Selective breeding programs based on genomic data are still not a common practice in aquaculture, although genomic selection has been widely demonstrated to be advantageous when trait phenotyping is a difficult task. In this study, we investigated the accuracy of predicting the phenotype and the estimated breeding value (EBV) of three Bayesian models and a Random Forest algorithm exploiting the information of a genome-wide SNP panel for European sea bass. The genomic predictions were developed for mortality caused by viral nervous necrosis, post-stress cortisol concentration, antibody titer against nervous necrosis virus and body weight. Selective breeding based on genomic data is a possible option for improving these traits while overcoming difficulties related to individual phenotyping of the investigated traits. Our results evidenced that the EBV used as a pseudo-phenotype enhances the predictive performances of genomic models, and that EBV can be predicted with satisfactory accuracy. The genomic prediction of the EBV for mortality might also be used to classify the phenotype for the same trait.Dicentrarchus labrax L.), the viral nervous necrosis mortality (MORT), post-stress cortisol concentration (HC), antibody titer (AT) against nervous necrosis virus and body weight (BW) show significant heritability, which makes selective breeding a possible option for their improvement. An experimental population (N = 650) generated by a commercial broodstock was phenotyped for the aforementioned traits and genotyped with a genome-wide SNP panel . We compared the predictive accuracies of three Bayesian models  and a machine-learning method (Random Forest). The prediction accuracy of the EBV for MORT was approximately 0.90, whereas the prediction accuracies of the EBV and the phenotype were 0.86 and 0.21 for HC, 0.79 and 0.26 for AT and 0.71 and 0.38 for BW. The genomic prediction of the EBV for MORT used to classify the phenotype for the same trait showed moderate classification performance. Genome-wide association studies confirmed the polygenic nature of MORT and demonstrated a complex genetic structure for HC and AT. Genomic predictions of the EBV for MORT could potentially be used to classify the phenotype of the same trait, though further investigations on a larger experimental population are needed.In European sea bass ( In fish species, improvements in selection accuracy due to genomic predictions and benefits of genomic selection over pedigree-based methods have been documented both in simulation studies  and realSelective breeding applied to aquaculture for economically important traits is a relatively young practice compared to plant and livestock species , and itsDicentrarchus labrax L.) industry, one of the major threats is viral nervous necrosis (VNN), and interest in enhancing genetic resistance to VNN has recently greatly increased [h2) estimates for VNN resistance [For the European sea bass  of antibody titer against nervous necrosis virus (NNV) antigens in sea bass and a negative genetic correlation (ra = −0.39) of the antibody titer with VNN mortality. These results suggested that the NNV antibody titer could be used as an indicator trait to enhance VNN resistance.In a previous paper , we estiLow stress-sensitive fish have shown an increased ability to cope with environmental challenges typical of intensive systems, and they have also shown better performance traits . Yet, tTraditional selective breeding approaches make use of estimated breeding values (EBV) predicted from individual phenotypes of the breeding candidates or their relatives through additive genetic relationships between animals. Such selection programs are currently most commonly adopted by European breeding companies ,10; theiRoutine individual phenotyping of breeding candidates or their relatives for complex traits such as VNN resistance, antibody titer or cortisol concentration is either difficult or unfeasible, as well as being costly and time-consuming. For this reason, genetic improvement of such traits cannot be achieved efficiently with traditional methods ,22. Yet,This study aimed to investigate the performance of genomic models by making use of the individual genotypes of 16,075 genome-wide SNPs as predictors of the VNN mortality, post-stress cortisol concentration, NNV antibody titer and body weight, predicting both trait phenotypes and pseudo-phenotypes (EBV). The performance of those predictions in the classification of VNN mortality was compared with the performance of classification based on EBV estimated from full information including the individual phenotype, or from information of full- and half-sibs alone.The procedures for the production, rearing and NNV challenge test of the experimental fish were detailed in . Briefly8.30 TCID50 per mL), the fish were subjected to a stress test , anesthetized (30 ppm of MS-222), individually tagged with passive integrated transponders (PIT-tag) and individually blood-sampled to perform an indirect ELISA assay and cortisol analysis by radioimmunoassay (RIA).The challenge test with NNV was carried out using 652 randomly chosen fish at an age of 548 d post-hatching, with an average body weight (BW) of 146.4 g (CV% 29.18). Before being infected by intraperitoneal injection of 0.1 mL viral suspension . Dead fish were classified as 1. The experiment ended at 29 d post-challenge and a cumulative survival rate of 52% was recorded. All fish that were alive at day 29 were euthanized with an overdose of anesthetic (MS-222) and classified as survivors (MORT = 0). Tissue samples (muscle and fin) were collected from each fish (dead or survived) and preserved in absolute ethanol for genomic DNA extraction. The experimental protocol was evaluated and approved by the Italian Ministry of Health .An indirect ELISA assay for antibodies against NNV and cortisol extraction and measurement were carried out as described in . Briefly7 TCID50 of virus preparation  diluted in 95 µL 0.05 M carbonate–bicarbonate buffer (pH 9.4) was employed to coat 96-well polystyrene plates . After an incubation period (4 °C) and washing procedure , the remaining sites were blocked with 3% BSA in TTN (TBT). Plates were incubated at room temperature and washed again, then incubated (4 °C) with various dilutions of pAb 283 diluted in TBT. Sera were analyzed in duplicate wells. The wells were carefully washed with TTN and then incubated for 2 h at 4 °C with a 100 µL dilution of horseradish peroxidase (HRP)-conjugated goat IgG fraction to rabbit IgG (GAR-HRP). The wells were carefully washed and the reaction was developed with 100 µL/well of 0.04% o-phenylenediamine in 50 mM phosphate-citrate buffer (pH 5.0) containing 0.001% H2O2. The reaction was allowed to proceed for 20 min at 22 °C, stopped with 50 µL of 3 N sulfuric acid, and the absorbance was read at 450 nm with an automatic plate reader . The ELISA optical density  values’ sample-to-positive ratio (S/P ratio) was applied to evaluate the status of the sample. The trait was abbreviated as AT (antibody titer).To do so, 5 µL 10Cortisol was extracted from the blood serum, following the protocol by , and the® Spin Tissue Mini Kit and Invisorb® DNA Tissue HTS 96 Kit , which was then quality-checked and quantified. A 2b-RAD library was constructed for each individual following the protocol by [http://seabass.mpipz.mpg.de/DOWNLOADS/dicLab1_scaffold.fasta, accessed on 17 May 2018) and then secondly identifying SNPs. The results were filtered to exclude loci shared by less than 75% of the individuals, resulting in a dataset of 18,097 SNP genotypes per fish. Some SNPs were discarded based on a minor allele frequency (MAF) of lower than 1%, genotype frequencies deviating from the expected Hardy-Weinberg equilibrium frequencies (p < 0.001) and the presence of a missing genotype in more than 15% of the individuals. Missing genotypes for the remaining SNP were imputed using the FImpute software [All the phases of genotyping, parentage assignment and pedigree reconstruction were detailed in . Genomictocol by  with mintocol by , firstlytocol by   was performed on MORT, SRHC and AT to test the association between phenotypes and SNP genotypes using the R package GASTON . Due to 1, µ, I is an identity matrix of appropriate order and For SRHC and AT, hypothesis testing was carried out on the basis of solutions to the following linear mixed model:ted from .p = 0.05/N where N is the total number of genetic markers considered in the association analysis [The genome-wide significance threshold was set to analysis , thus apanalysis .Animal additive genetic effects  for MORT, BW, SRHC and AT were predicted using mixed animal models. A Bayesian approach employing Monte-Carlo Markov Chain (MCMC) and Gibbs sampling methods was implemented using the software TM . For MORiability , and was1 is an all-ones vector, X is an incidence matrix relating b, A is the numerator relationship matrix, I is an identity matrix of appropriate order and A matrix computed from the reconstructed pedigree, the estimate of In our study, the liability was modeled as:orted in  and by s1, µ, For BW, SRHC and AT, the prediction of EBV was based on the following linear mixed model:orted in .The prior densities used in all Bayesian analyses were uniform densities for the fixed effects and normal densities for the additive genetic and residual effects. The Gibbs sampler was run using 100,000 iterations with a burn-in of 5000 iterations and a thinning interval of 10 samples.FULL to indicate that they were estimated using all available information, which included the observed phenotype of the individual and those of its full- and half-sibs.The predicted EBVs were used as “pseudo-phenotypes” in the development of genomic predictions described in the next section of the paper and will be referred to as EBVFULL for the investigated traits by means of three Bayesian regression models fitted to the data using the BGLR package in the R software [FULL for the trait under investigation, 1 is an all-ones vector, I is an identity matrix of appropriate order and Genotypes of the 16,075 SNPs were used as predictors of the phenotypes or EBVsoftware . These msoftware , the prisoftware  using, am variables is randomly selected from the set of p available predictors and evaluated to identify the rule splitting the parent node into two smaller daughter nodes. The splitting rule is based on a single variable belonging to the selected subset of m predictors.Random Forest (RF)  is a macFULL) for different values of the following parameters: the number of trees to grow  and the minimum size of terminal nodes . The predictors  were the genotypes coded as described previously. The number m of predictors randomly sampled for node splitting was set as the default  [FULL. Analyses were implemented using the randomForestSRC package [We finetuned the RF algorithm by comparing the error rate in the prediction or classification of the response variable (phenotype or EBVression) . It was  package  in the RFULL for the trait under study were set to missing, and they were predicted or classified from the SNP genotypes using the solutions of the Bayesian models or the RF obtained in the analysis of the training data. Predictions and classifications for each test set were saved, and at the end of the CV, aggregated with those of the other test sets to compute performance metrics.The metrics used to predict and classify the performance of the Bayesian models and RF algorithm were computed in a set of 16 independently-generated five-fold cross-validations (CVs). In each CV, the data (N = 650) were randomly split into five equally-sized data segments. Four of these data segments (80% of the data) were used as a training set to obtain solutions for allele substitution effects, whereas the remaining segment served as a test set. In the test set, the phenotypes or EBVFULL of any trait, the Pearson product-moment correlation (r) between the observed and predicted values was computed as a measure of prediction accuracy. The accuracy in the prediction of the additive genetic component of the phenotype  or the EBVorted in .FULL for MORT, the genomic predictions of the phenotype for MORT and those of the EBVFULL of all the investigated traits provided by the Bayesian models and the