Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits . Bread wheat lines  were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated . Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle. Including NIR-predicted data into multi-trait prediction models increases the prediction accuracy of genetically correlated end-product quality traits in wheat, supporting the selection of desirable lines in early breeding cycles1An exponentially growing human population and a rapidly changing and more variable climate present major risks to food security. Wheat is the most important grain food source for humans and is used for a diversity of products . World wCurrent major objectives in wheat breeding include enhanced grain yield, improved agronomic performance, and durable disease resistance, all production-oriented traits. While of considerable value, breeding for end-product quality traits is generally a secondary target met through the application of quality thresholds for release and consumer acceptance . As suchIn complex genetic phenotypes, the application of marker-assisted selection (MAS) is limited due to its low power to detect minor quantitative trait loci (QTL), genotype-by-environment interactions, and genotype-by-genotype interaction (interaction of QTL and plant genetic background). Numerous linkage mapping studies have shown that a large number of QTL with small effects control most end-product quality traits, and, therefore, MAS is unlikely to be able to capture sufficient variance of these traits to be useful in breeding programs . GenomicWhen compared to single-trait analysis, multivariate and/or multi-environment predictive models generally have improved accuracies, especially for traits with high genetic correlations . High geCross-validation schemes can be implemented that mimic real breeder circumstances when predicting lines in environments/years that have not been observed in the field. In forward prediction, previous years are used to predict the following year’s progeny. This method represents a common breeding situation where previous data are available, but no data are available for phenotypes or environments in the future year(s). Predicting the performance of lines for future years is a significant missing data problem and is challenging . Here, wThe main objectives of this study were to (1) estimate the genetic correlations of end-product quality and NIR-predicted data, (2) evaluate the influence of adding NIR-predicted data in improving prediction accuracies of end-product quality traits using fivefold cross-validation, and (3) investigate the forward prediction of line performance across years in a multi-environment context.22.1b*; color), flour water absorption , hardness/particle size index of flour , flour yield , protein , and flour swelling volume . All data across trials and years were used in the analysis, with a summary of the number of records and lines in each trait and their range and means provided in Phenotype records for six quality traits from hexaploid bread wheat breeding lines measured in laboratory assays along with NIR-predicted data from approximately 27,000 lines were used in this study. Wheat lines were evaluated for 8 years from 2012 to 2019, grown in NSW, VIC, WA, and SA, in more than 150–197 trials of end-product traits and 409–450 trials for NIR-predicted data. Lines were evaluated for crumb yellow/blueness  . End-use quality tests were performed using approved methods of the American Association of Cereal Chemists International . The PSI, Japan) . FSV  and 42.5% ethanol and two washes with 70% ethanol. DNA was eluted in 15 µl of 10 mM Tris-HCl at pH 8.0.Genomic DNA was extracted from six seeds per sample using a modified CTAB method , where aThe reference panel was genotyped with the Illumina Infinium Wheat Barley 40K XT SNP array v1.0  accordinGenotypes were imputed to exome density following the procedure described in 2.3y was a vector of quality phenotypes, µ was the trait mean, trial was a group effect of year, location, and nursery and fitted as fixed effect in the model.Phenotypic records were edited for possible outliers (mean ± 4SD). Phenotypes were also adjusted for fixed effects using a linear mixed model (Eq. 1) in ASReml , where yThe line was fitted as fixed to estimate BLUEs as adjusted phenotypes in GEBV estimation and random to estimate the variance due to lines in order to determine the broad sense heritability using:where 2.4Genomic Restricted Maximum Likelihood (GREML) was used for estimating GEBVs and variance components in the MTG2 software .2.5The single-trait GREML model used in this study was a linear mixed model described asμ is the overall mean, 1n is a vector of ones, Z is a design matrix relating records to breeding values, u is a vector of GEBVs, and e is a vector of residual effects. It was assumed that u~N of G using the prcomp function in R (h2) was calculated as the ratio of the additive variance to the total phenotypic variance using the GREML model.where y is the vector of adjusted BLUEs for the trait, ion in R  to invesion in R  in R. NaSingle-trait genomic prediction accuracy was evaluated with fivefold random cross-validation. In each cross-validation run, onefold was used as a validation set with their phenotype data masked in the analysis Figure 12.6A basic multi-trait mixed model was used as follows:yEP and yNIR are the vectors of adjusted phenotypes, 1 is a vector of ones, μEP and μNIR are general means, ZEP and ZNIR are the design matrices of breeding values, uEP and uNIR are the vectors of genomic breeding values, and eEP and eNIR are the vectors of random residual effects, for trait end-product quality and NIR-predicted data, respectively. Residuals  andwhere Residuals are assumed to be unstructured.Wheat end-product quality traits are costly to assay directly and therefore cannot be rapidly determined for a large number of lines in breeding programs. NIR-predicted data can be generated easily across many lines and used to predict end-use traits, making them an interesting test case for examining bivariate models. The benefit of using NIR to predict end-product quality traits was investigated using three different cross-validation scenarios.2.6.1The end-product quality phenotypes of validation lines were masked and predicted with a reference population with equal numbers of end-product and NIR-predicted data. This aimed to assess the effect of multi-trait analysis in the improvement of prediction accuracy of end-product quality traits where both traits are available on the same lines .2.6.2Validation lines were masked from both end-product and NIR-predicted traits; however, the reference set included all additional lines with only NIR-predicted data. This aimed to study the effect of extra NIR-predicted data on improving prediction accuracy.2.6.3Assessment of adding NIR-predicted data on validation lines to increase prediction accuracy, assuming that NIR-predicted data are easy and cost-effective to generate on all lines. In this scenario, end-product phenotypes were masked only in the validation set, while their corresponding NIR measurements were included in the reference set.2.7In breeding programs, the aim is to predict future years/environments using previous years’ data. These independent predictions were performed by training the model on previous years’ data and predicting future environments using GREML. Data from 2012 to 2015 were used to predict 2016, 2012 to 2016 to predict 2017, and 2012 to 2018 to predict 2019.33.1H2 being high for b*, Wab, and PSI, moderate for FlrYld and Protein, and relatively low for FSV (h2.Phenotype records of wheat lines from different breeding cycles (2012–2019) were used to estimate broad- and narrow-sense heritabilities as well as genomic prediction analyses. Records were unbalanced across years and trials, with, in total, 1,400–1,900 end-product wheat lines and 27,000 NIR-predicted lines available. For both end-product and NIR-predicted data, the lowest number of individuals was recorded in 2012, while in 2018 and 2019, the largest number of lines were measured. Phenotypic records by trait and year are summarized in Genetic correlations between end-product quality and NIR-predicted traits were estimated using a multi-trait model Table 3.3.2A principal component analysis was used to investigate population structure. The two first components jointly explained 56.2% of the variation Figure 23.3b* , NIR-predicted data were limited to the lines that had been assessed for end-product quality traits, while in the second scenario (S2), a large number of additional lines with NIR-predicted data were added to the training population. In both S1 and S2, end-product measured phenotypes of the validation set and their corresponding NIR-predicted data were removed from training; however, in the third scenario (S3), NIR-predicted data of lines in the validation set were included. The aim of this approach was to investigate whether early NIR phenotyping of candidate lines improves the prediction accuracy of end-product traits. Fivefold cross-validation within the population was used to estimate prediction accuracy from the GBLUP. In S1, adding NIR-predicted data increased the accuracy of predictions in all end-product quality traits compared to single-trait prediction, except for protein, in which there was a reduction in prediction accuracy and l change Figure 3b* also positively responded to including more NIR-predicted data, where in S3 an accuracy of 0.41 (flour yield) and 0.64 (b*) were observed compared to the single-trait model .To identify whether the inclusion of additional NIR-predicted data in the model could further improve the predictive performance of end-product quality traits, S2 and S3 scenarios were studied. Adding more information through the inclusion of NIR-predicted data improved the mean prediction accuracy of all traits Figure 3The scale of the genetic correlation between end-product and NIR-predicted traits was reflected in the level of genomic prediction accuracy. For example, genetic correlations and prediction accuracies were high for Wab and PSI. There was a high correlation between the magnitude of prediction accuracy in the multi-trait scheme and the genetic correlation of end-product and NIR-predicted data (0.69–0.77). Correlations between broad- and narrow-sense heritability with multi-trait prediction accuracies in different scenarios ranged between 0.53 and 0.60 and 0.43 and 0.47, respectively. Broad- and narrow-sense heritability also had a correlation of 0.58 and 0.45, respectively, with mean prediction accuracies across all three scenarios.3.5b*, which was similar to single- and multi-trait scenarios.Despite some fluctuations, there was a gradual increase in prediction accuracy through the addition of phenotypic data over the years, though PSI accuracy was variable Figure 4Forward prediction in a univariate scheme showed similar average prediction accuracy across years with a lower upward trend than the multi-trait, demonstrating more accurate predictions were achieved when using correlated NIR-predicted traits to increase training size Table 4.4Wheat end-product quality traits have been difficult to include in early selection as testing is labor intensive, costly, and requires large grain samples that are usually not available until late in the breeding cycle. These limitations cause breeding program inefficiency with high-yielding lines, which incur significant field trial resources before often being discarded late in the breeding cycle based on quality testing results. The genomic selection offers a new way to include end-use quality traits into the early breeding cycle; however, genomic selection success requires having training populations of sufficient size for accurate prediction, and