The genetic architecture of corneal dysfunction remains poorly understood. Epidemiological and clinical evidence suggests a relationship between corneal structural features and anthropometric measures. We used global and local genetic similarity analysis to identify genomic features that may underlie structural corneal dysfunction.rg) between traits using linkage disequilibrium (LD) score regression and local genetic covariance using ρ-HESS, which partitions the genome and performs regression with LD regions. Finally, we identified genes located within regions of significant genetic covariance and analyzed patterns of tissue expression and pathway enrichment.We assembled genome-wide association study summary statistics for corneal features  and anthropometric traits  in Europeans. We calculated global genetic correlations  and CRF . Local analysis revealed 68 genomic regions exhibiting significant local genetic covariance between CRF and height, containing 2874 unique genes. Pathway analysis of genes in regions with significant local rg revealed enrichment among signaling pathways with known keratoconus associations, including cadherin and Wnt signaling, as well as enrichment of genes modulated by copper and zinc ions.Global LD score regression revealed significant negative correlations between height and both CH (Corneal biophysical parameters and height share a common genomic architecture, which may facilitate identification of disease-associated genes and therapies for corneal ectasias.Local genetic covariance analysis enables the identification of associated genes and therapeutic targets for corneal ectatic disease. Although the biophysical features underlying corneal shape contribute directly to ocular diseases such as keratoconus, they also demonstrate associations with systemic disorders, particularly those of the connective tissue.–Currently, the specific genomic drivers of corneal biomechanical dysfunction remain poorly understood, despite evidence that genetic polymorphisms explain a large proportion of such phenotypic variation.,,Although association studies have identified some genomic variants associated with keratoconus, traditional GWAS are limited in their ability to resolve common, low-effect variants that likely underlie a large proportion of corneal ectasia heritability.Because summary-level genetic data used in this study are publicly available and deidentified, institutional review board review for this project was not required. Bulk corneal gene expression data were made available from a previous study, which was approved by the institutional review board at Augusta University.http://www.nealelab.is/uk-biobank/.We obtained summary statistics for publicly available GWAS of keratoconus-related and anthropometric traits, including CCT, corneal hysteresis (CH), corneal resistance factor (CRF), 3 mm index of keratometry result, body mass index (BMI), weight, and height. All GWAS studies considered in the analysis are summarized in Both corneal biophysical parameters and anthropometric traits showed significant heritability explained in their respective GWAS data, with explained heritability proportions ranging from 0.14 (BMI) to 0.46 (height). The 3 mm index of keratometry result was an outlier, with an explained heritability of 0.0049.https://github.com/bulik/ldsc),,Genetic correlations occur in the presence of shared single nucleotide polymorphism (SNP) heritability. SNP heritability is the proportion of variability in a trait that is due to genetic contributions, and it can be estimated by classical twin studies or from regressing test statistics from SNPs in a GWAS across linkage disequilibrium (LD) blocks.https://huwenboshi.github.io/hess/)We used ρ-HESS . Genome mapping was performed using PyEnsembl (release 97). A full list of the genes is provided as We prioritized individual genes for downstream analysis by the significance of local covariance between CRF and height. CRF was selected as the endophenotype of choice for this analysis because it had the largest set of significant SNPs of the biophysical traits investigated.We performed single-cell expression analysis of genes in regions of high local covariance using an atlas of gene expression in 19 cell types of the anterior chamber of healthy human eyes.We compared the overlap of gene candidates in regions of significant local covariance with genes associated with either keratoconus or body height in the existing literature. Literature-based gene-phenotype associations were obtained from the DisGeNET ontology, which integrates clinical associations, experimental evidence, and computational predictions to produce a confidence score for gene-phenotype associations.