Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean’s profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross’s offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding. Most often, these crossing decisions are made on the basis of pedigrees, genetic diversity, yield, and agronomic traits . The folper se evaluation, while it is used for parental line selection in hybrid development  that a cross will generate, though there are key differences between predicting crosses for hybrids and inbred varieties. First, marker effects must be assessed across all RILs within the breeding population rather than on a per population or heterotic group basis, as is done in hybrid genomic selection . Additiovia estimation of population mean and genetic variance in inbred species such as barley and wheat  to parental lines, the UC used to determine cross value, as well as the marker density.22.1The TS used in this study consisted of 702 elite, inbred soybean lines from the University of Georgia (UGA) Soybean Breeding Program’s advanced yield trials (AYTs). Thirty-five of these lines were used as parents in crosses made between 2012 and 2014. These parental lines were selected to be included in this study due to the availability of seed and their parentage in crosses which generated more than 15 progeny lines that were tested in replicated field trials Table S1Yield trials were conducted at three locations with three replicates in each location: Athens, Plains, and Tifton in Georgia and Florence, South Carolina. Plots consisted of four rows with a row length of 4.9 m and 76.2 cm row spacing, and a planting density of 27 seeds/meter. All plots were end-trimmed to a row length of 3.7 m the before harvest with only the middle two rows harvested for yield to reduce edge effects. Each trial followed a randomized complete block design and included a set of two checks with similar maturity. The 35 parental lines were primarily evaluated during 2007 to 2013 growing seasons, with all other lines evaluated between 2015 and 2019.Additional plant materials were also included to calculate the value of the validation crosses drawn from the UGA Soybean Breeding Program’s preliminary yield trials (PYTs). For each of the 42 crosses utilized in validation, progeny from both the UGA Soybean Breeding Program’s AYT and PYT were used to calculate the observed value of the cross. PYTs were conducted similar to the AYTs with the exception that plots consisted of only two rows and were tested at two locations with two replicates in each location. These plant materials from the PYT were not included in the TS or used to predict cross values.2.2via gel electrophoresis and fluorimeter DNA quantification with a minimum desirable concentration of 100 µg/mL for genotyping.DNA was extracted from bulked, lyophilized leaf tissue collected from ~15 plants of each genotype. Extraction was accomplished using a modified CTAB, chloroform method detailed in Genotyping was accomplished using the SoySNP6k iSelect BeadChip. This genotyping method was developed based on the analysis of the linkage disequilibrium and minor allele frequency using >18,000 accessions in the USDA Soybean Germplasm Collection. In validation using a diverse set of germplasm, the chip had a minor allele frequency of >0.1 for >90% of the markers included in the chip . Followi2.3Grain yield at each location was collected from the middle two rows within each four-row plot after end-trimming. Yield was calculated based on the weight gathered from the combines during harvest of the yield plots, with yield adjusted to a standardized moisture of 13%. Maturity was evaluated continuously and collected as the days from planting to R8 when maturity was achieved by 95% of plants within a plot.via a mixed model, which accounted for environmental, maturity, and genotype effects along with genotype by environment interactions. Environment was defined as a combination of year and location. Genotypes and genotype by environment interactions were treated as a random effect and environmental and maturity effects were treated as fixed effects in BLUP calculation. Histograms and residual plots were used to check for outliers in the raw phenotypic data, as well as the BLUP values, with flagrant outliers excluded from the analysis.Best Linear Unbiased Prediction (BLUP) values for each line were calculated 2H) was calculated for each yield trial in the TS utilizing data from the two testing locations where maturity data was available. Variance components were determined using a linear model equation in the form of:The broad-sense heritability (e is the error term. G and E(G) were treated as random effects, while M, E, and E(r) were treated as fixed effects. Calculation was performed using the lme4 package in R  is the effect of the replication in a given environment, G is the genotypic effect, E(G) is the genotype by environment interaction, and age in R . H2 wase is the number of environments for the test and r is the number of replications.Where 2.42.4.1via a simulation of the single seed descent method. Each parent consisted of 3,762 SNPs coded as 2, 1, or 0, corresponding to the number of alleles from the nucleotide variant first in alphabetical order for said marker. SNPs coded as 2 and 0 are therefore homozygous, with heterozygotes coded as 1. For each cross, 500 progeny were generated using the sim.cross function of the R/qtl package  , Bayes B progeny . These tg is the genetic standard deviation among cross progeny of m  that were used to predict genetic values as well as the UCs , consisted of the full TS using all 702 lines, including the 35 parental lines used for validation. The second TS, without full siblings (WFS), removed all lines from the TS which were direct progeny of the crosses to be predicted, but still maintained all parental lines. The removal of full siblings from the TS is most similar to a breeding program’s TS, where a cross combination is only used once and is not repeated in later years. An additional TS composition was used to assess if any change in accuracy between FTS and WFS was due to the loss of relatedness between the TS and validation set or only due to a reduced TS size. The third composition, termed reduced training set (RTS), removed as many lines from the TS as WFS. In RTS all materials removed were randomly chosen lines that were not direct progeny of the predicted crosses. Therefore, the relatedness of the TS to the predicted crosses would be minimally impacted and WFS and RTS would have an equivalent number of plant materials. Additionally, predictions generated by OCS when utilizing the SoySNP6k marker set were compared to those generated when utilizing the SoySNP3k SNP marker set.0.1, UC0.2). Accuracy was defined as the Spearman correlation between the values predicted for a given set of cross and the observed value of said set. Spearman correlation provides a comparison of rankings, making it an effective method to evaluate a OCS, which generates values that are used to rank and select crosses. The observed value of the crosses was the mean yield BLUP value of all progeny that were tested in AYTs for said cross.The validation for OCS consisted of predicting the value of 42 unique cross combinations made within the UGA Soybean Breeding Program. All crosses had at least 16 progenies evaluated in multi-location yield trials between 2015 and 2019  and marker sets (SoySNP6k & SoySNP3k). The validation process was identical for all six combinations. Validation for each marker set and TS consisted of 50 validation tests. In each test all 12 OCS evaluation methods predicted the progeny values of a random sample of 40 crosses from the 42 possible crosses. After progeny generation each genomic prediction method  was utilized to determine the GEBVs of said progeny. Following this, OCS then assessed the value of each cross using all three UCs . Variation in the UC only led to a statistical difference when using EGBLUP with the WFS TS. In this scenario MV had a statistically higher accuracy than UCd EGBLUP Table 1.The relationship between the crosses predicted and TS had an additional, statistically significant impact on prediction accuracy. For all OCS evaluation methods using the SoySNP6k marker set, there was a statistically significant increase (P< 0.001) in prediction accuracy when using RTS over WFS and FTS over RTS  were used. Overall, there was a statistically significant increase in accuracy for all OCS methods when using the higher marker densities  and FTS (0.06) Table 3.These results suggest that genetic coverage is more important to successful OCS than it is for genomic selection. Considering that OCS must generate and predict progeny genotypes and values, the increased marker density will lead to more accurate generation of progeny genotypes. With a reduced marker density, the linkage between markers and QTL is minimized, therefore progeny generated using lower marker densities less accurately represent the progeny which could result from a cross. This makes the subsequent prediction of progeny values and cross values more difficult, making SoySNP6k preferable for OCS over SoySNP3k .4.2Within this study the highest prediction accuracies were observed when using the mean value EGBLUP method with the SoySNP6k marker set, 0.40 (WFS) and 0.56 (FTS). Therefore, the MV EGBLUP OCS model using the SoySNP6k marker set is recommended for the prediction of yield cross values in soybean. Limited studies have validated OCS in an empirical manner, and none have done so for soybean yield . NeyhartThe successful use of the OCS method recommended here depends on a number of factors and is beholden to some limitations. The predictive accuracy of OCS is constrained by the phenotypic and genotypic data used in the TS. The phenotypic data used in the TS and for validation was gathered in the same geographic area in the southern United States. The prediction accuracy of OCS would be reduced if it were used to predict the performance of crosses in a different geographic region than the TS was evaluated in. Furthermore, OCS requires an efficient genotyping pipeline to operate, as genotypic and phenotypic data for the parental lines must be available prior to planting. This could cause issues dependent on the genotyping resources and throughput available to a breeding program. Computational resources are additionally essential to OCS as the prediction and evaluation of crosses can be computationally demanding. This is especially true for methods involving EGBLUP which took ~12x longer to finish validation than the other genomic evaluation models on a high-performance desktop computer. Given sufficient access to computational resources, the time to execute OCS in a breeding program would be less than 24 hours.This study presents multiple future research direction and utilizations. First, OCS could be used to select for multiple traits at once, with crosses selected based on an index of weighted traits values such as yield, seed composition, disease, or nematode resistance. This method could identify populations with progeny that have both desirable yield and seed composition traits, which can be antagonistic. Additionally, QTLs with known effects could be included as weighted covariates in progeny evaluation for more heritable traits, such as maturity, disease, and nematode resistance . For pes5Determination of cross combinations is one of essential steps in soybean breeding. OCS enables parental selection decisions based on genetic evaluation and simulation prior to crossing, which could lead to higher efficiency in the breeding pipeline and greater genetic gains. OCS considers complementation between parental genotypes and allows a breeder to evaluate the distribution of progeny values from a cross combination. Result indicated that MV EGBLUP using the SoySNP6k marker set is the most accurate OCS method of those tested, with a prediction accuracy of 0.4 when using a breeding program TS. OCS is highly impacted by the relatedness of the TS to the cross combinations predicted, marker density, and the genomic evaluation model used. The predictive methodologies generated in this study can be utilized by soybean breeding programs as well as breeding programs for other crop species leading to improved rates of genetic gain.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.MM conducted genetic experiments, analyzed data, interpreted the results and drafted the manuscript. ZL conceptualized the project, provided oversight of the experiments, interpreted the results, and edited the manuscript. QS carried out the genotyping of all experimental lines, analyzed data, and interpreted the results. BF conducted the field tests and collected agronomic trait data. All authors contributed to the article and approved the submitted version.


