Pork belly is a cut of meat with high worldwide demand. However, although the belly is comprised of multiple muscles and fat, unlike the loin muscle, research on their genetic parameters has yet to focus on a representative cut. To use swine breeding, it is necessary to estimate heritability against pork belly traits. Moreover, estimating genetic correlations is needed to identify genetic relationship among the traditional carcass and meat quality traits. This study sought to estimate the heritability of the carcass, belly, and their component traits, as well as the genetic correlations among them, to confirm whether these traits can be improved.A total of 543 Yorkshire pigs  from 49 sires and 244 dam were used in this study. To estimate genetic parameters, a total of 12 traits such as lean meat production ability, meat quality and pork belly traits were chosen. The heritabilities were estimated by using genome-wide efficient mixed model association software. The statistical model was selected so that farm, carcass weight, sex, and slaughter season were fixed effects. In addition, its genetic parameters were calculated via MTG2 software.The heritability estimates for the 7th belly slice along the whole plate and its components were low to moderate (0.07±0.07 to 0.33±0.07). Moreover, the genetic correlations among the carcass and belly traits were moderate to high (0.28±0.20 to 0.99±0.31). Particularly, the rectus abdominis muscle exhibited a high absolute genetic correlation with the belly and meat quality (0.73±52 to 0.93±0.43).A moderate to high correlation coefficient was obtained based on the genetic parameters. The belly could be genetically improved to contain a larger proportion of muscle regardless of lean meat production ability. Pork is one of the most consumed meats in the world and constitutes a major source of animal protein, especially in Asian countries such as South Korea ,2. MoreoAmong other factors, pig breeding primarily aims to improve lean meat production and carcass yield. Therefore, current systems to evaluate pig breeding performance have mainly focused on the longissimus thoracis (LT) muscle, which corresponds to the loin eye area (LEA). However, focusing on improving the lean percentage of the carcass has the unintended consequence of reducing the pork belly percentage ,9. ThereUnlike the LT, which is made up of a single muscle, pork belly is comprised of multiple muscles with fat content between them . TherefoTherefore, our study sought to improve pork belly traits that are directly associated with consumer preference by collecting the characteristics of the belly of the pig, the carcass, and the meat quality in a purebred Yorkshire pig population. Normally, crossbred pigs were used for commercial. However, it needs to estimate the genetic parameters of each purebred to use in swine breeding for pork belly. This study was conducted to estimate genetic parameters, including heritability of the detailed belly features of representative cuts, lean meat production, and meat quality traits, and their genetic correlations. Previous studies have analyzed pork belly parameters but were limited to belly weight (BEW), width, and length ,13. MoreThis study followed the guidelines of the Institutional Animal Care and Use Committee of the National Institute of Animal Science, Korea (2015-137). This study was conducted from February 2016 to June 2017.In total, 543 Yorkshire pigs  from 49 sire and 244 dam were used in this study. The pedigree information presented in The carcass weight (CW) and backfat thickness (BFT) were measured immediately after slaughter. The BFT was measured with a ruler at the 11th and last thoracic vertebrae on the left half of each carcass, and the average of the two measurements was used for the analysis. The LEA was measured at the level of the last rib after chilling the carcasses for 24 h.In this study, we measured and estimated multiple pork belly characteristics using a set of pork belly slices. The carcasses were cooled at 4°C for 24 h. Then, the left side of each carcass was divided into seven primal cuts following the standard cutting lines of the Korea Institute for Animal Products Quality Evaluation, and the belly was separated from the shoulder between the 4th and 5th thoracic vertebrae with a straight cut perpendicular to the axis of the carcass. The BEW was measured before dividing the belly into vertical slices.2) of the belly muscle components in each section was recorded through image scanning using the Image-Pro Plus software . The belly area (BA) and muscle area (MA) were then measured for each image slice of the pork bellies. The fat area was calculated by subtracting the MA from the BA. The volume of the belly (VB), belly muscle (VTM), and belly fat (VTF) were computed by summing all the corresponding slice values and multiplying them by the slice thickness. The belly muscle ratio (BMR) and belly fat ratio (BFR) were calculated by dividing the VTM or VTF by the VB and multiplying the value by 100 to convert it to a percentage.The belly was cut using a meat cutter  at every vertebra (6th to 14th thoracic vertebra and 1st to 5th lumbar vertebra), resulting in 14 cuts with a thickness of approximately 3 cm each. Slice numbers were labelled from 1 to 14, from cranial to caudal. Each of the resulting pork belly slices was scanned  with a steel ruler for calibration. The area  was measured at the 13th to 14th ribs using a spear-type electrode , which was previously calibrated with pH4 and pH7 standard solutions with automatic temperature compensation. The carcasses were chilled at 4°C for 24 h and the LT muscle was obtained to evaluate the meat quality traits. Additionally, samples were cut from the pork loin at the 8th to 9th thoracic vertebrae at 24 h post-mortem and placed on a table for 30 min to expose their surfaces to the air prior to measuring the meat color. The average lightness (L) was recorded using a chromometer , and the results were expressed as CIE  lightness . The sam2-test for Hardy-Weinberg equilibrium errors (<0.000001). A total of 42,399 SNP markers on 18 autosomes were left after filtration and quality control.The genomic DNA of the experimental animals was isolated from belly muscle tissue using the DNeasy Blood & Tissue Kit . The animals were genotyped for 55,232 single nucleotide polymorphism (SNP) markers using the Axiom Porcine Breeders Genotyping Array . Quality control and filtration of genotyped SNP markers were performed using the PLINK software (ver. 1.90) . The genPrior to the statistical analyses, we calculated the descriptive statistics and validated the normal distribution of the phenotype data. Putative outliers were excluded based on the ascertainment of normality using the Shapiro-Wilk’s method in SAS 9.4 .The heritability for all the traits were estimated for 543 Yorkshire pigs using the univariate linear mixed model (LMM) method in the genome-wide efficient mixed model association (GEMMA) software . The LMMy is the phenotype, b is the vector of fixed effects including sex, farm, season (summer or non-summer based on month slaughtered), and CW; u is the vector of random additive genetic effects following a normal distribution G is the n×n genomic relationship matrix (GRM) of which matrix elements are composed of pairwise genetic relationship coefficients computed using genotypes of 42,399 SNP markers, and X and Z are the incidence matrices for b and u.where Heritability was estimated as follows:h2≥0.4), moderate (0.2≤h2≤0.4), or weak (h2<0.2).Moreover, the outcomes of this estimation were classified as high variances equal to 0. All genetic parameters were computed using the MTG2 software, which is based on genomic REML analysis [r) were classified as strong (r≥0.7), moderate (0.3≤r<0.7), or weak (r<0.3).where analysis . The cor2 (LEA) and 21.95± 5.13 mm (BFT).The descriptive statistics are summarized in 3 for the VTM, 3,107.01±597.80 cm3 for the VTF, 46.50%± 6.54% for the BMR, and 53.50%±6.54% for the BFR. Regarding the belly components in Section 7, the CTM area was 14.96±2.62 cm2, the RAM area was 7.07±1.55 cm2, and the EAM area was 10.87±2.17 cm2. For meat quality, the mean pH45 was 6.13±0.28, the mean L was 46.34±2.75, and the mean DL was 2.69%±1.38%.The means of the six belly traits were 7.05±1.00 kg for the BEW, 5,954.81±688.54 cm3 for the VB, 2,758.04±381.16 cmh2 = 0.21±0.08), the LEA had the highest heritability values compared to those of the other two carcass traits (h2 = 0.47± 0.08), and the BFT showed moderate heritability (h2 = 0.36± 0.07).The estimated heritability values are summarized in h2 = 0.15 ±0.07), whereas the VTF had the highest heritability of all the traits (h2 = 0.33±0.07). The belly traits VB, VTM, BMR, and BMF exhibited heritability values of 0.21±0.08, 0.19±0.08, 0.25±0.08, and 0.25±0.08, respectively.The heritability of the belly trait BEW was weak (h2 = 0.07 ±0.07). In terms of meat quality, the h2 of pH45 was 0.09±0.05, and that of L was 0.24±0.07. Furthermore, the estimated heritability of DL was 0.36±0.08.For the belly muscle components in Section 7, the heritability of the CTM was 0.23±0.07, and that of the EAM was 0.19±0.06. The RAM had the lowest heritability (r = −0.31). Moreover, there was no correlation between the CW and meat quality traits. The LEA showed no correlation with the BMR, BFR, and meat quality traits (p>0.05). The LEA had weak correlations with BFT, VTF, RAM, and EAM . The other traits had moderate positive correlations with the LEA . Positive phenotypic correlations were observed between the BFT and several traits, including the BEW, VB, VTM, VTF, BFR, CTM, and L . BMR, one of the muscle-related traits, had a negative correlation coefficient with the BFT (−0.38). The other traits had no correlation with the BFT.The estimated phenotypic correlations among traits are shown in Among the belly traits, BEW exhibited a moderate to high absolute phenotypic correlation coefficient with some belly traits including the VB, VTF, BMR, and BFR . Moreover, the BEW had a weak absolute correlation with VTM, CTM, RAM, and DL . However, the other traits seemed to have a high significance. The VB trait was positively correlated with almost all the belly and belly component traits , whereas other traits such as VTF and BMR had negative correlations , and three meat quality traits had no correlations with high p-value. The VTM had no correlation with the VTF. Nevertheless, some of the belly and belly component traits without the BFR (−0.61) had moderate positive correlations . The other traits showed no significance with the low absolute correlation coefficient. The VTF trait had a negative correlation with the belly muscle-associated traits, including the BMR, RAM, and EAM . VTF and BFR had a positive correlation coefficient (0.63), whereas the meat quality traits without L had no correlation with the VTF . The belly trait BMR had a low positive correlation with the belly association traits . Additionally, L had a weakly negative correlation (−0.14). The BFR was negatively correlated with all three belly components in Section 7 , and the meat quality trait L had a weakly positive correlation (0.14). However, the correlation of BFR with pH45 and DL seemed to no significance.For the belly components in Section 7, all traits had weak to moderate positive correlation coefficients . The CTM trait had a weakly positive correlation with the meat quality trait DL (0.12). RAM had no correlation with the meat quality traits. Similarly, no correlations were identified between EAM and the meat quality traits. For the meat quality traits, pH45 had a weak negative correlation with the other traits . The L trait had a weak positive correlation with DL (0.25).The genetic correlations among the traits were estimated and are presented with the phenotypic correlations in For the belly traits, the BEW had a moderate to strong absolute correlation coefficient for almost every belly trait . The CTM was the only trait of the belly components in Section 7 that was positively correlated with BEW (0.28±0.20). The other traits had a poor to moderate correlation coefficient with the BEW, but they also had a higher standard error than their absolute coefficients. Positive correlations with the VB were found among the belly and belly components in Section 7. The correlations between VB and the VTF and BFR traits were above moderately positive . Moreover, VTM had above moderately positive correlation with the belly trait BMR and the belly components CTM, RAM, and EAM . Additionally, the VTM was positively correlated with the meat quality trait L (0.35±0.26). BFR (another belly trait) and the meat quality traits pH45 and DL had negative correlations with the meat quality trait L . The standard errors of the correlation between the VTM and the VTF were higher than their absolute correlation coefficients. The VTM had a moderate to high absolute correlation with the belly traits and the belly components in Section 7 . The BMR trait was above moderately positively correlated with the CTM, RAM, EAM, and L . On the other hand, there was no correlation between the BMR and the other traits. Negative genetic correlations with the BFR were found among three of the belly components in Section 7 . However, the BFR had no correlation with the meat quality traits.The CTM of the belly component in Section 7 and the RAM appeared to have a strong positive correlation coefficient (0.98±0.34), and the correlation coefficient between the CTM and EAM was moderately positive (0.44±0.21). The RAM also showed a strong positive correlation with the EAM (0.75±0.49). Therefore, RAM and pH45 exhibited a strong absolute correlation with the meat quality trait L . The EAM had no correlation with the meat quality traits.The meat quality parameter pH45 had a negative correlation with L (−0.55±0.35). Moreover, the standard error of the correlation coefficient between pH45 and DL was higher than the absolute coefficient, and L had a moderately positively correlation with DL (0.63±0.18).Pork belly is among the most demanded cuts in South Korea ,4,19 andh2 = 0.28) in Yorkshire pigs [h2 = 0.56) in Yorkshire pigs [The estimated heritability values for the carcass traits in this study ranged from 0.21 to 0.47. A previous study demonstrated that the heritability of the carcass traits CW and BFT of Yorkshire pigs were 0.21 and 0.54, respectively . These rire pigs . Howeverire pigs . In otheh2 = 0.33). Additionally, another study in South Korea previously reported a heritability of 0.44 [h2 = 0.67) [For the belly traits, the estimated heritability ranged from 0.15 to 0.42. Kang et al  previous of 0.44 . Moreove of 0.44  reported of 0.44 . However of 0.44 . However of 0.44  also est of 0.44  reported = 0.67) . Another = 0.67) . Additio = 0.67) . Therefo = 0.67) . TherefoThe heritability of the belly components of Section 7 were low to moderate in the present study. In a previous study, Section 7 was chosen as the representative parameter for the belly . InteresThe present study also estimated the heritability of meat quality traits as low to moderate in Yorkshire pigs. A previous study reported that meat quality traits had a low heritability in Jeju black pigs . The resIn this study, we also estimated the phenotypic correlations among the carcass, belly, and belly components in Section 7 and the meat quality traits. The correlation coefficients between the carcass and meat quality traits were estimated to be low. Low correlations were also previously reported in other studies ,35. HoweOther phenotypic correlation results between the belly and belly components in Section 7 in this study showed that the BEW and VB had a higher correlation coefficient with VTF than with VTM. Similar findings were also observed for the BMR and BFR. Fredeen  reportedIn the present study, we estimated the genetic correlations among traits, including the belly and belly components. The CW was found to be correlated with the LEA, BEW, VB, VTM, VTF, CTM, RAM, and EAM. Additionally, other carcass traits were genetically related to the belly and belly component traits. The genetic correlation between the CW and LEA is known to be genetically associated . MoreoveAmong the genetic correlations in the belly and belly components in Section 7, we observed that the BEW had a positive correlation with the VB, VTM, VTF, BFR, and CTM, whereas the BMR had a negative correlation. Given that all traits belonged to the same or similar category as the BEW, these traits had to be related. Most importantly, our findings indicated that the fat-related traits  were related to the increase in fat traits according to the increase in the CW or weight, which was consistent with the findings of early studies . In the Interestingly, we observed that the belly component traits were genetically related to economic traits and meat quality. Particularly, from a genetic perspective, the RAM of the belly component traits in Section 7 was strongly positively associated with the meat quality trait L, and negatively related to pH45. As mentioned above, the L trait has been previously linked to type I muscle fiber characteristics ,45,46. TIn this study, we estimated the genetic parameters of pork belly, carcass, and meat quality traits. The heritability among the belly and its components in Section 7 were found to be moderate. These results provide a basis for improving the quality of pork belly and its components to obtain a product with less fat and more muscle. Moreover, the RAM was genetically correlated with carcass traits and meat quality. Additionally, pork belly with less fat are generally less firm, which is an undesirable trait for meat processing. Therefore, only de soft fatty portions should be reduced rather than reducing too much fat. Taken together, our findings suggest that the muscle components in pork belly could be genetically enhanced through selective breeding without affecting the carcass traits. Furthermore, as these traits could be also related to the meat quality traits, it is possible to concurrently improve the meat quality.


Hybrid breeding is an established and effective process to improve offspring performance, while it is resource-intensive and time-consuming for the recurrent process in reality. To enable breeders and researchers to evaluate the effectiveness of competing decision-making strategies, we present a modular simulation framework for reciprocal recurrent selection-based hybrid breeding. Consisting of multiple modules such as heterotic separation, genomic prediction, and genomic selection, this simulation framework allows breeders to efficiently simulate the hybrid breeding process with multiple options of simulators and decision-making strategies. We also integrate the recently proposed concepts of transparent and opaque simulators into the framework in order to reflect the breeding process more realistically. Simulation results show the performance comparison among different breeding strategies under the two simulators. This phenomenon is known as heterosis. The concept was validated by some early recorded experiments . A numbeHybrid breeding attempts to take advantage of dominance effects  by breedOwing to the resource-intensive and time-consuming nature of the RRS process, it is challenging to design, validate, and compare algorithms for the many decisions to be made in RRS. As a result, it becomes important to use a simulation framework that can quickly and realistically simulate the process, as alluded to in In this paper, we performed comparisons among different breeding strategies under the simulation framework for RRS using transparent and opaque simulators. The concepts of transparent and opaque simulators were formally defined and formulated in 2The workflow of the RRS simulation framework is illustrated in As shown in 2.1Here, we define the notations used in this paper.We further define the indicator of accumulating additive effects at the and use Define the hidden genotypic information as For the phenotypic values The variance for the environmental effects is controlled by broad-sense heritability 2.2Genotyping is the process of obtaining genomic information, and phenotyping is the evaluation of traits of interest of plant individuals. We describe the simulation of genotyping and phenotyping steps using transparent and opaque simulators as follows.transparent simulator makes two major assumptions: (1) the whole genome of an individual contains no more information than what is revealed by the genetic markers and (2) the true genetic effects (jβ) for all markers are the same as the results from the genomic prediction. As such, the phenotypic value of individual * A conventionally used where opaque simulator, both assumptions made in transparent simulators are relaxed. A separate genotype is assumed to represent the ground truth genome, which is a superset of the observed genotype; the phenotypic value is determined by the whole genome, whose ground truth genetic effects are never revealed to the genomic prediction module. As such, the phenotypic value of individual * In the proposed where 2.3Genomic prediction is a technology that builds the quantitative relationships between phenotypic responses Bayesian predictor. We use the following Bayesian linear mixed model . GCA is designed to measure the average performances of test cross as evaluation over the row-wise (or column-wise) means of GEBV matrix * Specific combining ability (SCA). SCA is designed as the evaluation of the row-wise (or column-wise) maximum of GEBV matrix * 2.7After the identification of homogeneous candidates that have the satisfying ability to reasonably compensate with individuals from the other genetically different population, the breeders need to cross the candidates by certain mating designs to improve their current GEBVs. We use Adjacent. One direct way to produce progeny with high GEBV based on two superior parents, i.e., * Complementary. Consider the possible complementary desirable alleles from parents so that mating with inferior ones may produce progeny with even higher GEBV, i.e., * 2.8The last step of one breeding cycle is the conversion from heterozygous individuals to homozygous so that another cycle of hybrid breeding could be initialized. Doubled haploid, which is the replication of one gamete, and self-cross can both achieve the goal.33.1This paper uses the same dataset as  methods .To facilitate the simulation, we chose lgorithm . To obtaVectors for the additive effects, and We designed 24 experiments and conducted simulations based on these settings to test the performance of framework. The layout for experiment settings is shown in Step 1. Randomly choose 200 individuals from the total of 369 and arbitrarily separate them into heterotic pool Step 2. Let Step 3. Mutually cross the two new heterotic pools, and record and analyze the average GEBV of the top 100 hybrid offspring Step 4. Repeat Step 2 and Step 3 until the pre-specified cycle numbers are achieved. Here, we choose We actually presented the “ground truth” GEBVs for all the results since all the authentic genomic effects are assumed and the true GEBV reflects the true enhancement of breeding on genomic effects.3.2We presented average GEBVs for two parental populations and their hybrid children population during From Genetic gains of the children population 3.3In real-life production, the influence of the environment on plants cannot be ignored. We use broad-sense heritability The results showed that genetic gains of both the Bayesian predictor and the phenotypic predictor were amplified with increasing 4In this paper, we extended the concepts of transparent and opaque simulators to RRS-based hybrid breeding and compared the performances of various strategies for genomic prediction, genomic selection, and mating. In previous genomic prediction and selection models, researchers mostly assumed transparent simulators, in which the same set of markers were deployed in both phenotype simulation and genomic prediction. Recently, the concept of opaque simulators was defined in The framework also incorporates broad-sense heritability as an adjustment for environmental effects to bring it closer to reality. A sensitivity analysis was performed on the environmental effect This study is not without its limitations. For example, the proposed framework only considered dominance effects in the simulation modules, but did not explicitly incorporate epistatic effects or genotype-by-environment (G×E) interactions, which also play critical roles in the breeding process. The purpose of this omission was to avoid complex interactions between epistasis or G×E and opaque simulators. After observing the differences between the transparent and opaque simulators, a natural follow-up direction is to introduce epistasis and G×E to make the breeding simulator more realistic.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.LW and ZZ conceived the project and wrote the manuscript. ZZ coded the algorithms and performed the computational experiments. The authors are grateful to the editor and reviewers, whose feedback greatly improved the quality of this manuscript. All authors contributed to the article and approved the submitted version.