RF algorithm. Moreover, to mimic a scenario of “traditional” selective breeding where only full- and half-sibs of breeding candidates are enrolled in NNV challenge tests, we also considered—as additional classifiers of MORT—the estimated EBV when omitting the phenotype of the individual (EBVFH). Hence, in each CV, we omitted the phenotypic information of the animals of the test set from the data, predicted the EBVFH for the investigated traits for all the animals following the methodology described in Classification of the phenotype for MORT was achieved using several classifiers. These included the EBVThree metrics were used to evaluate the performance in classifying the phenotype for VNN MORT: the area under the receiving operating characteristics curve (AUC), which describes the natural trade-off in classification between sensitivity (true positive rate) and specificity (true negative rate), the classification accuracy (ACC) and the Matthews correlation coefficient (MCC).The AUC is the most appropriate metric to evaluate the model performance in classification . This meThe ACC is a measure of the proportion of correctly classified samples and was computed as follows:The Matthews correlation coefficient  is the cage ROCR .FULL, predicted according to the methodology described in FULL of all animals except the offspring of the sire (dam) used as a test set. The EBVFULL of the parent in the test set was then predicted from the parent genotypes by making use of the genomic prediction equation. This was replicated as many times as the number of parents. The predictions and EBVFULL for each parent were saved, and at the end of the LOFO, aggregated with those of the other parents to compute, as a measure of prediction accuracy, the Pearson product-moment correlation (r) between the EBVFULL and the genomic-predicted EBVFULL.An additional validation procedure, focused on the parents of the challenge-tested fish, was arranged to mimic a real genomic selection scenario where the genomic prediction equations are obtained by training models with information from a reference population that does not include the progeny of the animals to be predicted. We will refer to this procedure as leave-one-family-out (LOFO) validation. In such a procedure, the parents of the experimental fish are used one at a time as an independent test set. When we carried this out, initially, the EBV10 transformation was 5.51. The genome-wide association study failed to identify genome regions exceeding the statistical significance threshold  than for SRHC (r = 0.21) and AT (r = 0.26). The prediction accuracy of the RF algorithm exhibited a similar pattern, with greater accuracy for BW (r = 0.35) than for SRHC and AT . The accuracies adjusted for the square root of the trait heritability were moderate and similar across traits .In general, the Bayesian models exhibited slightly greater prediction accuracies than the RF algorithm . When prFULL of the traits as pseudo-phenotypes, the accuracies increased in comparison with those obtained with models trained on the trait phenotype. For the Bayesian models, the predictive accuracy increased to a similar extent across models. The EBVFULL predicted with the largest accuracy were those of MORT and SRHC (r = 0.90). The prediction accuracies were somewhat lower for EBVFULL of BW (r = 0.71) and AT (r = 0.79). The accuracy of the RF algorithm in predicting the EBVFULL was greater for MORT (r = 0.88) and SRHC (r = 0.86) than for AT (r = 0.76) and BW (r = 0.68).As expected, when the models were trained on the EBVFULL were correlated with the observed trait phenotypes (data not presented in tables). Such correlations were greater than those between the observed and genomic-predicted phenotypes for both SRHC and AT, with estimates of r ranging from 0.22 to 0.25 and 0.32 to 0.36, respectively.The genomic-predicted EBVFULL and its genomic predictions obtained in the LOFO validation procedure for the 49 parents of the challenge-tested fish are presented in FULL were predicted with the largest accuracy were SRHC  for MORT exhibited an AUC of 0.833, ACC of 0.757 and MCC of 0.544  when randomly guessing the classes was used as a classification strategy. The ACC (0.52) and MCC (0.07) metrics also indicated poor classification performance.FULL for MORT, the metrics were more favorable , indicating that the genomic classifier contributed an added value to the classification of mortality. The results were consistent across Bayesian models and RF, although the performance of RF was slightly poorer  for BW, SRHC and AT were favorable in our study, at 50, 47 and 42%, respectively, of the maximum attainable accuracy. Studies focused on the common carp [Genomic selection is a recent field of investigation for aquaculture species, and estimates of the predictive accuracy of genomic models are scarce in the literature. Moreover, to our knowledge, this is the first study to have investigated the performance of genomic models applied to the prediction of traits such as SRHC and AT. The accuracy of the investigated genomic predictions of the phenotype for BW, SRHC and AT ranged from 0.25 to 0.38. The genomic information enabled the additive genetic component of the phenotype to be predicted, which for the investigated traits, accounted for 14 to 57% of the observed phenotypic variation . The uppmon carp  reportedmon carp , while fmon carp .The results obtained for genomic prediction of continuous traits are encouraging in the context of aquaculture production, suggesting that selective breeding approaches using genomic information are of great value in enhancing the aforementioned traits, and become even more important for traits like SRHC and AT, where routine individual phenotyping is a critical issue for various reasons .FULL for MORT was the best classifier of the observed MORT , with limitations due to utilizing only the between-family genetic variation [FULL. Though training genomic models on estimated breeding values as pseudo-phenotypes is not as common as for terrestrial species, the development of genomic tools providing accurate predictions of the EBVFULL for MORT and other traits can increase the selection efficiency in fish breeding, thus adding value to selective breeding programs in the aquaculture industry. Since moderate genetic correlations between MORT and BW or AT were estimated in a previous study [FULL for traits correlated with MORT when used as classifiers of the observed mortality.The EBVved MORT . In a trariation . Likewisus study  on the sFULL led to much greater accuracies than those observed in the prediction of the trait phenotypes; the accuracies were very high for MORT and SRHC and moderately high for BW and AT. Moreover, the correlations between the observed phenotypes and the genomic predictions of the EBVFULL were greater when compared to those between the observed and predicted phenotypes for both SRHC (r between the observed phenotypes and genomic predictions of the EBVFULL ranging from 0.22 to 0.25) and AT (r ranging from 0.32 to 0.36).The genomic prediction of the EBVFULL as pseudo-phenotypes. As expected, the LOFO accuracies decreased in comparison with those estimated in the random five-fold cross-validation, but to different extents across traits. For MORT and SRHC, the accuracy remained satisfactory. This is of great interest in the context of application, particularly when considering phenotyping difficulties and complexity for such traits. Omitting the progeny information in the training of the genomic models seems not to affect the accuracy of the genomic-predicted breeding values of the parents, and this means that in a real selection scenario applied to commercial populations, where it is likely that the genomic models are trained using a reference population consisting of distant relatives of the animals to be predicted, the predictive performances of such models would be still acceptable. Conversely, the correlations between the observed and predicted EBVFULL dropped remarkably for BW and AT . Such trait-specific differences between the LOFO and random validation results are not easy to interpret. It could be argued that factors affecting the accuracy of genomic prediction—like the degree of genomic similarity between the reference (training) and testing  populations, the size and composition of the reference population and the density of the marker array [However, the accuracies provided by random k-fold cross-validation are optimistic because in a real genomic selection scenario, the prediction of the genetic merit of future breeding candidates is obtained from data for a reference population that does not include close relatives  of the animals to be predicted. To obtain more realistic insights and for strict validation, we developed a LOFO procedure, using the EBVer array —could haer array  pointed er array , a consiUnder perfect linkage disequilibrium between markers and causal loci, the prediction accuracy has an upper bound that depends on the trait heritability and the realized genomic relationships between the training and testing sets . With unRelative to the maximum accuracy under perfect linkage disequilibrium, the prediction accuracy under imperfect linkage disequilibrium decreases to an extent that depends on the regression of the realized genomic relationships at the markers on those at the causal genes . Hence, FULL for MORT to classify the observed phenotype of MORT resulted in better classification performance, as measured by the AUC, in comparison with classification based on genomic predictions of the phenotype. The AUC was lower than that (AUC = 0.70) reported by Palaiokostas et al. [Using the genomic prediction of the EBVs et al. , who uses et al. , was apps et al. . Hence, FH in the classification of MORT might be explained by the sample size of our experimental population, or by “true” genetic correlations that are weaker than the estimates. The sample size of the reference population used to estimate the allele substitution effects is a major factor in the prediction accuracy, and it also plays a key role in the precision of the estimated genetic correlation between traits. Increasing the number of genotyped animals has been proven to enhance the prediction accuracy [FH as a classifier of MORT has a considerable drawback: each member of a full- or half-sib family has an identical EBVFH, implying that there is no distinction between members [The unsatisfactory performances exhibited by both the genomic-predicted EBV for continuous traits and EBVaccuracy ,61. The  members  and no p members ,63.The results of this study indicate that genomic predictions of the estimated breeding values can be used in selective breeding for VNN resistance and response to stressors. This removes the need to phenotype breeding candidates or their relatives, which is necessary in traditional breeding programs. Despite the limited size of the experimental population, which suggests that further investigations are required, the accuracy of the classification of VNN mortality and the prediction of the stress response, as ensured by the genomic prediction models, was greater than that attainable when the genetic merit of breeding candidates is estimated from sibs information, as would occur in selective processes based on challenge tests. Training genomic models with estimated breeding values in place of phenotypes increases the accuracy in classifying or predicting the trait phenotype. The sensitivity of the results of this study to the characteristics of the adopted validation procedure raises questions about the suitability of random cross-validation procedures when the development of genomic models is envisaged in the context of genomic selection. Experimental populations to be subjected to NNV challenge are generated by full or incomplete factorial mating designs that include close relatives. Family linkage disequilibrium may lead to optimistic results when the accuracy of genomic predictions is evaluated on the basis of random cross-validation procedures. The impact of the method of infection used in NNV challenge tests on the type of and variation in the infection response observed should be elucidated in future studies on the genetic background of VNN resistance in European sea bass.


