Phenotypic traits were simulated based on previously established means and heritability estimates ; traits were distributed in a genome with 10 linkage groups, considering two alleles per marker. Four different scenarios were considered. For the principal trait, heritability was 50%, and 40 control loci were distributed in five linkage groups. Another phenotypic control trait with the same complexity as the principal trait but without any genetic relationship with it and without pleiotropy or a factorial link between the control loci for both traits was simulated. These traits shared a large number of control loci with the principal trait, but could be distinguished by the differential action of the environment on them, as reflected in heritability estimates . The coefficient of determination were considered to evaluate the proposed methodologies. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the tested traits. Computational intelligence and machine learning were superior in extracting nonlinear information from model inputs and quantifying the relative contributions of phenotypic traits. The R2 values ranged from 44.0% - 83.0% and 79.0% - 94.0%, for computational intelligence and machine learning, respectively. In conclusion, the relative contributions of auxiliary traits in different scenarios in plant breeding programs can be efficiently predicted using computational intelligence and machine learning.The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant breeding. Data of an F Its main objective is to increase the frequency of desirable alleles in plant populations to develop superior crops with high productivity, disease and pest resistance, abiotic stress tolerance, and environmental adaptability , 2. Quanh2, and are closely correlated with the principal trait. Indirect selection through a secondary trait may be more efficient than direct selection if h2 is higher for the secondary trait than for the primary trait, and if the genetic correlation between the primary and secondary traits is sufficiently strong [The efficiency of selection can be increased by selecting secondary traits that are easy to measure, have high y strong .Although the simultaneous evaluation of traits in a plant breeding program provides massive data, identifying the most important predictive phenotypic trait is a challenge for breeders. Conventional methods of phenotypic trait selection are based on multiple linear regressions. For instance, mixed models are often used in plant breeding programs . In prinIn this context, computational intelligence may serve as an alternative to predict complex traits from auxiliary traits , 8–11. AMultilayer perceptron (MLP) and radial basis function (RBF) networks are the most commonly used ANNs. The MLP classifier has the typical architecture of an ANN with at least one hidden layer and one output layer, both with nonlinear and differentiable transfer functions –20. The Other interesting alternatives for predicting and quantifying the importance of auxiliary traits are machine learning-based methodologies, such as decision trees , 7, 19 aTo this end, the present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools useful in plant breeding programs.2 population represented by 500 individuals, derived from a cross between contrasting homozygous parents, was used.A set of simulated data of an Fh2 estimates. The h2 values used were 30%, 50%, and 80%, respectively (Eleven phenotypic traits (PTs) were simulated using previously established means and ectively . The traPT1 was used as the principal trait of prediction with 50% heritability and 40 control loci of the trait distributed in LGs 1, 2, 3, 4, and 5 . Ten auxi is the simulated observation of the trait of the ith individual, μ is the general average of the trait, whose value is specified by the breeder, Gi is the effect associated with the ith individual, with i is the random error, where εi~N, and PT3, PT4, and PT5 in different scenarios represented other important traits ; they shαj = ai+di and μ+aj, μ+dj, and (μ−aj) are the genotypic values associated with classes AA, Aa, and aa, assumed to be equal to 1, 0, and -1, respectively, when coded; pj is the contribution of the locus j to the expression of the trait, with a uniform distribution; and di is average degree of dominance of each trait (di = 0.5).The total phenotypic value in the epistatic model, expressed by an individual belonging to the population, was estimated using the following equation:A total of 1,000 codominant molecular markers, with two alleles per marker, were used. These markers were distributed in a genome established by 10 LGs, reflecting a diploid species with 2n = 2x = 20. Each LG was 100 centimorgans; thus, 100 markers were evenly spaced. All the markers are described in Four different scenarios were considered to predict phenotypic traits (PT1). These scenarios differed in terms of the four loci controlling quantitative traits PT3 to PT11 . ScenariGSy(x) is the indirect selection gain in y (x), which is selection intensity (0.9659); x is the principal trait under selection; hx is the square root of heritability; sgy is the phenotypic standard deviation.Indirect selection gain through principal traits was estimated as described by  using thTo predict PT1 and determine the importance of other traits (PT2 to PT11), multiple regression, computational intelligence, and machine learning were used.R2) between the dependent and independent traits. For prediction using stepwise multiple regression, PT1 was used as the principal trait and the others as auxiliary traits. The R2 values were used to verify the extent to which the independent traits explained the total variation in the dependent trait. The following model was used to predict PT1:y is the response variable (PT1); x1 to xk (PT2 to PT11) are the explanatory variables; β0 represents the intercept; β1 and βk are the coefficients associated with the variables x1 to xk; and ε is the residual effect.Stepwise multiple regression selects a predictor trait at the expense of the coefficient of determination  layer located between the input and output layers. For the best efficiency of this network, before training, the data were normalized by an interval between − -1 and 1. The maximum number of training periods was set to 5,000, and the minimum mean square error (MSE), to stop processing the network, was set at 1.0 × 10R2 estimates. This importance was estimated by exchanging information on or making the phenotypic value of each trait constant and verifying the changes in the R2 estimates. When the values of a trait are disturbed, the value of R2 decreases, indicating that the trait is important relative to the others for the purpose of prediction.To quantify the importance of phenotypic traits using the MLP network, two methodologies were used, namely Garson’s algorithm (1991) and modified by Goh . In this2 and obtained by permuting its values, was quantified using the following equation:R2 of the ANN topology obtained using the original predictor variables and established by the square of the correlation between the predicted and observed values, and R2 of the same topology as the ANN and was obtained from a dataset in which the values of xi were altered by the permutation procedure.The relative importance of the variable, measured by the reduction in RR2, and the relative importance of each trait was measured by de-structuring the information on each explanatory phenotypic trait, as described for MLP above.RBF network is characterized by having only one hidden layer and using a Gaussian activation function. The structure of the RBF that best predicted PT1 was established with 10 to 30 neurons (increased by 2 at each processing step) and a radius of 5 to 15 . The efficiency of prediction was measured based on R2, and the MSE was used to quantify the importance of phenotypic traits.Machine learning is one of the techniques used in artificial intelligence, and it allows for the detection of patterns in large datasets and the development of predictive models . LearninThe importance of the explanatory trait was determined by estimating the percent increase in MSE (%IMSE). %IMSE was derived for each predictor variable from the difference in MSE between the predictive measure based on the original dataset and that based on a permuted dataset, where the predictor in question was randomized . To imprxi values were changed by the permutation procedure. MSE is the mean square error of the same methodology obtained from the original predictor variables and is a function of the square of the deviations between the predicted and observed values.The relative importance of the variable (IV) was obtained by permuting its values and quantified using the following equation:The dataset was divided into two parts: a training and validation set. The training set included the same individuals for modeling using all methodologies and was composed of 80% individuals in each class, selected at random. The remaining 20% of individuals constituted the validation set. In previous studies, 60% to 90% of individuals constituted the training set , 31. ForData simulation and analysis were performed using the R package NeuralNetTools  and GeneR2 estimates for all methodologies, using the explanatory traits for PT1, are shown in R2 values indicate that the principal trait of prediction is more adaptable than the other explanatory phenotypic traits [The c traits .R2  and multiple linear regression  [R2) to verify the effectiveness of methodologies for the prediction or classification of simulated populations [The en layer . The difctively) ; moreoveulations , 42, 43.R2 of 82.14% was obtained for scenario 4. For scenarios 2 and 3, 29 neurons were established, and R2 values of 77.89% and 75.49% were obtained, respectively. In scenario 1, 30 neurons were established, and a maximum R2 value of 83.02% was obtained  of PT5. Meanwhile, the correlation between PT1 and PT2 was not significant (P > 0.05) in all scenarios, except in scenario 2, which is consistent with the simulation results and the absence of shared control loci between these two traits. These results were expected because PT2 had no genetic relationship with PT1 due to the absence of pleiotropy or a factorial link between the loci controlling these phenotypic traits.PT1 showed a positive and significant correlation with PT5 (P < 0.01) in all scenarios, probably because of the large number of shared control loci and high h2 value. The placement of groups 4 and 5 relative to PT1 was consistent with the simulation of genetic structures, allowing satisfactory prediction of importance, albeit lower than expected relative to group 3 but higher than expected relative to PT2.The phenotypic correlation network is shown in In breeding programs, understanding the meaning and degree of association between traits plays an important role in the development of selection strategies that facilitate the production of superior genotypes. One of the most used techniques to estimate these associations is the Pearson correlation, which is interpreted as the strength of the linear association between a pair of characteristics . When moThe estimates of the correlation coefficients can help improve our understanding of a complex character, such as production, but they do not determine the relative importance of the direct and indirect influences of the other characters on production . This ish2 = 80%) in all scenarios. In scenario 2, the highest indirect selection gain was achieved with PT3 and PT4 . Meanwhile, in scenarios 3 and 4, the highest selection gain was achieved with PT4 (h2 = 50). Therefore, a greater number of shared control loci affected indirect selection gain. The success of indirect selection depends mainly on heritability and genetic correlation between the primary and secondary traits [y traits ; therefoR2 shows a great reduction after disturbing the values of a trait, the phenotypic trait is important relative to others for prediction. The relative importance of phenotypic traits based on the reduction in R2  modified by Goh . This alThe method proposed by Garson (1991), modified by Goh , identifThe percent relative contributions of the 10 phenotypic traits relative to PT1 in the four scenarios estimated by this method are listed in ANNs most often exhibit satisfactory performance compared with other machine learning-based predictive algorithms . The MLPThe results of quantification of the importance of phenotypic traits using RBF after the permutation of the traits and assignment of a zero value to the input trait are shown in R2  in four different scenarios are shown in PT5 was estimated as the most important phenotypic trait in all machine learning methodologies and in all scenarios. This result is consistent with that of the computational intelligence methods Tables –8. AlthoThe discriminatory power, redundancy, precision, and complexity can influence the indices or statistics used to quantify the importance of auxiliary traits in the prediction of a principal trait. Thus, the selection of a prediction method and index reflecting the true importance of each auxiliary trait is imperative. In the present study, we propose some genetic structures that can allow us to estimate the efficiency of procedures using computational intelligence and machine learning to quantify the relative contribution of phenotypic traits in different scenarios for the implementation of these techniques in other studies. The lack of information on the implementation of computational intelligence and machine learning in breeding programs to predict phenotypic traits is a challenge for breeders. However, with the relevant recent advances in biotechnology and high-throughput phenotyping, more information can be obtained for the identification of genetically superior individuals.Breeding for desired traits in crops has long been a time-consuming, labor-intensive, and expensive process. Breeders study generations of plants and identify and modify desired genetic traits, as they assess how traits are expressed in offspring , 58. Thea priori knowledge of prediction is not required if the data produce these effects, and no assumptions are made regarding the distribution of phenotypic values [Methodologies based on machine learning and computational intelligence do not depend on stochastic information and tend to be more efficient, whereas conventional methodologies depend on the normality of the distribution of phenotypic traits. Moreover, machine learning and computational intelligence methodologies make no assumptions regarding the model and can capture complex factors, such as epistasis and dominance, in prediction models. In machine learning, c values . Machinec values . Unlike c values –64 reporComputational intelligence and machine learning can efficiently predict the relative contributions of auxiliary traits in different scenarios in plant breeding programs. PT5 was identified as the most important predictor of PT1.S1 File(RAR)Click here for additional data file.