Wheat is a major crop worldwide, mainly cultivated for human consumption and animal feed. Grain quality is paramount in determining its value and downstream use. While we know that climate change threatens global crop yields, a better understanding of impacts on wheat end‐use quality is also critical. Combining quantitative genetics with climate model outputs, we investigated UK‐wide trends in genotypic adaptation for wheat quality traits. In our approach, we augmented genomic prediction models with environmental characterisation of field trials to predict trait values and climate effects in historical field trial data between 2001 and 2020. Addition of environmental covariates, such as temperature and rainfall, successfully enabled prediction of genotype by environment interactions (G × E), and increased prediction accuracy of most traits for new genotypes in new year cross validation. We then extended predictions from these models to much larger numbers of simulated environments using climate scenarios projected under Representative Concentration Pathways 8.5 for 2050–2069. We found geographically varying climate change impacts on wheat quality due to contrasting associations between specific weather covariables and quality traits across the UK. Notably, negative impacts on quality traits were predicted in the East of the UK due to increased summer temperatures while the climate in the North and South‐west may become more favourable with increased summer temperatures. Furthermore, by projecting 167,040 simulated future genotype–environment combinations, we found only limited potential for breeding to exploit predictable G × E to mitigate year‐to‐year environmental variability for most traits except Hagberg falling number. This suggests low adaptability of current UK wheat germplasm across future UK climates. More generally, approaches demonstrated here will be critical to enable adaptation of global crops to near‐term climate change. Wheat bread‐making quality is strongly affected by the environment, both directly and through genotype by environment interaction (G × E) effects. Augmentation of genomic prediction models with environmental covariables enabled successful G × E prediction in a large historical field trial dataset. Prediction models were projected into future UK climate scenarios. Negative impacts due to increased summer temperatures were predicted in the main wheat growing area in the east, but the climate in the North and South‐west may become more favourable. For most traits, only limited potential was found for breeders to exploit predictable G × E and select stable genotypes to mitigate year‐to‐year climate variability. Of these trials, 323 were part of the Agriculture and Horticulture Development Board (AHDB) Recommended List national variety testing series between harvest years 2001 and 2020. Additional trials included evaluation of 19 high quality milling wheat varieties as part of 32 National Institute of Agricultural Botany (NIAB) membership trials between the harvest years 2009 and 2019, and trials from 2017 to 2019 described in Fradgley et al.  and Scot−1), were all measured at between 313 and 327 trial environments. Gluten quality traits: Zeleny sedimentation volume, Chopin alveograph W and P/L values, were measured at between 81 and 86 environments and for fewer genotypes  were used for which pedigree and/or genetic marker data were also available. The wheat quality traits, grain protein content (%), Hagberg falling number  and specific weight kg hl−, were alW and Chopin P/L were recorded for two genotypes. The genotype occurrences per environment were therefore highly unbalanced where each genotype only appeared in trials in a limited number of years  trials were included each year, with the exception of 2001 when there were only three trials and only Chopin s Figure . However2.2SoilGrids global resource , soil organic carbon (dg kg−1), nitrogen (cg kg−1), pH and cation exchange capacity at pH 7 (mmolc kg−1). All values were means from the 5‐15 cm depth layer. The weather variables were selected to ensure direct comparison and integration between observed and projected climate datasets and are summarised in Table q environments × k environmental covariates matrix of EC values per trial environment. The EC matrix was scaled and mean centred and used to compute a q environments × q environments enviromic relationship matrix (ERM) which describes the similarities among pairs of field trials across years using a Gaussian kernel. Preliminary analysis investigated performing feature selection using gradient boosting machine learning models , θ is a scaling factor assumed as the mean value of the Euclidean distance matrix and h is a bandwidth factor which was assumed to be 1 as default. The ERM therefore had dimensions of n environments × n environments. This approach was implemented in R using similar methods to those detailed in the EnvRtype package  was conducted considering environmental covariables (ECs), including weather information and soil properties, for past trial data and future projections. Soil physical and biochemical properties for each trial location were derived from the 2.3The genomic relationship matrix (GRM) was computed by combining pedigree and single nucleotide polymorphism (SNP) marker array data to characterise the genetic relationships among genotypes in the study. While marker data offer a more accurate representation of realised identity by state of inherited genetics, ancestry and pedigrees can offer complementary information for average identity by descent of inherited haplotypes and can often be used together in genomic prediction models  among all 240 genotypes included in the trials dataset was calculated based on information both from an updated version of pedigree data for released varieties  based on SNP marker data available for 213 varieties in common with the A matrix. The majority of these genotype data for released varieties were from the 90 k SNP array  was calculated as described by VanRaden .Secondly, we calculated the realised genomic kinship matrix , Kg is the relationship matrix of the genotypes of size nL×nL (in this case the H matrix as defined above), and Ug is a variance–covariance matrix of genetic effects between the traits of size t × t. The matrix E denotes the random environmental effects of order n×t and follows a normal distribution E∼Nn×t0,ZEKEZE′,UE where ZE is an incidence matrix for trial environments of order n × nE (nE represents the number of environments), KE is the relationship matrix of the environments of size nE×nE (in this case is an identity matrix) and UE is a variance–covariance matrix of environmental effects between the traits. Random errors are represented by the matrix ε of order n×t that follows a normal distribution ε∼Nn×t0,I,∑t where the identity matrix I is of dimension nT×nT. Multi‐trait models were run with 10,000 iterations of Markov Chain Monte Carlo (MCMC) to optimise prior distributions, of which 5000 were burn‐in, and a thinning value of 2.To assess the extent of common genetic and environmental influences between different quality traits, genetic and environmental trait correlations were estimated using available data. However, due to the unbalanced nature of available observations across environments for each trait we fitted pairs of traits  for all trait combinations with Bayesian multi‐trait linear mixed models (multi‐response) with unstructured residual covariance using the multi‐trait function from the ‘BGLR’ package in R  , μ1 is the fixed‐effect overall mean (n × 1 dimension), E is the random environmental effect, assumed as E~N where σE2 is the variance of E and KE is the n × n dimension kernel for environmental variation, computed as: KE=ZEZE′. matrix ZE is the incidence matrix for the environments in the dataset. The vector of random genetic effects were assumed as g∼N0,Kgσg2, where σg2 is the genetic variance and Kg is the n × n dimension kernel for genetic effects, computed as: Kg=ZgHZg′, in which Zg is the incidence matrix for the individuals, and H is the genomic relationship matrix (GRM) as described above. Finally, for this model, we assume an independent structure for the residuals, as ε∼N0,σε2I where σε2 is the error variance.The baseline model of this study considers only the main genetic effects (hereafter named: MM) . According to Lopez‐Cruz et al. , this moΩ), resulting in the updated enviromic‐aided environmental kernel, hereafter named as KW=ZEΩZE′. Consequently, the addition of a kernel‐based reaction‐norm structure (gW) is given by: gW∼N0,Kg⊙KWσgW2. Thus, this reaction‐norm extension of the MM (here after named as RNMM) can be described asgE) and a reaction‐norm structure (gW), which can be described asgW∼N0,Kg⊙KWσgW2 and gE∼N0,Kg⊙KEσgE2.We then extend both the MM and MDs models to include predictable G × E interaction terms between the genomic and enviromic kernels (e.g. soil and weather variates), resulting in kernel‐based reaction‐norm models that include genotype specific response over an environmental gradient. The environmental variation considers a nonlinear enviromic‐aided ERM ; fut = Future period (2050–2069); raw = unadjusted; bc = bias corrected; t = climate data time series; σ = standard deviation of climate data. Overbar indicates average. A transform was applied to rainfall  and relative humidity (100−relative humidity) prior to bias‐correction, to ensure the bias‐corrected values remained within physical bounds. All 12 km grid boxes and weather covariates were therefore comparable between the observed average and future climate environments. For soil data extracted from the SoilGrids global resource . The models were then used to predict phenotypes for 20 randomly selected wheat variety genotypes under both the recently observed climate and projected future climate conditions in all 1707 of the 12 km gridded square locations across the UK used in the HadUK‐Grid and UKCP18 datasets. Therefore, a total of 68,280 new genotype–environment combinations were predicted for each trait. The recently observed climatological average was derived by averaging monthly weather covariates over the years 2000 to 2020 in the HadUK‐Grid weather dataset. Similarly, the future climatological average was derived by averaging the projected