Bos indicus beef cattle, covering the definition of measurable traits, genetic parameter estimates, genomic analyses, and a case study of selection for sexual precocity in Nellore breeding programs.Tropical pasture-based beef production systems play a vital role in global food security. The importance of promoting sustainable intensification of such systems has been debated worldwide. Demand for beef is growing together with concerns over the impact of its production on the environment. Implementing sustainable livestock intensification programs relies on animal genetic improvement. In tropical areas, the lack of sexual precocity is a bottleneck for cattle efficiency, directly impacting the sustainability of production systems. In the present review we present and discuss the state of the art of genetic evaluation for sexual precocity in Increasing productivity through continued animal genetic improvement is a crucial part of implementing sustainable livestock intensification programs. In Zebu cattle, the lack of sexual precocity is one of the main obstacles to improving beef production efficiency. Puberty-related traits are complex, but large-scale data sets from different “omics” have provided information on specific genes and biological processes with major effects on the expression of such traits, which can greatly increase animal genetic evaluation. In addition, genetic parameter estimates and genomic predictions involving sexual precocity indicator traits and productive, reproductive, and feed-efficiency related traits highlighted the feasibility and importance of direct selection for anticipating heifer reproductive life. Indeed, the case study of selection for sexual precocity in Nellore breeding programs presented here show that, in 12 years of selection for female early precocity and improved management practices, the phenotypic means of age at first calving showed a strong decreasing trend, changing from nearly 34 to less than 28 months, with a genetic trend of almost −2 days/year. In this period, the percentage of early pregnancy in the herds changed from around 10% to more than 60%, showing that the genetic improvement of heifer’s sexual precocity allows optimizing the productive cycle by reducing the number of unproductive animals in the herd. It has a direct impact on sustainability by better use of resources. Genomic selection breeding programs accounting for genotype by environment interaction represent promising tools for accelerating genetic progress for sexual precocity in tropical beef cattle. Tropically adapted cattle under a grass-fed system characterizes the beef production in the tropics . RelyingTo be sustainable, cattle production systems must attend to the farmer, industry, and society requirements, which include cost-effectiveness, high production, animal welfare, and minimal environmental pollution . Indeed,An effective way to increase productivity is through continued animal genetic improvement, making it a crucial part of implementing sustainable livestock intensification programs . SelectiSelecting for female sexual precocity involves reducing the age at first calving, which is a trait of easy and of a relatively early measurement, but usually presents low heritability . An alteIn general, the lack of sexual precocity is a bottleneck for female cattle efficiency in Zebu cattle, directly impacting the herd profitability in tropical areas . The ageBos indicus cattle [Female sexual precocity is related to the age at puberty, a polygenic trait regulated by a complex network of biochemical processes that culminates in the first ovulation generally followed by a short estrous cycle and subsequent regular cycles . The ages cattle ,21. Thuss cattle .In general, selection for sexual precocity aims to reduce the age at puberty and at first calving, with a consequent increase in the herd profitability. In this case, sexual precocity is an economic trait (selection objective) that could be expressed as various indicator traits (selection criteria) such as heifer pregnancy at about 14 months of age  or even In practice, an objective of selection is represented by a combination of economic and genetic values for a number of traits that influence the income and/or expenses of a specific production system . In beefcorpus luteum or albicans (CL or CA), as an alternative to determining heifer puberty, has been suggested in tropical cattle [The direct selection for puberty requires measuring female age at puberty, which can be based on oestrus observation and progesterone concentration . To obtal cattle . Neverthl cattle . Even adl cattle , adaptatDespite the difficulty to directly measure puberty-related traits, heritability estimates of moderate to high magnitude have been reported for such traits in Zebu cattle raised under tropical conditions. Vargas et al.  reportedBeyond heritability estimates, Johnston et al.  reportedScrotal circumference at different ages, age at first conception or at first calving, and heifer early pregnancy are among the most commonly used sexual precocity indicator traits in beef cattle ,38. Thescorpus luteum. Favorable genetic correlation estimates between SC and age at first calving (−0.61 to −0.13) [In beef cattle breeding programs, SC is routinely measured at young  ages to be used as a selection criterion for improving both male and female sexual precocity. Moderate to high (0.25 to 0.58) heritability estimates have been reported for SC ,44,45,46o −0.13) ,41,43,48o −0.13) ,42,48, iAFCo and AFC refer, respectively, to the age, in months or days, at the first positive pregnancy diagnosis and at the first calving. These traits are easier to measure than puberty traits but usually present low to moderate (0.07 to 0.24) heritability estimates ,51,52,53AFCo and AFC are highly genetically correlated to each other (0.96) , suggestScenario 1HP is a binary trait expressed as success or failure of the heifer in conceiving or calving precociously, given a specific age. Unlike the AFC trait, HP is obtained for all heifers and normally presents moderate to high heritability. Heritability estimates for the probability of heifer conceiving at 16 months of age in Nellore range from 0.44 to 0.55 ,29,56. IFor Patterson et al. , optimizThere are evidences that selection for early heifer pregnancy at younger ages would produce a rapid response, causing no negative impact in reproductive, growth, carcass, and feed efficiency indicator traits in tropical beef cattle . It is iScenario 2Common reproductive management adopted by breeding programs in Brazil is to consider two breeding seasons: (1) fall season—females with approximately 15 to 17 months of age are exposed to mating for 60 days, in an attempt to identify sexually precocious females; (2) summer season—all females are allowed to breed for a period of 70–90 days. In this scenario, heifers that fail to conceive in the fall season (non-precocious) have a second opportunity in the summer season, and precocious heifers have the advantage of having a longer period from their first calving to conception 10 to 11 months) than non-precocious (3 to 5 months). Females (dams and heifers) that fail to conceive in the summer season are culled  months t. This maAs well as for sexual precocity, improving the probability of a cow to remain productive in the herd for a specific period of time (stayability) has a great impact on the herd profitability . Indeed,High-throughput phenotype technologies generate accurate real-time animal-level information, allowing to evaluate the dynamic nature of several traits, which might be used to enhance the genetic improvement of sexual precocity of heifers. In this context, digital image analysis and computer vision systems (CVS) are promising tools for applications in animal science . These tFemales’ sexual precocity is a complex trait highly influenced by heifer development, which is characterized by several phenotypic changes. Since traditionally, it is recommended that heifers reach 60 to 65% of their adult body weight at the beginning of the breeding season ,74, suchk) of growth curve models and AFC for Guzera (−0.67) and Canchim (rg = −0.83) cattle, and Inoue et al. [k and conception rates (0.91) in Japanese Black cattle. These results suggest that the selection for a higher maturation rate will positively impact the age at first calving and conception rates. Using high-throughput phenotyping technologies, traits such as weight at different ages (growth curves) and body condition could be more easily and accurately evaluated and used to assess heifer sexual precocity in large-scale production systems. Some growth curve parameters were shown to be good predictors of sexual precocity. For instance, Oliveira et al.  and Gavie et al.  reportedAn alternative to increase the genetic progress of sexual precocity is the adoption of genomic selection (GS). GS refers to the evaluation of animals based on direct genomic values derived from the sum of the estimated effects of single nucleotide polymorphism (SNP) genotypes of selection candidates . When imBasically, the implementation of GS for a specific trait, e.g., heifer’s sexual precocity, requires an equation that predicts breeding value from SNP genotypes . This eqIn Nellore cattle, accuracies ranging from 0.23 to 0.63 were reported for age first calving ,84 and fGenomic information becomes especially relevant to accurately select young sires for traits that are difficult or expensive to measure and/or those that are measured late in an animal’s life or only measured on females ,81,87. TRecently, Warburton et al.  showed tG × E basically refers to the change in variation and/or in the ranking of genotypes evaluated in different environments. G × E represents an important source of sexual precocity variation in beef cattle raised in tropical environments ,85,91, wEvaluation of animal’s response to environmental changes has been widely studied using reaction norms (RN) ,93. SuchAs beef cattle are raised in highly heterogeneous environmental conditions in tropical production systems, heterogeneity might exist in the (co)variance components for economically relevant traits across environments . For sexModeling the effect of G × E allows selecting animals based on their resilience or robustness. The selection based on resilience aspects aims to improve