some of the limitations listed above also make the accumulation of adequate training populations problematic . Studies4.1The broad-sense heritability of end-product quality traits evaluated varied from 0.40 to 0.87, with most of them having a value above 0.60. These heritability estimates for quality traits are consistent with values reported in other studies . The lowEnd-product and NIR-predicted traits showed a high genetic correlation. This is consistent with other studies, where the genetic correlation of end-quality traits and their corresponding NIR prediction were notably high . High coIn general, traits with lower genetic correlations of end-product and NIR-predicted data showed a smaller improvement in prediction accuracy. This is similar to the result reported by 4.2Multi-trait models combine information of correlated traits to deliver higher prediction accuracy and are thus useful in enhancing the prediction accuracy of favorable traits, which are difficult to assess in the early cycles of the program. We showed higher prediction accuracy across quality traits in multi-trait models , which is consistent with b*, and water absorption in different multi-trait scenarios, although the strategy used to predict performance was different. Overall, the addition of NIR-predicted data substantially improved trait prediction accuracy, and it is worthwhile perusing as a strategy in breeding programs.Using NIR-predicted data in a multi-trait scheme, prediction accuracies showed on average a 0.05 to 0.2 (8% to 73%) rise across all scenarios and traits (excluding Protein). This is comparable to Higher accuracies of different multi-trait scenarios also suggested that an expanded reference set of predictors would improve the prediction accuracy of correlated traits (S2 and S3). While using NIR-predicted data of the reference population (S1) showed an 11% increase in prediction accuracy over the single-trait model, increasing NIR measures as a correlated trait in the second scenario (S2) had a 33% increase over the single model. Including NIR measures of candidate lines in the training population (S3) also showed a 30% increase in prediction accuracy across all traits but did not considerably affect prediction accuracy compared to S2. The additional NIR information in S3 compared to S2 involved adding only a small number of observations. It could be that the addition of a relatively small number of NIR-predicted data would be unlikely to substantially affect the prediction accuracy. Particularly, focusing on improving the genetic correlation of NIR predictions with end-product assays and therefore increasing NIR-predicted data will positively affect prediction accuracy for target traits. The findings of the present study can be potentially applied in plant breeding to achieve more accurate and improved predictions compared to single-trait predictions for end-product quality traits of high importance.4.3Predicting the performance of new individuals lacking phenotypes is always a challenge in breeding programs. We demonstrated continuous increases in forward prediction accuracy across years as the size of the training population (and relatedness to the training set) increased. The importance of large training population sizes in increasing forward prediction accuracy has been previously demonstrated . JarquínPrediction ability could be improved by optimizing other factors affecting GS accuracy ; for exa5We investigated the potential benefit of including NIR-predicted data in multi-trait models for predicting six end-product quality traits in a commercial wheat breeding program. Different scenarios of multi-trait models were compared with a single-trait prediction model. We demonstrated that multi-trait models combining direct measures of end-product quality traits with their NIR predictions had higher predictive accuracy than their respective single-trait models. The increased prediction accuracy was observed with the inclusion of NIR-predicted data in the training population, which was likely driven by the increased size of the reference set and the relationship between the reference and validation populations. The prediction accuracies we achieved using the combined data were at a level that breeding selections could be confidently applied in the breeding process to increase breeding efficiency and genetic gain. Integrating NIR-predicted data into the prediction is a cost-effective way to improve the prediction accuracy of end-product quality traits, enabling breeders to confidently validate large numbers of lines in early breeding cycles. While our results state the efficiency of multi-trait analysis in end-quality traits in bread wheat, the method can be generalized to any other plant breeding program that may want to benefit from NIR-predicted data in improving the prediction accuracy of laborious-to-phenotype traits .The data analyzed in this study is subject to the following licenses/restrictions: The datasets analyzed during the current study are not publicly available due to third party commercial restrictions but will be made available from the corresponding author on reasonable request and with permission of Intergrain Pty Ltd. Requests to access these datasets should be directed to shiva.azizinia@agriculture.vic.gov.au.SA: analyzed data and wrote the manuscript. DMu, AR, JG, HR, and DMo: provided samples and phenotypes. KF, GK-G, and MH: genotyped samples. MH, JT, and HD: supervised the study. HD: edited the manuscript. All authors have read and approved the manuscript.


There is widespread interest in identifying genetic variants that exhibit parent-of-origin effects (POEs) wherein the effect of an allele on phenotype expression depends on its parental origin. POEs can arise from different phenomena including genomic imprinting and have been documented for many complex traits. Traditional tests for POEs require family data to determine parental origins of transmitted alleles. As most genome-wide association studies (GWAS) sample unrelated individuals , the study of POEs in such datasets requires sophisticated statistical methods that exploit genetic patterns we anticipate observing when POEs exist. We propose a method to improve discovery of POE variants in large-scale GWAS samples that leverages potential pleiotropy among multiple correlated traits often collected in such studies. Our method compares the phenotypic covariance matrix of heterozygotes to homozygotes based on a Robust Omnibus Test. We refer to our method as the Parent of Origin Inference using Robust Omnibus Test (POIROT) of multiple quantitative traits.Through simulation studies, we compared POIROT to a competing univariate variance-based method which considers separate analysis of each phenotype. We observed POIROT to be well-calibrated with improved power to detect POEs compared to univariate methods. POIROT is robust to non-normality of phenotypes and can adjust for population stratification and other confounders. Finally, we applied POIROT to GWAS data from the UK Biobank using BMI and two cholesterol phenotypes. We identified 338 genome-wide significant loci for follow-up investigation.https://github.com/staylorhead/POIROT-POE.The code for this method is available at  Most genome-wide association studies (GWAS) implicitly assume the magnitude and direction of effect of a genetic variant on expression of a phenotype is independent of whether the variant was maternally or paternally inherited. However, there exists a distinct class of genetic variants for which this assumption is violated. Such variants harbor a parent-of-origin effect (POE) wherein the effect of an allele on a trait depends on whether it was transmitted from the mother or the father . A substThere are multiple cited biological mechanisms by which POEs can arise in mammals. These include maternal intrauterine environment effects and effects of the maternal mitochondrial genome. However, the most frequently considered mechanism is genomic imprinting . This epTo detect variants demonstrating POEs, studies have historically required genotype data from related individuals to ascertain parental ancestry of the inherited alleles. In the case of available parent-offspring trio or other forms of familial genomes, there are well-established methods to detect POEs . These aRather than rely on family studies of limited sample size to detect POEs, researchers have recently transitioned to detecting the phenomenon in GWAS-scale cohorts. This practice requires innovative statistical methods to deal with missing parental ancestry information. For example, More recently, A sizable proportion of genes in the GWAS catalog are pleiotropic . These gpost hoc test that can help distinguish variants with POE effects from variants demonstrating more general gene–gene/gene–environment effects . We apply our method to GWAS data of BMI, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol from the UK Biobank and identify 338 significant potential POE loci. We conclude with a discussion of our findings, limitations, and proposed research to extend this work.Here, we expand on the concept initially suggested by Hoggart et al. to develop a test for POEs in genetic studies of unrelated individuals that considers multiple quantitative phenotypes. We show that a pleiotropic POE variant will not only induce differences in the variance of POE traits between heterozygotes and homozygotes, but also in their corresponding covariances. In our method, POIROT (Parent-of-Origin Inference using Robust Omnibus Test), we test for equality of phenotypic covariances matrices between heterozygous and homozygous groups. Specifically, we use the robust omnibus (R-Omnibus) test  to accomK > 1 continuous phenotypes on all subjects and that we have already adjusted these phenotypes for the effects of non-genetic confounders like principal components of ancestry.Using the notation of ith AA individual. We can represent We first model phenotypes in homozygous AA subjects. Let Within (1), ith BB individual. Further, let kth phenotype. If there is no association between this SNP and the kth phenotype, it follows that kth phenotype, but there is no POE present, then We next model phenotypes in homozygous BB subjects. Let k phenotypes, and Here, ith heterozygote. We can model this vector asLastly, we consider heterozygous AB individuals who carry only one copy of the alternative allele B. Let i received the B allele from the mother and i received the B allele from the father. We assume In (3), Based on the derivations above, we can calculate the phenotype covariance matrix for each genotype category. Based on k, then kth diagonal element of k and k’, then kk’ element of This observation motivates the use of a test of equality of two covariance matrices for detecting POEs in a population-based sample where we cannot explicitly observe We can test the null hypothesis that no POEs exist at a given SNP for any of the K phenotypes under study ( O’Brien . POIROT  O’Brien  to test  O’Brien .kth centered phenotype of the ith heterozygote (kth phenotype of the jth homozygous (AA and BB combined) individual . This step is necessary if a marginal association exists between the alternative allele and a given phenotype. In that event, the variance of original phenotype values among aggregate homozygous subjects  would be erroneously inflated. We next group these centered phenotypes by homozygote versus heterozygote status. Let Z measures by dividing by the square root of their absolute values. We consider W values for the ith heterozygous subject, and W values for the jth homozygous subject. We then perform a two-sample Hotelling’s T2 test (In (7) and (8), we standardize the  T2 test  comparint+nhom-2 . The tespost hoc test that can be utilized to differentiate the two phenomena. The