monthly weather covariates over the years 2050 to 2069, as well as across all 12 individual ensemble members in the UKCP18 weather dataset. Prior to averaging across ensemble members, the UKCP18 projections were bias‐corrected, in which individual model biases in the mean and variance of each variable were corrected to match those observed in the HadUK‐Grid weather dataset using the following formula2.8To further test to what degree the predictable G × E in prediction models could be exploited to mitigate year‐to‐year climate variability impacts on UK wheat quality traits, we also fitted models using all 240 available genotypes and the full observed dataset to predict their performance under a wide range of climatic conditions by sampling years from each individual UKCP18 ensemble member. In contrast to using the ensemble mean as above, this represents physically plausible year‐to‐year climate variability, under the RCP8.5 emissions pathway. By sampling each of the 20 years between 2050 and 2069 and across all 12 ensemble members, we obtained 240 independent year environments in which to assess the genotypes' performance. We sampled this climatic variability at three locations in the East, North and South‐west of the UK which were projected to experience contrasting climate change impacts so that a total of 172,800 new genotype–environment combinations were predicted for each trait. In summary, these represent a wide range of 720 simulated environments that are self‐consistent throughout the year, with realistic temporal variation around the expected future average climate of 2050 to 2069, and that are independent from the years observed in the trial dataset.b) was used as the stability index of genotypic sensitivity to temporal environmental variation due to the G × E variance component of prediction models. An index of risk was also defined as the percentage of the 240 projected year environments in which the predicted trait value for each genotype did not meet the thresholds as defined by the UK variety quality criteria for bread wheat export .All combinations of traits were found to have positive genetic correlations suggesting some degree of positive pleiotropic genetic control. However, both positive and negative environmental correlations were found Figure . ProteinW, a small but statistically significant increase in mean within‐trial prediction accuracies of genotype performance was found with the inclusion of environmental covariates (RNMM and RNMDs models) compared to models that included only genetic data (MM and MDs models)  for year‐mean (across locations and genotypes) trait values for HFN and specific weight Figure  varied b) Figure .W and Chopin P/L) had particularly large genetic and G × E variance components, which also reflected the generally high prediction accuracy for these traits , we predicted the quality traits for a representative subset of 20 wheat genotypes at both the observed average (2000–2019) and projected future climate conditions (2050–2069) so that 68,280 simulated genotype–environment combinations were modelled. On average, positive effects of future projected climates were predicted for grain protein content and HFN, with negative effects for specific weight , but a few locations in the South‐west and Wales were predicted to increase by over 2 kg hl−1  that would enable selection to mitigate environmental effects for most traits  indicating that genotypes with higher average specific weight were generally also more stable (low b stability coefficients). This was also the case for HFN in two out of the three locations, suggesting that the value of high HFN or specific weight genotypes is greater in more challenging environments. In contrast, mean protein content was positively correlated with b stability coefficients at two of the three locations and Zeleny sedimentation at one location. This suggests that positive selection for these two traits may also select for reduced stability and climate resilience.Taylor's power law describes how the variance of a trait increases logarithmically with mean trait value  and genomic‐enabled prediction models to predict G × E. We explored the impacts of projected climate change on wheat quality in the UK for the period 2050–2069 under a high emissions pathway, RCP8.5. Our results showed positive effects of climate change on key quality traits in some regions that are not currently suited to growing high quality wheat. This represents an opportunity for expanding high quality wheat production, potentially mitigating negative impacts in the current primary high‐quality wheat growing regions in the East of the UK. We found limited potential for wheat breeding to exploit predictable G × E and develop varieties that are resilient to projected year‐to‐year climate variability for most traits, with the exception of HFN. Moreover, different regions of the UK will experience contrasting environmental challenges to quality traits so that it will be particularly difficult to breed varieties that are resilient across the UK for multiple quality traits. The low correlations between modelled genotypic stability at contrasting regional locations also suggests the potential and the need for breeding programmes to select for local rather than broad adaptation.Inclusion of environmental covariables in genomic prediction models resulted in modest but statistically significant increases in prediction accuracies, which is similar to other studies for wheat yield  over winter months of the same year (Atkinson et al., Taking a similar approach to de los Campos et al. , who madWe emphasise that more substantial and immediate climate change impacts will occur in more marginal wheat growing regions at lower latitudes than in the UK (Harkness et al., Supplementary figuresClick here for additional data file.Table S1Click here for additional data file.


Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data from 2014 to 2017 of the Genomes to Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments representing combinations of locations and years, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the low-yielding environments. GWAS was carried out for yield and five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships. Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study. These results identify regions of the genome for future selection to improve yield and a methodology to increase the number of hybrids evaluated across locations of a multi-environment trial through genomic prediction. Extensive private sector hybrid development and testing along with improved agronomic and management practices have contributed to sustained yield improvement in the United States  are an important tool to uncover the complex genetic structure of quantitative traits, such as grain yield, which have many small-effect quantitative trait loci (QTL) involved. GWAS utilizes dense genomic data to identify associations between genomic markers and phenotypic traits. Causal markers or markers in linkage disequilibrium (LD) with QTL are detected as being associated with the phenotypes they control . GWAS haMarker-assisted selection (MAS) where hybrids are selected based on major QTL has been an important breeding technique for traits controlled by few, major effect QTL. Genomic prediction (GP) that uses high-density markers to predict hybrid yield performance has been shown to improve genetic gain approximately three-fold compared to selection based on single or few genetic markers (MAS) in quantitative traits . A trainPrevious work has demonstrated that genomic prediction models are improved by incorporating GEI analysis . One appwww.genomes2fields.org) is a large-scale partnership between the public and private sector to organize a MET where inbred and hybrid maize genotypes are evaluated across a range of environments across North America. Phenotypic, genotypic, and environmental data collected in each site are publicly available (The Genomes to Fields (G2F) initiative . Phenotypic information was collected in MET across North America through a collaboration of 67 principal investigators and sponsors across 18 universities and federal agencies (numbers from 2022) . ExperimGenotyping-by-sequencing (GBS) data for 1,577 inbred lines with 945,574 SNPs is publicly available at Cyverse . Linkage disequilibrium (LD) was calculated using the hybrid dataset for the 2,126 hybrids with 100,878 SNPs. LD was calculated using a sliding window of 100 SNPs using PLINK v1.9  Supplem.y is the vector of phenotypic measurements, X is a design matrix associating the phenotypic measurement to the environment (combination of location and year), Z is the design matrix associating the phenotypic data and the hybrid identification, g is the vector of BLUPs with I represents an identity matrix, and I is an identity matrix. This model utilized an identity rather than genomic relationship matrix as the goal was to estimate i, i, and Reaction norm models were used to model grain yield of the 2,126 hybrids in 86 environments. The merit of an environment was estimated as the fixed effect of environment in a BLUP model to mitigate differences in average yield performance due to different hybrids evaluated in different environments. The environmental gradient (EG) was estimated as e “lme4”  in Eq. 1nd order Legendre orthogonal polynomial RNM model is defined in Eq. jth hybrid in the ith EG coefficient, Env is the vector of the fixed effect of the location-year combination, bm is the mth fixed regression coefficient for the average curve of the population, i, jth hybrid regressed on n  using the first method defined in I is an identity matrix.Genomic analyses were performed through GBLUP based on RNM. The 2be effect in this context is similar to the permanent effect in random regression models. Differently from a traditional random regression model , the repeated records for the same hybrid are spatially-repeated measurements . Model performance was compared with and without be using AIC (data not shown). This effect was found to reduce AIC and was considered throughout the remainder of the study. The reasoning for this effect is that be is capturing variation due to environmental effects on the hybrid’s phenotype caused by the high degree of similarity between the