animals’ capacity for dealing with adverse environmental conditions ,103,104.Selecting for robustness, in turn, would improve sexual precocity combined with resilience to stressor factors, allowing a high expression of this trait in a wide variety of environmental conditions . ImproviThe G × E is a mostly tropical issue that must be addressed in this region. Despite its importance in animal breeding by frequently causing a reduction in response to selection , G × E iThe performance of tropically-adapted beef cattle is likely to be affected by G × E interactions. Carvalheiro et al. , workingDifferences in environmental conditions, mainly related to nutritional aspects, affect cattle’s sexual precocity by influencing metabolic status through changes in glucose, insulin, and reproductive hormones ,113,114.A better understanding of molecular mechanisms underlying G × E effects on sexual precocity in tropical beef production systems is important to design efficient selection strategies to improve puberty in stressful conditions. Probably, the accuracy of selection for heifer sexual precocity will be improved with statistical models accounting for genomic regions presenting environment-dependent sensitivity. Additionally, modeling G × E is necessary to enable international reference populations for genomic selection across environments and countries . corpus luteum, age at first insemination, age at first service, age at puberty , and early puberty in cattle, a total of 10,623 QTL were reported. A better understanding of the genetic control of puberty-related traits may contribute to designing improved selection strategies for sexual precocity. With this purpose, genome-wide association studies (GWAS) were developed in order to identify candidate genomic regions or genes with major effects on the phenotypic expression of traits of interest. To date, a total of 108 QTLs were described at the release 45 of the QTLdatabase website  for the Bos indicus and tropical composite breeds. In general, results from these association analyses , also plays an important role in lipid metabolism, regulating fatty acid and glucose levels [XKR4  was associated with climate adaptation in Bos indicus cattle [GWAS for sexual precocity in Nellore heifers, for instance, have suggested various candidate regions and genes such as the d traits ,130,131.e levels ,133, ande levels . The XKRs cattle , fat deps cattle ,137, fees cattle . Such pos cattle  and corrs cattle . PLAG1) that is a transcription factor encoding the zinc finger protein PLAG1. Strong GWAS signals at this genomic region for a number of traits in cattle, including heifer puberty, are generally attributed to a causative mutation present in PLAG1 [TMEM68, NPBWR1, and OPRK1) were also associated with reproductive performance in different species of mammals [TMEM68 and OPRK1 are related to neurons development, associated with the glial cell, a key component to promote responses to growth factors and GnRH [TMEM68 gene was significantly associated with residual feed intake and average daily gain in crossbred steers [The region located on BTA14 at 20–28 Mb (UMD3.1) is one of the most known large-effect pleiotropic regions affecting the expression of several economic traits, including those related to sexual precocity across different breeds . This rein PLAG1 ,141, but mammals ,143,144.and GnRH . In addid steers .As pointed out by Fernandes Júnior et al. , an impoCandidate genes, identified by GWAS or meta-analysis, are generally used in enrichment analysis in order to identify over-represented pathways underlying complex traits. This is important for a better understanding of the genetic mechanisms involved with the expression of complex phenotypes. Basically, in pathway-based association analysis, candidate genes are assigned to biological processes based on their function and properties of their encoded products, and then a statistical test, e.g., Fisher’s exact test, is used to identify a possible overrepresentation of the list of candidate genes used as input in a given pathway . For earSMC2 mutation that causes embryonic loss [In general, the combination of GWAS, meta-analysis, and functional pathway enrichment analysis have provided great advances in the understanding of genetic control of pubertal-related traits in tropically-adapted beef cattle . Such knnic loss . Such vaWhen variants are found in gene regions, especially in exons, determining the cause and effect of these variations is a crucial issue. For instance, exome sequencing made it possible to identify causal mutations in underlying defective bovine embryo development  and fertIn the tropical environment, the effect of heat stress, food, and water scarcity during summer affects reproduction performance in most livestock species. Low reproductive hormones secretion, the incidence of abortion, and embryonic death are among the problems compromising reproductive efficiency . InvestiRocha et al.  used theIn Brahman heifers, transcriptomic studies showed a large list of differentially expressed genes  related to puberty ,156. GenUsing advanced proteomic technologies, Nguyen et al.  and TahiMetabolomics is another emergent “omics” area that has provided a deeper knowledge of the role of metabolites in reproduction, such as the metabolomic signature of the ovarian follicular fluid and embryos development. These results could improve natural and assisted reproduction in bovine . The relSundrum et al.  discusseThe big data generated by the omics approach and the joint studies with different levels of information, called multi-omics integration, have been used to investigate the dynamics of complex traits. Cánovas et al.  and FonsThe biochemical and molecular knowledge of complex traits in cattle raised in the tropics, combined with sustainable environmental management, would lead to new study proposals in the area of systems biology. In addition, the knowledge acquired using functional approaches leads to a better understanding of the biological mechanisms at all levels. Elucidating interconnections involving the different network members  is essential for the comprehension of the organism as a whole.Nellore is a zebu breed, introduced in Brazil from India at the end of the XIX and the beginning of the XX century. The last importation was in the 1960s. In total, around 6200 animals were imported. Due to its adaptation to tropical conditions, including resistance to heat stress and endo and ectoparasites, in special ticks, this breed has spread around Brazilian tropical regions. Today, 80% of the beef cattle population in Brazil is composed of Nellore and its crosses . In the 1980s, genetic evaluation programs were implemented by independent groups, starting to use EPDs and oriented mating systems , accelerThe results presented in this section are from one of the breeding programs composing the Nellore Alliance, chosen because it imposes a strong selection intensity on female sexual precocity. Presently, in this program, about 90,000 cows from 90 herds are annually controlled. In 12 years, the age at first calving phenotypic (AFC) means showed a strong decreasing trend, changing from nearly 34 to less than 28 months . NaturalThe phenotypic and genetic progress, verified in this period, was possible due to the so-called “precocious challenge.” Historically, in the breeding programs, females used to be first exposed to reproduction after achieving minimum weight and/or age, around 24 months. In the 1990s, in a few breeding programs, young females (16–18 months of age) started to be exposed in an earlier breeding season aiming to identify the “precocious” one. In this system, non-pregnant females at the end of this breeding season are given another chance to conceive in the regular breeding season, when all females are exposed. For several years, rates of pregnancy in the earlier breeding season were around 10–18% ,168,169.Results from the specific breeding program presented here show that the number of herds exposing young females to reproduction expressively increased from 2007 ~15) to 2020 (62). Likewise, the percentage of early pregnancy, including precocious and super precocious females, changed from around 10% to more than 60% in the same period . Importa5 to 2020Economic advantages of decreasing age at first calving from 36 to 24 months of age are well documented ,171. In For Capper , the keyIn general, puberty-related traits would quickly respond to selection by presenting moderate to high heritability estimates. In this context, the heifer early pregnancy (HP) trait, which expresses the success or failure of a heifer in conceiving or calving precociously at a specific age, is a promising selection criterion for improving sexual precocity in beef cattle. As a future perspective, the use of high-throughput phenotyping strategies in the evaluation of sexual precocity has been emerging with advances in digital image analysis and computer vision systems.Some Nellore breeding programs have achieved a considerable decrease in both phenotypic and genetic trends for age at first calving by exposing heifers to mating at 14 months of age in order to identify and select precocious females. Consequently, the proportion of early pregnancy in these herds has increased from around 10% to more than 60%. The genetic improvement of heifer’s sexual precocity contributes to reducing the number of unproductive animals in the herds and promotes sustainability through better use of resources. Genome-wide association studies have pointed many quantitative trait loci affecting puberty traits in tropical cattle, and multi-omics approach, still at the beginning in livestock, has already presented promising results in multiple levels . These molecular approaches have contributed to uncover specific molecular information, e.g., SNPs, genes, and biological pathways, with major effects on heifer’s sexual precocity. Modeling such information in genomic selection evaluation may increase prediction accuracies. Additionally, with whole-genome sequencing data, phenotype causal mutations could be identified and directly included in the analysis. Genomic selection breeding programs accounting for genotype by environment interaction also represent promising tools for accelerating genetic progress for sexual precocity in tropical beef cattle.