Milk production is an important trait in the breeding and genetic improvement of Xinjiang Brown cattle. To obtain the best strategy for improving the reliability of the breeding value estimation for each trait, we used single-trait and multitrait models based on the A-array pedigree-based best linear unbiased prediction (PBLUP) and H-array single-step genomic best linear unbiased prediction (ssGBLUP) to perform the genetic evaluation of different strategies using the restricted maximum likelihood (REML) and Bayesian methods. Upon comparison, the ssGBLUP calculation results of the multitrait models obtained using the REML and Bayesian methods were better than those of other strategies. Considering the calculation time, the multitrait model REML method is recommended for ssGBLUP calculation to accurately predict the breeding value of young animals; thus, this strategy should be used for the early breeding selection of Xinjiang Brown cattle.One-step genomic selection is a method for improving the reliability of the breeding value estimation. This study aimed to compare the reliability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP), single-trait and multitrait models, and the restricted maximum likelihood (REML) and Bayesian methods. Data were collected from the production performance records of 2207 Xinjiang Brown cattle in Xinjiang from 1983 to 2018. A cross test was designed to calculate the genetic parameters and reliability of the breeding value of 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS) of Xinjiang Brown cattle. The heritability of 305 dMY, MFY, MPY, and SCS estimated using the REML and Bayesian multitrait models was approximately 0.39 (0.02), 0.40 (0.03), 0.49 (0.02), and 0.07 (0.02), respectively. The heritability and estimated breeding value (EBV) and the reliability of milk production traits of these cattle calculated based on PBLUP and ssGBLUP using the multitrait model REML and Bayesian methods were higher than those of the single-trait model REML method; the ssGBLUP method was significantly better than the PBLUP method. The reliability of the estimated breeding value can be improved from 0.9% to 3.6%, and the reliability of the genomic estimated breeding value (GEBV) for the genotyped population can reach 83%. Therefore, the genetic evaluation of the multitrait model is better than that of the single-trait model. Thus, genomic selection can be applied to small population varieties such as Xinjiang Brown cattle, in improving the reliability of the genomic estimated breeding value. Xinjiang Brown cattle was the first breed of cattle used for milk and meat purposes after the founding of the People’s Republic of China . The breThe large-scale breeding of Xinjiang Brown cattle population is limited, and the number of the grazing or semihouse feeding and semi-grazing population is high. Although the production performance database of core breeding farms has been preliminarily established, the limitations of Xinjiang Brown cattle production performance data, coupled with the lack of pasture management and the presence of a significant amount of truncated data, have led to the genetic evaluation of Xinjiang Brown cattle in failing to achieve better reliability in terms of genomic estimated breeding value, thus influencing the accurate selection of excellent breeding animals. Compared with PBLUP, genome prediction can reduce the breeding cost and generation interval by at least 50% , leadingThe multitrait BLUP (MBLUP) has been studied for decades. It is a method for the genetic evaluation of individuals for two or multiple traits using information such as the phenotypic and genetic correlations of traits. An advantage of MBLUP  is the iTherefore, this study aimed to use the REML and Bayesian methods to estimate the genetic parameters of the milk production traits of Xinjiang Brown cattle using the A and H matrices, respectively, with a goal to ultimately obtain the variance components and genomic breeding values of each trait of the single- and multitrait models; this will improve the reliability of the estimated values and provide theoretical support for a more accurate estimation of the genetic parameters of the Xinjiang Brown cattle population.Data of Xinjiang Brown cattle were obtained from 7516 production performance measurement records and 16,795 pedigree records of the abovementioned four Xinjiang Brown cattle breeding pastures  from 1983 to 2018 as well as DHI measurement records from 2010 to 2018. The pedigrees of Xinjiang Brown cattle breeding bulls, Xinjiang Brown cattle adult cows, and Swiss brown cattle breeding bulls were traced as well. This included 676 Xinjiang Brown cattle breeding bulls, among which 1 breeding bull had the most offsprings (619 offsprings), and 221 breeding bulls had only one offspring; 583 adult cows of Xinjiang Brown cattle had only one offspring, and 6199 adult cows of Xinjiang Brown cattle had ≥2 offsprings; the maximum number of offsprings was 12. The following milk production traits were obtained after sorting: 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS). SCC conversion to SCS is based on the formula determined by the American Dairy Cattle Improvement Program Board: Effect Division: The field effect was divided into four levels, where the calving year was divided into seven levels according to phenotypic records: 1985–1995, 1996–2000, 2001–2005, 2006–2008, 2009–2011, 2012–2014, and 2015–2018; the calving season effect, according to the unique climatic conditions in Xinjiang, was divided into spring (April and May), summer , autumn (September), and winter  using the pentad mean temperature method; the birth order effect was divided into six levels: 1, 2, 3, 4, 5, and 6 (including >6 births).p-value of >1 × 10−6 were excluded. Finally, 118,021 SNP loci of Xinjiang Brown cattle were retained for subsequent analysis.The chip data included 403 Xinjiang Brown cattle cows, 71 Xinjiang Brown cattle bulls, and 11 Swiss brown cattle bulls, where a total of 139,376 single nucleotide polymorphism (SNP) loci were detected using the GeneSeek GGP Bovine 150 K chip (Illumina). The Beagle v.4.1 software was used for imputation, which mainly inferred the presence of haplotypes in the population based on the principle of linkage disequilibrium. Therefore, the quality of the chip data needed to be controlled to ensure the accuracy of imputation. Quality control standard: the SNP loci with an individual genotyping detection rate of <90%, individual genotype deletion rate of <10%, minor allele frequency of >0.01, and Hardy Weinberg equilibrium A or H matrix) were constructed using single-trait models, PBLUP and ssGBLUP, and the genetic parameters of milk production traits of Xinjiang Brown cattle were estimated using the REML method.Different relationship matrices , b is the fixed effect vector , a is the random additive genetic effect vector, e is the residual effect vector, X is the fixed effect coefficient matrix, and Matrix expression formula of each trait model is shown below:A is the additive genetic relationship matrix based on the pedigree, H is the genomic-pedigree joint relationship matrix, I is the unit matrix, and A and G values, H is calculated using the following formula:A represent the populations of non-genotyped and genotyped animals, respectively; G is the genomic relationship matrix, and its calculation formula is M is the incidence matrix of SNP effect, where elements jth SNP; m is the number of markers; and Kth SNP. Therefore, the formula for H−1 is For PBLUP  or ssGBLith trait, ith trait, ith trait, and A is the additive genetic effect matrix; ith trait, respectively; i ≠ j) are the additive genetic covariance and random error effect covariance between the ith and jth traits, respectively. The formula for H−1 is The REML and Bayesian methods were used on the multitrait animal model to perform genetic parameter estimation based on PBLUP and ssGBLUP, where the matrix form of the multitrait model  is as foThe REML method of the REMLF90 module of BLUPF90 software  was usedThe calculation formula of heritability is as follows:The formula for the standard error of heritability is shown below:The definition of reliability of GEBV was:a and PEV is the prediction error variance and If GEBV is unbiased, 2h > 0.3). However, the heritability of SCS was approximately 0.07 (0.02), which suggested a low heritability trait (2h < 0.1).In this study and the one by Zhou Jinghang, the heritability of 305 dMY and SCS estimated using the REML method of the DMU software based on the pedigree data of Xinjiang Brown cattle was 0.40 (0.017) and 0.08 (0.009), respectively, which were both higher than the heritability results of PBLUP calculated using the REML and Bayesian methods in this study; the standard error of the former was lower. However, the heritability results were similar to those of ssGBLUP calculated using the REML and Bayesian methods in this study. The estimated heritability of MFY and MPY was 0.30 (0.013) and 0.20 (0.011), respectively, which were significantly lower than the results estimated using the REML and Bayesian methods for different genetic relationship matrices of the multitrait model in this study . In addiIn this study, the heritability obtained using the REML and Bayesian methods in the multitrait model was higher than that in the single-trait model. Furthermore, in all model methods, except for the decrease in SCS, the heritability of other traits calculated based on ssGBLUP was slightly higher than that calculated based on PBLUP ; the staeviation ,44. For eviation ,46. Thereviation . In addieviation . As the eviation , it is feviation ; howeverWhen using the REML method of the single-trait model, only the reliability of the breeding value of 305 dMY was >0.4. The reliability of the breeding value of milk production traits estimated based on PBLUP and ssGBLUP of the REML and Bayesian methods in the multitrait model ranged from low to moderate. In general, the increased ratio of the reliability of genotyped population was 0.9%–3.6% higher than that of the overall population, and the GEBV reliability of the genotyped population was up to 83%. Compared with PBLUP, ssGBLUP method with chip information added significantly and simultaneously improved the reliability of breeding value estimation in both REML and Bayesian methods , which iRelying on the linkage disequilibrium between SNP markers and quantitative trait locus of target traits, genomic prediction promotes the association between SNP markers and individual phenotypic values. Thus, the accurate estimation of SNP’s genetic effect based on the phenotypic value has become a key factor in genomic prediction; the accuracy of genomic prediction  can be iWith increased reference population size and improved pedigree data integrity, the accuracy of EBV or genomic breeding estimation increases . HoweverIt is feasible to use ssGBLUP to perform the genomic evaluation of the milk production traits of Xinjiang Brown cattle. However, the single-trait model does not consider the covariance between traits, and thus has a large error. The use of the multitrait model, regardless of the use of PBLUP or ssGBLUP, can greatly improve the reliability of the breeding value estimation. Moreover, the estimated genetic parameters provide a basis for calculating a more accurate breeding value of Xinjiang Brown cattle. However, if it is needed to obtain a higher level of breeding value reliability, the number of genotypes needs to be simultaneously expanded when using high-density chips (150 k). Nevertheless, applying the ssGBLUP method to the genetic evaluation of Xinjiang Brown cattle necessitates further study and analysis. The next challenge is to build a joint reference group through cross variety GS to expand the number of genotyped animals with the objective to improve the reliability of the breeding value estimation of various traits of Xinjiang Brown cattle.