Maternal effects influence juvenile traits such as body weight and early growth in broilers. Ignoring significant maternal effects leads to reduced accuracy and inflated predicted breeding values. Including genetic and environmental direct-maternal covariances into prediction models in broilers can increase the accuracy and limit inflation of predicted breeding values better than simply adding maternal effects to the model. To test this hypothesis, we applied a model accounting for direct-maternal genetic covariance and direct-maternal environmental covariance to estimate breeding values.This model, and simplified versions of it, were tested using simulated broiler populations and then was applied to a large broiler population for validation. The real population analyzed consisted of a commercial line of broilers, for which body weight at a common slaughter age was recorded for 41 selection rounds. The direct-maternal genetic covariance was negative whereas the direct-maternal environmental covariance was positive. Simulated populations were created to mimic the real population. The predictive ability of the models was assessed by cross-validation, where the validation birds were all from the last five selection rounds. Accuracy of prediction was defined as the correlation between the predicted breeding values estimated without the phenotypic records of the validation population and a predictor. The predictors were the breeding values estimated using all the phenotypic information and the phenotypes corrected for the fixed effects, and for the simulated data, the true breeding values. In the real data, adding the environmental covariance, with or without also adding the genetic covariance, increased the accuracy, or reduced deflation of breeding values compared with a model not including dam–offspring covariance. Nevertheless, in the simulated data, reduction in the inflation of breeding values was possible and was associated with a gain in accuracy of up to 6% compared with a model not including both forms of direct-maternal covariance.In this paper, we propose a simple approach to estimate the environmental direct-maternal covariance using standard software for REML analysis. The genetic covariance between dam and offspring was negative whereas the corresponding environmental covariance was positive. Considering both covariances in models for genetic evaluation increased the accuracy of predicted breeding values.The online version contains supplementary material available at 10.1186/s12711-023-00829-8. Juvenile traits are influenced by maternal effects in many species –3. The mmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimi. j. i.Fitting a model that considers the direct-maternal genetic covariance ) using common breeding software is straightforward, and thus, the relation between the direct additive genetic effect and the maternal genetic effect has been widely discussed and investigated for decades , 2, 6, 7To determine which model has the best fit to the data, the likelihood ratio test (LRT) is often used, and to evaluate the accuracy of prediction of breeding values, cross-validation (CV) is a common strategy. A common CV strategy is to train the models on a subset of the data, the training dataset, and then validate it on another subset, the validation dataset. A way to do this in animal breeding is to mask the phenotypes for some of the individuals, often the last few generations, and their breeding values are predicted based on the information of their relatives in the training population. Then, the accuracy of the prediction of breeding values based on pedigree information only (before the phenotypic record is obtained) is estimated as the correlation between the predicted breeding values and the phenotype corrected for fixed effects.Legarra and Reverter  proposedThe accuracy of the estimates of the maternal genetic variance and of the covariance between direct and maternal genetic effects depends highly on the data structure, the number of dams having their own performance and on the number of progeny per dam , 15. BroThe aims of this paper were to: 1) propose an extension to common standard REML methods to estimate the environmental direct-maternal covariance, by applying models that are similar to that proposed by Koerhuis and Thompson [ propose The commercial broiler data were provided by Cobb-Vantress Inc. , and the trait analyzed was BW measured in g at a fixed age after birds were hatched. The pedigree consisted of 407,473 birds, and phenotypic records were collected on 358,196 birds from 41 selection rounds (SR). At each SR, parents were selected based on their breeding values estimated with a pedigree-best linear unbiased prediction (BLUP) model. Individuals were mated following a hierarchical mating system where one dam is mated to only one sire, whereas a sire is mated to multiple dams. Table The animal model for traits that are affected by maternal effects was developed by Willham in 1963 . This moIn broilers, sexual dimorphism affects the variances of BW, thus we considered that the BW in females and males were different but correlated traits. For the real data, five bivariate models were tested. The general model was defined as:The first model, “Moda,” ignores the genetic maternal variance and assumes that both the direct-maternal genetic and environmental covariances are null.Note that the residuals for the males were independent of the residuals for the females since they belong to records on different individuals. This model was included because it has been the commonly used model in several broiler-breeding programs.For each of the following models, different assumptions on the variance–covariance structure were made as presented below. The second model, “Modam,” assumes that the genetic and environmental covariances are null, but a maternal genetic effect is included in the model.The third model, the “Coram model,” assumes that the genetic covariance is different from 0 and is the commonly used model to study direct and maternal effects in many species , 3.\docuThe fourth model, the “Corepe model,” assumes that the environmental covariance between dam BW and the permanent effects of the dam on offspring is different from zero and that the genetic covariance between maternal and direct genetic effects is zero.Note that an environmental covariance exists between the residual of the dam and her subsequent maternal environmental effect on both females and males  and predictors, which are described below. Variance components were always estimated using the full dataset . Before computing the correlations and inflations, all the estimated breeding values were corrected for genetic trend. This was done using a multivariate analysis of the variance approach. Thus, the variances and covariances conditional on the genetic trend were used to estimate the correlation, and inflation was corrected for the effect of SR.reduced and in the second case, as EBVfull.We divided our data into two subsets: a training dataset that consisted of the first SR and a validation dataset that included the population of the last five SR. The breeding values of the individuals of the validation population were predicted using information on past relatives only, and thus their own phenotypes were masked (denoted as reduced dataset), and using all the phenotypes, thus their own performance was included (denoted as full dataset). In the first case, we will refer to the breeding values as EBVreduced) and (1) the phenotype corrected for the fixed effect (yc) and (2) the breeding values estimated including their phenotypes (EBVfull). In the following sections, these correlations will be referred to as “cor” and “cor,” respectively. The “cor” corresponds to the method proposed by Legarra and Reverter [The accuracy of prediction for each model was computed as the correlation between the breeding values of the individuals estimated without their phenotypes  the phenotype corrected for fixed effect (yc), which will be referred to as “yc ~ EBVreduced” and (2) the breeding values estimated using the full datasets (EBVfull) as proposed by Legarra and Reverter [full ~ EBVreduced.Inflation of predicted breeding values is an important component when comparing models for genetic evaluation because it affects the comparison of birds that are affected differently by inflation. Here, the inflation of breeding values, sometimes incorrectly called bias, was estimated as the slope of the regression of the response on the predictor. When the breeding values are neither inflated nor deflated, the expected value of this slope is 1. The predictor used to estimate inflation was in all cases the EBVReverter , which wThis strategy follows the proposal of Chu et al.  to assesreducedHS of its paternal half-sib yc, EBVreducedHS)”. Compared with the cor, described in the “yc and EBVreduced belonged to the same individuals, here the yc belonged to paired paternal half-sibs.Within the validation population, paternal half-sibs were randomly paired. For the real data, because performances in males and females were considered as different traits, the pairing of the half-sibs was done within each sex. Thus, 5267 pairs of paternal half-sisters and 5147 pairs of paternal half-brothers were created, which were the maximal number of pairs possible. The sampling of pairs was performed 50 times. Here, the predictive ability of the model was defined as the correlation between the phenotype corrected for the fixed effects of an individual reduced)) was also computed, and this is considered to be the most accurate test. In the same way, a third test was added for the inflation of the breeding value. This test corresponds to the slope of the regression of the breeding values of the individuals estimated without their phenotypes on the TBV, which will be referred to as TBV ~ EBVreduced. In the simulation, BW was modeled as being the same trait in males and females. Therefore, the paternal half-sibs pairs were done across sex resulting in 10,855 pairs. Moreover, for the simulation study, the reported accuracy of prediction and inflation of breeding values are the average of 15 replicates.The same tests as described above were used to assess the predictive ability of the models in the simulated populations. However, in this case, the true breeding values (TBV) were known. Therefore, to assess the accuracy of the prediction of the models, the correlation between the breeding values of the individuals estimated without their phenotypes and the TBV variance components estimated using the real dataset for each of the five models are presented in Table Adding genetic (the Coram model) or environmental (the Corepe model) covariance to the model led to an increase in the estimates of both the genetic and environmental maternal effects. This increase was greater when both covariance components were added to the model (the Coramepe model).Adding environmental covariance led to a decrease in the estimated additive genetic variance and an increase in the estimated environmental variance, which is reflected by a decrease in the estimate of the heritability . This decrease in the estimate of the heritability was even greater when the genetic covariance was added to the model. In contrast, adding the genetic covariance only led to an increase in the estimate of the additive genetic variance, which is reflected by an increase in the estimate of the heritability.It is interesting to note that ignoring the direct-maternal environmental covariance seems to lead to an estimate of direct-maternal covariance that is less negative. These findings highlight the changes in the definition of the direct additive genetic variance using different models. If the direct-maternal genetic or environmental covariances are non-null, then ignoring them may induce bias in the estimation of variance components and in the prediction of breeding values.yc, EBVreduced) and Halfsibs cor, relied on the correlation between the breeding values estimated for the individuals of the validation population using the training dataset (EBVreduced) and the phenotypes corrected for the fixed effects (yc). The third test, cor is the correlation between the breeding values estimated with the full dataset (EBVfull) and the breeding value estimated using the reduced dataset (EBVreduced).The observed accuracies of predicted breeding values in the real data using three different tests are in Table yc, EBVreduced), ranged from 0.192 to 0.216 in females and from 0.230 to 0.248 in males , ranged from 0.195 to 0.197 in females and from 0.202 to 0.205 in males , ranged from 0.587 to 0.618 in females and from 0.659 to 0.680 in males . However, if the cor is used, then the Corepe model  yields the best accuracy. Finally, if the test used is Halfsibs cor, then the Modam model  results in the highest predictive ability. Regarding the inflation of the breeding values, when the corrected phenotype was considered as