This article introduces a review of research that has implemented oxytocin measurements in different fluids such as plasma, cerebrospinal fluid, urine and, mainly, saliva. The main purpose is to evaluate the level of evidence supporting the measurement of this biomarker implicated in a variety of psychological and social processes. First, a review of the technical developments that allowed the characterization, function establishing, and central and peripheral levels of this hormone is proposed. Then, the article approaches the current discussions regarding the level of reliability of the laboratory techniques that enable the measurement of oxytocin, focusing mainly on the determination of its concentration in saliva through Enzyme-Linked Immunosorbent Assay (ELISA). Finally, research results, which have established the major physiological correlates of this hormone in fields such as social neuroscience and neuropsychology, are collected and discussed in terms of the hormone measurement methods that different authors have used. In this way, the article is expected to contribute to the panorama of debates and current perspectives regarding investigation involving this important biomarker. Oxytocin, saliva, ELISA, RIA, social behavior One of the molecules whose study has generated high interest is oxytocin (OXT). This nonapeptide is one of two neuro-hormones released by the posterior hypophysis, along with arginine vasopressin (AVP). OXT is produced by the supraoptic and paraventricular nuclei of the hypothalamus and then transported to the gland by structures denominated tuber cinereum and infundibulum . It has Nowadays, OXT levels determination are used beyond the investigations involving reproductive physiology. Probably linked with these functions that are crucial for survival in mammals, OXT is also involved in several social and affective processes. OXT has been widely studied due to its role as a facilitator of maternal care behaviors in many mammal species and has also been associated with trait attitudes of paternal behavior, that, depending on the species, have great relevance in offspring care . It has Over the past decades, saliva has been the fluid in which many efforts to measure OXT have focused, in order to establish non-invasive correlates of these psychological and physiological functions. Nevertheless, measurements of physiological levels of this hormone have been difficult, due to its small molecular size and homology with other peptides.In such a context, the purpose of this narrative review is to focus on the different techniques that allow the measurement of OXT and current discussions of its validity, mainly in the field of social neuroscience and affective neuroscience. In order to avoid possible biases associated to the search of results in scientific literature, this review included papers that reported positive and negative results related to the validity in OXT measurements. The search criteria for articles were broad, based on key words such as Oxytocin measurements, saliva samples, plasma, urine, spinal fluid, ELISA, EIA and RIA. In some cases, relevant original articles referenced in different papers or reviews were searched and revised, mainly those that involved protocols and measurement techniques of OXT. Databases used in this review were PubMed, Scopus, ScienceDirect, SciELO and Google Scholar.2There are different approaches to detect and quantify OXT. Since its discovery in 1909 by Henry Dale, the peptides detection was performed in pituitary tissue, which was carefully selected to collect only the posterior part of the gland. Then, the macerate was processed in order to remove any blood residue and finally, through Biuret's reagent, the peptides presence in the sample were evidenced .The investigation on the peptide's functions required its extraction and, at the time, it was not yet identified as a biomarker. However, in 1928 the peptide was separated from vasopressin at Parke-Davis & Co Research Laboratory . FurtherIn addition to the extraction of the nonapeptide via acetic acid, other techniques such as gel filtration and ether-acetone  were useThe field of study of peptides released by the pituitary gland had its turn in the seventies, when an interest to quantify them started to emerge. A fusion between extraction and immunoassay techniques, allowed a better precision in the analysis and quantification of OXT . The RadIn contrast, the sensitivity of the Enzymatic Immuno Assay technique (EIA), developed by Van Weemen and Schuurs in 1971, was not as thorough as RIA; however, it did not have as many disadvantages as the second. In parallel, during that same year the Swedish scientists Engyvall and Perlmann developed the Enzyme-Linked ImmunoSorbent Assay or ELISA. These investigators, driven by the search of an alternative to RIA, created a technique that had better outcomes in contrast with other immunoassays, it even opened the door for a possible substitution of RIA, as Ekins and Watson explained in “ELISA: a replacement for radioimmonoassays?” (1976). Despite the similarities between both techniques, ELISA changed the radioactive isotope that harmed the laboratories budget and eliminated the risk for the researchers working with these reagents . Other bThe antibody specification for both techniques was improved through the years and it did not take long before it was used for the detection and quantification of oxytocin. 3In 2007, authors of the same research group were pioneers in making an effective measurement of salivary oxytocin using ELISA, modifying the sample in order to concentrate it and facilitating the subsequent analysis. The main advantage of this type of measurement is the non-invasive procedure for taking the sample, which reduces ethical impediments in non-clinical research and contributes to reduce the possible noise of venipuncture stress. They reported a higher antibody specificity for OXT while RIA showed more cross-reactivity with AVP. The authors’ findings support that OXT is present in human saliva, showing a consistent variation pattern of salivary OXT with lactation (with ranges of nearly 4 pg/ml during the feeding and nearly 10 pg/ml 30 min prior to feeding). They also tested changes in salivary OXT after a massage in males, reporting a range of variation between 1,75 before and 2,1 pg/ml after the massage, which follow the variation pattern measured in plasma OXT: 175 pg/ml before and 220 pg/ml after the massage, nearly one order of magnitude above .However, these results contrasted what was discussed in 2005 by Horvat-Gordon, Granger, Schwartz Nelson and Kivlighan, who denied the soundness of the measurement of salivary OXT and its correlational inaccuracy with behavior. Nonetheless, Carter and colleagues approached the problem modifying the sample, drying it through vacuum centrifugation, and then hydrating it in order to make it four times more concentrated. Experimentally, they performed a comparison between recovered OXT in saliva vs. blood, before and after a massage treatment in males. The results showed consistency in the increase of OXT taken after the treatment in both blood (pre – 175 pg/ml and post – 225 pg/ml) and saliva  . FollowiOther attempts to measure salivary OXT were executed through the ELISA technique in addition to the treatment of vacuum centrifugation implemented by Carter, but the results were still paradoxical, as was evidenced in “Correlation of plasma and salivary oxytocin in healthy young men – Experimental evidence” by As was implied, being able to assess the relation between central OXT from peripheral measurements  is crucial to understand the effect of this hormone in social behavior or to postulate its usefulness as a clinical marker in different psychological or psychiatric disorders . The effThe evidence on evaluation of peripheral oxytocin as an indicator of concentrations at a central level is contradictory. There are diverse physiological stimuli that relate to the increase of oxytocin production at both central and peripheral levels, for instance, during pregnancy or childbirth, during nursing and during sexual intercourse. In several of these conditions, an increase in the oxytocin concentration in CSF and plasma has been found, although they are not necessarily correlated .Likewise, there are studies performed in humans in which a positive relation is found between oxytocin levels in CSF and plasma . In a reIn contrast, there are studies in humans in which a relation was not found between the central and peripheral values  view Ta. These nRegarding the diverse peripheral measurements and the link between them, there is currently no definitive consensus either. There are studies in which no relation between salivary and plasmatic OXT is found  or betweLikewise, urine use has been attempted as a peripheral measurement of OXT levels, although more evidence is required to implement it as a reliable measurement. p < 0.001) than plasma and CSF , and even more than that between plasma and salivary OXT . In studies performed in humans, a positive relation between plasmatic and salivary oxytocin levels was found , In another study exploring romantic attachment anxiety, forty-five healthy subjects filled the Italian version of the "Experiences in Close Relationships" (ECR), a self-report questionnaire to evaluate romantic attachment. Using the OXT RIA kit with a sensitivity of 10–30 pg/tube, authors found that attachment anxiety and OXT are positively associated in romantic bond to a statistically significant degree . Range of variation of OXT level in this study appears unusually low: 0,13–4,59 pg/ml .To assess the role of OXT in romantic attachment, Conversely, in a study carried out by 7It has been found that both neurohormones, OXT and vasopressin (AVP), interact with the hypothalamic-pituitary-adrenal axis (HPA) and the stress response . While tIn the same way, p < 0,001). Authors used Furthermore, p = 0.03) between salivary OXT, measured through an ELISA kit following the protocol of To assess the effect of acute stress on OXT secretion, r = -0.40, p = 0.04), and insecurity . They used a RIA protocol developed by t (34) = 2.335, p = 0.077, d = -0.651). However, after being submitted to the TSST they found no significant statistical changes intra or intergroups. Authors used an ELISA kit based in Although most studies point to an inverse correlation between OXT and stress, and cortisol, there evidence is mixed. For instance, Finally, to explore the relation between a physiological condition that induces OXT and a possible stress reduction, one of the first studies in this topic evaluated changes in OXT, Cortisol and ACTH after suckling (in lactating women) and breast stimulation (in no lactating women). Authors measured these hormones through a RIA protocol with a detection limit of 2 pmol/l for OXT. Results showed