Lens culinaris Medik.) is one of the major cool-season pulse crops worldwide. Its increasing demand as a staple pulse has led to the unlocking of diverse germplasm collections conserved in the genebanks to develop its superior varieties. The Indian National Genebank, housed at the Indian Council of Agricultural Research (ICAR)-National Bureau of Plant Genetic Resources, New Delhi, India, currently has 2,324 accessions comprising 1,796 indigenous and 528 exotic collections. This study was conducted to unveil the potential of lentil germplasm by assessing its agro-morphological characteristics and diversity, identifying trait-specific germplasm, and developing a core set. The complete germplasm set was characterized for two years, i.e., 2017–2018 and 2018–2019, and data were recorded on 26 agro-morphological traits. High phenotypic variability was observed for nine quantitative and 17 qualitative traits. A core set comprising 170 accessions (137 Indian and 33 exotic) was derived based on the characterization data as well as geographical origin using a heuristic method and PowerCore software. This core set was found to be sufficiently diverse and representative of the entire collection based on the comparison made using Shannon–Weaver diversity indices and χ2 test. These results were further validated by summary statistics. The core set displayed high genetic diversity as evident from a higher coefficient of variance in comparison to the entire set for individual traits and overall Shannon–Weaver diversity indices . In addition, the total variation explained by the first three principal components was higher in the core set (70.69%) than in the entire collection (68.03%). Further, the conservation of pairwise correlation values among descriptors in the entire and core set reflected the maintenance of the structure of the whole set. Based on the results, this core set is believed to represent the entire collection, completely. Therefore, it constitutes a potential set of germplasm that can be used in the genetic enhancement of lentils.Lentil ( Lens culinaris Medik.; 2n = 2 × = 14) is a diploid, self- pollinating, and cool-season pulse that is grown worldwide  and carbendazim (1 g) per kg of seed. Manual weeding was completed at 25–30 and 45–50 days after sowing. The experimental design consisted of an augmented block  and 528 exotic collections (ECs). The indigenous accessions were collected from different lentil-growing areas in India after exploring several crops from 1976 to 2017. Geo-coordinates of each collection site were mapped using geographic information system (GIS) tools . Geo-refed block  with fouTwenty-six agro-morphological descriptors  were selected for phenotypic characterization using descriptor lists of lentil  Table 1Table 1. 2n), where n and K mean the number of observed accessions and number of classes, respectively. The number of classes can be modified. The modified heuristic algorithm was implemented to identify and select accessions of core collections using the strategy of advanced maximization or allelic richness. Subsets created using PowerCore represented all observation classes with the least allelic redundancy, ultimately ensuring a highly reproducible entry list. Core sets were extracted with different combinations of variables to assess their efficacy using PowerCore and using qualitative traits, quantitative traits, a combination of quantitative and qualitative traits, and a combination of quantitative and qualitative traits and passport data. Another method, known as principal component score strategy (PCSS), was used to extract the core (2 test. The Shannon–Weaver diversity index (SDI) was used to compare the representativeness of the whole set and the germplasm selected as core entries.A statistical software, PowerCore, was used to constitute a core collection based on the characterization data generated using 26 different descriptor traits . Continuthe core . This methe core  for desc2 test. For the quantitative traits, classes were formed based on Sturges’ formula. The observed number of accessions in the core set in each class was determined and was tested against the expected number of accessions using the χ2 test. Further, the SDI (H’) was computed as a measure of phenotypic variability using the phenotypic frequencies of quantitative and qualitative traits  method of SAS 9.3 for augmented design. The accessions were considered random and year as a fixed effect. Levene’s test was performed to assess the homogeneity of error variances before performing the combined analysis . Descripe traits  in the ei is the proportion of germplasm accessions in the ith class of an n-class characteristic and n is the number of phenotypic categories for a character. Phenotypic correlation coefficients (r) among descriptors in the core collection (Cored) and entire set were estimated. Moreover, the contribution of different descriptor traits to multivariate polymorphism and conservation of contribution in the core collection were assessed based on PCA. The SAS 9.3 program was used to estimate the correlation and PCA  lentil accessions were grouped into 12 clusters with a varied number of accessions in each cluster . A list Potential accession numbers for unique traits were identified from characterization datasets and validated over the years and locations at NBPGR, New Delhi, The International Center for Agricultural Research in the Dry Areas (ICARDA), Amlaha, and NBPGR, Regional Station, Ranchi. Some of them are depicted in a, Coreb, Corec, Cored, and Coree when generated using qualitative traits, quantitative traits, a combination of both traits, a combination of both traits and passport data, and PCSS. A total of 170 accessions were selected based on the trio of qualitative and quantitative traits and passport data using PowerCore (H’ > 0.5) for all descriptors, except for EPV, SSP, FGC, PP, SS, CC, and SP. The average SDI for quantitative traits was higher than qualitative traits. The SDIs were consistently higher in the core sets derived from different datasets than in the entire set, signifying an enhanced representation of the available diversity in each core set. PowerCore yielded a better representation of diversity than PCSS. The average SDIs for all traits revealed that Cored  was more diverse, followed by Coreb  and Corea   .a, Corec, and Cored captured the allelic richness of the entire INGB collections. The range of quantitative descriptors was classified into 12 groups for frequency distribution and χ2 analysis based on PowerCore. Frequency distribution analysis indicated the homogeneity of distribution of several traits by Corea(10), Coreb(13), Corec(11), Cored (18), and Coree(9)  . Thus, textremes .r = 0.539) and between days to 50% flowering and days to 80% maturity . Plant height was positively correlated with days to 50% flowering (i = 0.378), days to 80% maturity (r = 0.287), pods per plant (r = 0.496), seeds per pod (r = 0.521), and number of secondary branches (r = 0.542) and negatively with seed thickness (r = –0.034) and seed diameter (r = –0.234) in the core set. Similar trends were recorded in the entire lentil collection. The number of secondary branches was positively correlated with days to 50% flowering, days to 80% maturity, plant height, pods per plant, and seeds per pod negatively with 100-seed weight, both in the core collection and entire set  accounted for 39.36% of the total variation, whereas PC2 and PC3 accounted for 17.73% and 13.60% variance, respectively. These values were comparable with those of the entire lentil collection, explaining 68.03% of the variation . The negative variation in PC1 was mainly contributed (>50%) by days to 50% flowering, days to 80% maturity, plant height, number of secondary branches per plant, and pods per plant and 100-seed weight in the entire and lentil core set .Genebanks are reservoirs of germplasm variability containing vital genes. These play a crucial role in crop-breeding programs . EfficieGeo-referenced maps of India revealed that most of the Indian accessions were from the Gangetic plains and eastern part of India belonging to Uttar Pradesh (254), Bihar (203), Uttarakhand (181), and Madhya Pradesh (96). These are the most populated regions of India, with the massive scope of pulse production in their rice fallow areas . The majThe major steps in crop improvement are assessing the variability for desired traits and their utilization in breeding programs . PreviouLentil is a less vigorous grain legume that faces competition from weeds during the early growth stage. Lentil germplasm with increased vigor can dominate the weeds, ultimately enhancing the yield . The preIC241529, IC241531, and IC241532 (less than 100 days), and cluster X comprised late maturing accessions such as EC267554, EC267615, IC258265, and EC440747 > 125 days. Similarly, Clusters XI had accessions with low plant height IC241488, IC241501, IC241529, and IC241531 (<20 cm) and cluster V had accessions with larger plant height, IC329110, IC398793, and IC59038 (>50 cm). The accessions such as EC223214, IC241543 (with high 100-seed weight) grouped in cluster XII and may be crossed with accessions with low seed weight IC361467, IC521438 (<1.4 g) from cluster III for genetics and mapping purposes.The entire lentil germplasm was more diverse for quantitative traits such as number of secondary branches per plant, pods per plant, plant height, days to 50% flowering, seed thickness and qualitative traits, leaf pubescence, biomass score, pod shedding, growth habit, leaflet size, and pod dehiscence. Plant height (cm) in the INGB lentil core set ranged from 9.70 to 52.13 with mean value of 34.04, while it was ranged from 15 to 40 cm with mean value of 23 cm in the USDA core set sown at Pullman, Washington, United States, in 1996 . An averIC317520 collected from Rajasthan was identified as a unique seed morphotype with intact funiculus at maturity  (Pisum (up to five FPP) (Lens (up to seven FPP)  developed in this study were shared with the National Agricultural Research System (NARS) partners under the material transfer agreement for utilization in the varietal development programs.The ultimate aim of germplasm characterization is to support crop-breeding programs . Similarmaturity . This wamaturity . Rice–whmaturity . A substmaturity . Similarmaturity . However lentils . Indigen lentils . Genetic lentils . Identif lentils . In seveine FPP) , Pisum (ive FPP) , and Lenven FPP) . HoweverGlobally, the first and foremost reason behind the limited use of conserved germplasm by plant breeders is huge and uncharacterized germplasm collections in certain genebanks . CharactA subsampling procedure based on 26 agro-morphological descriptors and geographical origin data resulted in the selection of 170 germplasm accessions as the lentil core set, whereas only 79 accessions were sampled without passport information. Therefore, the inclusion of stratification into the geographical origin and places should be a choice for the sampling procedure. The INGB lentil core set amounted to 7.31% of the entire collection  conserved in the INGB, New Delhi. Similarly, r = 0.52 in the entire collection, r = 0.731 in core set), and