environments adjacent on the environmental gradient. Nevertheless, be could be capturing variance caused by non-additive genetic or systematic effects.The Multiple models were compared for performance using AIC including a linear and a 2nd order polynomial model with homogeneous or heterogeneous residual variance. The linear model was like the previously defined model in Eq. variance . Models variance .T is the matrix of covariates including the intercept, slope, and polynomial for each environment. C is the genomic covariance matrix for the coefficients. This covariance matrix was used to define the narrow-sense heritability (h2) and genetic correlation of the environments in Eqs ith environment and ith environment (in the heterogeneous model). The genetic correlation of yield across environments is defined in Eq. ith and the i'th environments, ith and the i'th environments, respectively. GEBVs are defined in Eq. jth hybrid. The predicted values, The genetic covariance matrix .Candidate genes were evaluated for the top five SNPs with the highest magnitude of effect in each environment . Candidaund 1 kb . Gene fuANOVA was performed to determine if there were significant differences between the yield performance of hybrids that were homozygous for the minor allele, heterozygous, and homozygous for the major allele for the selected SNPs in each environment which resulted in 1,806 comparisons . Where tGenomic prediction was performed to simulate two breeding program scenarios: previously tested hybrids from another environment could be included in the training dataset to predict performance in an untested environment (CV0) or untested hybrids evaluated in an untested year (CV00) and were previously described in The environmental merit, an environments placement along the environmental gradient, is unknown where yield has not been evaluated, as in the testing set of genomic prediction. To assess the environmental merit, environment-specific weather and soil data from public application programming interfaces (APIs) were used in a Random Forest model using the R packages “caret”  and “ranAdditionally, GP was performed where the known environmental merit was used in the environmental gradient to evaluate the impact of the discrepancy between the predicted and known environmental merits and was labeled CV0-Known EG.b1) was evaluated for each environment using a linear regression model (BLUP = b0+ b1*GEBVtesting) or (GEBVwhole = b0+ b1*GEBVtesting). Bias was centered around 0 by subtracting one from the regression coefficient.GEBVs from GP were either associated to single-environment BLUPs or to the GEBV of the whole-dataset model where all data was used in training. Prediction accuracy was assessed using Pearson’s correlation of these values in each environment. Bias  with any other selected SNP. Twenty-one SNPs were selected, which indicated that many environments had overlapping highest-effect SNPs  were shown to have a positive effect for the minor allele. S5_199708288 was the only SNP not within 1 kb of an annotated gene models. S2_236187932 was within 1 kb of candidate gene −1 across 57 environments for S3_215373678, 0.42 Mg ha−1 across 64 environments for S3_215380499, and 0.21 Mg ha−1 across 68 environments for S5_199708288. Thus, these SNPs could be important selection targets for population improvement.Grain yield of hybrids from varying SNP states were compared to verify the advantage of the minor allele for these four SNPs. Grain yield was often improved in the homozygous minor allele or heterozygous hybrids for S3_215373678, S3_215380499, and S5_199708288 compared to homozygous major allele hybrids. For comparison of yield performance differences between hybrids with the minor allele and the major allele, we selected environments where there were at least ten hybrids representing the minor allele and ten hybrids with the major allele. The average yield increase was 0.21 Mg haGRMZM2G126260, was found by Zm00001d051502 was a calcium dependent protein kinase which contributes to the growth and development, as well as the abiotic and biotic stress tolerance of a plant  and CV00-Predicted EG (r = 0.21). When GEBV from GP were compared to whole-dataset GEBV, RNM models outperformed GBLUP. Average prediction accuracy was 0.72, 0.80, and 0.81 in CV0-GBLUP, CV0-Predicted EG, and CV0-Known EG. CV00-Predicted EG (r = 0.60) had a higher average prediction accuracy than CV00-GBLUP (r = 0.57). In all of these scenarios, the GBLUP models consistently had a bias further from 0 than the RNM models. This could be due to the lack of GEI captured in the GBLUP model.Accurately predicting hybrid performance without growing field-experiments offers a major opportunity to improve genetic gain in maize. As prior knowledge and the GWAS analyses indicate grain yield in these environments is polygenic in nature, genomic prediction was assessed in this study. A standard GBLUP model was used for comparison to understand the value of the RNM. The GBLUP model outperformed the RNM models in prediction accuracy when GEBV were compared to single-environment BLUPs. Average prediction accuracy was 0.37, 0.34, and 0.32 in CV0-GBLUP, CV0-Predicted EG, and CV0-Known EG, respectively. Similar prediction accuracies were observed from CV00-GBLUP (r = 0.34) compared to untested hybrids in an untested year   . A benefr = −0.14); however, including environment data in the model in their study improved the prediction accuracy of this environment (r = 0.42). Other environments in their study performed worse when environmental data was included which indicated there was no ideal model for prediction in all environments.In this study, the within-environment BLUP prediction accuracy varied substantially across environments with ranges from −0.40 to 0.70 in CV0-Predicted EG and from −0.24 to 0.54 in CV00-Predicted EG. As such, a few environments were negatively impacting the average prediction accuracy. Prediction accuracy in MNH1_2014 was −0.40 in CV0-Predicted EG and was far worse than the next worse prediction accuracy of −0.02 in KSH1_2015. r = 0.38. This model has since been applied in the 2022 G2F yield prediction contest using data from 2014 to 2021, and an increased environmental merit prediction accuracy was observed due to the increase in the number of environments observed.The merit of an environment in this study was estimated as the fixed effect of environment in a BLUP model to mitigate differences in average yield performance due to different hybrids evaluated in different environments. Where yield is unknown in an environment, such as in the testing set of genomic prediction, it is possible to use environmental characteristics  to estimate the merit of a given environment. In this study, a Random Forest model was trained to associate the environmental data from 3 years of data to their known environmental merits and was used to predict environmental merit of the other year (that corresponded to the testing dataset of genomic prediction). The correlation between known and predicted environmental merit was Since there was a discrepancy between known and predicted environmental merit in this study, there was reranking between environments on the environmental gradient that could have impacted prediction accuracy. Genomic prediction was assessed with the known environmental merit to see if this would improve prediction accuracy. Average GP accuracy with the known environmental merit (CV0-Known EG) was 0.32 with a range from −0.21 to 0.68 and did not improve prediction accuracy over the GP model with predicted environmental merit (CV0-Predicted EG). This lack of change is likely due to the high genetic correlation across environments in this study. As such prediction accuracy was less impacted by placement on the EG.Often GP accuracy is compared to observed yield of hybrids in a given environment from a BLUP model. As within-environment BLUP models do not account for the relationship between environments and occasionally use an identity matrix to describe the relationship between hybrids, it is likely that these are not the most accurate representation of a hybrids true value. In this study, GEBVs from GP were compared to a whole-dataset model where phenotypic and genotypic data of all hybrids in all environments was modeled. Prediction accuracy of CV0-Predicted EG and CV00-Predicted EG were 0.80 (range: −0.14–0.99) and 0.60 (range: 0.05–0.86) and was greatly improved in comparison to the prediction accuracy of the BLUP model.The results of this study indicate the value of RNM to better understand GEI and predict hybrid yield performance. Twenty-one SNPs with associated candidate genes were found to be associated to yield in environments across the EG, and many of the candidate genes were previously reported as stress adaptations. Genomic prediction was performed with an increased correlation to models that incorporated genomic and environmental relationships than within-environment BLUP models. While yield was used to describe environmental performance in this study, this strategy is potentially limited in breeding for stress adaptation. Large-scale genomic sequencing capabilities enables genome-wide association studies and genomic prediction with the benefits of assessing hybrid merit and minimizing the phenotyping cost of multi-environment trials. A reaction norm model was applied to model hybrid yield across a range of environmental conditions. Heritability was improved in the higher-yielding environments which indicated that hybrid performance in the low-yielding environments was relatively more dependent on the environment. Genome-wide association study was used to estimate SNP effect, and many candidate genes were found to be associated with stress tolerance in previous studies. Multiple genomic prediction scenarios were performed where prediction accuracy was greater when hybrids had previously been tested than when untested hybrids were evaluated in an untested environment. Reaction norm models outperformed GBLUP when compared to GEBV derived models that utilized the whole-dataset. This approach could be useful for improving genetic gain in maize by increasing the number of hybrids and environments evaluated while limiting cost associated with phenotypic evaluation in multi-environment trials.


