Sire estimated breeding values (EBVs) for a given trait between statuses were very linearly correlated, with the exception for AB between PRRS and PED. For this trait, PED sire EBVs tended to plateau at high PRRS sire EBVs. However, this dataset consisted of only 100 sires, and with the large SE of estimates, further studies are needed to better understand the relationship between these diseases at the genetic using a sire model. Nonetheless, positive high genetic correlations between PRRS and PED were found for NBA, SB, NBD, and PROP, indicating that reproductive performance will be reflective of the genetic merit of the individual regardless of whether performance was recorded in PRRS or PED. This is of major importance to the swine industry because of the increased interest in breeding a more robust pig that excels in both the Clean and dirty environments. If selection was done for an increase in performance during PRRS, this would also result in increased performance in PED. Moreover, the very low genetic correlation between PRRS and PED for NW might be due to the major impact that PEDV has on NW, and thus, the genetic control for this trait between the two diseases should be quite different. Nonetheless, these results for NW indicate that genetic improvement for response to one disease would not impact the response the other disease.Phenotypic and genetic differences were observed in commercial sows as a function of disease status  in this study. Mean performance under PRRS was different than for performance recorded in Clean and PED affected environments. In contrast, PED and Clean statuses had more similar phenotypic performance. The greater heritability and additive genetic variance estimates obtained during PRRS and PED statuses compared to Clean indicate that selection for improved reproductive performance under these diseases is possible. The high genetic correlations obtained between PRRS and PED statuses indicate that selection for improved reproductive performance under one disease would also be favorable for the other disease. In addition, genetic correlations between Clean and Diseased environments were overall positive, and thus, the reproductive performance in PRRS and/or PED would also be informative of the animal’s genetic merit during Clean. Overall, our results indicate that there is an opportunity to select for improved reproductive performance during PRRS and PED outbreaks in commercial sows.


Infant vocalizations are one of the most fundamental and innate forms of behavior throughout avian and mammalian orders. They have a critical role in motivating parental care and contribute significantly to fitness and reproductive success. Dysregulation of these vocalizations has been reported to predict risk of central nervous system pathologies such as hypoxia, meningitis, or autism spectrum disorder. Here, we have used the expanded BXD family of mice, and a diallel cross between DBA/2J and C57BL/6J parental strains, to begin the process of genetically dissecting the numerous facets of infant vocalizations. We calculate heritability, estimate the role of parent-of-origin effects, and identify novel quantitative trait loci (QTLs) that control ultrasonic vocalizations (USVs) on postnatal days 7, 8, and 9; a stage that closely matches human infants at birth. Heritability estimates for the number and frequency of calls are low, suggesting that these traits are under high selective pressure. In contrast, duration and amplitude of calls have higher heritabilities, indicating lower selection, or their importance for kin recognition. We find suggestive evidence that amplitude of infant calls is dependent on the maternal genotype, independent of shared genetic variants. Finally, we identify two loci on Chrs 2 and 14 influencing call frequency, and a third locus on Chr 8 influencing the amplitude of vocalizations. All three loci contain strong candidate genes that merit further analysis. Understanding the genetic control of infant vocalizations is not just important for understanding the evolution of parent–offspring interactions, but also in understanding the earliest innate behaviors, the development of parent–offspring relations, and the early identification of behavioral abnormalities. Vocal communication is important for social interactions in all mammals, and it begins in rodents on the first postnatal day with the communication between an infant and its parents . Mouse pDifferences in infant vocalization are prominent among genotypes and strains of mice and their intercrosses , but no The BXD family is widely used for mapping QTLs that influence behavioral, anatomical, and physiological phenotypes . The BXDAcoustic characteristics such as fundamental frequency and sound amplitude, have different effects on parental care , and we In this study, we have used a diallel cross of C57BL/6J (B6) and DBA/2J (D2) to estimate heritability and parent-of-origin effects of all USV traits. We follow up with a larger study using 41 member strains of the BXD family, to map loci for both the frequency and amplitude of calls. We ask four questions:•Can we detect parent-of-origin effects, such as parental genetic effects (PGEs), contributing to infant USVs?