secondary branches per plant and pods per plant , showed that future germplasm characterization might use days to 50% flowering and secondary branches per plant during preliminary evaluation. This trait is easier to measure than other traits, such as days to maturity and pods per plant. Similar observations were reported by While sampling for a core set, the phenotypic associations in the core set need to be preserved for maintaining coadapted genetic complexes  and effiThe present study was based on the characterization of 2,324 accessions of cultivated lentils that resulted in the development of a core set of 170 accessions. The core set possessed a high diversity and allelic richness for major descriptor traits as revealed by the summary statistics, chi-square test, and SDI. The lentil core set described in this study represents the variability existing in the germplasm conserved in the INGB and provides insights into the traits for earliness, seed size, growth habit, and other vital agronomic traits. Identified and validated TSGs could serve as a potential source of novel alleles and genes. Characterization data of INGB lentil germplasm and developed multipurpose core set may serve as a valuable resource for lentil workers and open the avenues for germplasm utilization in the selection, mapping, genomics, and trait discovery to attain sustainable lentil production under climate changing regime.The original contributions presented in the study are included in the article/KT and AK planned and designed the research. KT, PG, AKS, GC, RBh, RBa, and RM assisted in data recording. JK, KT, and DM analyzed the data. NS, VG, and PG contributed to the materials. DS and PG performed the passport data analysis and geo-referenced map. KT and PG prepared the manuscript. AS, GM, JR, and HD edited the manuscript. KS provided the necessary support for inter-institutional collaboration. All the authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.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.


Workability traits are a group of functional traits that affect the economics of dairy production and are increasingly included in selection indexes. The most important of them include milking speed and temperament. The aim of this study was to estimate genetic and phenotypic parameters of workability traits. The estimation was carried out by considering two approaches: the first using pedigree data and the second using pedigree and genomic data. The obtained results indicate that workability traits belong to low heritable traits and are positively correlated genetically and phenotypically, which means a possibility of their effective improvement in the population, taking into account that the genomic information of sires did not have a significant effect on the estimated genetic parameters.Heritabilities of workability (WT) traits—milking speed (MS) and temperament (MT)—as well as genetic and phenotypic correlations between these traits in the population of Polish Holstein-Friesian (PHF) cows were estimated. The estimation of genetic parameters was performed twice: first with the use of pedigree data; and second with the use of pedigree and genomic data. Phenotypic data from routinely conducted MS and MT evaluations for 1,045,511 cows born from 2004 to 2013 were available; the cows were evaluated from 2011 to 2015. The main dataset was reduced based on imposed restrictions . The dataset prepared in this manner comprised 391,615 cows. It was then reduced to daughters of 10% randomly selected sires for computational reasons. Finally, for genetic parameter estimation, 13,280 records of cows were used. The linear observation model included additive random effects of animal, fixed effects of herd-year-season of calving subclass (HYS) and lactation phase, fixed regressions on cow age at calving and the percent of HF breed genes in the cow genotype. Heritabilities estimated based on pedigree data were 0.12 (±0.0067) for MS and 0.08 (±0.0063) for MT, the genetic correlation between MS and MT was estimated at 0.05 (±0.0002) and the phenotypic correlation coefficient was estimated at 0.14 (±0.0004). The inclusion of genomic information of sire bulls had no clear effect on the size of the estimated WT genetic parameters. The heritabilities of MS and MT were 0.11 (±0.0065) and 0.09 (±0.0012), respectively. The genetic and phenotypic correlation coefficients were 0.07 (±0.0003) and 0.12 (±0.0005), respectively. The sizes of the obtained heritabilities of WT and of the genetic and phenotypic correlation between these traits indicate the possibility of effective population improvement for both WT traits. In a review of selection indexes in countries with developed cattle breeding, Miglior et al.  found thHeritabilities for MT published in the literature typically range from 0.05 to 0.12 ,12,13. HIn recent years, more and more countries in the world with highly developed HF cattle breeding have included WT into their national breeding value (WH) evaluation systems and selection indexes. However, to be able to estimate WH for this group of traits, it is necessary to know the genetic parameters. Currently, no genetic parameters of WT have been estimated in Poland. However, since 2006, data concerning phenotypic evaluation of this trait group have been collected routinely as part of the performance control system conducted by breeders’ associations. Based on the collected data, it became possible to undertake studies on the estimation of genetic parameters of WT in this work. Moreover, by using an additional source of information contained in the genome, it became possible to implement a new methodology of WH estimation based on genomic prediction. Currently, there are no papers in the scientific literature on the possibility of a more in-depth estimation of genetic parameters of WT, i.e., taking into account genomic information. Therefore, the aim of this study was to estimate genetic parameters of MS and MT and phenotypic and genetic correlations between these traits by using both pedigree and genomic data.The study material consisted of MS and MT phenotypes from 1,045,511 Polish Holstein-Friesian cows born between 2004 and 2013. The phenotypic evaluation of WT cows was carried out between 2011 and 2015. The dataset was created from the SYMLEK system database belonging to the Polish Federation of Cattle Breeders and Dairy Farmers . WTs are measured in Poland by trained classifiers by using the subjective scoring method that is developed in accordance with the recommendations of the International Committee for Animal Recording  and implemented since 2006 . WTs areThe basic dataset included cow numbers, dates of birth and first calving, percent of HF breed genes and phenotypic scores for WT and parental numbers. Next, the sizes of the half-sister groups and of the herd-year-season of calving (HYS) subclasses were calculated, and their distributions were made. The half-sister group was taken as the daughter cows of the same sire and the HYS subclass was defined as the group of cows calved in the same farm, year and season, with the summer calving season covering April to September and the winter calving season covering October to March. A total of 85,172 HYS subclasses and 16,720 half-sibling groups were created. Approximately 40% of the study population consisted of cows with at least 10 half-sisters.The distribution of half-sibling groups and HYS subclasses was presented in the Next, the date of phenotypic evaluation of workability traits was added to the basic dataset. Age at first calving was calculated from birth and calving dates. Calving dates and dates of phenotypic evaluations were used to calculate the day of lactation when the evaluation of WT was performed. Based on the day of evaluation, the cows were assigned to eight 10 day lactation stages covering the period from day 21 to day 100 of lactation. The next step was to reduce the size of the basic dataset by introducing the following restrictions: Cows with phenotypic evaluation of WT traits made later than the 100th day of lactation were removed, and WT evaluations made during trial milking performed earlier than 26 days after the first trial milking were discarded. Cows from farms where no variation in MS or MT was observed were also removed. The dataset prepared in this manner (Dataset A) counted 391,615 cows.Based on the data contained in the genotype database of the National Research Institute of Animal Production, a dataset was created containing the genotypes of 2228 bulls whose daughters were evaluated in Poland for WT (Dataset G1). Each bull was genotyped using the Illumina BovineSNPv2 BeadChip, 54609 SNP, and genotype data were stored in the cSNP database system. Next, the imputation findhap software was used to generate genotypes dataset . As a fiy is a vector of observed traits MS/TM; X is the incidence matrix of fixed effects; β is a vector of fixed effects ; Z is the incidence matrix of random effects; a is a vector of additive genetic random effects; and e is a vector of random error effects.A multitrait linear model of observation including the following fixed and random effects was used to estimate the genetic and phenotypic (co)variance components of WT:In advance, for the estimation of the genetic and phenotypic (co)variance components of WT traits, additional restrictions were imposed on dataset A. Only the daughters of cows of 10% randomly selected sires were left in the dataset, and HYS subclasses consisting of less than 10 cows were removed. This dataset (Dataset B) of 13,280 cows was treated as the basic dataset for estimating (co)variance components of genetic and phenotypic WT traits. Then, a smaller dataset (Dataset G2) was created, containing the genotypes of 319 sire bulls from Dataset G1, which were sires from dataset B. The (co)variance components were estimated by using programs implemented in the BLUPF90 package  that areGenetic and phenotypic parameters were calculated using variance components estimated with 2 different relationship matrices: pedigree-based A (BLUP) and realized H (ssGBLUP). The inverse of matrix H that combines pedigree and genomic relationships was constructed according to Aguilar et al. . Cow andThe first stage of the study was to perform a detailed characteristic of the data, including the calculation of means and standard deviations and the percentage distribution of each grade for both WT by year of birth. The results of this stage of the work are presented in The average scores of both WT were close to the optimum in intermediate values: these values were 3.05 points for MS and 1.97 points for MT. In the following years of the birth of cows, two trends can be observed. Between 2004 and 2008, the average scores in terms of both WT were decreasing, after which the trend changed to an increasing one. Until the last analysed year of birth (2013), the phenotypic scores were increasing quite regularly, finally slightly exceeding the optimal value for each trait. The value of the coefficient of variation CV (about 20%) indicates that the right arm of the distribution of phenotypic scores in terms of WT descends more smoothly than the left one.The characteristics of the Polish population