–Functional pathway enrichment among gene candidates was measured using gene set enrichment analysis.rg = 0.64; standard error (SE) = 0.06; P = 3.0e-24), as was CH with CRF  and CCT with CRF . Furthermore, there were extreme positive correlations between weight and both height  and BMI , as well as a weaker negative correlation between height and BMI , consistent with previous evidence.Both corneal biophysical parameters and anthropometric traits showed highly significant correlations among themselves, as expected. CH was highly positively correlated with CCT , as well as between CH and height . No other pairwise correlations reached significance. The sex-stratified CRF and height genetic correlations .Genetic correlation analysis revealed significant inverse global associations between corneal biophysical features and anthropometric traits . Specifielations  were simrg = 0.88) and the higher heritability for CRF, we restricted our analysis to the local drivers of the CRF-height genetic association. After adjusting for multiple hypothesis testing, 68 genomic regions showed significant local genetic covariance . Furthermore, expression in corneal epithelial cells was also high, being significantly greater  than that in all but four other cell types . We finally compared expression levels of genes in regions of high local covariance in corneal tissue between patients with keratoconus and healthy controls, observing that genes in regions of high genetic covariance showed 13% higher median expression levels in corneas with keratoconus than in healthy corneas (P = 0.003; We identified 2874 unique genes in regions of significant local covariance. To determine whether these genes could represent shared genetic drivers between corneal dysfunction and height, we evaluated tissue expression patterns among genes in highly correlated regions A using aNOX4, FNDC3B, and ADAMTS17, had known associations with both.We also evaluated whether genes in regions of high local genetic covariance included genes with known associations with both keratoconus and body height in a literature-based ontology of gene-phenotype associations.We next measured functional pathway enrichment among the list of regions of high covariance using gene set enrichment analysis. Regions with high covariance were significantly enriched for signaling pathways involved in cell-cell adhesion and connective tissue maturation, including cadherin signaling, Wnt signaling, and Rho-GTPase–mediated cytoskeletal regulation A. We als,Structural corneal dysfunction is associated with significant visual morbidity in corneal ectatic diseases like keratoconus. While corneal biophysical properties are highly heritable, their genomic basis remains poorly understood.,–,,The observed mild but statistically significant global association of height with CH and CRF suggests that corneal structural development relies on genes involved in connective tissue maturation throughout the body. This result provides a quantitative genomic context for the phenotypic associations observed with several corneal conditions, including keratoconus.–,,NOX4, FNDC3B, and ADAMTS17, present in regions of significant local covariance that also have known associations with both keratoconus and body height. In particular, NOX4 mediates oxidative stress response, which is dysregulated in corneal ectasias, whereas FNDC3B is broadly involved in cellular differentiation.–The genes contained within the 68 regions of high covariance facilitate a variety of processes with experimental or associative links to corneal ectasia. Cadherin signaling, which is broadly involved in connective tissue maturation, represented the single most enriched functional pathway among genes in correlated regions, providing additional evidence for its importance in corneal function specifically. For instance, several cadherin-family proteins, such as cadherin 11 and desmoglein 1, are known biomarkers for keratoconus, and N-cadherin is critical for corneal epithelial cell maturation.––We also observed enrichment of gene sets interacting with metal cations such as zinc, copper, and calcium and the selective estrogen receptor modulator tamoxifen. Although the therapeutic potential of such compounds in corneal ectasia remains to be established, these results provide additional support for the involvement of metal ion homeostasis and sex hormone signaling in keratoconus.,Our analysis has several limitations. The study population is predominantly of European ancestry, which may limit the generalizability of the results. We also note that although LD regression analysis identifies correlative relationships between genomic landscapes, it does not assess causal relationships, is limited in its ability to infer significance at the individual gene level, and may be susceptible to bias because of phenotype-specific mating patterns.Corneal structural abnormalities underly ectatic disease and yet remain poorly understood at a genetic level. Our results suggest that corneal development shares a common genomic architecture with gross body habitus, informing the design of both clinical biomarkers and therapeutic interventions. Further investigations, including in vivo interrogation of candidate genes, will provide additional insight into this relationship.