•How are the functions of different aspects of vocalization reflected in their heritability?•What regions of the genome modulate different facets of USVs, and are these different facets under selective or joint control?•Within these genomic regions, can we identify putative candidate genes with mechanisms that link to different aspects of infant vocalizations?n = 29), DBA/2J (D2) (n = 24), and reciprocal F1 hybrids—B6D2F1 (n = 30) and D2B6F1 (n = 30). The four genotypes of pups consisted of: (a) B6 (B6 dam and B6 sire), (b) D2 (D2 dam and D2 sire), (c) B6D2F1 (B6 dam and D2 sire), and (d) D2B6F1 (D2 dam and B6 sire). For the second part of the study we collected USV data for 41 strains of the BXD recombinant inbred family (n > 310). This family is the most deeply phenotyped mammalian model system, consisting of experimentally tractable and genetically defined mouse strains capturing a large amount of naturally genetic variation, analogous to that found in a normal human population . Infants were weighed and recorded at postnatal day (P)7, P8, and P9, between 2 and 5 PM (CST). Postnatal days 7–9 were chosen as they closely correspond to full term birth in humans. This was estimated using the Translating Time website2). Prior to the end of each session, body temperature was measured on the chest using an infrared thermometer . Recordings were transferred to Avisoft-SAS Lab Pro (version 4.40), and sonograms over the range of 20–250 kHz were generated using the following software parameters: (1) FFT length of 1,024 points , (2) frame size of 100%, (3) a Hamming smoother with a bandwidth of 317 Hz and a resolution of 244 Hz, (4) temporal resolution 1.024 ms with a Hamming smoother overlap of 75%, and 1/bandwidth of 3.2 ms.Individual pups were separated from their parents and placed into a freshly cleaned plastic container (12 cm × 8 cm × 7 cm) that was located in the center of large polystyrene foam box (28 cm × 21 cm × 18 cm). The end of an UltraSoundGate 116–200 microphone—sensitive to frequencies up to 340 kHz—was placed 8–10 cm above the pup and the temperature within the box was ∼21°C. Vocalizations were recorded for the first 3 min of isolation at a sampling rate of 300 kHz using 16-bit A-to-D converter  the number of calls, (2) the duration of calls, (3) the mean fundamental frequency, and (4) the mean peak amplitude of calls. These parameters were measured automatically using Avisoft-SAS Lab Pro software for the entire 3-min recording session on P7 to P9. Automated measurements excluded calls with insufficient signal-to-noise ratio. SAS-lab Pro allows the investigator to overlay spectrographic images and fundamental frequency tracking results. The accuracy of call detection by the software was verified manually by an experienced user. The user verified all the calls in all recorded files and, when necessary, missed calls were marked by hand to be included in the automatic parameter analysis. With regard to peak amplitude measurement, this was considered as the point with the highest energy within the spectrum of the call, and was recorded as the relative output voltage of the microphone signal. Due to the consistent placement of the microphone, some clipping of high-volume calls was noted, but this did not correlate with strain, and therefore will simple result in reduced estimates of amplitude. Peak frequency was defined as the frequency at the location of the peak amplitude within the spectrum of the call. All measurements were carried out using the same equipment in the same chamber. No differences in quantitative parameters of calling were detected in a comparison of sexes; therefore, data was collapsed. This is in line with previous findings of no differences between sexes . AverageWaveform patterns of calls recorded on P8 in D2, B6, B6D2F1, and D2B6F1 mice were examined in detail. Every call was classified as one of 10 distinct types  and the distance (in mm) to the next section level.Specimens were fixed in formalin for 3 days and decalcified for 8 h. Body mass, skull width and length and femur length were measured on each pup. The head-neck region was embedded in paraffin. Coronal serial sections (5-μm-thick) were taken every 50 microns starting ventral. Sections were stained with hematoxylin–eosin for a general overview. Sections were scanned with ScanScope CS2 . Volume of cricoid cartilage, thyroid cartilage, arytenoid cartilage, thyroarytenoid muscle (TA), lateral crico-arytenoid muscle (LCA), and cricothyroid muscle (CT) were estimated from digital images of sections from 10 equally distant levels from ventral to dorsal. All measurements were made using ImageJ . Volumes were estimated by adding up the products of area measurements  and