of PHF sires, depending on the country of origin and the method of import to Poland, are presented in The genetic parameters of workability traits estimated based on pedigree data and pedigree and genomic data of cows are presented in First stage was to estimate parameters based on pedigree data. In the next stage of the study, the data used for estimating the values of WT genetic parameters were enriched with information derived from the genotypes of sires. The genetic parameters of WT were estimated using information enriched from the genotypes of sires. The genotypes of 319 bulls collected in the database of the National Research Institute (Dataset G2) were included in the calculations.Heritability estimated with genomic information was 0.11 for MS and 0.09 for MT. Next, the estimated genetic and phenotypic correlation coefficients between these traits were 0.07 and 0.12, respectively. The values of these parameters are slightly different compared to the parameters estimated without using genomic information on sire bulls. The differences between heritabilities are 0.01 and between correlations are 0.02.The descriptive statistics (mean and Monte Carlo error) and highest posterior density (HPD) regions of the a posteriori distribution for all co(variance) estimates are presented in Knowledge of genetic and phenotypic parameter, i.e., heritability and coefficients of genetic and phenotypic correlations between traits enables the optimization of selection and increases the accuracy of the prediction of its results. Typically, genetic and phenotypic correlations are used to predict changes in the value of one trait during improvement of another trait or, when the values of one trait are difficult to measure, it can be inferred from its relationship with another trait that is easier to determine.The accurate estimation of genetic parameters of functional traits such as WT, for example, has become possible due to the increase in computational capabilities of computers, mathematical method development and increasingly sophisticated genetic parameter estimation software. The (co)variance components of functional traits were estimated by using multivariate linear observation models . The autIn our study, the convergence diagnosis (results not shown) was analyzed by using the Geweke method , using tIn this study, a two-trait animal model based on a linear model was used for the observations including both WT. The coefficient of heritability estimated in this work for MS was 0.12 (±0.0067). This result is similar to the results obtained in the study of Sewalem et al. , who obtSimilar values of heritability coefficients obtained in our study may be a result of the population structure of bulls in Poland: As much as two-thirds of the population consisted of bulls imported from different countries, with the vast majority from world leading countries of cattle breeding. Such a population structure shows that the genetic variability in Poland is similar to that in the whole population of HF cattle.Rensing and Ruten  estimateSewalem et al.  concludeThe heritability 0.08 estimated from this work for MT was lower than that of MS, and this result is consistent with those published in the literature. The same exact heritability for MT was presented by Sewalem et al. . A slighThe inclusion of bull genotype data had little effect on the value of heritability estimates for MS and MT; however, slightly higher estimates of genetic and phenotypic correlation coefficients between MS and MT were obtained. It is worth noting that the genetic correlations between these traits are small but positive, meaning that if the daughters of sires are milking faster, they are also generally slightly more excitable. The positive genetic correlation 0.247 (±0.075) and phenotypic correlation 0.10 between MT and MS were estimated by Sewalem et al. . Wethal The small changes in genetic parameters after WT after the enrichment of pedigree data with genomic information can probably be explained by their structure. In this study, data including phenotypic values of cows and genotypes of their sires were used. The results of the analysis of cow genotypes were not available; consequently, there was a large disproportion between the amount of information coming from both sources. This data structure is not conducive for a high accuracy in genetic parameter estimates. In their study, Dehnav et al.  found thIn practice, low coefficients of heritability of a trait indicate the possibility of its effective genetic improvement in a selected animal population (direct selection), but can simultaneously mean a slower genetic gain than in the case of highly heritable traits. One solution is the indirect selection for other moderate and highly heritable traits that are strongly and positively genetically correlated with low heritable traits. Therefore, the continuation of these studies should be the estimation of genetic and phenotypic correlation coefficients between WT traits and other genetically improved production and functional traits.In conclusion, the obtained coefficients of heritability and genetic and phenotypic correlations between WT indicate a possibility of effective improvement of the population in terms of this group of traits.The analysis of the values of coefficients of genetic parameters of WT estimated on the basis of pedigree and genomic data showed that the enrichment with information derived from the genotype of bulls did not bring significant changes in the values of the heritability and correlation coefficients. However, further studies with larger data sets included genotypes of cows are recommended.


Syndromes, wherein multiple traits evolve convergently in response to a shared selective driver, form a central concept in ecology and evolution. Recent work has questioned the existence of some classic syndromes, such as pollination and seed dispersal syndromes. Here, we discuss some of the major issues that have afflicted research into syndromes in macroevolution and ecology. First, correlated evolution of traits and hypothesized selective drivers is often relied on as the only evidence for adaptation of those traits to those hypothesized drivers, without supporting evidence. Second, the selective driver is often inferred from a combination of traits without explicit testing. Third, researchers often measure traits that are easy for humans to observe rather than measuring traits that are suited to testing the hypothesis of adaptation. Finally, species are often chosen for study because of their striking phenotypes, which leads to the illusion of syndromes and divergence. We argue that these issues can be avoided by combining studies of trait variation across entire clades or communities with explicit tests of adaptive hypotheses and that taking this approach will lead to a better understanding of syndrome‐like evolution and its drivers. Syndromes, wherein multiple traits evolve convergently in response to a shared selective driver, form a central concept in ecology and evolution. Recent work has questioned the utility and indeed the existence of some of the classic syndromes, such as pollination and seed dispersal syndromes. Here, we discuss some of the major issues that have plagued research into syndromes in macroevolution and suggest an approach to avoid these issues.  When thHistorically, “syndromes” have been in two primary arenas. In macroevolution, syndromes are typically tested by examining correlated evolution of traits along a phylogeny. In community ecology, species are often classified into different syndromes based on their trait combinations. In both arenas, the “syndrome” of traits is typically assumed to result from adaptation to a single selective driver . In some cases, adaptation to a single selective driver does seem likely, such as the repeated changes in phenotype following transitions to lake or stream habitats in sticklebacks  is often held up as a prototypical example of this phenomenon, whose persistence, despite the extinction of putative dispersers more than 10,000 years ago  have real implications for conservation and for our ability to accurately predict responses of species to climate change, both in terms of species' abilities to migrate or evolve in new environmental conditions, as well as their ability to maintain vital species interactions. The validity of results based on syndromes depends largely on the degree to which those syndromes can actually be used to predict ecology  and “adaptive syndromes” (where such traits can reliably infer adaptation to a particular driver). Then, we explore a hypothetical example to illustrate common approaches to the study of syndromes and their potential pitfalls. We identify a number of problems with the way that researchers typically approach the study of syndromes, including the assumption that correlated evolution is strong evidence of adaptation, sampling bias that creates the illusion of syndromes, and the study of traits that are not especially relevant to the hypothesized evolutionary driver. Finally, we propose an approach to studying syndromes that overcomes these problems and allows us to build evidence‐based evolutionary narratives about syndromes of traits and adaptation.1.1The diversity of scenarios in which the term “syndrome” has been applied, as well as the variety of other terms with partly or completely overlapping meanings, means that the definition of a “syndrome” varies considerably across studies. Broadly speaking, the literature contains two major kinds of syndromes: syndromes which have been described based solely on convergent evolution of a combination of traits with no tests of adaptation  convergent evolution of (2) multiple traits (3) adapted to a particular driver. Typically, different states of the driver—such as different pollinators or different habitats—will result in distinct clusters of traits, although discrete clustering is not necessary. We include convergent evolution as a criterion because the repeated appearance of the trait combination is key evidence for the generality of the association. A collection of traits selected simultaneously in a single lineage serves as a compelling story of the complexity of adaptation, but does not provide the predictability that makes syndromes a valuable concept in the literature.Trait syndrome”. We propose the term “trait syndrome” to refer to cases where two of the three criteria have been met, namely that there has been (1) convergent evolution of (2) multiple traits. However, in trait syndromes, the adaptive driver remains a hypothesis rather than well‐demonstrated. We propose this terminology in order to differentiate scenarios in which only traits have been studied from those scenarios in which adaptation has also been studied.