Chorthippus dorsatus. Predator–prey interactions represent a behavioral context in which we expect unpredictability to be particularly beneficial. By exposing grasshoppers to an immune challenge, we explore if individuals in poor condition become more or less predictable. We quantified three aspects of escape behavior  in a standardized setup and analyzed the data using a multivariate double-hierarchical generalized linear model. The immune challenge did not affect (un)predictability in flight initiation distance and jump angle, but decreased unpredictability in jump distances, suggesting that unpredictability can be costly. Variance decomposition shows that 3–7% of the total phenotypic variance was explained by individual differences in (un)predictability. Covariation between traits was found both among averages and among unpredictabilities for one of the three trait pairs. The latter might suggest an (un)predictability syndrome, but the lack of (un)predictability correlation in the third trait suggests modularity. Our results indicated condition dependence of (un)predictability in grasshopper escape behavior in one of the traits, and illustrate the value of mean and residual variance decomposition for analyzing animal behavior.(Un)predictability has only recently been recognized as an important dimension of animal behavior. Currently, we neither know if (un)predictability encompasses one or multiple traits nor how (un)predictability is dependent on individual conditions. Knowledge about condition dependence, in particular, could inform us about whether predictability or unpredictability is costly in a specific context. Here, we study the condition dependence of (un)predictability in the escape behavior of the steppe grasshopper  Choosing a good strategy for escaping predators is essential for the survival of prey species. Having unpredictable escape paths seem to be the preferred strategy among insects, but not much is known about what causes such behavior. We find that aspects of the escape behavior of grasshoppers in poor health conditions decreased in unpredictability. This suggests that unpredictability (sometimes) does not occur by mistake but rather represents a costly and desirable feature. Animal behavior varies both between individuals and within individuals. While variation in individual average behavioral responses has been intensely studied under the label of animal personality , the un and neurThe still widespread neglect of individual differences in (un)predictability incurs costs of lost opportunities. Meta-analyses show that in animal behavior studies, only about 40% of the behavioral variability is accounted for by differences in average behavior among individuals , meaningUnpredictability in behavior can arise for multiple reasons. One reason is that microenvironmental conditions differ causing different optimal trait values in repeated observations. This can lead to differences in (un)predictability if environments experienced by individuals differ, even if plasticity itself is identical across individuals. While this might be a common occurrence under natural conditions, it will apply less to highly controlled experimental settings. However, current evidence does not suggest systematic differences in (un)predictability between field and lab studies . This suA few studies have shown that (un)predictability has a heritable basis and can thus evolve by natural selection . UnprediA context in which unpredictability seems particularly advantageous is the interaction between predators and prey . In caseChorthippus dorsatus) to an immune challenge that impairs individual condition. If being unpredictable poses a challenge, we predict impaired individuals to become more predictable (stereotyped) in their responses. However, if organismal error makes it challenging to perform in a stereotyped way, we predict control individuals to be more predictable. The manipulation thus allows us to assess if (un)predictability is condition-dependent and if unpredictability is a potentially desirable feature or a sign of failure in performance.It has been hardly explored if being predictable or unpredictable in risky or competitive situations is actually challenging. To our knowledge, only one study looked at the condition dependence of unpredictability . We hereC. dorsatus to an immune challenge (n = 66) and recorded the effect of the treatment on three escape behavior traits, in comparison to untreated individuals (n = 66).In order to test the condition dependence of (un)predictability, we exposed steppe grasshoppers 3. After their final molt, individuals were transferred in pairs of the same sex and the same final molt date to smaller mesh cages of dimensions 22 × 16 × 16 cm3. Individuals were maintained with ad libitum access to freshly cut grass provided in small vials with water and a water tube for moisture. Dead individuals and individuals that lost legs were replaced, if possible, with other individuals that matched the cage mate. When a replacement was impossible, the remaining cage mate was not used in the experiment.Individuals were sampled as nymphs in June/July 2021 from Jena, Germany (50°56ʹ41.2″N 11°36ʹ37.8″E). Subjects were transferred to the laboratory, separated by sex, and maintained in groups of up to about 60 individuals in large mesh cages of dimensions 47.5 × 47.5 × 93 cmAround 15 days after the final molt, a behavioral assay was performed as described below. To achieve standardized conditions across the behavioral essay, individuals were placed in refrigerators at 8 °C on the evening prior to phenotyping. During overnight cooling, individuals were kept individually in cylindrical vials of dimensions 8 cm height and 5 cm diameter, with 2–3 leaves of grass. For each