an increase of OXT and a synchronic reduction of ACTH and cortisol, which had a similar pattern but appeared earlier in lactating women compared to no-lactating women who received breast stimulation. Authors reported their results in units pmol/l, with a range of variation of OXT between 2 and 15 . In comp8Different researches relate peripheral oxytocin with different psychiatric disorders such as psychopathy, anxiety, depression, autism and others , or someIn order to evaluate the effect of OXT in mood changes, Personality profiles with lack of empathy and emotional insensitivity  are related to antisocial behaviors . Levy etOn the other hand, levels of oxytocin in saliva are positively associated to the gaze fixation time of images containing people interacting and showing emotions in children with normal development. In contrast, autistic children do not show the same correlation . It was Additionally, there is a correspondence between anxiety, suicidal ideation and negative social interactions, and this correlated with salivary OXT levels . LebowitDespite the above findings, there is still a wide controversy discussing whether this peptide may be used as a biomarker for detection of psychiatric diseases. Some meta-analyses point out that it is not reliable, regardless of the type of sample taken . For exa9Recent studies have remarked other possible stimulus able to induce OXT secretion. On the other hand, In a research with 68 beginner and advanced practitioners of Jujitsu, a form of traditional martial arts originating in Japan, In another study, Finally, in a recent article 10Over the past decades, many researchers rather evaluate the effect of exogenous administration of OXT on behavior and mental states, instead of measuring peripheric hormone concentration or induce secretion through physiological stimulus such as the mentioned above. Intranasal administration of OXT (IAO) is the most common way to deliver the hormone, with a low-risk procedure based of a nasal spray. It has been shown that neuropeptides go through the blood-brain barrier after intranasal administration, which provides a useful method to study OXT effects on the central nervous system in humans .IAO has been related to an increase in plasmatic and central levels in humans , and in On the other hand, an increase in salivary oxytocin has been found after IAO . AlthougIn this field, two recent reviews are strongly recommended. The First one focused on the potential application of IAO for treatment of mental diseases associated with altered socio-emotional competence, mainly in the mental health crisis associated to the Covid-19 pandemic . In the Keeping in mind these considerations, some findings regarding social-affective correlates of OXT using IAO will be mentioned. For instance, there is evidence that suggests the central system of oxytocin is altered in different mental disorders that are characterized as social disorders, personality disorders and early traumas. Regarding that, it has been found that the IAO in patients with autism spectrum disorders (ASD), schizophrenia and borderline personality disorder, improves performance in social cognition tasks that measure the ability to comprehend affective content, recognition of others’ emotions, social and facial perception, sarcasm comprehension and empathy .Another example is the study from On the other hand, in a study that implemented functional magnetic resonance imaging (fMRI) in humans, A study conducted by Additionally, it has been documented that IAO modulates the action of other hormones such as cortisol  and testFinally, it has been found that the administration of IAO increases the expression of prosocial skills and behaviors such as parental care , empathyIn sum, regarding the studies described, OXT modulates social perception, social cognition and social behavior, possibly promoting the approach between individuals and affiliation. These effects might be associated with its capacity to modulate functions linked with emotional processing and recognition. Additionally, functional imaging studies support the idea that anxiolytic effects related to the exogenous OXT administration are due, at least partially, to the deactivation of amygdala excitation. Therefore, reduction in emotional excitation during social encounters might foster social approach and contribute to the positive effects of OXT in trust and social cognition.11Different techniques have enabled the rigorous study of OXT since its discovery in 1909, and, even though more than 100 years have passed, its effect on behavior is still assessed. Despite findings in immunochemical techniques, some difficulties persist in making an optimal measurement of OXT, placing at risk the validity and scope of the experiments requiring this type of analysis.Different samples and methods could yield a wide range of results, that vary from 1 to nearly 1000 pg/ml, and this makes their comparison and the establishment of clear trends and conclusions difficult. Distinct protocols of extraction and processing of fluid samples could also introduce differences in OXT measurements. Furthermore, many authors report their results in different measurement units , which makes data comparison, extrapolation and discussion harder. Some studies do not even report the absolute values of OXT, but only percentages of variation or levels of statistical significance. On the other hand, given the different ranges of absolute values, the levels of statistical significance in comparative analyzes can change dramatically.As for IAO techniques, which are widely used today, they can induce false increases in salivary OXT levels by a direct passage of the exogenous substance to the oropharynx. At other times, elevated peripheral oxytocin levels may be associated with the measurement of other substances chemically similar to the hormone, such as other neuropeptides.In addition, the complexity of measuring OXT in a standardized way is not only related to the measurement methods used, but to many other physiological factors that can induce changes in its concentration and limit behavioral and psychological correlations. Among these, there are not only hormonal interaction phenomena that can affect OXT levels, but also possible interactions with other chemical signals, as shown by studies in which allergy sufferers have higher baseline levels of oxytocin in their blood . All of Despite the fact that one of the most used protocols in recent years for the measurement of saliva OXT is that of “However, the amounts of OXT present were low and the methods used here for concentrating samples are labor-intensive. A more sensitive assay is desirable, if salivary OXT is to become a readily available “biomarker” for use in studies of human behavior or physiology”.Probably, changes in the treatment of salivary samples and monoclonal antibody use might be the key to obtain more reliable results. Some authors recently suggest that other methods like liquid chromatography-mass spectrometry (LC-MS) could yield better results to measure salivary OXT , but expAll authors listed have significantly contributed to the development and the writing of this article.Universidad Externado de Colombia.This work was supported by No data was used for the research described in the article.The authors declare no conflict of interest.No additional information is available for this paper.


Heterosis has been widely used in maize breeding. However, we know little about the heterotic quantitative trait loci and their roles in genomic prediction. In this study, we sought to identify heterotic quantitative trait loci for seedling biomass-related traits using triple testcross design and compare their prediction accuracies by fitting molecular markers and heterotic quantitative trait loci.A triple testcross population comprised of 366 genotypes was constructed by crossing each of 122 intermated B73 × Mo17 genotypes with B73, Mo17, and B73 × Mo17. The mid-parent heterosis of seedling biomass-related traits involved in leaf length, leaf width, leaf area, and seedling dry weight displayed a large range, from less than 50 to ~ 150%. Relationships between heterosis of seedling biomass-related traits showed congruency with that between performances. Based on a linkage map comprised of 1631 markers, 14 augmented additive, two augmented dominance, and three dominance × additive epistatic quantitative trait loci for heterosis of seedling biomass-related traits were identified, with each individually explaining 4.1–20.5% of the phenotypic variation. All modes of gene action, i.e., additive, partially dominant, dominant, and overdominant modes were observed. In addition, ten additive × additive and six dominance × dominance epistatic interactions were identified. By implementing the general and special combining ability model, we found that prediction accuracy ranged from 0.29 for leaf length to 0.56 for leaf width. Different number of marker analysis showed that ~ 800 markers almost capture the largest prediction accuracies. When incorporating the heterotic quantitative trait loci into the model, we did not find the significant change of prediction accuracy, with only leaf length showing the marginal improvement by 1.7%.Our results demonstrated that the triple testcross design is suitable for detecting heterotic quantitative trait loci and evaluating the prediction accuracy. Seedling leaf width can be used as the representative trait for seedling prediction. The heterotic quantitative trait loci are not necessary for genomic prediction of seedling biomass-related traits.The online version contains supplementary material available at 10.1186/s13007-021-00785-8. Hybrid breeding has successfully been used in maize over the last decades due to the direct utilization of heterosis, which refers to the agronomic performance superiority of heterozygous hybrids over corresponding homozygous parents . The breRecent studies have indicated the usefulness of various genomic prediction models involved in ridge regression best linear unbiased prediction (rrBLUP), genomic BLUP (GBLUP), and the general combining ability (GCA) model in prediction of yield-related traits and biomass-related traits (BRTs) in the harvested mature maize materials based on genomics, transcriptomics, and metabolites data –13. CompZ1, Z2, and Z3 of the performance data of three populations involved in the F2 population or recombinant inbred line (RIL) population crossed with each parent and their F1, was used to test the significance of the epistatic effect of the QTL contributing to heterosis, with superiority in detection precision and efficiency [QTL mapping approaches have been extensively applied to identify genetic loci responsible for the complex traits and heterosis –24. Amonficiency . As an eficiency . Thus, tficiency .Z1) data set and DIFF (Z2) data set