the response, the best model was Moda, which does not include a maternal genetic effect and no direct-maternal covariance. In contrast, when the breeding values estimated using the full dataset are considered as the response, the Coramepe model  was the best model. Traditionally, the predictive ability of the models is estimated using cor or cor, but in the case of a trait influenced by maternal effects, it seems that the conclusions will differ depending on the tests used. In terms of inflation of breeding values, it will even lead to opposite conclusions. Because of the conflicting results, a simulation study was conducted to understand why the different tests led to such different rankings of the models. The results of the simulation study are presented in the following section.To summarize, depending on the test used, the models rank differently. If we focus only on the accuracy of prediction of the models, the Moda model  will be the best model if the test used is corvariance components estimated with the true prediction model, denoted as the Coramepe model, on the full dataset, after 40 SR were similar to the one used to simulate the population Table .With the Moda model, which did not include a maternal genetic effect, the additive genetic variance was overestimated compared with the true direct additive genetic variance. In fact, the additive genetic variance in this case was almost equal to the sum of the true direct additive genetic variance and the true maternal genetic variance.When ignoring both direct-maternal genetic and environmental covariances (Modam model), the maternal genetic variance and the permanent environmental maternal were halved compared with the true variance components.When adding the direct-maternal genetic covariance (Coram model), the direct additive genetic variance was highly overestimated, whereas the residual variance was underestimated compared with the true variance components.When adding the direct-maternal environmental covariance (Corepe model), the additive genetic variance was highly underestimated, whereas the residual variance was overestimated compared with the true variance components.reduced) of an animal from the validation population was correlated with the TBV ). In the second test, the EBVreduced of birds from the validation population was correlated with the phenotype corrected for the fixed effect ). In the third test, the EBVreduced of one animal from the validation population was correlated with the phenotype corrected for the fixed effect of one of its paternal half-sibs from the validation population ). Finally, in the last test, the EBVreduced of birds from the validation population estimated without including their phenotype was correlated with the EBVfull estimated using their own phenotype ). The four different correlations cannot be compared directly, however, the ranking of the models can.Accuracies of prediction in the simulated dataset using four different tests are in Table reduced), accuracies of prediction ranged from 0.352 to 0.374 , accuracies of prediction ranged from 0.154 to 0.162, with the Coramepe model resulting in the lowest accuracy and the standard model resulting in the highest accuracy , accuracies of prediction ranged from 0.203 to 0.211. The Coramepe model resulted in the highest accuracy and the Moda model in the lowest accuracy , the accuracies of prediction ranged from 0.596 to 0.628. Here again, the Coramepe model resulted in the highest accuracy and the standard model in the lowest accuracy , which is the most accurate test, Halfsibs cor and cor were identical. However using the cor as is commonly done in animal breeding leads to a biased ranking of models in terms of prediction accuracy.Overall, the ranking of the models using cor observed here is not due to a negative direct-maternal environmental covariance that was ignored in some of the models. The main consequence of this negative Cor would be that although the dams were selected for their higher genetic potential which is transmitted to offspring, it will have a negative impact on the maternal component of the genetic potential of their offspring. This negative direct-maternal genetic covariance may be explained by the fact that broilers are selected mainly on production traits, including their growth potential. It is well known that a high-level of production can have a negative effect on fitness and therefore on reproduction ability , 22. Basyc might be biased in the same direction by maternal effects, making validation of the model difficult. Based on the variance components, we can see that when the model changes, the definition of the predicted additive genetic effects will change. When the maternal genetic effect is ignored then the direct additive genetic effects capture part of these missing effects. Thus, the EBV obtained from the Moda model is different from that obtained with the Coramepe model. Indeed, with this model, the direct additive genetic effect did not capture some parts of maternal effects and of the direct-maternal covariance. Thus, because we compared models that change the definition of the additive genetic effects, the models are not compared on the same scale. For example, with the Moda model, which did not include any maternal genetic effect, the additive genetic variance was affected by the maternal genetic variance, when estimated from simulated data with a non-null maternal genetic effect. Thus, the EBV used to compute the accuracy is heavily biased by the maternal effects and the maternal effects are also included in yc making the CV based on cor problematic. In contrast, with the Coramepe model, it is possible to disentangle quite accurately the different elements. Thus, in this case, the EBV is not biased by the maternal additive genetic effect. Using the half-sib correlation, we were able to rank the models correctly in the simulation study although the differences were small. Thus, the power of this test for model comparisons is limited. The same is true for the correlation between the EBVfull and EBVreduced. The difference in predictive ability between the models was small. Nevertheless, when comparing models that might change the interpretation of the additive genetic or other random effects in the model, our simulation study highlighted the need to design the CV strategy carefully to be able to compare those models on the same base. Dividing the correlations by an adjusted heritability estimate based on the trait definition might provide different results; however, this can make their interpretation more complex because heritability estimates from mis-specified models might also be biased.In the presence of maternal effects, it is necessary to adopt a specific strategy of cross-validation , 12, 13.In the simulation, accounting for direct-maternal genetic and/or environmental covariance led to breeding values with no significant inflation and was associated with a gain in accuracy compared with Moda. The main advantage of the models that include a direct-maternal genetic and/or a environmental covariance is, as expected, that they control the inflation of breeding values. It seems that environmental covariance has the biggest impact on the reduction of inflation of breeding values because adding the genetic covariance does not lead to further gain. In fact, adding the genetic covariance to the prediction model led to a slightly greater inflation of the breeding values, but it was not significant. This is because the permanent environmental effect is associated with the residual, that is, the components with the largest variance in all models.Surprisingly, with real data, the LRT indicated a better fit using the Coramepe model, but the prediction of breeding values with this model led neither to an observable greater accuracy nor to a decrease in the inflation of breeding values compared with the Modam model. Because the LRT assesses the fit of a model with the given dataset, it does not assess the fit of this model to a new dataset as opposed to the CV strategy. Therefore, the Coramepe model is the model that fits our training population best, however, we are not able to confirm its superiority in the validation population. As discussed in the previous section, this might be because of the statistical test we used. However, we did not observe a reduction in the deflation of the breeding values. This absence of change in deflation might be due to the heterogeneous residual variance, which was neither simulated nor modeled, but which might be present, especially in the latest SR that constituted our validation population. Further model development, to account for residual heterogeneous variance and environmental covariance between the dam and its offspring is needed. In addition, other unknown events that might have occurred in the real data, were not considered. Identification of such effects could have resulted in a validation population that differed from the training population because of changes in diet or age at which BW was measured. Indeed, we observed that the estimates of some of the fixed effects presented a trend over SR and such a trend was also not considered in our simulation. A simpler explanation for this absence of gain in accuracy and reduction of deflation/inflation of the breeding values when using the complete model might be that in our population, environmental and direct-maternal genetic covariances are not important enough to impact the prediction of breeding values. Applying our method to other populations might help to investigate the impact of adding environmental and direct-maternal genetic covariances in other populations of broilers but also in different species.Considering the direct-maternal environmental covariance in prediction models led to a gain in prediction accuracy and a reduction of the inflation of the breeding values in the simulation. Thus, this may contribute to increasing genetic gain in the breeding program. Because of the negative direct-maternal genetic covariance, it might be interesting to select the dams not only on their direct additive genetic potential but also on their maternal genetic effect. In fact, with the Moda model, which does not consider the maternal genetic effect, the estimated breeding values capture a part of the maternal genetic effect, so that the individuals are already based on a composite EBV. Although the breeding values estimated with the different models were highly correlated (> 0.9), re-ranking among the candidates were observed (data not shown). Thus, depending on the model used to estimate the breeding values, different individuals will be selected to produce the next generation. We can also speculate on the consequences of ignoring the direct-maternal covariances for the breeding program, if these, in reality, are not significantly different from zero. To check the possible consequences of over-specified models, an extra simulation study was conducted for which the only difference compared to the simulation described in the "full and EBVreduced seem to be promising but we were not able to completely confirm the results obtained with the simulation studies when applied to real data, which indicates that there might be other unknown factors that influence BW and are not included in our analysis. In the simulation, we showed that when the direct-maternal environmental covariance is not null its consideration in model prediction will increase the accuracy of prediction and reduce inflation of breeding values, compared with a model that ignores it. Over-specifying the model by including covariances when in reality they did not exist did not lead to loss of accuracy. Therefore, implementing a model that includes the direct-maternal genetic and the direct-maternal environmental effects may lead to improved genetic gain, without impacting computation time in the routine genetic evaluations.In this paper, we demonstrate a straightforward way to estimate the direct-maternal environmental correlation using standard software and to use it in models for the prediction of breeding values. This can be implemented without significant increases in computing time. The direct-maternal environmental correlation was positive while the direct-maternal genetic correlation was negative. Standard CV methods do not rank models correctly in the presence of maternal effects that are not included in models for genetic evaluation. The half-sib correlation and the correlation between EBVAdditional file 1: Table S1. Variance components estimated with Modam and Coramepe models and with the true model. Table S2. Predictive ability of the Coramepe and Modam models in the absence of genetic and environmental direct-maternal covariance. Additional file Additional file 2: Table S3. Computational time and convergence criteria for the five models using real data. This document provides details on the convergence criteria used and on the computational time needed for the five models.