test is motivated by the observation that, for a general interaction effect, the variance of a quantitative phenotype among BB homozygous individuals is different from that of AA homozygotes. This observation is in contrast to the variance pattern expected under a POE, in which the variability of each homozygous group is the same after phenotype centering. Thus, we can craft a post hoc test that assesses the null hypothesis of a POE (trait variance/covariances are the same between the two homozygous categories) versus the alternative of a general interaction effect (trait variance/covariances differ between the two homozygous categories). We create such a test by implementing the R-Omnibus framework as previously outlined in Section 2.2 but restricted to comparison of the two homozygous groups .As detailed above, POIROT tests for a variant demonstrating POE by comparing/contrasting trait variances and covariances by genotype. However, trait variances can also differ by genotype when a variant exhibits a gene–gene (GxG) or gene–environment (GxE) interaction effect . To incrK = 3, 6, or 10 phenotypes and n = 3000, 5000, or 10 000 unrelated individuals. To generate phenotypes for each round of simulation, we first randomly generate K intercepts from a standard normal distribution to form the K phenotypes exhibit one of three possible levels of pairwise correlation . We assume the pairwise trait correlations are randomly drawn from a uniform distribution. To simulate phenotypes exhibiting “low” correlation, we assume this is a Uniform distribution. For phenotypes of “medium” and “high” correlation, we assume a Uniform and Uniform distribution, respectively. These random draws are used to populate the off-diagonal elements of We conducted a variety of simulation studies to determine POIROT’s ability to detect POEs while maintaining proper rates of type I error. We considered n maternal and paternal genotypes for a given SNP by sampling twice from a Bernoulli [p = MAF ] distribution for each parent. To generate offspring genotypes, we sample from a Bernoulli (p = .5) distribution to determine which maternal allele and which paternal allele is transmitted. Thus, we can now assign all n offspring to one of four genotype groups: (i) AB with maternal reference/paternal alternative, (ii) AB with paternal reference/maternal alternative, (iii) AA, and (iv) BB. We then simulate the phenotypic error vector for all n unrelated offspring by drawing from a multivariate distribution with mean 0 and variance-covariance matrix Once we have constructed For type I error rate simulations, as described above, we assume these phenotypes have pairwise-trait correlation of levels low, medium, or high. To reflect the scenario where there exist no POEs or marginal effects of the alternative allele at the locus for any phenotype, we assume that henotype . An examK = 3, 6, or 10 normally distributed phenotypes. We assumed 1, 2, or 3 had parent-of-origin specific associations with the variant. When the number of affected phenotypes is greater than one, this corresponds to pleiotropy. For these scenarios, we assumed k harboring a POE. All other elements of the maternal effect vector are 0 for the phenotypes with no POE associations. We again considered low, medium, and high pairwise-trait correlations. We assumed a MAF of 0.25 and sample sizes of 5000, and 10 000. We applied our method to 5000 simulated datasets for each of the 162 settings and calculated power at significance level Next, we investigated the power of our test when POEs do in fact exist for a locus. We again considered P-value across the K phenotypes tested was less than K independent tests, this approach can be overly conservative. Thus, we implemented a second more liberal approach that estimates the true number of independent tests, K phenotypes. We then calculated power of the univariate approach as the proportion of loci for which the minimum observed P-value was less than We also compared the performance of POIROT to the corresponding univariate test of n α/Keff . We thenpost hoc test for distinguishing POEs from general interaction effects. Under the null hypothesis (i.e. there exist POEs but no interaction effects for any of the phenotypes considered), we looked at type I error of the R-Omnibus test comparing phenotypic covariances of the two homozygous groups. Similar to above, we considered a MAF of 0.25 and 3, 6, or 10 tested phenotypes, of which 1, 2, or 3 had POEs but no interaction effects. We considered sample sizes of 5000 and 10 000, maternal POE effect sizes {0.5, 0.6, 0.75}, and low/medium/high trait correlation. We also evaluated the power of this post hoc test to identify GxE effects when present. Simulation parameters were informed by prior work of post hoc interaction test.Finally, we performed several simulations to investigate the performance of our proposed 2), HDL cholesterol (mmol/l), and LDL cholesterol (mmol/l)]. Relevant covariates included genotyping array, PCs, sex, age at recruitment, and smoking status . Prior to analysis, we removed all individuals identified as outliers according to pre-calculated metrics of genotype missingness, heterozygosity, and excess relativeness. We excluded those with putative sex chromosome aneuploidy and those who were not included in PCA calculation. We included individuals of self-reported white British ancestry only.To assess the utility of POIROT for detecting POEs on continuous phenotypes using published population-based GWAS data, we utilized genotype and phenotype data from the UK Biobank (UKB), a large-scale biomedical database housing data collected from approximately 500 000 individuals from the UK (see Acknowledgements). This study allows for widespread investigation of the genetic variation associated with hundreds of lifestyle and health factors. To identify potential POE variants, we obtained data on three quantitative phenotypes . We then utilized the GSEAPreranked tool to test for enrichment of variants in this set among those top ranked variants by -log10  .P-values under the null hypothesis of no POEs for any single phenotype. The distribution of the P-values is again as expected under the null when we have non-normality of phenotypes  plots in enotypes , suggestSimulation results comparing the performance of POIROT to the competing univariate test under the assumption of true POE(s) are summarized in Simple Bonferroni correction tends to be overly conservative in the presence of correlated traits. We therefore used two multiple-testing correction approaches for the univariate method. As power generally increases with increasing sample size and POE magnitude, the scenarios shown in ect size .The one exception to these trends is the top right panel of post hoc test for distinguishing POE (null) from general interaction effects  are shown in post hoc test had the power to differentiate interaction effects from POEs . We identified a total of 338 variants with POIROT p-values falling below the Bonferroni-adjusted genome-wide significance threshold of 1.5 × 10−7 . These sP < .001) from the GSEA follow-up test, indicating that variants within 500 kb of imprinted or predicted-imprinted genes tended to lie at the top of our list ranked by increasing POIROT P-value. We next applied our post hoc test to these 338 identified variants to evaluate whether any demonstrated general interaction effects and observed that approximately two-thirds (230) had P > .05/338 and failed to reject the null of a POE. We similarly applied the univariate test for POEs genome-wide using each individual phenotype separately. Results are provided in We also saw a significant positive normalized enrichment score  in our analysis. Among them, we highlight one exonic variant  and one intronic variant  on chromosome 2 for gene ABCG8. Polymorphisms in this gene have previously been associated with direct LDL in UKB samples , rs74629722 ]. In particular, rs550619 lies 3299 bp from a previously published POE variant for BMI (rs1367117)   and has 1367117) . NeitherIn this article, we introduce a multivariate method, POIROT, for identifying common variants exhibiting POEs on one or more quantitative phenotypes in unrelated subjects. This work is motivated dually by the widespread evidence of pleiotropy in the genetics literature, as well as the limited statistical options for detecting POEs in unrelated cohorts. Our proposed method is an inherently simple statistical test of whether the phenotypic covariance matrix of heterozygotes is equal to that of homozygotes at a given locus. It represents a multivariate extension of the POE test of a single continuous phenotype proposed by post hoc test. Two of these variants map to gene ABCG8, a gene with high confidence of maternal imprinting in humans based on previously published work, and another lies nearby a known POE variant for BMI in the gene APOB.Through simulations, we demonstrate POIROT achieves appropriate type I error under the null. It further displays superior power to detect POEs than the competing univariate approach under most settings. Our method is indeed robust to non-normality of phenotypes across several simulation scenarios. We further applied our method to real GWAS data on white individuals of European ancestry from the UKB. In this analysis, we considered BMI as well as HDL and direct LDL cholesterol as potential imprinted phenotypes. The analysis revealed 338 variants meeting the stringent genome-wide significance threshold. Of these, 41 of may warrant particular focus in future investigations. They were not identified by the existing univariate approach to detect POEs and did not show evidence of significant gene–gene or gene–environment interaction effects using our proposed post hoc test for interaction effects.While the results presented here are promising for the utility of our proposed multivariate method for POE detection in practice, there are inherent limitations that we must address. Firstly, we propose POIROT as a method to detect SNPs wherein the effect of the variant allele in offspring differs according to which parent transmitted it. We do not evaluate the ability of our method to detect other trans-generational effects that may appear as imprinting effects at surface evaluation . FurtherPOIROT is a variance/covariance-based test for detecting POEs applicable to large population samples where allelic parental origin is unknown. If parental genetic information is known (i.e. through collection of parent-offspring trios), then it is well-established that variance-based tests within the offspring are often considerably less powerful than mean-based tests (like those described in the Section 1) that leverage allelic parental origin and look for differences in phenotypic means between heterozygous offspring with maternally- versus paternally inherited effect alleles . We perfThere are several avenues we are interested in pursuing to extend the work presented here. Rather than testing genome-wide variants, implementation of a two-stage screening procedure may mitigate the multiple testing burden. In the first stage, we propose to perform a standard GWAS for marginal (not parent-of-origin dependent) variant associations that considers multiple traits jointly. We restrict consideration to marginal association tests that are orthogonal to POIROT and thus provide complementary information. We can then efficiently test a smaller subset of top SNPs identified from the first stage for POEs. Another limitation we acknowledge is the requirement of continuous phenotypes. We are interested in the possible extension of our approach to accommodate dichotomous multivariate traits. One potential solution would be to use liability-threshold models  that canbtad199_Supplementary_DataClick here for additional data file.