However, the molecular markers of body size in Yangyuan donkeys have yet to be explored. In this study, we performed a genome-wide association study to identify the genomic variations associated with body size traits in a population of 120 Yangyuan donkeys. We screened 16 single nucleotide polymorphisms that were significantly associated with body size traits. Some genes distributed around these significant SNPs were considered candidates for body size traits, including SMPD4, RPS6KA6, LPAR4, GLP2R, BRWD3, MAGT1, ZDHHC15, and CYSLTR1. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated that these genes were mainly involved in the P13K-Akt signaling pathway, Rap1 signaling pathway, regulation of actin cytoskeleton, calcium signaling pathway, phospholipase D signaling pathway, and neuroactive ligand-receptor interactions. Collectively, our study reported on a list of novel markers and candidate genes associated with body size traits in donkeys, providing useful information for functional gene studies and offering great potential for accelerating Yangyuan donkey breeding.The Yangyuan donkey is a domestic animal breed mainly distributed in the northwest region of Hebei Province. Donkey body shape is the most direct production index, can fully reflect the donkey’s growth status, and is closely related to important economic traits. As one of the main breeding selection criteria, body size traits have been widely used to monitor animal growth and evaluate the selection response. Molecular markers genetically linked to body size traits have the potential to accelerate the breeding process of animals  Domestic donkeys (Equus asinus) have facilitated the movement of goods and people for millennia , providiAs a complementary tool for conventional breeding, molecular marker-assisted selection has accelerated the breeding process of various domestic animals . The incIn this study, we detected significant SNP markers and candidate genes associated with body size traits within a population of 120 Yangyuan jennies using GWAS. GO and the KEGG pathway analyses were conducted. Our results may serve as a reference for molecular marker-assisted breeding in donkeys.ad libitum and water. Blood samples were taken from the jugular vein and rapidly stored at −80°C. Genomic DNA was extracted from the blood using a TIANamp genomic DNA kit . The quality and integrity of the extracted DNA were examined based on the A260/A280 ratio and 1% agarose gel electrophoresis. We also conducted measurements of the body height, body length, chest circumference, and shin girth. EXCEL 2019 was used to generate the descriptive statistics of the four body size traits.A total of 120 donkeys were selected for whole-genome sequencing in Yangyuan County, Hebei Province, China. All donkeys were adult jennies with unknow pedigree information. The animal feed consisted of grass and hay http://www.bioinformatics.babraham. ac.uk/projects/fastqc). Fastp (Genomic libraries with an insert size of ∼400 bp were constructed and sequenced using an Illumina NovaSeq instrument (2 × 150 paired-end mode), which was conducted by the Personalbio Biotechnology Co., Ltd. . FastQC was used to provide quality control checks on the raw sequence data . Fastp  was usedHigh-quality trimmed read pairs were aligned to the reference donkey genome (GenBank: PRJNA431818) by Burrows-Wheeler Aligner  with default parameters and SAM files were generated . The SAMHigh-quality mapped reads were used for variant calling with GATK (version 3.8) . To imprPCA of the 120 samples was conducted with GCTA  softwarehttp://genetics.cs.ucla.edu/emmax.)  by the efficient mixed-model association expedited (EMMAX) software (/emmax.) . The modp-value) = 7 were determined as the significant loci. The search for candidate genes was extended from 50 kb upstream and downstream from the significant SNPs.The Manhattan and quantile-quantile plot (Q-Q) plots were drawn by R packages. The SNPs reached the genome-wide significant level -log10  to predict the main biological pathways involved in the candidate genes.To provide insight into the functional enrichment of the candidate genes, we performed GO enrichment analysis  and the The body height, body length, chest circumference, and shin girth were collected from 120 Yangyuan jennies. The raw measurement data are detailed in To sequence the Yangyuan donkey genome, libraries with 400 bp inserts were sequenced and 861 Gb of raw sequence data were produced. The quality of the raw data is shown in We performed principal component analysis (PCA) with 9,735,956 SNPs to analyze the population structure of 120 Yangyuan donkeys. PCA revealed the relationship between the first two principal components, and we found that most of the individuals were clustered together and only a few individuals showed outlier distribution, which demonstrated that the genetic background of the population was relatively uniform . The phyp-values against the expected p-values are presented in Based on the detected SNPs, correlation analysis was performed according to the SNP and body height, body length, chest circumference, and shin girth traits to identify the markers and candidate genes that were closely associated with the target traits. In total, 16 SNPs reached a significance threshold of 7, and most of the SNPs were within the intronic, intergenic, and downstream locations of chromosomes 8, 13, and 31. The Manhattan plots for the four body size traits and corresponding Q-Q plots of the p-value, indicating the most significant enrichment, were selected for display, and the results are shown in The GO function annotation and KEGG pathway enrichment analyses were utilized to explore the biological functions of the candidate genes. The GO enrichment analysis results were classified based on the molecular function, biological process, and cellular components. The top 10 GO terms with the smallest The Yangyuan donkey is considered a valued Chinese indigenous breed, famous for its high disease resistance performance and strong adaptability. The donkey industry has been vigorously developed in many areas of northern China; however, the genetic mechanisms of body size traits of these donkeys have rarely been studied. Due to the long average generation interval (5 years) of donkeys, traditional approaches to donkey breeding yield slow progress. Undoubtedly, compared to conventional breeding, marker-assisted selection (MAS) could accelerate breeding programs . Moleculvia whole-genome sequencing, we reported some significantly correlated SNPs and candidate genes that could serve as potential candidates associated with body size traits. By annotating candidate regions, sphingomyelin phosphodiesterase 4 (SMPD4) was found to be a candidate gene greatly associated with body height and body length. SMPD4 is a sphingomyelinase from lipid rafts that hydrolyzes sphingomyelin into phosphorylcholine and ceramide, and regulates membrane composition in lipid rafts (With the development of molecular markers, the GWAS approach has been used extensively to understand the genetic basis of complex traits in animals , and is id rafts . The losid rafts . SMPD4 cid rafts . A key eid rafts . Researcid rafts . In rat id rafts . HoweverGO analysis showed that in terms of cellular components, the candidates focused on the components of chromosome remodeling complex, lysine methyltransferase, and membrane. Molecular function enrichment analysis highlighted the membrane protein activities, and biological processes were mainly enriched in response to certain organic substances and ion channel activity. According to KEGG analysis, pathways were mainly enriched on the P13K-Akt signaling pathway, the Rap1 signaling pathway, the regulation of actin cytoskeleton, calcium signaling pathway, phospholipase D signaling pathway, and neuroactive ligand-receptor interactions. Previous studies have shown that by regulating the P13K-Akt signaling pathway, the proliferation and differentiation of bone marrow mesenchymal stem cells and bone formation could be promoted . BlockinIn summary, from the GWAS results for the body size traits with SNPs detected from the whole-genome sequence in 120 Yangyuan donkeys, we identified 16 SNPs significantly associated with body size traits. All of the results provided good ideas for further revealing the Yangyuan donkey body size trait mechanisms, improving donkey production performance, and conducting marker assisted selection in donkey breeding.


Wolbachia have been developed as a tool for protecting humans from mosquito populations and mosquito-borne diseases. The success of using Wolbachia relies on the facts that Wolbachia are maternally transmitted and that Wolbachia-induced cytoplasmic incompatibility provides a selective advantage to infected over uninfected females, ensuring that Wolbachia rapidly spread through the target pest population. Most transinfected Wolbachia exhibit a strong antiviral response in novel hosts, thus making it an extremely efficient technique. Although Wolbachia has only been used to control mosquitoes so far, great progress has been made in developing Wolbachia-based approaches to protect plants from rice pests and their associated diseases. Here, we synthesize the current knowledge about the important phenotypic effects of Wolbachia used to control mosquito populations and the literature on the interactions between Wolbachia and rice pest planthoppers. Our aim is to link findings from Wolbachia-mediated mosquito control programs to possible applications in planthoppers. Wolbachia are a group of gram-negative bacteria that live inside invertebrate cells and have been successfully developed to control mosquitoes and mosquito-borne diseases by decreasing host population density or decreasing host virus transmission. Unlike chemical control approaches, which result in collateral destruction of beneficial insects, Wolbachia-mediated population control has proven to be an excellent vector-control agent because it targets a single species. Moreover, as the target population is suppressed, the chemical control approaches become less effective, while Wolbachia-mediated pest population control is more effective. Because of Wolbachia pervasiveness in nature and lack of genetic modification, Wolbachia-mediated control programs are accepted as environmentally friendly biocontrol strategies to control insect pest populations and disease vectors. To date, the Wolbachia control strategies successfully used have been limited to mosquitoes. There is a question of whether Wolbachia control strategies could be applied more broadly to other pest insects and insect-borne diseases.Oryza sativa), cultivated extensively in the tropical and subtropical regions of the world, is the staple food for billions of people worldwide  and groEL genes are used to distinguish the major phylogenetic subdivisions of Wolbachia. Due to extensive recombination and strong diversifying selection in the wsp gene, wsp should therefore be unsuitable for use alone for reliable Wolbachia strain characterization when trying to type and quantify strain diversity   . Based o studies . Overall species .Wolbachia strains are subdivided into 17 supergroups from A to R, except for supergroup G, which is controversial , which is located at the germarium and supports somatic stem cells   . In 1971 strains . A rangencreases .Wolbachia manipulate the reproduction of hosts, especially Wolbachia-induced CI, has attracted great attention in recent decades. Although the means by which Wolbachia mediate CI are currently unknown, there is a general consensus that Wolbachia modify sperm at an early stage of spermatogenesis, and a rescue activity takes place in the same Wolbachia-infected egg to reverse or neutralize the modification of sperm following fertilization  modify the infected sperm, resulting in CI when the modified sperm fertilizes an uninfected egg , brown planthopper , and white-backed planthopper  are the three serious and destructive pests of rice that directly cause 20 to 40% of crop loss globally each year , the Special Fund for Scientific Innovation Strategy-Construction of High-Level Academy of Agriculture Science (Grant Nos. R2021YJ-YB2007 and 202117TD), and the National Natural Science Foundation of China (grant no. 31901879).