Given the economic relevance of fertility and reproductive traits for the beef cattle industry, investigating their genetic background and developing effective breeding strategies are paramount. Considering their late and sex-dependent phenotypic expression, genomic information can contribute to speed up the rates of genetic progress per year. In this context, the main objectives of this study were to estimate variance components and genetic parameters, including heritability and genetic correlations, for fertility, female precocity, and semen production and quality  traits in Nellore cattle incorporating genomic information.The heritability estimates of semen quality traits were low-to-moderate, while moderate-to-high estimates were observed for semen morphological traits. The heritability of semen defects ranged from low (0.04 for minor semen defects) to moderate . For seminal aspect (SMN_ASPC) and bull reproductive fitness (BULL_FIT), low (0.19) and high (0.69) heritabilities were observed, respectively. The heritability estimates for female reproductive traits ranged from 0.16 to 0.39 for rebreeding of precocious females (REBA) and probability of pregnancy at 14 months (PP14), respectively. Semen quality traits were highly genetically correlated among themselves. Moderate-to-high genetic correlations were observed between the ability to remain productive in the herd until four years of age  and the other reproductive traits, indicating that selection for female reproductive performance will indirectly contribute to increasing fertility rates. High genetic correlations between BULL_FIT and female reproductive traits related to precocity (REBA and PP14) and STAY were observed. The genetic correlations between semen quality and spermatic morphology with female reproductive traits ranged from -0.22  to 0.48 (REBA and sperm vigor). In addition, the genetic correlations between REBA with semen quality traits ranged from -0.23 to 0.48, and with the spermatic morphology traits it ranged from -0.22 to 0.19.All male and female fertility and reproduction traits evaluated are heritable and can be improved through direct genetic or genomic selection. Selection for better sperm quality will positively influence the fertility and precocity of Nellore females. The findings of this study will serve as background information for designing breeding programs for genetically improving semen production and quality and reproductive performance in Nellore cattle.The online version contains supplementary material available at 10.1186/s12864-023-09216-5. Fertility and reproductive performance directly impact the beef cattle industry's profitability . NumerouBos taurus taurus) breeds without incorporating genomic information. Furthermore, there is a lack of estimates of genetic correlations between male and female fertility and reproduction traits, especially in Zebu (Bos taurus indicus) breeds such as Nellore, one of the most important beef cattle breeds in Brazil (one of the largest beef cattle producers in the world) , where: r = radius (testicular width); π = correction factor (3.14); and L = testicular length [The testicular format (TF) was defined as long-moderate, long-ovoid, ovoid-spherical, and spherical , 71 The r length .A drop of semen from each ejaculate was placed on a slide, previously heated to 37 °C, for observation of TURB  under an optical microscope at 100 × magnification. An additional drop of semen was placed between the slide and coverslip, previously heated to 37ºC, to evaluate MOT (expressed in percentage) and VIG , with an increase of 100 to 400 times. Part of the ejaculate aliquot was utilized to evaluate SMN_ASPC with four classes: creamy (1), milky (2), watery (3), and opalescent (4). An aliquot from the ejaculate was diluted in buffered saline formaldehyde solution and utilized in the sperm morphology analyses .In phase-contrast microscopy, the sperm pathology was evaluated in moist preparations, between a slide and coverslip, at a magnification of 1,250 times under an immersion objective lens. In each sample, 400 cells were evaluated, and the percentage of normal sperm and anomalies of the acrosome, head, intermediate piece, and tail were determined . Later, Contemporary groups (CGs) were created considering animals born on the same farm, year and season of birth, of the same sex, and belonging to the same management group, for both, male and female traits, except for SC and PP14 in which management group was an uncorrelated random effect. Additionally, only records within ± 3.5 standard deviations from the CG mean were maintained for further analyses. CG with less than five animals and without phenotypic variability were excluded. Any CG with progeny from less than two bulls or formed by animals with unknown pedigree was also removed.A total of 7,975 animals were genotyped using the GeneSeek SNP Beadchip Bovine GGP-HDi 50 K . The quality control (QC) was performed using the PREGSf90 package , duplicaA−1) is replaced by the H−1 matrix [H is a combined relationship matrix of genotyped and non-genotyped individuals; A is the pedigree-based relationship matrix; A22 is a numerator relationship matrix for the genotyped animals, and G is the genomic relationship matrix calculated as [Z is the matrix containing adjustments for allelic frequencies. These factors are fitted to ensure that the mean diagonal of G is close to A22 [The single-step genomic BLUP (ssGBLUP) procedure based on Bayesian inference was used for all traits. The ssGBLUP is a modification of the BLUP model, where the inverse of the pedigree relationship matrix ; R is a residual (co)variance matrix; I is an identity matrix of proper order; Ga is the additive genetic (co)variance matrix, and ⨂ is the Kronecker product. For the systematic effects, a uniform a priori distribution was defined. A single Markov Chain Monte Carlo (MCMC) was generated with 800,000 samples, and the first 200,000 samples were discarded as burn-in. The remaining samples were saved in a range of 100 samples. Consequently, the inferences were made based on 6,000 samples from the posterior distribution of the parameters. The convergence was evaluated using the Geweke test [For the categorical traits, a priori, the distributions of vectors H matrix. For the genetic correlations, the Gibbs sampling method was applied using the software GIBBS1F90 or THRGIBBS1F90 [A threshold animal model considering the Probit link function was used to estimate the variance components and genetic parameters for the categorical traits using the BLUPf90 + family programs . Alpha  of REB: All records of rebreeding of females; REBB: Rebreeding of females that entered reproduction at two years old; REBA: Rebreeding of precocity heifers; IR: Interval of rest (in days); N: Number of animals in each class.