Sophora moorcroftiana is a leguminous plant endemic to the Qinghai-Tibet Plateau. It has excellent abiotic stress tolerance and is considered an ideal species for local ecological restoration. However, the lack of genetic diversity in the seed traits of S. moorcroftiana hinders its conservation and utilization on the plateau. Therefore, in this study, genotypic variation and phenotypic correlations were estimated for nine seed traits among 15 accessions of S. moorcroftiana over two years, 2014 and 2019, respectively from 15 sample points. All traits evaluated showed significant (P< 0.05) genotypic variation. In 2014, accession mean repeatability was high for seed perimeter, length, width, and thickness, and 100-seed weight. In 2019, mean repeatability for seed perimeter and thickness, and 100-seed weight were high. The estimates of mean repeatability for seed traits across the two years ranged from 0.382 for seed length to 0.781 for seed thickness. Pattern analysis showed that 100-seed weight was significantly positively correlated with traits such as seed perimeter, length, width, and thickness, and identified populations with breeding pool potential. In the biplot, principal components 1 and 2 explained 55.22% and 26.72% of the total variation in seed traits, respectively. These accessions could produce breeding populations for recurrent selection to develop S. moorcroftiana varieties suitable for restoring the fragile ecological environment of the Qinghai-Tibet Plateau. The areial land . Owing tial land . At the ial land .Sophora moorcroftiana (Benth.) Baker, is a perennial deciduous dwarf shrub of the legume family endemic to the Qinghai-Tibet Plateau. It has strong ecological adaptability, such as drought resistance, barren tolerance, and wind and sand resistance. It is mainly distributed in the valleys and hillsides of the Yarlung Zangbo River Basin at an altitude of 2,800–4,500 m above sea level (m a.s.l.) and is a dominant pioneer plant among drought-tolerant shrubs  accessions and found that accessions with a white hilum were associated with lower mature grain content. Compared to other plant organs such as flowers and leaves, using seed traits to characterize the genetic diversity of species has certain advantages because seeds are relatively easier to collect and store . Pinna eS. moorcroftiana populations collected from different locations on the Qinghai-Tibet Plateau was studied using digital image analysis techniques. Our research focused on estimating the genetic variation within and among populations. In addition, a combination of potentially beneficial seed traits has been evaluated in breeding programs. This study aimed to provide data support for the genetic diversity and taxonomy of S. moorcroftiana, and to provide valuable parameters and information for the selection and breeding of strong adaptability S. moorcroftiana varieties.Therefore, in this study, the variation in seed morphological characteristics of 15 22.1S. moorcroftiana seeds were collected at 15 sampling points during October 1–7, 2014, and October 1–7, 2019 ; SW, seed width (mm); W/L, seed width to seed length ratio; HL, hilum length (mm); HW, hilum width (mm); HW/HL, hilum width to hilum length ratio; Pe, perimeter (mm); ST, seed thickness (mm) and SY, 100-seed weight (g). 100 seeds were manually counted. Use an electronic balance  for weighing. Before trait measurements, a flatbed scanner (EPSON GT-15000) was used to obtain digital images of the seed samples. During the scan, the seeds were allowed to equilibrate before measurement (room temperature was maintained at 20 ± 3°C and 40 ± 5% relative humidity) . The scaThree replicates were scanned for each population and each replicate included 100 seeds. Seed samples were prepared and scanned according to methods described by 2.3Data analysis was based on (1) variance component analysis to assess the magnitude and significance of genotypic variation between populations and (2) pattern analysis, including a combination of clustering and principal component analysis (PCA)  to proviS. moorcroftiana population accessions were analyzed within and over two years (2014 and 2019). The analyses were conducted using the variance component analysis procedure, residual maximum likelihood (REML), in GenStat 7.1 (2003) (Data on seed traits from 15 1 (2003) . Analysi1 (2003) .All seed trait means were derived from the best linear unbiased predictor (BLUP) analysis . These Bnl (number of years), and nr (number of replications) obtained from REML analysis were used to estimate the population accession mean repeatability (R).Referring to Fehr (1987) method, the estimated genotypic (Accession mean repeatability within a single year:Accession mean repeatability across years:rp) analysis was performed using GenStat 7.1 (2003), and multivariate analysis of variance (MANOVA) was used to assess accessions for 15 populations over two years, resulting in the sum of the estimated cross-products of the multi-trait data matrix.Phenotypic correlation : a) based on the variance components over the two years 2014 and 2019 to obtain an adjusted mean matrix of genotype × trait BLUP and finally obtain a graphical summary of accession traits for eight populations; and b) to analyze the type of association (positive or negative) among the nine seed traits in 2014 and 2019.33.1P< 0.05) for all the traits evaluated in the 15 S. moorcroftiana accessions (The genotypic variance components of the nine seed traits in 2014 and 2019 showed significant differences (cessions 2.R1) was high for seed perimeter, length, width, and thickness, and 100-seed weight, ranging from 0.933 to 0.992 .In 2014, accession mean repeatability (P< 0.05) among the 15 S. moorcroftiana accessions for nine seed traits (R2) of hilum length/width and seed thickness was higher than that of the other traits . The R2 values for the hilum length and width, and 100-seed weight were intermediate at 0.597, 0.643, and 0.626, respectively. R2 for seed perimeter, length, width, and width/length ratio were lower than those for the other traits, ranging from 0.097 to 0.493.Analysis of variance for over two years, 2014 and 2019, showed significant genotypic variation , which may help to investigate their taxonomic relationships. Analysis of the seed morphological characteristics of S. moorcroftiana showed that the JC, JD, SR, and BL accessions were significantly different (P < 0.05) from the GG, RB, NML, XTM, NM, DG, and SJ accessions, which could be divided into two groups. Our study shows that these heterogeneous seed traits can provide valuable information on the evolutionary relationship of S. moorcroftiana, and the seed morphology database has the potential for taxonomic screening , indicating their potential for genetic improvement. Pattern analysis showed that the 100-seed weight was significantly and positively correlated with seed perimeter, length, width, and thickness. The significant correlation between these traits provides key information for S. moorcroftiana breeding programs that focus on developing varieties with high seedling establishment success rates. This study not only deepens our understanding of the genetic diversity of S. moorcroftiana seed morphological traits but also provides important information for the development of breeding banks.This study estimated the phenotypic correlation, genotypic variation, and mean repeatability of nine seed traits in 15 The original contributions presented in the study are included in the article/RD and HL conceived the experiment. QG and JL collected the seeds. WZ and CL carried out the experiment. RD and QG analyzed the data. RD and HL wrote the paper. All the authors contributed to the manuscript and approved the submitted version. All authors contributed to the article and approved the submitted version.