recorded the 95th percentile of these as our 95% confidence intervals.h2). In the inbred BXD, offspring and parents are genetically identical, and therefore we cannot differentiate different components of heritability, and instead get a single, broad sense, heritability (H2).Different heritability values are given for the diallel cross as it is possible to deconvolute additive, dominance and parent-of-origin effects on heritability, and therefore we can report a narrow sense heritability (Va/(Va + Ve/n), where Va is the genetic variability (variability between strains), Ve is the environmental variability (variability within strains), and n is the number of within line replicates.We also calculated HRIx¯2 , the herdensity function in the stats package and plotted using the ggplot2 package ’). All mean strain data are publicly available at GeneNetwork.org .For QTL mapping, the average of each vocalization trait was calculated for each strain. Distributions of these means were generally close to normal and did not require scale transformations , and suggestive (p < 0.63) thresholds were calculated by 5,000 permutations of the phenotypes, using GeneNetwork. The suggestive threshold corresponds to approximately one false QTL per genome-scan. Likelihood ratio statistic (LRS) scores were converted to log of the odds (LODs) scores by dividing by 4.61, and confidence intervals were defined by a LOD drop of 1.5 on either side of the peak value . Using all four cofactors and their interactions , the F ratio associated with strain was 26.913, and while this value was highly significant  the corresponding estimate of narrow sense heritability (2h) was modest—approximately 0.11. Litter identifiers can be used to estimate parental effects and these effects are strong  explaining 25% of the variance. Neither body weight nor sex were a predictor over the 3-day recording interval, explaining < 1% variance (p > 0.05) each. Age did have a small, significant effect , but only explains 1% of the variance . Duration of calls did not differ between the reciprocal F1s . The estimate 2h was high: ∼0.51. Litter identifiers, used to estimate parental effects, were found to have moderate effects , explaining ∼10% of the variance. Again, neither age nor sex were predictors and explained < 1% of the variance (p > 0.5). As above, age has a small significant effect, but explains < 1% of the variance  = 51.997, p < 2E-16, 2h = 0.16; Figure p < 0.00001). But frequency produced by D2B6F1 and B6D2F1 pups did not differ significantly . Neither sex, age or body weight had any significant direct effect. Again, litter effects were significant, explaining 23% of the variance  = 304.633, p < 2E-16, 2h = 0.31, Figure p < 0.0000001). F1 pups produced low amplitude calls compared to B6 and D2 (B6D2F1 = -41 ± 1.6 dB and D2B6F1 = -37 ± 1.9 dB). Litter identifier explained 51% of the variance. Both sex and age had small, significant effects, but explain less than 1% of the variance  for all except harmonics, with only harmonics significantly different between B6D2F1 and D2B6F1 pups. Heritability was estimated for all qualitative traits and ranged from just 0.1 for harmonics to 0.65 for upward calls .The reciprocal F1 females inherit identical nuclear genomes, including their X chromosomes, whereas males inherit identical autosomes, but differ in their sex chromosomes. For this reason, differences between the reciprocal F1 females are highly likely to be caused by either indirect parental effects or genomic imprinting, whereas differences between reciprocal F1 males could also be caused by the specific effects of the Y chromosome. Statistical comparison was performed on quantitative USV traits between reciprocal F1 pups, combined and split by sex Figure . No signpost hoc test. Between the reciprocal F1 strains we saw no differences in morphological characteristics. Therefore, these were combined, and the ANOVA analysis rerun. Body size is significantly influenced by strain , we can now examine whether these genetic effects are likely to be caused by a small number of loci with large effects, or a large number of loci of small effect.BB and DD), whereas more genetically complex traits modulated by many variants of small effects will be close to normal. Several of these plots appear to be made up of a mixture of two normal distributions , characterized by a ‘shoulder’ on the normal distribution, indicative of one locus of moderate to large effect segregating in the family. Other plots appear to be more normally distributed , suggesting several variants of smaller effect. Normal probability plots show the same patterns, with breaks  indicating a variant of large effect  and duration and amplitude of calls had the highest heritability (H2 = 0.53–0.65), with relatively small confidence intervals  regression mapping .The first was on Chr 14, and was significant for mean peak frequency on P7 , suggestive for peak amplitude for all days combined (GN 16603), and for