were detected, as well as five additive × additive (aaij) and three dominance × dominance (ddij) epistatic interactions with two-dimensional scans through the SUM data set and DIFF data set, respectively [ai*, which includes the additive effect of QTL i (ai) minus half the sum of dominance × additive epistatic interactions (daij), and the augmented dominance effect di*, which includes the dominance effect of QTL i (di) minus half the sum of additive × additive epistatic interactions with the genetic background irrespective of linkage, corresponding to the effects reflected in the SUM and DIFF data sets with one-dimensional scans, respectively. Thus, two-way marker interactions detected in the SUM and DIFF data sets should not be interpreted as the interactions of corresponding effects in one-dimensional scans. In fact, digenic epistatic interaction in the SUM data set comprised aaij and ddij interactions, while digenic epistatic interaction in the DIFF data set comprised adij and daij interactions [aaij and ddij epistasis can be obtained with two-way analysis of variance (ANOVA) of H3 (the performance of progenies of F2 or RIL crossed with F1) and Z3 according to the genetic expectations of QTL effects for TTC designs reported by Melchinger et al. [Following the RIL-based TTC design, the heterotic QTLs of maize yield-related traits, including grain yield, number of kernels per plant, and kernel shape-related traits, were characterized , 27. Apaectively . Howeverectively  defined ractions . Unbiaser et al. . Hence, In this study, TTC populations were constructed to identify heterotic QTLs and the epistatic interactions contributing to seedling BRTs. Predictive ability of seedling BRTs and the impact of heterotic QTLs on the predictive ability were evaluated.Intermated B73 × Mo17(IBM) population consisted of 122 RIL genotypes  were use1 − MP)/MP × 100, where MP indicates the average value of the parents. For each trait, ANOVA was conducted on best linear unbiased estimates (BLUEs) of each genotype in TC populations across blocks. A t-test and Pearson’s correlation based on BLUEs were further used for significance testing and relationship analysis. To evaluate the heritability of seedling BRTs, H1i, H2i, and H3i (i = 1–122), respectively, the following linear transformations of Z1i = (H1i + H2i)/2, Z2i = H1i  − H2i, and Z3i = H1i + H2i – 2H3i  were used for augmented additive, augmented dominance, and epistasis heterotic QTL detection [B73 × Mo17, TC(B), TC(M), and their corresponding parents were subject to heterosis assessment by relative mid-parent heterosis (MPH), which was calculated using the equation MPH (%) = , partially dominant (0.2 ≤|di*/ai*|< 0.8), dominant (0.8 ≤|di*/ai*|< 1.2), or overdominant (|di*/ai*|≥ 1.2) [A total of 1631 markers, including 142 single nucleotide variations, 93 insertion/deletion, 57 presence/absence variations , and oth*|≥ 1.2) .The performance of TC(B) and TC(M) progenies was used as the response variable in the genomic prediction model. The genotypes of these two populations were derived from the corresponding genotypes of IBM individuals and either B73 or Mo17 defined by 1631 markers. For missing genotypes, the mean value of the marker was assigned by the R package ‘rrBLUP’ v4.6 . The uniμ is the fixed model intercept,  follows :N denotes the marker number, ument}GP .The prediction was carried out in the R package ‘sommer’ v4.1.2 . To achiANOVA based on three TC populations revealed a highly significant difference among TC populations , whereas the lowest significant correlation was found between LL and SDW (r = 0.40)  Fig. a. Hetero40) Fig. b. This t40) Fig. c. To det40) Fig. : Fig. S1A linkage map covering 6943.84 cM was constructed by 1631 markers, with an average interval distance of 4.28 cM between adjacent markers , colocalized with qLW4a, compared with qLA7a on chromosome 7, with the small contribution to the phenotypic variation of LA (5.1%), colocalized with qLW7b. Two QTL regions shared by LW and LA suggest that these two genetic loci may contribute to LA and LW simultaneously although the gene action of each locus affecting each trait differently  and TC(M) population with observations in three blocks were used for genomic prediction for seedling BRTs based on 1631 markers. Comparisons among models if incorporating SCA and/or spatial effects revealed that the universal model for GCA and SCA effects coupling with block effect could capture the best predictions across seedling BRTs, and therefore used in subsequent analyses  Relationships between performance of seedling BRTs with heterozygosity. (b) Relationships between MPH of seedling BRTs with heterozygosity. Fig. S2. Prediction accuracies of seedling BRTs based on different numbers of markers. Corresponding numbers of markers were sampled randomly from 1631 markers five times to represent five repeats of the marker group. The mean of the prediction accuracies with 1000 runs for each repeat was plotted.


The principal phenotypic determinants of market class in carrot—the size and shape of the root—are under primarily additive, but also highly polygenic, genetic control.The size and shape of carrot roots are the primary determinants not only of yield, but also market class. These quantitative phenotypes have historically been challenging to objectively evaluate, and thus subjective visual assessment of market class remains the primary method by which selection for these traits is performed. However, advancements in digital image analysis have recently made possible the high-throughput quantification of size and shape attributes. It is therefore now feasible to utilize modern methods of genetic analysis to investigate the genetic control of root morphology. To this end, this study utilized both genome wide association analysis (GWAS) and genomic-estimated breeding values (GEBVs) and demonstrated that the components of market class are highly polygenic traits, likely under the influence of many small effect QTL. Relatively large proportions of additive genetic variance for many of the component phenotypes support high predictive ability of GEBVs; average prediction ability across underlying market class traits was 0.67. GWAS identified multiple QTL for four of the phenotypes which compose market class: length, aspect ratio, maximum width, and root fill, a previously uncharacterized trait which represents the size-independent portion of carrot root shape. By combining digital image analysis with GWAS and GEBVs, this study represents a novel advance in our understanding of the genetic control of market class in carrot. The immediate practical utility and viability of genomic selection for carrot market class is also described, and concrete guidelines for the design of training populations are provided.The online version contains supplementary material available at 10.1007/s00122-021-03988-8. Daucus carota subsp. sativus) is a widely cultivated vegetable crop of both significant economic importance—globally, annual carrot production exceeds 40 million metric tons  were utilized in this study, composed mainly of Plant Introductions in the USDA National Plant Germplasm System (USDA-NPGS) collection of n et al. , as thesIn 2016 and 2018, the collection was grown at the Hancock Agricultural Research Station in Hancock, WI ; accessions were planted on May 16th and May 24th, and harvested on August 29th and 30th, respectively. In 2018–2019, the collection was grown at the University of California Desert Research and Extension Center in Holtville, CA . In this environment, planting occurred on October 10th, and roots were harvested on February 25th. In both locations, accessions were grown in 1-m long rows; in Wisconsin, one replicate per genotype was planted, while in California, two replicates of all genotypes were planted in a randomized complete block design. One to fifteen roots were harvested per replicate. In Wisconsin, higher disease pressure led to fewer mature, undamaged roots being harvested per plot, on average. Following harvest, carrot tops were removed, and roots were stored at 4 °C until phenotyped.https://github.com/shbrainard/carrot-phenotyping; MATLAB scripts for straightening binary masks and performing PCA on contours or curvature values are available at: https://github.com/jbustamante35/carrotsweeper.Roots were digitally phenotyped as described by Brainard et al. . In brieFollowing acquisition and pre-processing, phenotypes were extracted from the straightened, de-tipped binary masks. Root length was calculated as the distance from the center of the root crown to the root tip, following both straightening and elimination of the unexpanded, etiolated portion of the root. Maximum width was measured as the distance across the widest portion of the carrot, which is typically located just below the root crown. Total root size was defined as the 2D area of the entire binary mask. Aspect ratio was calculated as the ratio of length and maximum width. In addition, in order to quantify size-independent parameters of contour shape, PCA was performed on the root contour following a normalization procedure whereby each carrot was standardized to have a maximum width of 1, and a length of 1000. The scores along the first principal component quantify the degree of root fill, or how far down the length of the carrot the maximum width of the root is maintained; this trait accounts for over 80% of the variation in size-independent root shape. In addition, curvature values were computed at each point along the root contour in both the shoulder and tip regions as described by Driscoll et al. . PCA of Finally, prior to association analyses and construction of genomic prediction models, the diversity panel was restricted to those accessions that exhibited little to no branching of the taproot. Both the nature of the root-straightening algorithm—which depends upon identifying a carrot tip—and many of the phenotypes themselves , implicitly require that the root be a single unbranched taproot. Small root hairs were removed through smoothing operations, and just as forked or split roots were discarded prior to image acquisition, accessions with highly branched fibrous root systems were also excluded on the basis of being inappropriate to an analysis of market class traits. Together with the failure of some roots to produce new leaf tissue following vernalization, this reduced the total size of the diversity panel used in subsequent analyses to 662 unique cultivated accessions.ith genotype effect, jth environment effect, ijth genotype x environment interaction , library preparation, and sequencing was performed at the University of Wisconsin-Madison Biotechnology Center. In brief, restriction enzyme-digestion was performed with ApeKI, following which Illumina adapters and sample-specific barcodes were annealed. Samples were then multiplexed and