Genetic pleiotropy refers to the simultaneous association of a gene with multiple phenotypes. It is widely distributed in the whole genome and can help to understand the common genetic mechanism of diseases or traits. In this study, a multivariate response best-subset selection (MRBSS) model based pleiotropic association analysis method is proposed. Different from the traditional genetic association model, the high-dimensional genotypic data are viewed as response variables while the multiple phenotypic data as predictor variables. Moreover, the response best-subset selection procedure is converted into an 0-1 integer optimization problem by introducing a separation parameter and a tuning parameter. Furthermore, the model parameters are estimated by using the curve search under the modified Bayesian information criterion. Simulation experiments show that the proposed method MRBSS remarkably reduces the computational time, obtains higher statistical power under most of the considered scenarios, and controls the type I error rate at a low level. The application studies in the datasets of maize yield traits and pig lipid traits further verifies the effectiveness.The online version contains supplementary material available at 10.1186/s12864-023-09820-5. To date (LASSO) , Bayesia (LASSO)  and Elas (LASSO) . Moreove (LASSO)  introduc (LASSO)  proposedP-value combination method, such as Fisher’s method [P-value. (2) Multivariate analyses. The basic idea is to conduct association test between the candidate SNP and multiple phenotypes simultaneously. This kind of approaches encompasses two representative methodologies: model-based methods and dimension reduction methods. Model-based methods mainly include linear mixed model method and Bayesian model method for continuous data [With the deepening of GWAS and epidemiological studies, more and more evidence suggests the widespread existence of genetic pleiotropy, which refers to a phenomenon that a single gene is simultaneously associated with multiple phenotypes , 8. Accos method , weightes method  or Cauchs method , is emplous data , 15, proous data , and nonous data . On the ous data , canonicous data , and hieous data , are speThe aforementioned multi-trait methods implement association test for a single variant or gene at a time, there are only a few multi-trait multi-locus methods to detect genetic pleiotropy to our current knowledge. For example, some distance regression model methods have been proposed to perform association test based on the similarity matrices of genotype and phenotype –24. SamuIn this study, we apply the multivariate response best-subset selection method (MRBSS) to perform pleiotropic genetic association analysis. MRBSS method is recently proposed by Hu et al. , it can The rest of paper is organised as follows. “n independent samples derived from a source population. For each sample, data of q SNPs and p phenotypes of interest are collected. Different from the traditional genetic association model, the high-dimensional genotypic data are viewed as response variables and the multiple phenotypic data as predictor variables. Then, we consider a multivariate response variable selection genetic model as follows:Y is an X is an jth SNP is considered to be active , otherwise, if jth SNP is considered to be inactive . q linear regression models jth column of the residual error matrix Suppose that there are jth SNP has no association with any of the phenotypes, is equivalent to jth SNP has association with at least one of the phenotypes, is equivalent to Obviously, A, and tr denotes the trace of a matrix.In this study, we aim to find the best subset of active SNPs, so it is necessary to effectively distinguish between active and inactive SNPs. On one hand, we introduce a separation parameter To solve the response best-subset selection problem, which involves selecting the true active-SNP subset while excluding the inactive-SNP subset, we can convert it to a mixed 0-1 integer optimization problem:The solution of  can be eq individual objective functions, that is, jth objective function can be expressed as:Notice that the least-squares estimate of regression coefficients can be obtained from model , i.e., \}Θ^ into , and dec problem  is furthObviously, Hn(Δ) in  is equivThe solution in the estimated response best-subset selection matrix In the last section, we provide the solution set form for the response best-subset selection problem to find the active-SNP subset, which includes the separation parameter terature , we applp degrees of freedom, i.e., To find the optimal solution for the parameter pair e BIC in , we perfTo evaluate the performance of MRBSS, we compared it with other two existing methods, namely Multivariate linear mixed model (mvLMM) and 2HiGWAS. mvLMM method is powerful approach to detect pleiotropic associations with multiple correlated phenotypes while controlling for population stratification . It consY. The sample size n is set to be 100, and the number of SNPs q is chosen from X using the model p is set to be 5. Assume the proportion of active SNPs to be k, l)th element being P-values less than the significance level of 0.05. We evaluate the performance of MRBSS, mvLMM, and 2HiGWAS in terms of statistical power, type I error rate, and computation time.In simulation studies, the genotypic data of SNPs  are simulated in the R package PhenotypeSimulator  with ming et al. . The firThe statistical power results from nine simulation studies, considering two different covariance structure scenarios, are presented in Figs. q increases, the statistical powers of all three methods decrease under all scenarios. When the proportion of active SNPs Figure mentclass2pt{minimFigure P-values consistently hover around the predefined significance level of 0.05. Furthermore, the type I error rates of MRBSS and mvLMM demonstrate stability across various scenarios, exhibiting no significant fluctuations with respect to the number of SNPs q or the proportions of active SNPs q reaches 500, the type I error rate gradually converges towards 0.05. Similar results can be obtained in Table The type I error rates of the three methods are presented in Tables entclass1pt{minimaTable Corn is a globally significant food crop, serving as both animal feed and an industrial raw material. The yield-related traits of corn play a direct role in determining its final production. Therefore, investigating the shared genetic factors underlying these yield traits is crucial for achieving high crop yields. In this study, seven maize yield-related traits are applied for analysis, including ear length (EL), ear diameter (ED), cob diameter (CD), kernel number per row (KNPR), 100 grain weight (100-GW), cob weight (CW), and kernel width (KW) . Moreovewww.maizegdb.org). By searching within a 200kb range upstream and downstream of the SNPs, we identify a count of 101, 24, and 47 associated genes using the aforementioned methods, respectively. Moreover, we observed one gene that is identified by both MRBSS and mvLMM, as well as four genes that are identified by both MRBSS and 2HiGWAS. The Venn diagram  as well as literature repositories such as PubMed. The findings reveal that out of the genes identified by the MRBSS method, eight have previously been reported to be associated with maize yield-related traits. Similarly, one gene detected by the mvLMM method has been reported in previous studies. In contrast, none of the genes identified by the 2HiGWAS method have been previously reported. We supply a comprehensive overview of the identified genes and their corresponding information in Table SWe use the proposed MRBSS method, along with two other methods, namely mvLMM and 2HiGWAS, to conduct pleiotropic genetic association analyse on seven maize yield-related traits. The results reveal a total of 151, 30, and 66 significantly associated SNPs detected by MRBSS, mvLMM, and 2HiGWAS, respectively. To ascertain the corresponding mapped genes for these associated SNPs, we refer to the B73 RefGENV4 genome in the maize databases available at MaizeGDB (Blood lipids are ubiquitously present in the cellular milieu of animals, and they play essential roles in fundamental metabolic processes. Their intricate involvement in the pathogenesis of cardiovascular diseases, obesity, metabolic syndrome, and diabetes has been extensively documented . In thisSimilarly, we employ three distinct methods, namely MRBSS, mvLMM, and 2HiGWAS, to perform pleiotropic genetic association analyse on the six blood lipid phenotypes of the Laiwu pig. Through these analyses, we identify a total of 121, 8, and 41 SNPs significantly associated with the blood lipid traits using MRBSS, mvLMM, and 2HiGWAS, respectively. Assuming that the mapped genes are located within a 20kb range upstream and downstream of the associated SNPs, we observe a count of 69, 7, and 20 genes for the three methods, respectively. Furthermore, one gene is identified as associated by all three methods, and three genes are detected by both MRBSS and 2HiGWAS. To visualize the overlapping genes detected by the three methods in pig lipid traits, a Venn diagram (Fig. Additionally, through a comprehensive search of various databases, including those available in the literature, we discover that eight genes identified by MRBSS, four genes identified by mvLMM, and two genes identified by 2HiGWAS have been previously reported. Further information on the identified genes, along with their corresponding details, can be found in Table SPleiotropic genetic association analysis offers the potential to uncover complex relationships between genetic variants and multiple phenotypes. Nowadays, it is a hot topic to develop the statistical methodologies for it. In this study, we propose to use MRBSS method to detect pleiotropic associations. Our findings demonstrate that MRBSS has a high power for identifying association signals with varying strengths and different correlation structures. This highlights the versatility and robustness of MRBSS in capturing genetic associations in complex phenotypes. Moreover, MRBSS provides effective control over the type I error rate in all the considered scenarios, ensuring a low level of false positive results. In terms of computational efficiency, MRBSS shows the shortest running time.The good performance of the MRBSS method can be attributed to the following three aspects: First, in the construction of the multivariate response variable regression genetic model, all phenotypes are considered as predictor variables, taking into account the complex hidden correlation information among phenotypes. Second, SNPs are treated as response variables, and the response best-subset selection approach considers both the inherent interactions among SNPs and avoids multiple testing corrections. Third, by transforming the response variable selection procedure into a pure 0-1 integer optimization problem, redundant SNPs are removed. Parameter estimation is performed using the curve search method, thereby reducing computational complexity.In fact, the mvLMM used for comparison is a typical multi-trait single-locus association analysis method. As the number of phenotypes increases, its computational complexity increases dramatically . 2HiGWASAlthough MRBSS is developed for pleiotropic genetic association analysis, it can also be extended to other areas, such as association analysis on longitudinal phenotypes and transcriptome-wide association study, where the association between longitudinal phenotypes and genetic variants, the association between gene expression levels and genetic variants, are performed, respectively. MRBSS will contribute to a deeper understanding of the genetic basis of complex phenotypes and diseases. However, MRBSS has weak ability to explain the genetic effects of the pleiotropic genetic associations, further studies would be focus on addressing this tissue.In summary, we propose an efficient pleiotropic genetic association analysis method based on multivariate response best-subset selection, which not only considers the correlation structure in multiple phenotypes but also the internal interaction effect between multiple loci. Simulation experiments show that the method remarkably reduces the computational time, obtains higher statistical power under most of the considered scenarios, and controls the type I error rate at a low level. The application studies in the datasets of maize yield-related traits and pig lipid traits further verifies the effectiveness.Additional file 1.Tables S1-S6 are showed in the Supplementary Materials.