Glycine max) productivity is significantly reduced by drought stress. Breeders are aiming to improve soybean grain yields both under well-watered (WW) and drought-stressed (DS) conditions, however, little is known about the genetic architecture of yield-related traits. Here, a panel of 188 soybean germplasm was used in a genome wide association study (GWAS) to identify single nucleotide polymorphism (SNP) markers linked to yield-related traits including pod number per plant (PN), biomass per plant (BM) and seed weight per plant (SW). The SLAF-seq genotyping was conducted on the population and three phenotype traits were examined in WW and DS conditions in four environments. Based on best linear unbiased prediction (BLUP) data and individual environmental analyses, 39 SNPs were significantly associated with three soybean traits under two conditions, which were tagged to 26 genomic regions by linkage disequilibrium (LD) analysis. Of these, six QTLs qPN-WW19.1, qPN-DS8.8, qBM-WW1, qBM-DS17.4, qSW-WW4 and qSW-DS8 were identified controlling PN, BM and SW of soybean. There were larger proportions of favorable haplotypes for locus qPN-WW19.1 and qSW-WW4 rather than qBM-WW1, qBM-DS17.4, qPN-DS8.8 and qSW-DS8 in both landraces and improved cultivars. In addition, several putative candidate genes such as Glyma.19G211300, Glyma.17G057100 and Glyma.04G124800, encoding E3 ubiquitin-protein ligase BAH1, WRKY transcription factor 11 and protein zinc induced facilitator-like 1, respectively, were predicted. We propose that the further exploration of these locus will facilitate accelerating breeding for high-yield soybean cultivars.Soybean ( The climate of this site is a typical semi-arid continental climate with an annual temperature and rainfall of 7.7°C and 450-550 mm, respectively. Three replicates were performed under WW and DS conditions in a randomized block design. Each plot consisted of two rows, 0.6 m apart that were 2 m in length, and the planting density was 165,000 plants per ha. The water supply of WW condition was delivered by drip irrigation, while that of DS treatment was delivered by natural precipitation.The pot-culture experiments were conducted under open field conditions at Liaoning Academy of Agricultural Sciences, Shenyang , Liaoning Province, China, in 2020 and 2021 cropping seasons (hereafter referred as SY2020 and SY2021). Soybean seeds were planted in plastic pots (30 cm × 30 cm × 25 cm) with 16.0 kg soil. In a randomized block design, three replications (pots) contained three plants each. The DS treatment was carried out throughout the flowering and podding periods of soybean. Soil moisture content was maintained at 80% of the field’s capacity to hold water under WW conditions, whereas it was 60% under water stress conditions. We measured the soil water content every three days and replenished it as needed.2.2Data of three yield-related traits were collected at maturity (R8). In field trials (FX2018 and FX2019), a random sample of 10 plants from each plot were used to determine the yield-related traits, including pod number per plant (PN), biomass per plant (BM) and seed weight per plant (SW). In pot-culture experiments (SY2020 and SY2021), three plants of each pot were used to measure the above traits.http://www.R-project.org/) to reduce environmental variation  . Adenine was added to the 3’ end of the digested fragments, and the Dual-index was used to distinguish raw sequencing data from digested fragments (Oryza sativa L. ssp. japonica cv. Nipponbare) libraries (http://rice.plantbiology.msu.edu/), which were constructed and sequenced using the same procedures.Using a modified CTAB method, DNA from leaves of about 60 d after germination was extracted . SLAF-seragments . We obtaragments . Followihttp://bio-bwa.sourceforge.net/)  . In ordege.net/) . In addige.net/)  were sel2.4Admixture software was used to generate admixture ratios for K values 1-10 by analyzing population structure 1000 times. Using the valley value of cross-validation error rates, the optimal number of subgroups was determined according to cluster results . Taxonom2.5p-value was 0.05/67 929 . For simplicity, p<7.36E-07 was used as the threshold value. Manhattan plots were used to visualize significant markers, and quantile-quantile (Q-Q) plots to show important p-value distributions .A general linear model (GLM) was used for each SNP and trait to test for association between them using TASSEL 5.0. The GLM is based on P + Q matrices, where P is the phenotype matrix and Q is the population structure matrix. The statistical model for the GLM is: y = Xb + e. In this case, y is the data of individual environment or adjusted BLUPs for each trait, X is the known design matrix, b is the fixed effects vector, and e is the random residues vector. A 1000-permutation test was run for the GLM analyses. The Bonferroni-corrected threshold for the 2.6www.soybase.org) to identify genes underlying QTLs of interest.Based on the GWAS results, pairwise linkage disequilibrium measures were calculated between SNPs in the genomic regions containing significant SNPs. A QTL interval was defined as one where the squared allele frequency correlation between markers was higher than 0.4. We scanned the genome regions in Soybase  and the BLUP data for these traits was calculated. The PN, BM and SW under WW and DS conditions exhibited normal distribution, which was basically the same in the four environments as well as the BLUP data Figure 13.22 = 0.3, the average decay distance of LD was 178.7 kb for all 188 soybean accessions , we searched for genes associated with yield-related traits and drought tolerance in QTL regions detected under WW and DS conditions, respectively  at 178.7 kb, and LD contained a number of significant SNPs, suggesting that GWAS can be used to identify significant markers-trait associations  controlling PN , the prect yield , such asct yield , ZmGW2 ict yield , and TaGct yield .Glyma.17G057100, encoding WRKY transcription factor 11, was predicted here as the putative candidate gene. WRKY transcription factors participate in various physiological and developmental processes  controlling shoot dry weight in soybean . Within rocesses , such asrocesses , seed dorocesses , senescerocesses , and devrocesses . Plant hrocesses , jasmonirocesses  and gibbrocesses , are sigrocesses  and soybrocesses .Glyma.04G124800, encoding Protein Zinc induced facilitator-like 1, was predicted here as the putative candidate gene. Due to their specialized role in phytosiderophores efflux and auxin homeostasis, a subset of the zinc-induced facilitators are also proven to impart tolerance to micronutrient deficiencies. In the case of Zn deficiency, crop yield is affected  that evaluated for the SW for a population grown in a low phosphorus environment . Within affected , while Faffected . ZIFL gerophores Glyma.08G020900, encoding ethylene-responsive transcription factor CRF2, was predicted here as the putative candidate gene. In many species, members of the AP2/ERF superfamily regulate flower and seed development, and thus play a critical role in regulating seed weight and further controlling seed yield  analysis. Six locus could play a key role in determining PN, BM and SW of soybean. The favorable haplotypes for locus qPN-WW19.1 and qSW-WW4 had experienced strong selection during historical yield improvement, while those for qBM-WW1, qBM-DS17.4, qPN-DS8.8 and qSW-DS8 had not been fully utilized, especially for drought tolerant soybean breeding. It was believed that the superior haplotypes for these loci should be integrated to improve yield-related traits. As a result of this study, a better understanding of the genetic architecture driving high yields will be gained and the foundation for marker-assisted breeding will be laid in soybean.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI SRA database accession number PRJNA1014913.SL: Data curation, Funding acquisition, Investigation, Project administration, Writing- original draft, Writing- review & editing. YC: Investigation, Formal analysis, Writing- review & editing. CW: Investigation, Methodology, Writing- review & editing. CY: Investigation, Data curation, Writing- review & editing. XS: Investigation, Methodology, Writing- review & editing. LZ: Data curation, Investigation, Writing- review & editing. WW: Project administration, Writing- review & editing. SS: Project administration, Writing- review & editing.


The genomic estimated breeding value (GEBV) using data from Brangus heifers were obtained from genomic selection (GS) methods associating the single nucleotide polymorphisms (SNP) marker genotypes with phenotypic data for economically important growth  and carcass  traits using the linkage disequilibrium (LD) between SNP markers and quantitative trait loci (QTL) and/or the genomic relationship between animals. The heritability estimates were found similar across genomic best linear unbiased prediction (the GBLUP), and the Bayesian  GS methods for k-means and random cluster. The Bayesian methods resulted in underestimates of heritabilities and overestimates of accuracy of GEBV. However, the GBLUP method resulted in more reasonable estimates of heritabilities and accuracies of GEBV for growth and carcass traits of heifers from a composite population.The predictive abilities and accuracies of genomic best linear unbiased prediction (GBLUP) and the Bayesian  genomic selection (GS) methods for economically important growth  and carcass  traits were characterized by estimating the linkage disequilibrium (LD) structure in Brangus heifers using single nucleotide polymorphisms (SNP) markers. Sharp declines in LD were observed as distance among SNP markers increased. The application of the GBLUP and the Bayesian methods to obtain the GEBV for growth and carcass traits within k-means and random clusters showed that k-means and random clustering had quite similar heritability estimates, but the Bayesian methods resulted in the lower estimates of heritability between 0.06 and 0.21 for growth and carcass traits compared with those between 0.21 and 0.35 from the GBLUP methodologies. Although the prediction ability of the GBLUP and the Bayesian methods were quite similar for growth and carcass traits, the Bayesian methods overestimated the accuracies of GEBV because of the lower estimates of heritability of growth and carcass traits. However, GBLUP resulted in accuracy of GEBV for growth and carcass traits that parallels previous reports. The availability of high-density SNP genotypes from high-throughput genotyping technologies ,2,3,4 ann = 54,001 SNP).The objectives of this research were to characterize LD structure of Brangus heifers and to compare the predictive ability and accuracy of the GBLUP and the Bayesian methods for economically important growth  and carcass  traits using BovineSNP50 Infinium BeadChip SNP markers (Birth weight (BW), weaning weight (WW), and yearling weight (YW) were phenotypes for growth traits, and depth of rib fat (FAT), percent intramuscular fat (IMF), and longissimus muscle area (LMA) were phenotypes for carcass traits from yearling ultrasound evaluation. Phenotypes were collected from 738 Brangus heifers that were registered with International Brangus Breeders Association ,22,23. YBovineSNP50 Infinium BeadChips for 54,001 SNP markers were