peak amplitude on P8  have a cis-eQTLs in transcriptome data, indicating that variants within the gene alter its expression; expression of 4 of these 31 genes  correlate well with at least one of the peak frequency phenotypes, which suggests that altering expression of that gene may alter expression of the phenotype; and 12 of the 31 genes , Lama5, Ntsr1, Col9a3, Dido1, Slc17a9, Bhlhe23) are associated with brain, behavior, or cranio-facial morphology in previous datasets, e.g., in knockout animals, which suggests that the gene acts in tissues relevant to USVs  have a cis-eQTLs in transcriptome data; expression of 6 of these 27 genes  correlate well with at least one of the mean peak amplitude phenotypes; and 11 of the 31 genes  are associated with brain, behavior, or cranio-facial morphology in previous datasets  mean fundamental frequency is higher in B6, (b) sound amplitude is higher in D2 USVs, (c) ultrasonic calls are longer in D2 and (d) D2 pups vocalize at a higher rate Figure . We knowIn mammals, sound amplitude is predominantly controlled by lung pressure and secondarily by laryngeal configuration as well as interactions between the laryngeal sound source and the vocal tract filter . In rodeLonger call durations and a higher vocal rate can only be explained by differences in breathing movement control during vocalization. Cries are produced during the exhalatory phase of breathing . PreviouHow much are differences in brain development associated with acoustic variation between mouse strains? The brain is not only essential for controlling the breathing characterizes described above, but also for the ability to vocalize in response to appropriate stimuli. Selective breeding for certain traits, for example high call rates, also alters functions of brain systems and emotional behaviors throughout life . CandidaMorphological features were only measured in the diallel cross, and not in the BXD lines. However, future studies will be able to employ a battery of physiological methods to understand strain-specific vocal motor control  and new FoxP2, Cadherin-6, Fmr1, Drd2, and vasopressin receptor genes are already known to produce abnormal USVs  was only carried out on a single postnatal day. This was due to the significant manual labor that was necessary for this analysis by a highly trained investigator, which made the analysis low throughput. Therefore, any conclusions we draw from this qualitative analysis should not be generalized to represent the entire postnatal period. Future work will make use of new, high-throughput, less labor intensive systems, such as the MUPET-Mouse machine learning system . These nSimilarly, our morphological analysis was only carried out in the diallel cross. This allowed us to demonstrate that morphological features which may be associated with vocalization can be detected and analyzed, but does not allow us to perform any genetic analyses. In future studied, we hope to be able to do this analysis in the BXD population, allowing direct correlation between our findings and morphological features, potentially detecting genomic variants underlying both.Another large contributor to variance was litter effects. Litter effects encompass a large number of different aspects, from vivarium-related factors  to social effects  to maternal effects . It is therefore unsurprising that for some traits they can account for up to 51% of the variance  transferred from mothers of every other strain , and then each of these mother–offspring strain combinations would have to be cross-fostered postnatally to every maternal strain . Something similar to this has been done on a much smaller scale by We did not see a correlation between our USV data and maternal care data previously collected in the BXD family , 2017. AB or D allele in otherwise genetically identical strains. If large GWASs of infant vocalizations are carried out in humans, then they would provide an excellent dataset for comparison with this work, as we have done for other traits (We have produced heritability estimates for several features of infant vocalization in mice, and defined QTLs underlying some of these core innate behaviors. All contain plausible candidate genes , but theUnderstanding infant vocalizations in mice is not just important for understanding the evolution of mother–offspring interactions, but also in understanding vocalization in human infants, perhaps with applications to the understanding and early identification of developmental disorders, such as autism-spectrum disorders.TR, MS, DH, LL, and RW contributed to the conception and design of the study. SR, BC, and TR collected the data. BC, TR, and DA performed the statistical analyses. DA and SR wrote the first draft of the manuscript. SR, BC, TR, DA, and RW wrote sections of the manuscript. MS and TR contributed to the interpretation of the data. All authors contributed to manuscript revision, read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.