SL) were counted 24 h (SL24h) post-mixing. Individual behavioral responses to isolation in a weighing crate (CRATE) or when alone in an arena while a human directly approached them (IHAT) were assessed within 48 h post-mixing. Additionally, pigs were tested for behavioral responses to the presence of a single human observer walking in their home pen in a circular motion (WTP) within one (T1) and 4 wk post-mixing (T2) noting pigs that followed, nosed or bit the observer. Animal models were used to estimate genetic and phenotypic parameters for all studied traits. Heritabilities (h2) for SL, CRATE and IHAT responses were low to moderate (0.07 to 0.29), with the highest h2 estimated for speed of moving away from the approaching observer. Low but significant h2 were estimated for nosing (0.09) and biting (0.11) the observer at T2. Positive high genetic correlations (rg) were observed between CRATE and IHAT responses (0.52 to 0.93), and within SL traits (0.79 to 0.91) while positive low to high correlations between the estimated breeding values (rEBV) were estimated within the WTP test (0.24 to 0.59) traits. Positive moderate rg were observed between CRATE and central and posterior SL24h. The rEBV of CRATE and IHAT test responses and WTP test traits were low, mostly negative (−0.21 to 0.05) and not significant. Low positive rEBV (0.06 to 0.24) were observed between SL and the WTP test traits. Phenotypic correlations between CRATE and IHAT responses and SL or WTP test traits were mostly low and not significant. Under the conditions of this study, h2 estimates for all studied traits suggest they could be suitable as a method of phenotyping aggression and fear/boldness for genetic selection purposes. Additionally, genetic correlations between aggression and fear indicators were observed. These findings suggest selection to reduce the accumulation of lesions is likely to make pigs more relaxed in a crate environment, but to alter the engagement with humans in other contexts that depends on the location of the lesions under selection.This study estimated the genetic parameters for human-directed behavior and intraspecific social aggression traits in growing pigs, and explored the phenotypic correlations among them. Data on 2,413 growing pigs were available. Pigs were mixed into new social groups of 18 animals, at 69 ± 5.2 d of age and skin lesions ( Aggression and fear/boldness indicators in pigs are heritable and there is evidence of genetic associations between them. Selecting against extremely shy and extremely bold pigs could result in easier to handle pigs while performing certain routine farm procedures. In recent years, temperament traits such as aggressiveness or fearfulness have received increasing attention in farming operations, as they affect how the animals respond to different husbandry practices . The incHeritabilities for behaviors thought to measure fearfulness and the ability to cope in stressful situations are low to moderate  and it iThe procedures described were approved by the institutional Animal Ethics Committee (ED-AE-43-2012). Governmental licensing was not required.n = 1,202 females and n = 1,211 barrows [castrated males]) from a commercial sow herd belonging to the Pig Improvement Company (PIC) where multiple lines were crossed onto the sows. The farm was located in South Eastern USA. Each pig was individually identified with an ear tag. Pigs were progenies of 116 sires and 391 dams and originated from seven different PIC terminal genetic lines. Pedigree information was available for two generations . Pigs were mixed in single sex groups (n = 18 pigs per group) of mixed genetic line at approximately 69 ± 5.2 d of age and they remained in the same groups until the end of the test period. Groups were formed by mixing nine pigs from two non-adjacent weaning pens. Groups that were mixed on the same day were regarded as being in the same batch. Eight groups were formed per batch and 17 batches were used in total to generate a total of 138 groups (batch 1 contained 10 pen groups). On average, animals from 11.6 ± 2.1 litters were represented in each group, and the mean number of pigs per litter per pen was 1.5 ± 0.81 pigs. Animals were housed in pens with fully slatted floors with a minimum space of 0.65 m2 per pig. Dry pelleted feed was provided ad libitum and pigs had constant access to water via nipple drinkers.Data were collected between December 2013 and June 2014 on 2,413 growing pigs (CRATE). Pigs remained isolated in the weighing crate for approximately 1 min and they were scored based on their restlessness on a 4-point scale, where 1 = pig performing exploratory behavior including sniffing and rooting of the crate floor and walls; 2 = pig shifting from side to side, attempts to turn; 3 = pig performing vigorous movements, attempts to escape by turning or running backwards and forwards; and 4 = pig performing serious, persistent attempts to escape by jumping over crate wall. Once the crate response test was completed, the pig was released into an empty testing arena and the individual human approach test (IHAT) was conducted. Approximately 30 s after the pig entered the testing arena, the observer walked toward the pig at a steady pace starting in the same corner of the arena each time and recorded the pig’s reaction. Three separate scores were given for each pig based on the severity of their movement (MOVEMENT), vocalizations (VOCALISE), and vigilance  test was designed as a practical approximation of pig-human interactions that occur while a producer performs the daily walk around the pens to ensure appropriate animal care. Pigs were tested for behavioral responses to the presence of a single human observer in their home pen at 6 ± 4.9 (T1) and 25 ± 15.9 (T2) d post-mixing. To begin the test, the observer entered the pen by climbing over the gate and walked once around the perimeter of the pen at a normal speed to ensure all animals were alert and aware of the human presence. The observer then walked around the pen a second time and recorded the ear tags of each pig that followed the observer for more than 0.5 laps of the pen. At the end of the second lap, the observer paused for 1 min and noted individuals that performed the following behaviors: 1) NOSE ; 2) FOLLOW , or 3) BITE  the observer.The walk-the-pen (SL24h) by a single trained observer. Recently received lesions were counted separately on three regions of the body: 1) anterior , 2) central , and 3) posterior . One uninterrupted scratch was classed as a single lesion, regardless of length or severity. A lesion was considered as recent if it was vivid red in color or recently scabbed. The pre-mixing lesion count was subtracted from that taken 24 h post-mixing for each pig. This served to ensure that only those lesions that occurred as a result of mixing aggression were included in all analyses.Skin lesions, as a proxy of aggressive interactions, were counted immediately prior to mixing, and 24 h post-mixing  algorithm. Each trait was analyzed using single-trait animal models. Models for CRATE and IHAT responses and skin lesions followed the general formula:Skin lesion showed considerably skewed distributions  and thusv. 4.1.2 . PredictU v6.5.2  using thwhere:y = vector of recorded traitsb, a, c, and e = vectors of the fixed effects, additive genetic effects, common environmental effects , and the residual error, respectively. The fixed effect vector b contained genetic line, sex, and batch effects for all traits. Additionally, the order the animals were tested in was also included for CRATE and IHAT responses models. Body weight at mixing was fitted as a linear covariate for all traits.X, Z, and W = Incidence matrices of fixed, additive genetic, and common environmental effects, respectively.For the WTP traits, an animal model with the logit function for binary traits was used. Models followed the general formula:where:y = vector of recorded traitsb, a, and e = vectors of the fixed effects, additive genetic effects, and the residual error, respectively. The fixed effect vector b contained the genetic line, sex, and batch effects for all traits.X and Z = Incidence matrices of fixed, and additive genetic effects, respectively.A seven-trait model was built for all linear variables. Genetic, phenotypic, and residual variances resulting from the single-trait animal models were used as starting values for the multi-trait model. Heritability, genetic and phenotypic correlation estimates were obtained by using an accompanying R program provided by DMU based on the notes “Calculation of Standard Errors of estimates of genetic and phenotypic parameters in DMU” by Multi-traits models including the WTP test traits failed to converge. Spearman correlations between estimated breeding values for WTP, CRATE response, IHAT traits, and skin lesions were calculated in R v. 4.1.2  as a proDescriptive statistics for skin lesions, CRATE, and IHAT responses are presented in Estimated heritabilities for skin lesion, CRATE, and IHAT responses were low to moderate (0.07 ± 0.02 to 0.29 ± 0.05), with the highest heritability estimated for speed of moving away from the approaching observer . All herrg) between CRATE response, IHAT traits and skin lesions are presented in g were observed between CRATE and IHAT responses (0.52 to 0.93), and within the various skin lesions traits (0.79 to 0.91), while significant positive low to high correlations between the estimated breeding values (rEBV) were estimated for the measures recorded within the WTP test (0.24 to 0.59). Correlations between the estimated breeding values of CRATE and IHAT test responses and WTP test traits were low, mostly negative  were observed between skin lesions and the WTP test traits.Genetic correlations  tended to display more distress while in the weigh crate and were less likely to willingly approach a human in the IHAT. Conversely, pigs with a high number of lesions to the anterior part of the body 24 h post-mixing, which are typically the most numerous and received primarily during reciprocated attack, also showed an aversive reaction to being in the crate, but these animals were more willing to explore a human in their home pen in the WTP test. Exerting selection pressure to reduce the accumulation of lesions is therefore likely to make pigs more relaxed in a crate environment, but to alter the engagement with humans in other contexts that depends on the location of the lesions under selection. Future studies could consider using precision livestock farming technologies to assess animal-human interactions in a more detailed and objective manner and thus remove some of the possible confounding factors associated with the recording of behavioral observations. Finally, the findings reported in this study could have practical implications for the pig industry as they suggest that pigs selected for reduced aggression could be easier to handle while performing certain routine farm procedures such as weighing. Additionally, as less fearful animals have higher growth rates, higher carcass quality characteristics and better immune function  this cou