used to genotype each heifer . Genotyppairwise LD function of the Synbreed package in the R program [The success of GS and genome-wide association studies (GWAS) are dependent on LD, which is a non-random association among SNP markers. LD is measured using the square of correlation ) in the R program [The BGLR package ) in the R program [The Bayesian  methods were applied to estimate the SNP effects for genomic prediction using cross-validation datasets of BW, WW, YW, LMA, IMF, and FAT. The cross-validation data of BW, WW, YW, LMA, IMF, and FAT were modeled as a function of the individual SNP effects:y=Xb+Mm+ey=Xb+Mm+eo effect:mi|π,σmi2y=Xb+Mm+eo effect:mi|π,σmi2o effect:mi|π,σmi2of (1−π) . The veco effect:mi|π,σmi2 program  was usedGeneticsPed package in the R-program [factoextra package in the R-program [The animals for cross-validation were divided into 10-fold data sets by using the k-means clustering approach. K-means clustering maximizes genetic relatedness within each cross-validation set and minimizes it between cross-validation datasets based on the genetic dissimilarity  matrix :(7)dij=1-program  was used-program  implemen-program  algorithFor random clustering, the animals were randomly divided into 10-fold datasets for cross-validation, and this procedure was replicated five times.The training process in the 10-fold cross-validation from k-means and random clustering approaches was performed by excluding one validation set to train on the remaining nine validation sets, and then the GEBV of animas in the omitted validation set were obtained. The predictive ability of the GBLUP and the Bayesian BayesA, BayesB, BayesC, and Lasso methods in the 10-fold datasets for cross-validation were determined using Pearson’s correlation coefficient (The accuracy of datasets :(8)rBV,GWe retained 35,351 SNP after filtering markers based on the quality-control criteria. The distribution and density plots of SNP markers per chromosome are presented in The density plot of SNP markers in Pairwise, LD between 35,351 SNP markers were assessed using the squared correlation  and carcass  traits from 10-fold k-means and random cluster training datasets were obtained from the GBLUP and the Bayesian  methods. In the analyses of growth traits, the estimates of heritabilities across 10-fold k-means (random) cluster training datasets ranged between 0.20 (0.20) to 0.26 (0.30) from the GBLUP and between 0.05 (0.04) to 0.16 (0.16) from the Bayesian methods for BW; between 0.19 (0.17) to 0.24 (0.24) from the GBLUP; between 0.02 (0.02) to 0.13 (0.13) from the Bayesian methods for WW; between 0.30 (0.31) to 0.36 (0.36) from the GBLUP; and between 0.09 (0.08) to 0.22 (0.29) from the Bayesian methods for YW. In the analyses of carcass traits, the estimates of heritabilities across 10-fold k-means (random) cluster training datasets ranged between 0.28 (0.28) to 0.34 (0.33) from the GBLUP and between 0.09 (0.07) to 0.19 (0.19) from the Bayesian methods for FAT; between 0.31 (0.30) to 0.37 (0.37) from the GBLUP, and between 0.15 (0.14) to 0.25 (0.26) from the Bayesian methods for IMF; and between 0.30 (0.32) to 0.38 (0.42) from the GBLUP, and between 0.11 (0.11) to 0.23 (0.29) from the Bayesian methods for LMA . OverallFor the carcass traits, overall mean (±standard deviation) estimates of heritabilities in As presented in s et al.  reportedPedigree-based estimates of heritabilities were similar with those from GBLUP for growth traits, but were higher than those from the Bayesian  methods for growth and carcass traits. Genome-based (BayesC) estimates of heritabilies for growth and carcass traits were lower than those from GBLUP, but were similar with those from the other the Bayesian methods. The heritability  and carcass  traits. The means and standard deviations of Pearson correlations  and carcass  traits from k-means and random cluster training and validation datasets were found different within the GBLUP and the Bayesian methods, which depends on the genetic architecture of the traits. The similar predictive performances from the GBLUP and the Bayesian methods for growth  and carcass  also suggested that the genetic structures of growth and carcass traits controlled by many genes with small effects. The carcass traits also resulted in the higher heritabilities and then higher predictive performances than growth traits. These results also revealed that the 10-fold k-means and random cluster cross-validation datasets resulted in significantly lower correlations than training datasets for growth  and carcass  traits within the GBLUP and the Bayesian  methods. The decrease in mean correlations for growth  and carcass  traits ranged from 52% to 87% in the 10-fold k-means and random cluster cross-validation datasets. In addition, k-means cluster minimizing genetic relationship among cross-validation datasets produced lower correlations than the random cluster and the ranges of the decrease were from 16% to 22% for BW, 40% to 49% for WW, 23% to 26% for YW as growth traits, and 12% to 15% for FAT, 16% to 18% for IMF, and 25% to 30% for LMA as carcass traits across the GBLUP and the Bayesian methods.The accuracies of GEBV from the GBLUP and the Bayesian methods were obtained from Pearson’s correlations  for BW, 0.347 0.645) for WW, 0.593 (0.790) for YW, 0.575 (0.663) for FAT, 0.697 (0.831) for IMF, and 0.562 (0.774) for LMA in k-means (random) cluster cross-validation datasets. As seen in  for WW, Daetwyler et al.  determinThe accuracies of GEBV from the GBLUP and the Bayesian  methods averaged over all k-means and random clustering were 0.451, 0.713, 0.670, 0.732, 0.572 for BW, 0.313, 0.563, 0.572, 0.592, 0.439 for WW, 0.524, 0.726, 0.745, 0.745, 0.718 for YW in the growth traits, 0.442, 0.659, 0.671, 0.677, 0.645 for FAT, 0.617, 0.789, 0.791, 0.799, 0.824 for IMF, and 0.500, 0.692, 0.705, 0.706, 0.737 for LMA in the carcass traits. As presented in In order to explore the translation of genomic prediction for growth and carcass traits in Brangus cattle, the GBLUP and the Bayesian  methods were used to estimate GEBV for growth  and carcass  traits within k-means and random clusters in this study. The heritability estimates from k-means and random cluster were quite similar across genomic prediction methods. The Bayesian methods underestimated the heritabilities between 0.06 and 0.21; however, the heritability estimates from GBLUP were between 0.21 and 0.35 for growth and carcass traits, and they parallel these types of estimates in the literature. Including low-density SNP markers with low minor allele frequency would cause a poor performance to estimate the heritabilities of traits with the Bayesian methods compared with GBLUP using genomic relationship. K-means cluster appears to minimize the genetic relationships among cross-validation datasets and yields lower correlations than in a random cluster. These results of the current study suggested that the level of genetic relationship between the training and validation data influences the prediction ability of genomic selection methods and the accuracy of GEBV. The prediction ability of the GBLUP and the Bayesian methods within k-means and random clusters were quite similar for growth and carcass traits; however, the Bayesian methods overestimated the accuracies of GEBV because of the lower estimates of the heritability of growth and carcass traits. However, the GBLUP resulted in more reasonable accuracy of GEBV for growth and carcass traits collected from Brangus heifers.


A wide variety of traits is heritable and has genetic loading, including schizophrenia spectrum disorders (SSDs) and its associated neurocognitive features. The genetic architecture of SSDs is polygenic, with the contribution of thousands of single nucleotide polymorphisms of small effect with an estimated SNP-heritability of 24%. The same occurs with neurocognitive phenotypes such as intelligence or educational attainment. Therefore, the method of polygenic risk scores (PRS) is useful in estimating the genetic burden of such traits. Moreover, the use of PRS in a sample of genetically related individuals would allow analyzing the contribution of genetic and environmental factors involved in the development of the disorder and its candidate endophenotypes.To estimate PRS for schizophrenia, and polygenic scores for intelligence and educational attainment in patients with First Episode Psychosis (FEP), their first-degree relatives (siblings and parents), and a group of healthy controls.The sample is comprised of 579 participants of the PAFIP-FAMILIAS project in Santander, Spain . All provided sociodemographic information and completed the same neuropsychological battery. Participants’ DNA was extracted from venous blood samples, and genotyping was performed at the Centro Nacional de Investigaciones Oncológicas (CeGen) by the Global Screening Array v.3.0 panel (Illumina). Data quality control, imputation, calculation of PRS, and genetic association analysis are being performed using PLINK, SHAPEIT, IMPUTE2, SPSS and R.Data analysis is currently in progress, at the quality analysis stage, in collaboration with the Institute of Psychiatric Phenomics and Genomics (IPPG) in Munich, Germany. We expect to find higher PRS for schizophrenia in FEP patients, while their first-degree relatives will potentially show intermediate risk scores between patients and healthy controls. A similar finding is expected regarding intelligence and educational attainment, as FEP patients may show more genetic burden for low intelligence and education.The estimation of PRS has demonstrated to be valuable in studying complex traits such as schizophrenia. We believe that by applying this method in a family design can provide interesting insights on the development of SSDs and its potential endophenotypes, and potentially useful in their prevention.None Declared