“By differentiating “trait syndromes” (combinations of traits) from “syndromes” (where the link to selection has been demonstrated), we aim to provide linguistic tools to better highlight cases where adaptation has been well‐studied. We urge researchers to consider the strength of their evidence that their syndrome of study is adaptive to the proposed driver and to name their syndromes (if they choose to do so) with trait‐centric terminology  until they have sufficient evidence to support the adaptive connection.1.3One of the major problems in our hypothetical example is that we did not test explicitly for convergence of traits on a phylogeny. Because traits may be shared due to common ancestry rather than adaptation  and driver (habitat). Instead, we chose the traits that were obviously different to us, as humans. Choosing obviously different traits is an excellent way to identify interesting differences and may allow us to identify trait syndromes across species. However, such obvious traits may not be appropriate to use to test for adaptation to a particular driver.In our hypothetical example, let us say that we have examined the beetle community on our islands and observed that beetle size, but not color, is correlated with habitat (perhaps we have since found iridescent beetles in the leaf litter and black beetles in variable light environments). We spend several years observing these beetles in their natural habitats and eventually notice that beetles foraging on branches (which we once thought were camouflaged with iridescent coloring) have a peculiar behavior: They tap the branch several times before scurrying out to munch on leaves, upside down. This new observation leads us to consider other traits that might be adapted to this foraging lifestyle. We find that foot morphology, behavior, and body size reliably differentiate between branch‐foraging and litter‐foraging beetles. In fact, these other traits are highly predictive of foraging style across beetles displaying a variety of colors.What happened in this extension of our hypothetical example? The obvious trait—coloration—is easily measured. But, the important traits are more difficult to quantify, requiring behavioral assays and microscopic analyses of foot morphology. This may seem obvious from the safety of our office chairs, but measuring the relevant traits, at the appropriate scale, is critical to evaluating adaptation. A classic example of this occurs in ultraviolet (UV) nectar guides in flowers: Humans cannot see UV light, but bees can. Until the scientific community could measure UV reflectance, UV nectar guides remained unknown, yet are central to the ability of bees to find and pollinate flowers  convergent evolution, of (2) multiple traits, which (3) reflect adaptation. When testing for these features of syndromes, we urge readers not to rely solely on correlated evolution along a phylogeny as evidence of adaptation, but rather to assemble evidence from a variety of perspectives .Demonstrating whether a trait syndrome can predict its putative driver is an additional piece of evidence pertaining to adaptation and is especially important if the hypothesized adaptive syndrome is intended to be used to infer abiotic environment, species interactions, and other ecological factors. For example, observing that carnivorous plants only occur in low‐nutrient environments is evidence that carnivory may be especially adapted to those low‐nutrient environments  Trait syndromes have been used as evidence of adaptation, but should be considered hypotheses that must be tested. (2) Adaptation is inferred from traits without sufficient testing. (3) Easy to measure traits, which may not be relevant to the hypothesis of adaptation, are often used. And finally, (4) syndromes are often identified based on biased samples of the most morphologically divergent species. Together, these issues have meant that syndromes of traits are regularly described and attributed to an adaptive driver with little evidence actually linking the two.The study of syndromes is important because syndromes are regularly used to infer ecology in a variety of contexts, including fossil organisms and newly discovered species, as well as to predict responses to abiotic change and/or species interactions. Through studying syndromes more rigorously, we improve our ability to conduct studies that rely on adaptive syndromes and have greater confidence in our inferences and predictions. Furthermore, many questions about convergence and adaptation become accessible. For example, what is the evolutionary trajectory of different syndromes—do traits evolve in the same order, or in different orders, as a syndrome is assembled? To what extent do pleiotropy and other genetic linkage mechanisms explain the observation of syndromes? To what extent are syndrome traits convergent across biological scales  and to what extent do species evolve unique adaptations? Some theoretical questions about syndromes also remain. For instance, are there synergistic interactions between individual traits of a syndrome, such that their combined contributions are greater than the sum of its parts? As traits related to a syndrome accumulate in a lineage, do they offer diminishing returns, such that a subset of traits is sufficient for adaptation to the selective driver? How does the relative size of fitness contributions from individual syndrome traits affect evolutionary trajectories and derived phenotypes? These kinds of questions, and more, are facilitated by rigorous study of syndromes.The authors have no conflicts of interest to declare.Miranda A. Sinnott‐Armstrong: Conceptualization ; funding acquisition ; investigation (lead); visualization ; writing – original draft ; writing – review and editing (lead). Rocio Deanna: Conceptualization (supporting); investigation (supporting); writing – original draft (supporting); writing – review and editing (supporting). Chelsea Pretz: Conceptualization ; investigation (supporting); writing – original draft (supporting). Jesse C. Harris: Conceptualization (supporting); investigation (supporting); writing – original draft (supporting). Amy Dunbar‐Wallis: Conceptualization (supporting); investigation (supporting); writing – original draft (supporting). Sukuan Liu: Conceptualization (supporting); investigation (supporting); writing – original draft (supporting). Stacey D. Smith: Conceptualization (supporting); funding acquisition ; investigation (supporting); supervision (supporting); writing – review and editing (supporting). Lucas C. Wheeler: Conceptualization ; investigation ; supervision (lead); visualization ; writing – original draft ; writing – review and editing .No data were generated in this work.


Genome-wide association (GWA) studies have shown that genetic influences on individual differences in affect, behavior, and cognition are driven by thousands of DNA variants, each with very small effect sizes. Here, we propose taking inspiration from GWA studies for understanding and modeling the influence of the environment on complex phenotypes. We argue that the availability of DNA microarrays in genetic research is comparable with the advent of digital technologies in psychological science that enable collecting rich, naturalistic observations in real time of the environome, akin to the genome. These data can capture many thousand environmental elements, which we speculate each influence individual differences in affect, behavior, and cognition with very small effect sizes, akin to findings from GWA studies about DNA variants. We outline how the principles and mechanisms of genetic influences on psychological traits can be applied to improve the understanding and models of the environome. The oldest and perhaps most difficult question in psychological science is how and why people, as individuals, become who they become. There is broad consensus that people’s differences in affect, behavior, and cognition result from the interplay between genetic propensities and environmental conditions. However, the mechanisms and processes that drive this interplay are not yet well understood.Genes are sequences of base pairs in the genome that code for proteins; only about 2% of the genome comprises protein-coding genes. The other 98% of the base pairs, the majority of the genome, are known as “dark matter” because much of their function is not yet well understood , which are judiciously selected to provide the greatest possible coverage of the genome.As the methods for charting DNA advanced, commercial companies began producing data devices with the capacity to extract and store large proportions of a person’s genome . These dhttps://www.ukbiobank.ac.uk) started in 2006 and has enrolled 500,000 volunteers ages 40 to 69 years.The fourth development was the emergence of genomic biobanks, which store human biological material for research purposes . The firexposome to describe the exposures of individuals across the life span and how those exposures relate to health  studies have confirmed the polygenicity of many complex traits . GWA stupolyenvironicity, for the lack of a better term.Just like the genetic influence on complex traits is driven by many SNPs, the environmental influence on people’s differences also arises from many factors e.g., . Just liA second key finding from GWA studies is that genetic influences on complex traits are pleiotropic, meaning that a DNA variant associated with the phenotypic expression of one trait is also often implicated in the expression of other traits. That pleiotropy applies to genetic influences on related phenotypes, such as learning disabilities or psychiatric disorders, was already evident from twin studies, which compare the phenotypic resemblance of monozygotic twins, who are genetically identical, and dizygotic twins, who share on average only 50% of their segregating genes and are genetically as similar to each other as fraternal siblings . Yet theEmpirical evidence for the multitude of factors that inform environmental influence and their broad reach on psychological traits is abound . For exaAnalogous to genetic pleiotropy, an environmental factor that potentially affects many psychological traits is education provision. For example, Head Start, a program for providing comprehensive early childhood education, has been shown to improve cognitive performance as well as social-emotional development and physical growth in children who enrolled in the program compared with control groups e.g., , althougIn summary, recent GWA studies have confirmed that genetic influences on psychological traits are due to many SNPs  that are located across the entire genome and influence multiple traits . We have outlined here that the same principles of polyenvironicity and pleiotropy are likely to apply to environmental influences on psychological traits.nature of nurture, demonstrating that some of parents’ genetic effects on their children are environmentally mediated  and Gene × Environment interactions (G×Es). GrEs occur when individuals’ genotypes correspond to their environments, and they can be passive, active, and evocative . Passivemediated . Active mediated . People mediated  and thatmediated .Whereas GrEs indicate the extent to which genes and environments are assorted, G×Es imply that genotypes respond to environmental variation in different ways. n-way interactions. The required sample sizes for reliably identifying such interaction effects grow immeasurably. Yet the greatest challenge for identifying G×E effects in the prediction of psychological differences is not statistical power but the fact that individuals