The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter <1. The tuning process  improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor,  Genomic prediction can achieve a more accurate prediction of additive genetic values at an early life stage, compared to the conventional pedigree-based prediction. Genomic prediction has been applied to a broad range of disciplines, including animal breeding  and humaGenomic prediction requires genotypic information for both discovery and target samples. Genome-wide single nucleotide polymorphisms (SNPs) are typically used to estimate the genomic relationship matrix (GRM) for the genotyped samples so that breeding values (in livestock) can be estimated for the target samples, given the phenotypic information of discovery samples . In manySsGBLUP uses an H-matrix that is a harmonised matrix of a pedigree-based numerator relationship matrix (NRM) and a GRM; therefore, we will use the term H-matrix best linear unbiased prediction (HBLUP). The H-matrix allows us to use the information of non-genotyped individuals in genomic prediction using a data augmentation technique (see  and Legap (1 − p)] α, where p is the allele frequency ]α, considering a non-negligible relationship between allele frequency and per-allele effect size , and the genotypic information is available for the individuals from the last two generations (104–105th generations), noting that the sample size in each of the last five generations is 1,000. Furthermore, it is noted that the phenotypes are available for all individuals. We conducted 3,000 replicates of the simulations under three different scenarios with specified simulation parameters. By running multiple replicates, we were able to estimate the variance and uncertainty in the results and obtain a more accurate assessment of the effects of different factors on the population. Replicating the simulation multiple times is a common practice in simulation studies as it can increase the reliability and validity of the results by reducing the impact of chance events and providing a more robust assessment of the effects of the factors being studied.p-value threshold lower than 1e-04 (HWE). After QC, the number of individuals did not change, and the SNPs number was 42,795. The Hanwoo beef cattle data included five carcass traits: carcass weight, eye muscle area, back fat thickness, marbling score, and adjusted 12 months weight. The total number of animals with non-missing records for each carcass trait with and without genotypic information can be seen in In this study, we applied statistical analyses to genotypic and phenotypic data from Hanwoo beef cattle. The total number of animals with pedigree information was 84,020, and among them, 13,800 animals were genotyped for 52,791 genome-wide SNPs, and 25,502 animals were recorded for their phenotypes. The number of animals available for both genotypic and phenotypic information was 9,072. The following criteria were applied for quality control (QC) using PLINK: minor allele frequency below 0.01 (MAF), filtering SNPs with a call rate lower than 95% (GENO = 0.05), individual missingness more than 5% (MIND = 0.05), and Hardy–Weinberg Equilibrium In the HBLUP analysis for the Hanwoo cattle data, animals available for phenotypes and genotypes  see  are randA which is estimated based on the pedigree and has been used in Henderson’s mixed model equation (A matrix can be formulated as follows.NRM is denoted as equation  to obtaiWhere vi) can be expressed as a function of the allele substation effect  is computed based on genotypic information, and NRM (A) is estimated using the pedigree information of the population. Following G and A . We used the genotypic information of the last two generations and the full pedigree information across the five generations to estimate the ered see  to estimblending” which shrinks the genomic relationships toward the pedigree relationships, using an arbitrary weight, GRM is typically a non-positive definite matrix. In the process of HBLUP, it is usually required to modify GRM to be positive definite so that it can be inverted without any numerical problem . This mo to 0.99 . The bleJ is a matrix with the same size as GRM, all elements are equal to one, and I is an array with the size of The tuning process adjusts GRM, accounting for the allele frequencies in the base population, using the information from NRM that includes the information of founders in the base population through the pedigree . The tunPlease note that Eqs In the analyses, we used a linear mixed model that can be written asWe employed the restricted maximum likelihood (REML) method, fitting the  MTG2.22  genomic One of the well-known methods to find the best configuration of hyper-parameters is the grid search . In the grid search, all possible combinations of hyper-parameters are considered to evaluate the performance of prediction models. We considered two tuning methods and without tuning . The blending step size in the grid search is 0.1 from 0 to 1 and 0.02 from 0.9 to 1.0. Meanwhile, the step size for This study uses critical performance metrics to evaluate the accuracy and effectiveness of the prediction and estimation methods. The specific performance metrics will depend on the specific research question, the data type, and the prediction method’s goals. Using multiple performance metrics to provide a more comprehensive assessment of the model’s performance is common.In genomic analysis studies, we often use a performance metric called RMSE to see how good a model is at making predictions. It's like a measuring stick to compare the model’s guesses with the real answers. We calculate RMSE by taking the differences between the model’s predictions and the actual values, squaring them, and then finding the average of those squared differences. Finally, we take the square root of that average. RMSE is useful because it's simple to understand and tells us how far off the model’s predictions are from the real answers, on average. The smaller the RMSE is related the better the model is at making accurate predictions, which means the guesses are closer to the real answers.In the study of genes and their effects on physical traits, scientists often use a tool called the Pearson correlation coefficient . This helps them figure out if there’s a connection between the two things they’re studying. If the coefficient is high and positive, that means when one thing goes up, the other thing tends to go up too. If it’s high and negative, that means when one thing goes up, the other thing tends to go down.In genomic analysis studies, researchers use statistical models to understand the relationship between genes and traits. The Akaike Information Criteria (AIC) is a metric that compares different models and determines the best one. It was developed by Hirotugu Akaike in 1974  and is bted data . Figure imal α . In addiMimicking a real dataset in which multiple replicates are not possible, we used a single simulation data to assess the HBLUP accuracy, varying hyper-parameters . All posWe used pedigree, genotype, and phenotype data of Korean native cattle (Hanwoo), which is a unique and important breed in the beef industry , to asseng HREML . We obses traits . On the  general . Finally general  althoughWe also used a grid search to assess the performance of all hyper-parameters  in whichHBLUP or ssGBLUP has been widely used in livestock breeding programs . The HBLIn our simulation scenario, we employed random mating and random selection instead of artificial selection based on phenotypes or estimated breeding values because the purpose of this simulation study is to demonstrate how the novel hyper-parameter, alpha, works in a simplified simulation setting. Nevertheless, we have applied our approach to real cattle data that have been subjected to artificial selection. By doing so, we believe that we have verified the performance of the hyper-parameter in a realistic setting. In both simulation and real data, allele frequencies can be altered significantly due to genetic drift and selection .The scale factor, ulations . ImportaIn both simulated and cattle data, blending (nite GRM . In any aningful . In addianingful . One comaningful . Howeveraningful .The tuning process adjusts GRM, accounting for the allele frequencies in the base population, assuming that the founders in the base population are not genotyped but are linked through the pedigree. As expected, the widely used tuning method (tune = 1  implemenThe grid search benefits include being able to provide reproducible results, being fast to implement, being simple to develop for parallel computing, and being efficient in exploring a low-dimensional hyper-parameter space. Moreover, for the large-scale hyper-parameters search space, there are a large number of other hyper-parameters optimisation methods, such as genetic/evolutionary algorithms, swarm intelligence methods, stochastic/random search techniques, and co-evolutionary algorithms . These mIn conclusion, existing hyper-parameters such as blending and tuning in HBLUP are important in general, and their optimal values or options should be properly sought to achieve a reliable genetic evaluation. Depending on the data, optimal values can vary, and unnecessary or over-parametrised blending or tuning can produce adverse effects on the prediction accuracy. The scale factor, a novel hyper-parameter to be introduced in HBLUP, should be explicitly optimised to increase the prediction accuracy, given that the impact of the scale factor is competitive with other hyper-parameters, blending and tuning. We suggest including the scale factor,


Manihot esculenta Crantz) holds significant importance as one of the world’s key starchy crop species. This study aimed to develop core collections by utilizing both phenotypic data  and genotypic data  obtained from 1,486 cassava accessions. Six core collections were derived through two optimization strategies based on genetic distances: Average accession-to-nearest-entry and Average entry-to-nearest-entry, along with combinations of phenotypic and genotypic data. The quality of the core collections was evaluated by assessing genetic parameters such as genetic diversity Shannon-Weaver Index, inbreeding (Fis), observed (Ho), and expected (Hs) heterozygosity. While the selection of accessions varied among the six core collections, a seventh collection (consolidated collection) was developed, comprising accessions selected by at least two core collections. Most collections exhibited genetic parameters similar to the complete collection, except for those developed by the Average accession-to-nearest-entry algorithm. However, the variations in the maximum and minimum values of Ho, Hs, and Fis parameters closely resembled the complete collection. The consolidated collection and the collection constructed using genotypic data and the Average entry-to-nearest-entry algorithm (GenEN) retained the highest number of alleles (>97%). Although the differences were not statistically significant (above 5%), the consolidated collection demonstrated a distribution profile and mean trait values most similar to the complete collection, with a few exceptions. The Shannon-Weaver Index of qualitative traits exhibited variations exceeding ±10% when compared to the complete collection. Principal component analysis revealed that the consolidated collection selected cassava accessions with a more uniform dispersion in all four quadrants compared to the other core collections. These findings highlight the development of optimized and valuable core collections for efficient breeding programs and genomic association studies.Cassava ( BhattacWhile phenotypic variation plays a crucial role in practical selection within genetic improvement programs, the establishment of cassava core collections based on a substantial number of morphological and agronomic descriptors evaluated over multiple cultivation years, along with a large set of molecular markers, has not yet been proposed. Therefore, the objectives of this study are as follows: i) develop cassava core collections based on quantitative, qualitative, and molecular data descriptors, both individually and in combination; ii) assess the effectiveness of different selection methods for cassava core collections in retaining maximum genetic diversity, variance, and other genetic parameters compared to the complete collection; and iii) generate a consolidated core collection that represents the highest phenotypic and molecular variability among cassava genotypes.22.1A comprehensive evaluation was conducted on a total of 1,486 accessions from the cassava germplasm bank of Embrapa Mandioca e Fruticultura. These accessions originate from various regions in Brazil and some others acquired through exchanges with countries such as Colombia, Venezuela, Nigeria, Mexico and Uganda Table S1The evaluation and characterization of the germplasm bank accessions took place between 2011 and 2021 in three cities in the State of Bahia, Brazil: Cruz das Almas, Laje, and Valença Table S22.2The characterization of the cassava accessions involved the use of standardized scales for morpho-agronomic descriptors, which encompassed various aspects of the plant including leaf, stem, root, flower, and agronomic traits. The descriptors were categorized into qualitative and quantitative variables specific to the cassava crop, as outlined in 2.3DNA extraction from young cassava leaves was carried out using the CTAB method (cetyltrimethylammonium bromide), following the protocol described by ApeKI, a type II restriction endonuclease that recognizes a degenerate sequence of 5 bases  with fragment lengths of 100 bp  was used for the sequencing process. The obtained reads were aligned to the cassava v.6 reference genome  protocol  was emplf 100 bp . After de genome  using