Trait‐based approaches elucidate the mechanisms underlying biodiversity response to, or effects on, the environment. Nevertheless, the Raunkiæran shortfall—the dearth of knowledge on species traits and their functionality—presents a challenge in the application of these approaches. We conducted a systematic review to investigate the trends and gaps in trait‐based animal ecology in terms of taxonomic resolution, trait selection, ecosystem type, and geographical region. In addition, we suggest a set of crucial steps to guide trait selection and aid future research to conduct within and cross‐taxon comparisons. We identified 1655 articles using virtually all animal groups published from 1999 to 2020. Studies were concentrated in vertebrates, terrestrial habitats, the Palearctic realm, and mostly investigated trophic and habitat dimensions. Additionally, they focused on response traits (79.4%) and largely ignored intraspecific variation (94.6%). Almost 36% of the data sets did not provide the rationale behind the selection of morphological traits. The main limitations of trait‐based animal ecology were the use of trait averages and a rare inclusion of intraspecific variability. Nearly one‐fifth of the studies based only on response traits conclude that trait diversity impacts ecosystem processes or services without justifying the connection between them or measuring them. We propose a guide for standardizing trait collection that includes the following: (i) determining the type of trait and the mechanism linking the trait to the environment, ecosystem, or the correlation between the environment, trait, and ecosystem, (ii) using a “periodic table of niches” to select the appropriate niche dimension to support a mechanistic trait selection, and (iii) selecting the relevant traits for each retained niche dimension. By addressing these gaps, trait‐based animal ecology can become more predictive. This implies that future research will likely focus on collaborating to understand how environmental changes impact animals and their capacity to provide ecosystem services and goods. Most studies investigated in this review concentrated in vertebrates, terrestrial habitats, the Palearctic realm. Almost 36% of the datasets did not provide the rationale behind the selection of morphological traits. The main limitations of trait‐based animal ecology were the use of trait averages and a rare inclusion of intraspecific variability. Nearly one‐fifth of the studies based only on response traits conclude that trait diversity impacts ecosystem processes or services without justifying the connection between them or measuring them. Díaz and Cabido  employedThe attempts to uncover gaps in trait‐based literature have primarily concentrated on specific groups of animals or themes, as seen in studies by de Bello et al. , Etard eThese biases are directly or indirectly related to the knowledge gap on trait variation within and between species and their ecological function, known as the Raunkiæran shortfall  and several terrestrial zoogeographical and marine biogeographical realms. This systematic review aims to specifically identify the trends and gaps in trait‐based studies involving animals in regard to (i) the taxonomic groups, ecosystem types, and geographical regions examined and (ii) the methods used for selecting traits across different taxa. We present the most comprehensive review of literature to date on trait‐based animal ecology and examine its challenges, limitations, and knowledge gaps. Finally, we offer solutions to address the main gaps identified, to assist future studies (1) in selecting an appropriate set of traits, (2) relating traits to species performance and function, and (3) making comparisons of functional traits across taxa and scales.22.1Scopus and Web of Science. We search the literature in titles, abstracts, and keywords using the following terms: “functional diversity” OR “functional trait*” AND “animal” OR “vertebrate*” OR “invertebrate*” OR “ecdysozoa” OR “protostomia” OR “spiralia” OR “echinodermata” OR “macroinvertebrate,” OR “aves,” OR “amphibia,” OR “reptilia,” OR “chiroptera” OR “cnidaria” OR “porifera” OR “ctenophora” OR “crustacea*” OR “insect*” OR “nematoda” OR “tardigrada” OR “onycophora” OR “annelida” OR “brachiopoda” OR “mollusk*” OR “bryozoa” OR “arthropod” OR “coleoptera” OR “heteroptera” OR “homoptera” OR “lepidoptera” OR “hymenoptera” OR “gastropoda” OR “isopoda” OR “acanthocephala” OR “brachipoda” OR “cycliophora” OR “nemertea” OR “platyhelminthes” OR “sipuncula” OR “diptera” OR “trichoptera” OR “diplopoda” OR “chilopoda” OR “collembola” OR “ostracoda” OR “copepod” OR “rotifera” OR “decapoda” OR “amphipoda” OR “arachnid*” OR “actinopterygii” OR “chondrichthyes” OR “sarcopterygii” OR “tetrapoda” OR “squamata” OR “testudinata” OR “crocodylomorpha” OR “thynchocephalia” OR “echinozoa” OR “crinozoa” OR “asterozoa” OR “mammal*” OR “primate*” OR “bat” OR “ungulate*” OR “monkey” OR “rodent*” OR “amphibian” OR “reptile” OR “lizard” OR “snakes” OR “anuran*” OR “bird” OR “avian” OR “fish” OR “microcrustacean” OR “arachnid,” OR “spider” OR “earthworm*” OR “sponge*” OR “coral*” OR “bee” OR “shrimp” OR “crab” OR “snail” OR “zooplankton” OR “wasp” OR “ant” OR “macrofauna”. We used these taxonomic groups based on the hierarchical classification proposed by Ruggiero et al. ;studies using traits of plants, fungi, protists or bacteria solely ; andarticles that used the term “functional trait” to assess the variation in molecules and tissues in the context of histology, neurology, enzymology, or genetics without a clear ecological meaning.We evaluated all 7854 manuscripts in three stages: (1) first, we read manuscript titles and abstracts and selected only those studies using at least one measured trait of animal species. At this stage, we excluded the following:In the second stage, we screened the full text of 2154 manuscripts to confirm if it matches the minimum criteria cited above. As a result, we excluded 621 manuscripts that were not previously removed by reading titles and abstracts, and selected 1655 studies for the extraction stage. In the last stage, the selected articles were then fully read for data extraction Appendix . BecauseTo guarantee a standardized extraction protocol among authors of this study, we first selected 20 random manuscripts to be compared among all authors that independently extracted study data in a “training” spreadsheet. Then, we compared the information agreement and fixed (when necessary) potential issues in the information extracted by the authors. After correcting extraction bias and answering all doubts from authors about the extracted data, we randomly split the 1655 papers into seven blocks that were screened and extracted by seven authors. We organized weekly meetings to discuss potential problems in data extraction  or to solve general questions from one or more authors about that extraction. After the end of this stage, the leading author screened all papers to assess extraction quality and to fix potential incorrect information.i to x was available in the main manuscript text or Appendices All authors followed a standard spreadsheet to extract the following data from text, figures, tables, and Appendices 2.2We used the taxonomic unit informed in each study to determine the refinement of traits, that is, whether a specific trait was attributed to specimens, subspecies, species, genus, and so on. When the authors did not provide the information in the methods, we considered the taxonomic names in traits provided in tables or Appendices The taxonomic groups used in data extraction (lowest taxonomic unit and the most inclusive taxonomic group) were defined based on the higher‐level classification proposed by Ruggiero et al. . The det2.3We defined the ecosystem type of each study based on 20 zoogeographical  used traits at the species level (fine resolution), followed by genus (6.8%), and multiple taxonomic units (5.3%) Appendix . In addiWe found that Vertebrata (42.1%), Ecdysozoa (35.1%), and Protostomia (17.3%) represented almost 95% of all studied data sets Figure . The fiv3.2We extracted different trait information from each data set, including: (i) trait type , (ii) whether intraspecific variability was included in the study, and (iii) which and how many niche dimensions were evaluated. We found that only 97 data sets (5.4%) examined intraspecific trait variation, while the remaining 1790 (94.6%) used trait averages (interspecific variation). Most data sets collected only response traits (79.4%), while 7.2% used effect traits, 7.3% adopted both response and effect traits, and 5.9% did not specify trait type. This low frequency of effect traits was similarly prevalent across taxonomic groups , except for Porifera Figure .Furthermore, 235 (16.5%) of the 1422 studies that only used response traits addressed the potential impact on ecosystem functioning without providing justification for the link between the selected traits and their function. In fact, previous research has rarely examined effect traits in trait‐based animal ecology  were concentrated in 9 out of 18 taxonomic groups, being especially common in studies with Mollusca, Vertebrata, Cnidaria, Mammalia, and Nematoda , habitat (65.9%), life history 36.1%), metabolic (8.2%), and defense (6.9%). Although 468 data sets (28.3%) have used only one niche dimension, most of them used more than one niche dimension, varying from 2 (36.3%) to 3 (23.5%), 4 (4.7%), and 5 (0.7%). Importantly, even when analyzed with higher taxonomic levels, the trophic niche dimension was the most used  used morphological traits without establishing a specific niche dimension, while 108 (6.5%) did not use any niche dimension at all. Even the most studied animals, such as vertebrates (20.7%), mammals (17.2%), birds (16%), and arthropods (15.4%) had a significant usage of unjustifiable morphological traits. Taken together, these results indicate many studies did not provide a specific niche dimension or ecological process underpinning trait selection. According to Winemiller et al. , classif3.3n = 503, 32.7%), followed by South America (19.1%), North America (15.8%), Asia (9%), Oceania (5.8%), and Africa (5.5%) , Neotropical (n = 303), and Nearctic (n = 252) represented 66% of all data sets  studying terrestrial animals, followed by freshwater (27.7%) and marine (19%) organisms. These studies were performed in all continents and oceans, varying from local to global scales. For terrestrial and freshwater ecosystems, the most studied region was Europe  is unimportant because interspecific trait variation is more significant than intraspecific , derived from the study question , we suggest two directions: (i) develop a new experimental study to create new standards for a given taxonomic group or (ii) select only a subset of relevant traits with minimum evidence of trait–environment correlation (response traits). Furthermore, when there is no empirical support, it is desirable to prevent generalizing the impacts of trait or trait diversity on ecosystem functioning.5.2After selecting relevant traits and validating which trait type is used, it is essential to ask at which spatial scale a given trait is ecologically relevant? For example, what evidence is that the selected traits are connected to environmental variables operating at a given scale or that those traits influence ecosystem properties? This is particularly relevant when collecting trait information from databases that use a set of traits that respond to large‐scale environmental processes, but do not necessarily affect demographic rates locally  and the ecological questions. However, whereas information on plant traits is standardized and broadly available  and geographical regions (temperate), which goes beyond studies with functional traits ; data curation (lead); formal analysis (lead); methodology ; visualization ; writing – original draft (lead); writing – review and editing (lead). Leonardo S. Chaves: Data curation ; methodology (lead); writing – review and editing . Gabriel Boldorini: Data curation ; formal analysis ; methodology ; writing – review and editing . Natália Ferreira: Data curation ; methodology ; writing – review and editing . Reginaldo Gusmão: Data curation ; methodology ; resources (lead); visualization (lead); writing – review and editing . Phamela Bernardes Perônico: Data curation ; methodology ; writing – review and editing . Nate J Sanders: Funding acquisition (supporting); visualization (supporting); writing – review and editing . Fabrício Barreto Teresa: Conceptualization ; data curation ; methodology ; writing – original draft ; writing – review and editing .The authors have no conflict of interest to declare.Appendix S1–S2Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.