are systematically assorted to environments rather than randomly distributed across them . And these environmental conditions vary second by second. In contrast to the genome, the environome is pure change: No moment in people’s lives is like any other before or in the future. Notwithstanding, some environmental factors exert relatively stable influences over time, such as the characteristics of the family home that children are raised in e.g., .The second difference lies in the unit of analysis. For genetic influences, the smallest unit of variation is based on SNPs that all adhere to the same structural and functional (coding) mechanisms. Thus, an individual SNP’s effect on a complex trait is directly comparable with the effects of other SNPs . By contproximal processes, through which genetic potentials for effective psychological functioning are actualized” . Although the understanding of the corresponding processes is currently incomplete, a schematic framework exists that outlines how the genome relates to the transcriptome, proteome, and metabolome. For environmental influences, however, no systematic framework of transmission is readily available. Even for environmental factors whose influence on complex traits has been conclusively demonstrated, for example, the benefits of large quantities of child-directed speech for children’s own language development , it is uWhat, then, are the similarities between genome and environome? We argue that there is a parallel between the events that brought about the “DNA revolution”  and thosOne seminal example of an extensive, technology-driven approach from developmental research is the human speechome project. Deb Roy, professor of social machine analytics at the Massachusetts Institute of Technology, installed cameras and microphones in his house that recorded everything that happened for up to 10 hr per day—starting on the day that his son Dwayne was first brought home after his birth and lasting for the first 3 years of Dwayne’s life . The resOther examples of new tools that enable collecting real-world observations of individual differences in affect, cognition, and behavior in exceptional depth include technology-supported experience sampling methods . For exaThroughout this article, we have highlighted ways in which psychological science may take inspiration from genomic research to advance the understanding and models of environmental influences. Our aim is now to outline the steps that we believe are essential to bring about an effective research agenda for the environome.A first challenge—having the technical tools available to capture the environome—is under way, although it is far from being complete. The environome comprises an infinite number of dynamic processes, whose assessment requires robust technologies that enable collecting precise, in-depth observations at multiple time points with little measurement error . AlthougThe second challenge is to develop the computational methods required for modeling these rich data, for example using machine-learning approaches such as data mining and cluster analysis. This challenge is not specific to studies of the environome but shared with analyses of the genome. Although current GWA studies already incorporate a vast number of SNPs, they typically include only a fraction of the potentially available genomic information . Anotherhttps://www.med.unc.edu/pgc/) that focuses on mental health issues or the Social Science Genetic Association Consortium (https://www.thessgac.org/) that targets social science outcomes, as its name suggests. These and other consortia like them typically involve hundreds of researchers and organizations that engage in interdisciplinary collaborations and pool data across biobanks, population cohort studies, and independent samples. They offer extraordinary opportunities for scientific breakthroughs: The majority of the recent discoveries about the role of genetic influences of people’s differences in psychological traits emerged on the back of the work completed in consortia. For modeling the environome, longitudinal population cohort studies, which are typically defined by the year or decade of the cohort members’ birth and by the geographical scope from which they were recruited, will be of particular value , intellect , adversity , mating , positivity , negativity , deception , and sociality (i.e., social interaction; Rather than creating new consortia or shifting attention away from existing ones, we suggest broadening their scope to also pool data and expertise on the environome. Akin to the HapMap Project, a first step for a systematic research program into the environome would call for charting the breadth of environments that humans experience. A bottom-up approach, for example by creating comprehensive archives of environmental measures that are available across biobanks, population cohort studies, and independent samples, has some appeal. The alternative top-down approach would involve developing a theoretical taxonomy that could be applied to categorize observations of environments, including those already collected in previous studies, and then be subjected to empirical validation. An encouraging example is the DIAMONDS taxonomy that proposes eight dimensions to classify psychological situations by the extent to which they pertain to GWA studies serve to identify genetic predictors of developmental differences in psychological traits, but they currently offer little value for elucidating the causality that underlies this prediction . Likewis


Genetic improvement of wool and growth traits is a major goal in the sheep industry, but their underlying genetic architecture remains elusive. To improve our understanding of these mechanisms, we conducted a weighted single-step genome-wide association study (WssGWAS) and then integrated the results with large-scale transcriptome data for five wool traits and one growth trait in Merino sheep: mean fibre diameter (MFD), coefficient of variation of the fibre diameter (CVFD), crimp number (CN), mean staple length (MSL), greasy fleece weight (GFW), and live weight (LW).. LW) to 0.77 (GFW vs. LW). By integrating the GWAS signals with RNA-seq data from 500 samples , we detected tissues that were relevant to each of the six traits, e.g. liver, muscle and the gastrointestinal (GI) tract were the most relevant tissues for LW, and leukocytes and macrophages were the most relevant cells for CN. For the six traits, 54 quantitative trait loci (QTL) were identified covering 81 candidate genes on 21 ovine autosomes. Multiple candidate genes showed strong tissue-specific expression, e.g. BNC1 (associated with MFD) and CHRNB1 (LW) were specifically expressed in skin and muscle, respectively. By conducting phenome-wide association studies (PheWAS) in humans, we found that orthologues of several of these candidate genes were significantly (FDR < 0.05) associated with similar traits in humans, e.g. BNC1 was significantly associated with MFD in sheep and with hair colour in humans, and CHRNB1 was significantly associated with LW in sheep and with body mass index in humans.Our dataset comprised 7135 individuals with phenotype data, among which 1217 had high-density (HD) genotype data . The genotypes of 707 of these animals were imputed from the Illumina Ovine single nucleotide polymorphism (SNP) 54 BeadChip to the HD Array. The heritability of these traits ranged from 0.05 (CVFD) to 0.36 (MFD), and between-trait genetic correlations ranged from − 0.44 (CN vsOur findings provide novel insights into the biological and genetic mechanisms underlying wool and growth traits, and thus will contribute to the genetic improvement and gene mapping of complex traits in sheep.The online version contains supplementary material available at 10.1186/s12711-021-00649-8. Chinese Merino is a dual-purpose sheep breed that is widespread in Northwest China and renowned for its environmental adaptability and high-quality wool and mutton . The genGenome-wide association studies (GWAS) have become an efficient approach to identify single nucleotide polymorphisms (SNPs) that are associated with complex traits in humans and livestock –8. Sever emerged –16. In a emerged . Based o emerged –20.Although the GWAS approach has been useful for discovering trait-associated genomic variants, the causal tissues and cell types that are affected by such variants are largely unknown . The disThe objectives of our study were: (1) to estimate the genetic parameters  for five wool traits ; and one growth trait  in a dual-purpose Merino sheep population (n = 7135); (2) to identify tissues and genes that are associated with these traits by integrating the results of a WssGWAS with data from 500 RNA-seq samples of 87 tissues; and (3) to explore whether the results from human studies can help validate and explain the findings in this sheep study via a PheWAS.The Merino sheep included in this study were maintained at the Xinjiang Fine Wool Sheep Breeding Farm . Farm management of all the animals was previously described in . In totaEach wool quality trait was measured according to the standardized method established by the China Fibre Inspection Bureau (CFIB) and the International Wool Textile Organization (IWTO) . BrieflyThe complete pedigree  was used to construct the relationship matrix, which included 413 sires, 6476 dams and 483 yearling females.P < 10−6) [Genomic DNA was extracted from the blood samples of 1217 randomly selected female phenotypic sheep using the phenol–chloroform method. Among these, 707, 257 and 253 individuals were genotyped using the Illumina Ovine SNP54 BeadChip , the Illumina Ovine SNP600 BeadChip , and the Sheep 600 K Genotyping Array , respectively. The reference population for genotype imputation included 510 individuals genotyped with the 600 K arrays . The target population included 707 individuals genotyped with the Illumina Ovine SNP54 BeadChip . The physical positions of SNPs were based on the sheep reference genome assembly Oar_v3.1 . Genotyp < 10−6) , if its y is the vector of phenotypes for MFD, CVFD, CN, MSL, GFW and LW; β is the vector of fixed effects, including flocks (50 levels), years of birth (8 levels: 2011–2018) and seasons (2 levels: spring and winter); a is the vector of random additive genetic effects of animals, where A representing the pedigree-based relationship matrix and e is a vector of random residuals where I representing the identity matrix and X and Z are the corresponding incidence matrices.A multi-trait animal model was used to estimate (co)variance components using the average information REML procedure in the DMU package :\documenHeritability was defined as escribed .We performed an association study using the single-step genomic BLUP (ssGBLUP) approach . Genomicy is the vector of phenotypic observations; β is the vector of the same fixed effects as mentioned above; a is the vector of additive genetic effects and assumes that H is the matrix of pedigree and genomic information and I is the identity matrix and X and Z are the incidence matrices of β and a, respectively.The single-trait animal model for ssGBLUP was as follows:H matrix (H−1) was defined as follows [A is the numerator relationship