sequenced on an Illumina NovaSeq 6000, generating on average 4 million, 150 bp paired-end reads per sample.Following six weeks of vernalization at 4 °C, one root per accession was transferred to a greenhouse environment and planted in conical tubes containing Pro-Mix High Porosity potting mix . Roots were maintained at 20 °C with a 16 h photoperiod. Following emergence of new leaf tissue, 1 cmhttp://github.com/shbrainard/gbstools/). SNPs were then identified using the GBSv2 pipeline of TASSEL 5 . Filtering on the basis of LD was performed using the prune plugin.Raw, multiplexed.fastq files corresponding to forward and reverse reads of each lane were checked for quality, and demultiplexed using a custom a Java application  in the Department of Computer Sciences. Logarithm of the odds (LOD) thresholds were then calculated to control the family-wise error rate (FWER) at α = 0.05.R2 values and p-values for significant markers were calculated using backward elimination on the basis of a deviance parameter equal to the difference of likelihoods of the full model  and the reduced model (with the marker in question removed): p-values equal to the dth quantile of the cumulative distribution function of the χ2-distribution, with degrees of freedom equal to the number of SNPs in a given peak; peaks were then represented as the single SNP with a p-value < 0.05. Partial R2 values were then computed for these markers as: n represented the number of samples.Partial In addition to GWAS, marker data were used to calculate GEBVs, using best linear unbiased predictors (BLUPs) , 2 kb , 5 kb , 10 kb , 100 kb , 250 kb , 500 kb , 750 kb , 1 megabase (Mb) , 2 Mb , 3 Mb , 4 Mb , and 5 Mb . A separate VCF file was also generated containing only markers on chromosome 3, thinned to a maximum of one SNP every 0.1 kb ; this highly biased marker set provided an extreme case with which to evaluate the effects of distorting the genome-wide distribution of a relatively large number of markers.k-means clustering algorithm was applied to the principal components of the marker matrix containing 9535 SNPs. The most distant sub-grouping of 60 accessions was adopted as the most unrelated VP, and was then progressively diluted by replacing individuals in this sub-group with random draws from the larger population, to thereby simulate a gradient of relatedness between the TP and VP. Dilution amounts were set to 1, 3, 5, 7, 10, 13, 15, 17, 24, 27, 31, 35, 45, and 50 individuals, with 50 replications performed at each dilution level. “Distance” between TP and VP was then calculated on the basis of a similarity matrix, defined as the inverse of the distance matrix constructed from the first 100 PCs of the PCA of the marker matrix. Each individual in a given VP was then compared against the n most similar individuals in the TP, where n was allowed to vary between 1 and 660, depending on the distance metric being analyzed. These distances were then averaged across all individuals in the TP. The same 100-fold cross-validation approach as outlined above was repeated here for every TP/VP combination, and average predictive ability was then regressed onto the average similarity index.To evaluate the effect of similarity between the TP and VP, a To evaluate the effect of varying TP size, two distinct approaches were taken. First, absolute TP size was varied, while holding the relative size of the VP constant at 10%. This was performed by sampling subsets of sizes ranging from 10 to 660 from the full panel, repeating this sampling process 50 times at each population size, and performing the same 100-fold cross-validation approach as with previous analyses. Separately, relative TP size was also varied, by holding the absolute size of the VP constant at 60 individuals, and varying the total TP size from 75 to 660. As with the previous analysis, at each level of relative TP size, the sampling procedure was iterated 50 times, and for each iteration the 100-fold cross-validation approach was performed. Finally, relative VP size was varied, by holding the total population size constant at 662 individuals, and sampling VPs ranging from 10 to 650, and as above, iterating each VP size 50 times, and performing the 100-fold cross-validation with each iteration.Representative roots drawn from four common market classes are shown in Fig. The extent and rate of decay of LD across the genome is an important determinant of the potential resolution of association analysis, and a decisive factor in determining the marker density necessary for performing GWAS  and 0.1 (green line) at 796 bp and 19.7 kb, respectively, illustrate that within only several kb, there is a rapid approach to linkage equilibrium across the panel. The curated marker set (with filtering parameters described above) contained SNPs separated by an average distance of 3711 bp; while this distance varied across the genome , SNPs on average were therefore sufficiently close to provide adequate genome-wide coverage in terms of being in LD with putative QTL. Short-range genome-wide LD is visualized as a Manhattan plot in Fig. r2 > 0.1. This demonstrates both a consistent and relatively limited degree of LD across the genome. r2 values are accurate estimates of LD even using unphased genotypes . Due to the relatively limited number of detected SNPs, and their small effect sizes, a number of additional GWAS models were tested to determine if the mixed linear model utilized here was overly conservative. However, the generalized linear model implemented in GAPIT , as shown in Fig. Increasing relatedness between the TP and VP has generally been found to be associated with improvements in predictive ability  that are both highly precise in that they fit the exponential regression function extremely well, while also being highly informative, in that they facilitate discriminating between degrees of relatedness for a wide range of TP/VP combinations.In addition to this general trend, it is also clear that across all traits, both extremely high and extremely low values ofThe third parameter evaluated in terms of its effect on predictive ability was the size of the TP. There are three distinct ways in which the effect of TP size can be modified, as described above: absolute population size can be varied, relative TP size can be varied, or relative VP size can be varied. First, the effect of varying absolute population size, with VP size held constant at 10%, was evaluated. For all traits, predictive ability reached its asymptote at roughly 330 genotypes Fig. a. SimilaThis study identified a novel set of QTL for four of the most relevant morphological components of root market class in carrot. This included a highly significant SNP on chromosome 2 associated with root fill; as such, this represents the first genetic characterization of the control of the vast majority of the variance in root shape. For three of these four traits, only two QTL per trait were identified, and their effect sizes were relatively small, ranging from 0.01 (for the SNP on chromosome 6 associated with aspect ratio) to 0.06 (for the SNP on chromosome 2 associated with root fill), with total phenotypic variance explained ranging from 0.06 for root fill to 0.22 for aspect ratio. This is surprising, given the relatively high heritabilities observed for these phenotypes, both as estimated here using genomic data, and as previously reported using a diallel mating design , or between population groupings defined through clustering algorithms such as STRUCTURE . The crIt is interesting to note that not all traits exhibited an exponential relationship between predictive ability and relatedness equally well. Certain traits, such as root fill and aspect ratio, clearly followed this exponential relationship , the key determinant was primarily the number of individuals in the TP, scaled by the number of individuals for which one is attempting to predict performance. It is also interesting to note that the point at which the asymptotic maximum predictive ability is attained—roughly 50% of the total diversity panel—is consistent with reports of the minimum TP size needed to attain maximum predictive ability in squash Supplementary file2 (DOCX 19 kb)Supplementary Fig. 1 Image acquisition workflow . A Each black-bordered box within the overall image is first identified; B QR codes in the upper portion of each sub-box were scanned; C Carrot pixels were differentiated from background pixels to generate binary mask images; D Binary masks were saved to disk according to a file structure and naming scheme based on the information encoded within each QR code. E The midline of the carrot root was estimated by tracing a path from the carrot tip to the center of the shoulder, following the maximum of the smoothed Euclidean distance transform; Fdth measurements were made by sampling the binary mask normal to vectors tangent to points along the midline; G-HA random forest classifier was used to detect the point at which to “de-tip” any residual, unexpanded portion of the tap rootSupplementary Fig. 2 Histograms, represented as violin plots for each of the three carrot root production years, for the 6 component market class traitsSupplementary Fig. 3 Distribution of the SNP marker dataset used in GWAS analyses. Genome-wide coverage is shown for each of the 9 carrot chromosomesSupplementary Fig. 4 Manhattan plots for GWAS results for tip fill and shoulder curvature, using a diversity panel of 662 carrot accessions and 146,821 SNPsSupplementary Fig. 5 QQ-plots of p-values generated by the generalized linear GWAS model implemented in GAPIT, using the same diversity panel of 662 carrot accessions and 146,821 SNPs as used in the mixed linear modelsSupplementary Fig. 6 Manhattan plots generated using the multi-locus mixed linear model (MLMM) GWAS model implemented in GAPIT, using the same diversity panel of 662 carrot accessions and 146,821 SNPs utilized in the other GWAS analysesSupplementary Fig. 7 Manhattan plots generated using the dominance GWAS model implemented in GWASpoly, using the same diversity panel of 662 carrot accessions and 146,821 SNPs utilized in the other GWAS analysesSupplementary Fig. 8 Manhattan plots of GWAS results using a diversity panel of 662 carrot accessions and only those SNPs located on chromosome 3. No significant associations were found for four root traits for which associations were previously detected Supplementary Fig. 9 Prediction accuracy versus population structure at a variety of values of n, illustrating the variable goodness of fit of the asymptotic exponential function for 6 traits: a total root size; b length; c root fill; daspect ratio; e maximum width; f tip curvature. Phenotypic data from the entire diversity panel of 662 carrot accessions was used in all analysesSupplementary file12 (CSV 38 kb)Below is the link to the electronic supplementary material.