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In Tunisia, the recognition of the possibility of including longevity and disease resistance in dairy cattle selection objectives has been hypothesized as a useful strategy by both researchers and producers. However, in this paper, the state of the art, with a focus on health and longevity, is reviewed. Along the same lines, the heritability for the milk traits, fertility traits, and longevity of Tunisian Holstein dairy cows complies with the literature. Therefore, the influence of genetics on some diseases of the dairy cow was investigated. In addition, a decreasing efficiency in cow fertility has been observed over the last few years. The results showed that the risk of culling increased with common diseases. When analyzed with the Weibull model, functional lifespan was strongly influenced by milk yield; therefore, the risk increased with a reduced milk yield. In her first three lactations, the relative risk of selection increased gradually with lactation. Thus, the risk of thinning is highest at the beginning and end of the first feeding and the end of her second feeding. In conclusion, the risk of culling was reduced in parity. The factors that influence the life of the herd, such as health, husbandry, environmental conditions, and management, are often ignored when evaluating longevity. For the modern dairy cow, advances in genetics and breeding for productivity have resulted in an increased incidence of health disorders and reduced longevity. However, to maintain farm sustainability, farmers need to optimize the balance between maximum production and minimum production costs . MoreoveBrickell and Wathes  suggesteThe health and welfare of all animals in the dairy herd are paramount for profitable and efficient milk production. The disease is often measured by economic impact, but animal health is also part of animal welfare. The pain and discomfort caused by health problems affect an animal’s well-being, so animals must be in good health to improve animal welfare . Records3, being the highest in Autumn and associated with milk yield. Milk yield increased with the number of lactations and varied by the lactation stage. Smaller farms had lower somatic cell counts. The same study reported that the body condition score (BCS) ranged between 1.25 and 4 (lactating cattle) using a BCS scale between one and five. Most cows presented a BC score of 2.5 (50% of cows); however, most dry cows presented a BCS of 2.75 (65% of cows), ranging between a BCS of 1.5 and 4. A BCS of two or less was classified as ‘thin’. The mean number of lactating cows in this category on all farms was 18.9 ± 1.9%. As for lameness, the proportion decreased, with only 19 out of 350 cows (5.4%) showing moderate lameness. Lameness appears to have been the greatest welfare problem within the parameters investigated. In general, the avoidance distances are short, which is an indicator of good human-animal relations and may reflect good farming practices [Bovine tuberculosis, Campylobacter enteritis, Anthrax, Hemorrhagic septicemia, Mannheimia haemolytica, and Contagious bovine pleuropneumonia, etc.) are of paramount importance, particularly those considered zoonotic. Among these, Bovine tuberculosis (caused by infection with Mycobacterium bovis) is perhaps the most problematic. Heritability estimates on the observation and responsibility scales varied between 0.06 and 0.18; the standard errors varied between 0.012 and 0.044. The presumption of inheritance was based on the tuberculin test response and the presence of tuberculosis lesions confirmed during slaughterhouse testing. These results demonstrate that gene truncation can achieve a significant improvement in tuberculosis resistance [Bovine tuberculosis (bTB) is considered a major zoonosis in Tunisia and a break to the intensification of production. Tunisia has had a national bTB control program since 1984. It is based on the intradermal tuberculin skin testing of dairy cows and regular meat inspections at the slaughterhouse. Nonetheless, bTB remains prevalent, mainly in the private sector, where disease control is based on sparse veterinary practices and slaughterhouse testing without routine intradermal tuberculin skin testing [Lameness among dairy cows is widely recognized as one of the most serious (and costly) animal welfare issues affecting dairy cattle. Lameness is the third most common infection in cows in modern barns. Environmental factors  and genetic factors are responsible for this condition. Lameness is also recognized as an important welfare problem, causing pain, and impairing the cow’s ability to exhibit normal behavior ,13. In aractices . In dair testing  concludet”, given that it survived to time “t − 1”. A second genetic assessment model for survival is the multi-trait animal model (MT). Here, a trait is defined as survival (0 or 1) to a specified endpoint or within a specified time interval during the cattle’s lifetime [0 (t) = exp (−(ht) ρ) and its flexibility. Weibull regression can model constant (ρ = 1), increasing (ρN1), and decreasing (ρb1) hazards. Further analysis is greatly facilitated if h0(t) can be approximated. Due to its high economic value, longevity is an important part of dairy cattle breeding goals in many countries. Imbayarwo-Chikosi et al.  attributlifetime . Veerkamlifetime  proposedlifetime . Linear lifetime ,35. In plifetime , and thelifetime . In Tunilifetime . M’HamdiThe following Weibull model was used:h = hazard function of the cow at time t; 0h(t) = Weibull baseline hazard function at time t; hys = random time-dependent effect of the herd–year–season; plst = fixed time-dependent effect of the parity–stage of lactation; ml = fixed time-dependent effect of the class of milk production expressed as deviations within the herd–year; yb = fixed time-dependent effect of annual change in herd size; age1 = time-independent effect of age at the first calving; and sire = random time-independent effect of the sire of the cow.The average length of productive life was 37.5 months, and the heritability was 0.19. Ben Salem et al.  calculatThe linear model used to study the herd life was:ijklmnTHL is the true herd life µ: is an overall mean, HYj is the fixed effect of the herd by calving year j, Sk is the fixed effect of calving season k, NL is the fixed effect of lactation number, mAge is the fixed effect of age at first calving, and eijklmn is a residual effect.The longevity from the Tunisian studies varies between 37.5 . These rFemale fertility is one of the most important factors affecting the longevity of dairy cows. Many authors have reported antagonistic genetic relationships between reproductive and milk production traits ,46,47,48The estimated genetic correlation between the calving intervals and DO is the highest 0.85). The lowest estimated genetic correlation (0.25) was found between DFS and NCS. Makgahlela et al.  reported5. The loThe estimates of the genetic parameters for female fertility traits in Tunisian dairy herds are low, even lower than those reported in the literature. In short, to improve the reproductive performance of Tunisian Holstein breeds, selection should not only focus on fertility traits, but also on improving cattle reproductive management. The genetic correlations between health and fertility are good, and the selection for mastitis or lameness is associated with shorter calving intervals, shorter intervals from calving to first service, fewer inseminations to conception, and higher rates of non-reversion. This suggests that it is likely to lead to an increase . Poor heReproductive disorders reduce fertility, prevent conception, cause problems in delivering healthy calves, cause postpartum complications, increase inter-calving intervals, reduce milk yield, and affect overall life expectancy . Among tType traits are currently being measured as part of the genetic improvement programs aimed at linking trait types with milk production, conformation, reproduction, and longevity. The purpose of including type traits is to improve the conformation of cattle by providing better body and functional and reproductive structures so that they can meet the challenges of increased production . Linear During the last century, dairy cattle breeding schemes were based on the phenotypic data processed according to quantitative genetic rules. Evaluation accuracy requires heavy and expensive phenotype recording and the progeny testing of bulls by observing the performances of their daughters. Indeed, the application of quantitative genetics to dairy cattle breeding between 1960 and 2007 was very successful, increasing the milk yield and profitability of the production systems . In HolsFirst, a genetic marker approach based on the concept of the detection linkage relationships between genomic regions and quantitative traits-quantitative trait loci (QTLs)was utilized in the marker-assisted selection (MAS) process. The usual MAS idea is a three-step process: (i) detect one or more QTLs; (ii) find the causative gene (causative mutation); and (iii) increase the frequency of favorable alleles by selection or introgression. Except for a few cases , the impact of MAS in livestock breeding programs was minimal (10%), as a QTL above the selected significance level can usually explain only a fraction of the trait variance . In the SYT10 on chromosome 5, ADAMTS3 on chromosome 6, NTRK2 on chromosome 8, and DERL1 and SNTG1 on chromosome 14). Tiezzi et al. [BTA18) and were mostly located within the QTL regions associated with mastitis. In contrast, for the lifetime profit index, the strongest associations were detected on BTA14 and BTA18. They also observed an association between BTA6 and BTA20 for lactation persistency. For clinical mastitis, which is related to longevity, Bermotiene [Second, the recent availability and affordability of high-density panels of single nucleotide polymorphism (SNP) markers have opened up this new possibility. Mészáro et al.  proposedi et al.  concludermotiene  observedrmotiene . A totalrmotiene .The whole-Genome selection presents numerous advantages versus the traditional quantitative approach: (i) genomic information is not affected by environment; (ii) it can be available at an early age; (iii) it can be obtained on all selected candidates; (iv) it enhances the reliability in predicting the mature phenotype; and (v) the genomic approach helps us to select for a wide range of traits and, in turn, saves time and effort . The ‘deThe main conclusions of this review are:The genetic parameters observed for the Tunisian Holstein dairy cattle are in agreement with the literature used in the present manuscript.During the last few decades of the twentieth century, the application of quantitative genetics to dairy cattle breeding was very successful, increasing the milk yield and profitability of the production systems.At present, herd management focuses on the balance of functional traits  against production.The performance of an evaluation model depends on the definition of the trait, the quality of the data, and the strategy of processing the merit index, combining single-trait models or multivariate models for groups of traits.However, the revolution in genotyping provided by high-density SNP coupled with a cost reduction has resulted in large databases of individuals with genome-wide genotypic data. Moreover, in the last decade, genomic selection has been very successful and was quickly adopted in the largest populations worldwide; genetic trends have doubled, especially for traits with low heritability.In Tunisia, Genomic research is sporadic, with no implementation of a genomic selection program to date. Despite the success of several countries in increasing the annual genetic gain of different traits of economic interest, we recommend to Tunisia, as it is a member of the ICAR consortia, to reinforce the phenotypic recording system and extend it to functional parameters, mainly longevity, and initiate a genomic selection program. The implementation and validation of the genetic evaluation process, in the INTERBULL framework, would enable Tunisia and developing countries in a similar situation to overcome the gap caused by genomic selection.