the genome .A total of 20,023 SNP markers were obtained, and these markers are distributed across the 18 cassava chromosomes. Sequence analysis and quality filtering were performed using Tassel software version 5.2.37 . The fil2.4The accessions in the cassava germplasm bank underwent evaluations in various trials conducted from 2011 to 2021, leading to minor variations in the qualitative descriptors across different classes. To address this variability, we employed the mode as an indicator of the prevailing trend for accessions in terms of qualitative characteristics. The mode signifies the class that exhibited the highest frequency of observations recorded over multiple years.y is the vector of phenotypic observations, g represents the genotypic effects considered as random effect b is the aligned effects of blocks within trials considered as random i represents the effects of the genotype-trial interaction considered as random effect e represents the error effects considered as random effect The quantitative dataset was analyzed using linear mixed models. The dataset included information about the year and location of assessment, referred to as “environments” in this context. A analysis considering all environments for each quantitative descriptor was performed using the following statistical model: on 4.1.8  within ton 4.1.8 .2.5Six core collections were generated using different criteria based on distances between the accessions of the complete collection and the core collection Table 1.j), a similarity coefficient (l and k) is then calculated as follows: l and k can be compared with variable In the case of collections based solely on phenotypic data, the Gower dissimilarity matrix  was emplL represents the total number of markers, ilap is the allele frequency of allele a of marker l for accession i, jlap is the allele frequency of allele a of marker l for accession j, and lm denotes the number of matching alleles for marker l.For collections obtained solely using molecular marker data, the Modified Rogers distance  was utili for individuals A and B, respectively; L denotes the number of loci for which To form the pooled core collection, both genotypic and phenotypic data were utilized to generate distance matrices. Gower distance was employed for phenotypic data, while Manhattan distance was applied for genotypic data. The Czekanowski distance  is given by the equation: on 2.7.2  in R veron 2.7.2 .2.6The coincidence between the different methods of forming cassava core collections was assessed using the Kappa index . A binarHo) was calculated using the formula Hs) was determined using the formula Fis) was calculated as To evaluate the genetic diversity within core collections, consolidated collection, and complete collection, several parameters were considered. The observed heterozygosity . Morpho-agronomic data was analyzed using the AMR package version 2.0.0 , while m33.1The core collections were created by selecting 10% of the complete collection, resulting in 149 genotypes. Overall, there was a lack of significant overlap in the selected accessions among the core collections. The core collections formed using the EN algorithm, based on phenotypic data (PhenEN), and pooled data (phenotypic and genotypic) (GPmEN) had the fewest exclusive accessions, with 47 and 48, respectively. On the other hand, the remaining core collections had over 50% exclusive accessions, indicating minimal overlap in the selected genotypes Figure 1The Kappa coefficient was used to assess the concordance in accession selection among the different core collection formation methods, and the results ranged from moderate (0.48) to low (-0.09) Table 2.3.2Out of the 1486 cassava accessions, approximately 30% (445 accessions) were exclusively selected by a single type of core collection, while the majority of accessions (~56%) were not included in any core collection Table S5Among the core collections, the ones built using the EN algorithm on phenotypic data (PhenEN) and the EN algorithm on grouped data (phenotypic and genotypic) (GPmEN) had the highest number of accessions shared with the consolidated collection, accounting for approximately 50% of the overlaps. The remaining collections had 53 to 69 accessions that were also selected by the consolidated collection. The core collections created using grouped data with the EN and AN algorithms also had a significant number of accessions in common with the consolidated collection (79 accessions) Table S53.3Ho value (0.403). Moreover, the variations in the maximum and minimum values for the three parameters  and petiole length. Accessions with extreme values or low harvest index were not included in the GenAN core collection.For the majority of quantitative phenotypic traits, there was no significant difference (>5%) in means and variances between the core collections and the complete collection Table S6The qualitative data were analyzed based on the efficiency of the core collections in encompassing all classes of each evaluated trait Figure 4Some characteristics were not well represented in all core collections, possibly because some of the classes are rare occurrences, being seldom observed in the field. For example, only 28 cassava accessions (~1.88% of the complete collection) exhibit a zig-zag stem growth habit. Therefore, any variation in the method of forming the core collection can alter this frequency, as seen in the PhenAN collection, which was represented only by the straight growth habit. Other significant variations in the representativeness of the core collections based on qualitative data were identified for the stipule margin and root position traits, also due to the low frequency of certain classes in the complete collection.3.5The quality assessment of core collections was conducted using the Shannon-Weaver Index (ISW). Comparisons were made between the core collections and the complete collection, with variations greater than ±10% of the ISW considered significant for quantitative phenotypic traits Table 4.The ISW for qualitative traits exhibited variations greater than ±10% when compared to the complete collection, particularly in the GenEN, PhenAN, PhenEN, GPmAN, GPmEN, and CCons collections Table 5.3.6Principal Component Analysis (PCA) was utilized to evaluate the representation of diversity in the core collections based on phenotypic and molecular data Figure 5In the PCA analysis of SNPs, the first two principal components accounted for a smaller percentage of the molecular variation in the data  compared to the phenotypic data Figure 644.1Solanum tuberosum L.)  . The disosum L.) .Several core collections have been developed based on phenotypic data alone . AlthougDue to the low agreement in the selection of cassava accessions among different core collections and the risk of excluding accessions with important phenotypic or molecular characteristics, a consolidated core collection was created by including accessions selected by at least two methodological approaches (EN and AN) and different types of collections . This slightly increased the number of selected clones (from 10% to ~14% of the complete collection), which is still manageable within the scope of genetic resources and species breeding programs.Overall, the cassava core collections effectively retained a high number of SNP alleles from the complete collection, surpassing 94.5%. Notably, the consolidated and GenEN collections exhibited the highest allelic richness, retaining 97.1% and 97.73% of the alleles, respectively. This preservation of allelic richness in core collections holds significant importance for future studies on genomic associations, especially for traits controlled by rare alleles. Furthermore, the allelic richness retained in cassava core collections compares favorably to other species such as maize 93% -  and tomaHo, Hs, and Fis) compared to the complete and consolidated collections. Methodologically, the collections obtained through the AN algorithm demonstrated Ho, Hs, and Fis values more similar to the complete collection, likely due to the algorithm’s aim of achieving a similar representation of the complete collection’s characteristics in the core collection. In general, the variation of Ho (0.39 to 0.40) and Hs (0.29 to 0.30) observed in core collections for biallelic markers closely aligns with other cassava germplasms, such as the global cassava collection from IITA, which exhibited Ho values ranging from 0.33 to 0.39 and Hs ranging from 0.31 to 0.34   . These rIn specific cases, an increase in the variance of certain traits was observed, likely due to the removal of accessions that made redundant contributions to phenotypic diversity, with phenotypic values close to the sample mean. For instance, the mean root diameter variance in the consolidated collections, GPmEN, and PhenEN increased by 31%, 49%, and 62%, respectively, compared to the complete collection. However, this increase in variance within core collections is a desirable attribute as it allows for a greater representation of the complete collection while minimizing redundancy . This isThe ISW was also utilized to assess the efficiency of core collections in representing cassava germplasm diversity. Overall, variations greater than ±10% of the ISW compared to the complete collection were observed for only a few quantitative phenotypic traits. The characteristics that were most affected by the ISW reaching 0.00 in the collections obtained with the AN algorithm were those with the greatest imbalance in their distribution, such as the thickness of the root cortex. On the other hand, the collections obtained with the EN algorithm exhibited higher ISW values than the complete collection. Similar results were identified in the analysis of qualitative phenotypic data, where the collections based on the EN algorithm and the consolidated collection showed higher ISW compared to the complete collection for most traits. However, similar to other species with clonal propagation such as yam , cassava4.2The construction of core collections involves various methodological considerations that can impact the representativeness of genetic diversity. In this study, two criteria described by The second collection in this study, known as type 3 aimed to represent the distribution of accessions in the complete collection. Its objective was to ensure that the selected proportion of the complete collection reflects the numerical contributions of different categories in the core collection. The EN algorithm was used to form these collections, which selected accessions that were well-distributed, particularly at the extremes of the different quadrants of the PCA. This approach provided a better representation of the entire collection, resulting in more diverse collections where each selected individual was sufficiently different from others. As a result, subsets with low redundancy  and highThe consolidated collection was developed to address the issue of low coincidence in the selection of accessions among the core collections. It served as an alternative to better represent the cassava accessions among the six collections developed based on different types of data. The consolidated collection proved to be efficient not only in overcoming the low coincidence but also in improving allele retention. It resulted in less difference in genetic parameters among the collections and maintained maximum diversity in the ISW for all traits. Furthermore, it better represented the phenotypic and genotypic classes of the complete collection in the PCA.4.3Lagenaria siceraria, have also demonstrated that PCA analysis of core collections, utilizing various phenotypic data types, accurately represents the complete collection and preserves the geographic distribution of accessions , the consolidated collection remains manageable in size, allowing for efficient characterization and utilization of the germplasm. Overall, the formation of the consolidated collection addresses the challenge of low coincidence in accession selection and provides a robust and representative resource for further research and breeding programs in cassava.https://doi.org/10.6084/m9.figshare.23818875.v1.The original contributions presented in the study are publicly available. This data can be found here: LD, EO designed the study. CS performed the field evaluations. CS, LD, and CC analyzed the data. CS, and CC wrote the paper. All authors contributed to the article and approved the submitted version.