matrix applied for all pedigreed animals; A22 is the numerator relationship matrix applied for genotyped animals; and Gw is the genomic relationship matrix, which assumes the allele frequency of the current population and adjusts for compatibility with A22 [To solve the mixed model equations, the inverse of the  follows :\documenwith A22 .G matrix was calculated as follows:Z is the marker matrix , and G was close to that of A22 [The t of A22 .In the first iteration The GEBV is calculated for the entire data set using ssGBLUP;The SNP effects  are denoted as ‘1’ and samples outside the organ system  are denoted as ‘−1’; Z is the matrix of covariables, including age and sex . To detect genes that have a high expression in specific tissues, we used the following approach for each gene in each tissue , 22:\docmentclass2pt{minimWe fitted this model for each gene in each tissue using the least squares approach with R  and thenProfiler .P-values (FDR) less than 0.05 as significant. For visualization, we classified these complex traits into 12 trait domains based on the available knowledge on tissue biology [To explore whether the orthologues of candidate genes detected for wool and growth traits in sheep were associated with complex traits in humans, we conducted a PheWAS for each of these genes based on the human GWAS data in the GWASATLAS database , 46. Bri biology .P-value for a list of tissue-specific genes, we repeated the permutation procedure 10,000 times and applied a one-tailed test of the proportion of random summary statistics greater than that observed [P-values for multiple testing by the FDR method.We applied the sum-based marker-set test approach below using the R package QGG , 48 to deviously , 50. To observed , 48. We The number of animals included in the estimation of genetic parameters ranged from 5208 to 7135 depending on the trait analyzed, among which 4713 animals had phenotypic records for all six traits and 1217 animals had genotypes. To use the information from the animals with phenotypes but without genotypes, we used a single-step BLUP (ssBLUP) to estimate the genetic parameters for the six traits. The estimates of the heritability for these traits ranged from 0.05 to 0.36, with MFD and LW having the highest estimated heritability, i.e. 0.36 (SE = 0.04) and 0.33 (SE = 0.03), respectively, and CVFD and CN having the lowest heritability, i.e. 0.05 (SE = 0.02) and 0.07 (SE = 0.03), respectively. The details of the phenotypic records and estimated genetic parameters for the six traits are in Table r = − 0.44) correlated with CN. The details of phenotypic and genetic correlations among those traits are summarized in Table In general, the phenotypic correlations among the wool traits and LW were weaker than their genetic correlations. The strongest positive phenotypic and genetic correlations were observed between GFW and LW, with correlation coefficients of 0.50 and 0.77, respectively. MSL was genetically positively correlated with MFD, LW, and GFW, with moderate correlation coefficients of 0.29, 0.29, and 0.27, respectively. It should be noted that LW was negatively genetically (Ovis aries chromosome 13 (OAR13) between 55,897,362 and 56,044,564 bp was associated with MSL and GFW, and the region on OAR16 between 31,883,408 and 31,963,084 bp was associated with CN and MSL. Detailed information on these 54 QTL is summarized in Additional file As determined via the sliding window strategy with WssGWAS , which ir2) patterns between SNPs along their physical distances in the studied population  of 0.81, and four haplotype blocks were observed in the combined regions  of 0.64, and six haplotype blocks were observed in the combined regions . Genes associated with GFW, including MAFG and MAFF, were enriched in the regulation of epidermal cell differentiation (P = 0.035).We conducted gene ontology (GO) enrichment analysis for genes within the top 1% of the windows for each trait according to the explained genomic variance , EDC4  and PSKH1 . Several genes associated with wool traits were also significantly associated with immune-related traits in humans, such as the reticulocyte fractions of red blood cells (SPHK1) and white blood cells (NPTX1) , RAPSN (height), PSKH1 (height), PPP1R3D (standing height), SHISA4 , ADSL (standing height) and CHRNB1 (body mass index). Four candidate genes, BNC1 (associated with MFD in sheep), GHR (CN and MSL), CHRNB1 (LW) and SPHK1 (LW), are shown as examples in Fig. The basic assumption is that, among mammals, orthologous genes have similar functions. To explore the potential functions of these 81 candidate genes in humans, we used the human GWAS atlas to conduct PheWASs , 46, andr = 0.77) and moderate positive genetic correlations between MSL and LW (0.28), MSL and GFW (0.27) and MSL and MFD (0.29), which is consistent with previous studies [Genetic improvement of wool traits and LW is an essential goal in the sheep breeding industry. In this study, we did not directly measure carcass composition traits but recorded LW at the age of 15 months, which indirectly reflects the health and meat production of the sheep . Compare studies , 59. In  studies .AMN1 for CVFD and MSL, and CHRNB1 and YBX2 for LW. These results provide evidence that the region that is associated with the positive selection signal in Merino might be related to wool phenotypes. The remaining different results between these studies could be due to differences in the statistical methods used, and/or to differences in allele frequencies and in patterns of QTL segregations between the populations analysed [We detected multiple novel QTL for wool traits that were not in the sheep QTLdb . Most ofanalysed , 11.Comparison of the data from the sheep expression atlas and human PheWAS data revealed that several candidate genes showed tissue specificity and conserved functions among mammals. Multiple candidate genes were associated with immune traits in humans, which is in line with previous findings showing that the immune system has been involved in the development of hair follicles , 54, 61.BNC1, a candidate gene for MFD, encodes a zinc finger protein that is present in the basal cell layer of the epidermis and in hair follicles and plays a regulatory role in keratinocyte proliferation [IGFBP4, which was located in the top QTL of CN, explained 2.32% of the genetic variance. Based on the gene annotation in GeneCards [IGFBP4 is involved in the regulation of cell growth, glucose metabolic processes, and insulin-like growth factor receptor signalling. Previous studies have demonstrated that IGFBP4 is preferentially expressed in hair follicles [IGF1, which plays a key role in the development and growth of hair follicles [IGFBP4 is a strong candidate gene for CN. CHRNB1 is involved in muscle contraction and muscle fibre development [CHRNB1 was significantly associated with skeletal traits. A previous GWAS conducted by Kominakis et al. [CHRNB1 is associated with body size in sheep. Thus, we consider that CHRNB1 is a promising candidate gene for LW. It should be noted that 22 QTL regions did not contain annotated genes and it would be interesting to link these regions to target genes using a more precise functional annotation of the sheep genome, such as that developed by the Functional Annotation of Animal Genomes (FAANG) project [feration . It is mferation . The geneneCards , IGFBP4 ollicles , 71 and ollicles , 73. Theelopment  and is ss et al.  also rev project , 77.BNC1, ENDOV, CDC6, RARA, IGFBP4, GHR, PPP1R3D, CHRNB1, ADSL and SPHK1. Our findings shed light on the genetic and biological basis of wool traits and live weight, and provide valuable information for genome selection in Merino sheep.We estimated the genetic parameters for five wool traits and live weight in a dual-purpose Merino sheep and detected 81 candidate genes for these six traits using a WssGWAS approach. By integrating multiple biological datasets  with GWAS signals, we propose a list of the 10 most promising candidate genes for these traits: Additional file 1: Table S1. Details of the summary statistics for individual samples of ovine transcriptomes. The transcriptome data were obtained from Clark et al. [k et al. .Additional file 2: Table S2. Summary of the top 10 regions and their corresponding candidate genes for wool traits and live weight in Chinese Merino sheep. Chr, chromosome; start (bp); end (bp); var (%), percentage of the genetic variance explained by the window region; mean linkage disequilibrium (LD) estimate (r2); genes, genes located within the window region. –, no genes were located in the window region or in the top 10 ranked windows for the corresponding trait. The six traits in this study were: mean fibre diameter (MFD), coefficient of variation of the fibre diameter (CVFD), crimp number (CN), mean staple length (MSL), greasy fleece weight (GFW) and live weight (LW).Additional file 3: Figure S1. Dynamics of the linkage disequilibrium  of SNPs along their distances. The R-square (r2) was lower than 0.4 (moderate LD) when the average distance between SNPs was approximately 100 kb. The R-square (r2) decreased rapidly as the marker distance increased.Additional file 4: Figure S2. Haplotype blocks and pairwise linkage disequilibrium (LD) of adjacent QTL regions. (a) SNPs located within 421,64,611–42,373,451 bp and 42,375,920–42,553,316 bp on OAR10; (b) SNPs located within 22,741,289–22,871,683 bp and 22,871,683–23,005,179 bp on OAR18. The black triangle blocks are haplotype blocks. The values in the boxes are pairwise SNP correlations (D’), whereas the boxes without numbers indicate complete LD (D’ = 1). The colour in the box corresponds to the pairwise SNP correlation.Additional file 5: Figure S3. General characteristics of the candidate genes across the six traits analyzed. (a) Comparison of our newly discovered QTL with previously known QTL in the sheep QTLdb; (b) Enriched gene ontology (GO) terms  for genes within the top 1% of the windows in each trait.Additional file 6: Table S3. Top five enriched gene ontology (GO) terms  for genes within the top 1% of the windows for each trait. The six traits analysed in this study were: mean fibre diameter (MFD), coefficient of variation of the fibre diameter (CVFD), crimp number (CN), mean staple length (MSL), greasy fleece weight (GFW) and live weight (LW).Additional file 7: Figure S4. Correlation clustering of 87 tissues and cell types within 13 organ systems based on the expression of their genes. The top colour corresponds to the correlation coefficients, and the bottom colour corresponds to the organ systems.Additional file 8: Table S4. Enriched gene ontology (GO) terms  for the top 10% of genes expressed in specific tissues based on the t-statistic ranks in each organ system.Additional file 9: Table S5. Significant GWAS signal enrichment of complex traits across all tissues within organ systems, the gastrointestinal (GI) tract and the immune system.Additional file 10: Table S6. Expression of 77 of 81 candidate genes based on the sheep expression atlas. The gene expression levels were normalized as transcripts per million (TPM).Additional file 11: Table S7. Results of the phenome-wide association study (PheWAS) for 78 of 81 candidate genes.