Mus musculus domesticus) were introduced to the Americas by European colonizers and are now widely distributed from Tierra del Fuego to Alaska. Multiple aspects of climate, such as temperature, vary predictably across latitude in the Americas. Past studies of North American populations across latitudinal gradients provided evidence of environmental adaptation in traits related to body size, metabolism, and behavior and identified candidate genes using selection scans. Here, we investigate genomic signals of environmental adaptation on a second continent, South America, and ask whether there is evidence of parallel adaptation across multiple latitudinal transects in the Americas. We first identified loci across the genome showing signatures of selection related to climatic variation in mice sampled across a latitudinal transect in South America, accounting for neutral population structure. Consistent with previous results, most candidate SNPs were in regulatory regions. Genes containing the most extreme outliers relate to traits such as body weight or size, metabolism, immunity, fat, and development or function of the eye as well as traits associated with the cardiovascular and renal systems. We then combined these results with published results from two transects in North America. While most candidate genes were unique to individual transects, we found significant overlap among candidate genes identified independently in the three transects, providing strong evidence of parallel adaptation and identifying genes that likely underlie recent environmental adaptation in house mice across North and South America.Parallel clines across environmental gradients can be strong evidence of adaptation. House mice  as well as the identification of genes that show signals of selection. Here, we investigate the genetics of environmental adaptation in South America. We find that populations in South America evolve independently of populations in North America. We identify candidate genes for environmental adaptation with links to traits like body size, metabolism, immunity, eye function, and the cardiovascular and renal systems. We then bring together data from three transects across two continents to determine if environmental adaptation is predictable, with parallel genetic changes in response to shared conditions. We find that most evidence of environmental adaptation lies in regulatory regions and that, while most candidate genes are unique to individual transects, many are shared, providing significant evidence of parallel adaptation. We identify a core set of candidate genes independently identified in all three transects that likely contribute to environmental adaptation in the Americas. These results highlight the value of studying wild populations of this genetic model system. Drosophila melanogaster populations in the northern and southern hemispheres have identified shared responses to selection  provide an opportunity to study the genomic basis of environmental adaptation using natural replicates. Native to Western Europe, house mice have spread opportunistically around the world in association with humans during the last five hundred years (Trpm2) and body weight (Mc3r and Mtx3), suggesting some shared response to selection.House mice (ed years . In thised years . House med years . These oed years . Genomiced years  in tandeed years . FurtherM. m. domesticus) as mice in North America. However, sampling was limited, and it is not known whether there may be introgression from other subspecies. Patterns of genetic diversity and differentiation across the continent are also largely unknown as are the relationships to populations in North America. Importantly, some aspects of the environment, such as temperature, vary similarly across latitude in North and South America  as house mice in North America? House mice comprise three major subspecies which diverged ~150,000 – 500,000 years ago and have distinct ranges: M. m. musculus is found in Eastern Europe and northern Asia, M. m. domesticus is found in Western Europe and the Mediterranean region, and M. m. castaneus is found in SE Asia  identified eight genes associated with differences in body weight, all except one of which showed signatures of selection.Here, we explore genomic signatures of environmental adaptation in house mice from South America across a latitudinal transect from equatorial Brazil (~3° S) to southern Argentina (~ 55° S) using exome capture data of wild-caught individuals. We combined the data generated in this study with published data from eastern and western North America. We address five main questions. First, do house mice in South America derive from the same subspecies . We also detected a significant signature of isolation by distance among populations in SA , while there is no evidence of a correlation in ENA . These differences suggest that barriers to gene flow  may differ between the continents.The average 2R=0.85, p-value ≤ 2.2 x 10 −16; Fig. S1b). Mean annual temperature showed a very high degree of overlap, varying from near 5°C to over 20°C in all three transects. In contrast, precipitation of the driest month showed variation in all three transects, but little overlap among transects .We first explored variation in climatic variables across the sampled localities in South and North America. In a PCA analysis of the 19 bioclimatic variables from the WorldClim database, the first two principal components explained 71.53% of the total variance . As expected for latitudinal sampling in this region, the first principal component was largely driven by mean annual temperature  and other highly correlated temperature related variables. The second principal component was mainly associated with precipitation of the driest month  and precipitation of the driest quarter . Since our sampling design was based on a latitudinal transect, we were most interested in exploring the effects of climatic variables associated with PC1, for which MAT showed a significant negative correlation with latitude  correction as outliers. Genes that contained outlier SNPs were considered candidate genes in all following analyses.To identify candidate SNPs that show signals of environmental adaptation in South America, we used a latent factor mixed model (LFMM) that implements a Bayesian bootstrap algorithm and accounts for population structure . To avoiWe identified 9,600 outlier SNPs in >3,400 genes across the genome associated with variation in latitude and 7,481 outlier SNPs in >2,800 genes for MAT , S6. We p ≤ 0.05) in a variety of biological processes including those related to intracellular transport, heart development and function, retina development, lipid metabolism, behavior, and brain development (Table S8). We also identified top candidate genes for latitude  for which mutants are associated with phenotypes related to body weight and size, fat, metabolism , immunity, cardiac function, limb and organ morphology, locomotion, and eye function/development, among others , used MouseMine (MGI) to identify phenotypes associated with the genes , and Pang others , S6, S8.g others , S8. ForBcl2l11, Grem1, Jade2, Kif21a, Nlgn2, Plscr3, Sar1b, St18, Tbl1xr1), lipid and fatty acid metabolism, body fat, and related phenotypes , abnormal glucose homeostasis , immunity , abnormal eye/retina morphology , abnormal renal/urinary system , abnormal cardiovascular system morphology , abnormal limb and digit morphology (Grem1), and behavior (Nlgn2) . In humans, Plscr3 and Sar1b are linked to obesity and lipid metabolic disease, respectively, Kif21a is linked to ocular disease, and Nlgn2 is linked to schizophrenia , whereas candidates identified using PDM were largely unique , c. Cand (Nlgn2) . Four ofnia MGI, .The three transects were planned to capture responses to climatic variables that covary with latitude, allowing us to investigate the extent to which responses to selection are shared. As expected, all three transects have similar ranges in MAT, from near 5°C to over 20°C, although the shift is more gradual in South America, covering ~50 degrees of latitude compared to ~20 degrees of latitude in the other transects . To identify shared responses to selection among transects, we compared the candidate genes identified using each environmental variable in pairwise comparisons between transects (Table S10). We performed permutation tests using 100,000 replicates with replacement to evaluate whether the overlap in shared genes in pairwise comparisons was significantly greater than expected by chance (Table S11).To ensure standardization, we repeated genome scans using published exome-capture data for transects in eastern North America . Overlap among candidates identified using PDM was significantly more than expected by chance in only one comparison . The proportion of shared genes was also much higher for latitude  in at least one transect. Many of these have mutant phenotypes related to abnormal behavior (12), abnormal homeostasis (11) and related child terms, abnormal immune system morphology (6), and abnormal body size (6). Over half have GO annotations related to metabolic processes (14). Notable among these are genes like Mc3r which was identified as a top candidate in both North American transects and is functionally linked to body size and metabolism . Those 90 genes include many annotated to knock-out/mutant phenotypes for abnormal homeostasis (43), abnormal blood homeostasis (29), abnormal motor/coordination, movement (27), abnormal postnatal growth/weight/body size (23), abnormal cardiovascular morphology (21), abnormal immune system morphology (20), and abnormal glucose homeostasis (17), among others. GO annotations are also diverse, but many have biological processes annotations related to high level terms like regulation of biological processes (62), response to stimulus (51), metabolic processes (48), developmental processes (41), gene expression (21), immune processes (10), and growth (10). Of these 90 genes, 24 were top candidates  significantly associated with body weight .Multiple studies  have supody mass  and bothody mass . In SoutTrpm gene family. TRPM channels act as cellular sensors with impacts on diverse physiological processes, including temperature sensing, mineral homeostasis, cardiac rhythm, and immunity  and insulin secretion  and Trpm3 has been shown to function in heat sensitivity  point to great potential for continued investigation of the genetic basis of adaptation in this system and more broadly, the connection between genotype and phenotype in house mice .Mice were collected using live Sherman traps from sites within eight sampling locations along a latitudinal transect in South America and from two sampling locations in Mexico (DNA was extracted and exome capture libraries were prepared as in Escherichia coli genome (ASM584v2) to filter potential contamination. We mapped the exome raw reads against the E. coli genome, and we retained the unmapped reads using HISAT2 (https://broadinstitute.github.io/picard/)  . We follpicard/) .−6 , and we removed those sites with a minimum allele frequency (minMaf) less than 5%. Finally, we retained those variants that were present in at least 80% of the samples, obtaining a total of 271,720 SNPs.Based on the Bayesian method implemented in ANGSD , we estiMus musculus, we included the previously published genomic information for 50 individuals of M. m. domesticus from Eastern North America  using BWA-MEM. The mapped reads were sorted, duplicates were marked and removed using picard and samtools software. We used GATK pipeline to identify artifacts or technical errors (BSQR and ApplyBQSR). Additionally, we performed a local realignment for indels and a variant filtering using the variant quality score recalibrations (VSQR) . FinallyNGSadmix for several K values: K=2 to K=5, with 5,000 as the maximum number of EM iterations. We used the log-likelihood estimated for each K to calculate the Cluster Markov Packager Across K from Evanno using R scripts.We used the autosomal recalibrated bam files and the software ANGSD  to calcuM. spretus  (Table S2), as an outgroup.To investigate the phylogenetic relationships between the South American house mice populations and their close relatives from North America, we used the exome sequence data generated for this study, 86 individuals from Mexico and South America, and the exome data from the 100 individuals from North America included in the admixture analysis . We alsoM. m. domesticus and M. spretus. We carried out the SNP calling first using all the individuals and then excluding Mexico due to small sample size. We filtered to only include the most suitable SNPs for phylogeny construction, using Variant quality score recalibration (VQSR) and QD>= 20  to identify variants. Only biallelic sites were retained. Additionally, we pruned SNPs for linkage disequilibrium using non-overlapping 50 kb sliding windows, and an r2 = 0.5 (50 50 0.5), allowing a missing data cut-off of 20%. Recalibrated bam files, ngsDist from NGS Tools  was used to visualize the tree.We used Haplotype Caller from GATK pipeline to perform the SNP calling in autosomal genomic regions for all the individuals of GS Tools , and RaxGS Tools  were usep-value = 1x10−3 and 5% minimum allele frequency) generated from the autosomal bam files of 186 individuals. We used R to perform the eigenvalue decomposition and generated the PCA plot using the three principal components. With ANGSD, we estimated stF for each pair of populations (North and South America), using the unfolded pairwise site frequency spectra (SFS) as priors for the allele frequency probabilities at each site. Additionally, we used VCFtools to calculate pairwise Weir and Cockerman’s stF for all the populations. Finally, for the populations from South America, we analyzed the relationship between genetic distance and the geographic distance by performing a Mantel Test using the R packages vegan, adegenet and hierfstat  and the program ngsRelate  for the 186 individuals of house mice from South America, eastern and western of North America (Table S1). Based on the geographic coordinates, we extracted the 19 bioclimatic variables from WorldClim database . We identified candidate SNPs using a threshold q-value ≤ 0.05 and z-score ≥ 2.To identify candidate genes underlying environmental adaptation, we performed a population genomic scan for selection using the Latent Factor Mixed Model (LFMM) software  and threTo explore the potential functional significance of the candidate SNPs identified, we used Ensembl’s Variant Effect Predictor command line software  with thep-values were adjusted using the Fisher’s exact test and the false discovery rate. We generated a network correlation of the most significant nodes obtained with the package topGO and networkplot in R.Additionally, we extracted from The Mouse Genome Informatics Database MGI;  the geneWe used the R package “VennDiagram”  to identq-value ≤0.05 and z-score ≥ 2), we performed linear regressions between the allele frequencies for each candidate SNPs and latitude, and we retained those SNPs with a 2R ≥ 0.65 and p-value ≤ 0.01.We estimated the allele frequencies for the eight populations from South America  using the samtools. Based on the list of candidate SNPs  as described above.We conducted a genome wide association analysis to identify genes that contribute to variation in body weight using the program GEMMA  that impSupplement 1


To quantify 5-HTT availability, binding potential (BPND) of [11C]DASB was obtained using the simplified reference tissue model. The Temperament and Character Inventory was used to assess subjects’ levels of three character traits. There were no significant correlations between the three character traits. Self-directedness was significantly positively correlated with [11C]DASB BPND in the left hippocampus, left middle occipital gyrus, bilateral superior parietal gyrus, left inferior parietal gyrus, left middle temporal gyrus (MTG), and left inferior temporal gyrus (ITG). Cooperativeness was significantly negatively correlated with [11C]DASB BPND in the median raphe nucleus. Self-transcendence was significantly negatively correlated with [11C]DASB BPND in the right MTG and right ITG. Our results show significant correlations between the three character traits and 5-HTT availability in specific brain regions. In particular, self-directedness was significantly positively correlated with 5-HTT availability, suggesting that a goal-oriented, self-confident, and resourceful character may be related to higher serotonergic neurotransmission.To elucidate the potential roles of serotonergic activity in human character traits , we investigated the relationship between these character traits and serotonin transporter (5-HTT) in healthy subjects. Twenty-four participants underwent High-Resolution Research Tomograph–positron emission tomography scans with [ Three character traits from the Temperament and Character Inventory (TCI) suggested by Cloninger et al. , “self-dLike character traits, the serotonergic system is also associated with general psychological well-being and positive emotions ,7,8,9,102A receptor (5-HT2AR) genes.Moreover, several genetic studies in humans have shown relationships between human character traits and the serotonergic system ,14,15,1611C]DASB binding in the caudal and median raphe nuclei, respectively [11C]MADAM binding in the dorsal raphe nucleus DASB, a radioligand that specifically binds to 5-HTT, to elucidate the potential roles of serotonergic activity in these character traits.Therefore, we investigated the relationship between three character traits and in vivo cerebral 5-HTT availability in healthy individuals using High Resolution Research Tomograph (HRRT)-PET with DASB binding potential (BPND) in the left middle temporal gyrus (MTG) , left inferior temporal gyrus (ITG) , bilateral superior parietal gyrus (SPG) , left inferior parietal gyrus (IPG) , left middle occipital gyrus (MOG) , and left hippocampus (HIP) . Cooperativeness was significantly negatively correlated with [11C]DASB BPND in the median raphe nucleus (rapheM) . Self-transcendence was significantly negatively correlated with [11C]DASB BPND in the right MTG  and right ITG . Details of these results are shown in Region-of-interest (ROI)-based partial correlation analysis with age and sex as covariates revealed that self-directedness was significantly positively correlated with [11C]DASB BPND in the temporal pole of the left superior temporal gyrus (STG) (p < 0.0001), right calcarine fissure and surrounding cortex (V1) (p = 0.0001), left IPG (p = 0.0001), left MOG (p = 0.0002), and left MTG (p = 0.0002), and a significant negative correlation between self-transcendence and [11C]DASB BPND in the right ITG (p = 0.0002). Details of these results are shown in Further voxel-based partial correlation analysis with age and sex as covariates revealed significant positive correlations between self-directedness and [11C]DASB PET study to report significant associations between three character traits and in vivo 5-HTT availability in healthy subjects.In this study, we found that the 5-HTT availability in specific brain regions was positively correlated with self-directedness and negatively correlated with cooperativeness and self-transcendence, indicating that serotonergic neurotransmission is significantly involved in these character traits in healthy subjects. To date, only two molecular PET studies have demonstrated associations between one or two character traits and in vivo 5-HTT availability ,18, and 11C]MADAM PET study, higher 5-HTT BPND in the raphe nucleus was associated with higher self-directedness scores DASB BPND; however, these methods are invasive as they require radial artery cannulation, and the resulting discomfort might be a confounding factor and would also lead to limited recruitment of participants. Inaccuracies in determining the arterial input function can also be a source of bias in endpoint estimation [11C]DASB BPND values were obtained using simplified reference tissue model 2 (SRTM2) [11C]DASB BPND [11C]DASB BPND may be slightly underestimated by the use of the cerebellum as a reference region should not be completely ruled out DASB BPND values exhibited substantial variances. The wide dispersion of data points may have resulted in relatively low statistical power in examining the observed correlations. Consequently, our study may have been limited in detecting relatively weak associations between character traits and 5-HTT availability as assessed using the ROI-based correlation analysis.As illustrated in Future dual-tracer PET imaging studies with radiotracers that bind to presynaptic and postsynaptic markers of the 5-HT system should be conducted to completely understand the complex relationship between character traits and the 5-HT system in humans.This study was approved by the Institutional Review Board of the Gachon University Gil Medical Center, and all study procedures were conducted in accordance with international ethical standards and the Declaration of Helsinki. Written informed consent was obtained from all subjects after a full explanation of the study purpose and procedures before participation in the study. Twenty-four healthy subjects  were recruited through local poster advertisements, and met the following criteria: (i) age between 19 years  and 60 years; (ii) no past or current psychiatric disorders from the Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV) , establiThe TCI was used to measure human character dimensions . Among these character traits, self-directedness is defined as an individual’s executive ability to regulate and adapt behavior to situations in accordance with personal goals and values  and is c11C]DASB using an HRRT-PET scanner . A transmission scan was performed using a 137Cs point source prior to [11C]DASB injection for attenuation correction. After a bolus injection of 684.0 ± 51.3 MBq [11C]DASB with an average specific activity of 47.3 ± 18.3 GBq/μmol  equipped with a 20-channel receiving head/neck coil. A three-dimensional T1-weighted magnetization-prepared rapid gradient echo sequence was used for structural MRI data and the scan parameters were set as follows: repetition time = 1800 ms, echo time = 2.61 ms, inversion time = 900 ms, flip angle = 10°, voxel size = 0.5 × 0.5 × 1.0 mm11C]DASB PET was conducted using Statistical Parametric Mapping 12 ). For each subject, realignment was performed to correct for motion within all reconstructed PET frames, and the structural MRI image was coregistered to an averaged PET image derived from the realignment process. The coregistered structural MRI images and the corresponding PET frames were spatially normalized using the Montreal Neurological Institute template. Based on parameter estimation by kinetic modeling implemented in PMOD software v4.2 , [11C]DASB BPND was obtained using SRTM2 [11C]DASB BPND estimation step, regional TACs were extracted from spatially normalized PET frames by averaging all voxels within 52 a priori ROIs associated with the serotonergic system based on a previous PET study DASB BPND values in each ROI was assessed using Levene’s test. For these analyses, a two-tailed p < 0.05 was considered statistically significant.The ROI-based statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) v28.0 . Pearson’s correlation analysis was used to examine the relationships between three character traits, and the equality of variances of DASB BPND in specific brain regions, we also performed a voxel-based analysis with the same data using SPM12. In this analysis, we set a false discovery rate (FDR)-corrected p < 0.05 as the significance threshold for multiple correlations. However, if no significant correlation was found at the FDR-corrected threshold, we further investigated significant results at uncorrected p < 0.001 with an extent threshold of 10 voxels, which has been considered statistically significant in previous PET studies [To complement the ROI-based results on the relationship between each character trait and [ studies ,66.Our findings raise the possibility that there may be significant in vivo correlations between the three character traits and 5-HTT availability in specific brain regions in healthy subjects. In particular, self-directedness was significantly positively associated with 5-HTT availability, suggesting that a goal-oriented, self-confident, and resourceful character may be related to high serotonergic neurotransmission.