pair of the same sex and final molt date, one individual was randomly selected for an immune challenge, while the other was used as a control.8 cells of heat-killed Escherichia coli per microliter diluted in Ringer’s grasshopper solution. The cell concentration was determined after the efficacy of five different solutions  was tested in 20 individuals each . The chosen concentration caused the death of 25% of the individuals after 48 h and thus seemed sufficiently strong without being immediately fatal. Males received 1 µL of the solution, and females, 2 µL . In order to maximize the differences between control and treated groups (and because we were not interested in the effect of heat-killed E. coli as such), control individuals did not receive any injection nor were they anesthetized. After injections, all individuals (treatment and control) were color-marked dorsally with yellow paint . In order to keep track of individual identity, one of the individuals was marked with a stripe from the wings to the pronotum, and the other with a stripe on the wings and a dot on the pronotum. The type of marking was randomly assigned and thus not associated with the treatment groups. After being marked, individuals were left for 1 h under a heat lamp in mesh cages of dimensions 22 × 16 × 16 cm³, with fresh grass available, as preparation for the behavioral assay.To cause an impairment in the condition of individuals, we performed an immune challenge. Treated individuals were anesthetized by exposing them for 3 min to temperatures of −20 °C. They were then injected with a solution containing 1.6 × 102), mimicking an approaching predator. Each individual had ten escape jumps recorded on video, with 2 min time gap between chases. To streamline phenotyping, we alternately simulated attacks on the two individuals in the arena. On average, 14 individuals were phenotyped per phenotyping date. The software Ethovision XT , with the Social Interaction Module, was used to track the movements of the chaser and the grasshopper. Effectively, the software tracks the yellow dorsal color applied to all individuals and the dark brown color that marked the chaser. Flight initiation distance (FID), jump distance, and jump angle measurements were extracted from the tracks. FID was defined as the minimum distance (in cm) between the chaser and the grasshopper before the individual jumped. Jump distance was defined as the distance (in cm) between the point where the jump started and where the grasshopper landed. Finally, jump angle was defined as the angle of the jump direction (in degrees) in relation to the initial grasshopper body position. For jump angle, straight jumps scored 0°, while negative values marked jumps to the left, and positive values jumps to the right. When the grasshopper did not move after the simulated attack, the observation was excluded from the analysis.Each pair of treated and control individuals was phenotyped simultaneously, with the observer being blind to the treatment. The two individuals were placed into a 5.3 × 3.3 m² arena, where they could acclimatize for 5 min. The arena floor was coated with foam rubber to provide a non-slippery surface for the grasshoppers to jump. The room temperature was maintained constant at 27 °C. Individuals were approached with a moveable device , which was placed on the ground about 50 cm behind the grasshopper and moved smoothly and at a constant speed towards it predictability (as standard deviations and coefficients of variation) in separate univariate generalized linear mixed models (LMMs) for each trait. The second approach was done with a multivariate double-hierarchical generalized linear model (DHGLM). While we think the DHGLM model represents the more appropriate analysis, the two-step approach serves as a reference that is closer to traditional analyses. All models were fitted using the R package n 2.18.0  with defWe used the individual average trait values of flight initiation distance, jump distance, and jump angle in LMMs with sex and treatment fitted as fixed effects, centered to a meaningful zero , and pair = 0.67, P < 0.001 and r = 0.37, P < 0.001, respectively).For the (un)predictability, we used the standard deviations of each individual, with the same fixed and random variables used in the average trait models. For FID and jump distance, we additionally modeled the coefficient of variation, as the individual standard deviation and average behavior were correlated in these traits (We fitted a multivariate DHGLM with FID (log-transformed), jump distance, and jump angle as three response variables. DHGLMs consist of a mean and a dispersion part and thus allow modeling hierarchical variation in average behavior and unpredictability within a single modeling framework . Both meAll fixed effects were centered to a meaningful zero. Treatment and sex were centered as described in the two-step approach. Daytime and jump order were centered on the average value in our data. Phenotyping date (the date when the experiment was conducted) and individual ID were used as random effects in the mean and the dispersion parts of the model with unstructured variance–covariance matrices for estimating covariation among traits and between averages and (un)predictability. At the residual level, we estimated the covariance between trait means because covariances involving means and variances are undefined at the level of residuals. Fixed and random effects were defined a priori based on their biological importance. To assure that variables would not be selected for their significance concerning the condition treatment, we randomized the treatment vector to adjust the model before running the model with the real treatment vector (see Supplement 2 for code details).We assessed convergence using the Gelman-Rubin convergence criterion  and used to make inferences about the parameters in the model. We considered fixed effects (and covariances among random components) to have a statistically significant effect when its 95% HPD interval did not include zero.t-tests (Variance decomposition was done based on posterior distributions from the DHGLM using equations presented in t-tests . Supplemt-tests .n = 66). In 17% of the trials, individuals did not jump after the simulated attack, and this behavior was more frequent in impaired individuals . The following analyses include only trials in which individuals jumped.We recorded the escape behavior of 132 steppe grasshopper individuals . To test whether unpredictability is condition-dependent, half of the individuals were impaired by an immune challenge predictability of jump distance. Impaired individuals jumped on average shorter distances and were more consistent. This, along with the increased number of non-jumps in treated individuals and death rates in the pretest, illustrates that the treatment affected escape performance negatively. However, there was no effect of the treatment on (un)predictability in FID and jump angle. Variance decomposition shows that jump distance was primarily explained by inter-individual differences in both average behavior and (un)predictability, while FID and jump angle were mostly explained by inter-individual differences in (un)predictability alone. Correlations between averages and unpredictabilities were significantly positive for one trait (jump distance) and there was a significantly positive covariation between one out of three unpredictability pairs. Covariation among some components of behavior and independent variation of others suggest some modularity in escape behavior.Animal personality studies have long used models that assume residual variance to be uniformly distributed, thus not differing among individuals . HoweverCovariation among (un)predictabilities, and among trait averages and (un)predictabilities can be informative about behavioral syndrome structure. The relationship between average behavior and (un)predictability has been described for several taxa and behavioral contexts , althougWolbachia took less risk than healthy individuals but did not change their levels of (un)predictability predictability. We found that males showed higher FID, jumping earlier than females, which might be interpreted as reduced boldness. This could be related to lower costs of escape jumps in lighter males as compared to significantly heavier females. In trials recorded later in the phenotyping session (which consisted of 10 chases), grasshoppers allowed the chaser to get closer before jumping, which indicates some habituation to the stimulus. Besides these statistically significant effects, we found trends that—although not statistically significant—can be instructive for the most likely direction of potential effects. Males tended to respond more strongly to the treatment, jumping even shorter distances than females when immune-challenged. The effect may, therefore, depend on sex or correlates thereof. In the dispersion part of the model, there was a trend for males to be more consistent than females in both FID and jump angle. Females might compensate for increased boldness (if this is reflected in reduced FID) by increased unpredictability .It might be biological or coincidental that increased unpredictability co-occurs with increased trait expression . However, the dependency may also be a statistical by-product of increased variability as trait values increase. It is conceivable that traits like jump distance  have fixed boundaries and that a reduction of unpredictability is simply a consequence of a reduction of the upper boundary . Cause and effect remain unknown since we cannot distinguish if individuals aim to reduce their mean trait values or variability in trait expression. Changes in unpredictability  may possibly be caused by changes in mean trait values. This interpretation might apply to our manipulations of condition that reduced the mean as well as the unpredictability in jump distance, but also to the sex difference with reduced distances and variability in males. The analyses of coefficients of variation suggest that changes in unpredictability are not explained by changes in the mean alone. Besides dependencies of unpredictabilities on the mean, differences in predictability in our experimental setup could have resulted from individual-specific reaction norms in response to habituation to repeated chases.Our results support the emerging trend that individual differences in behavior should not be summarized by individual average values, but that (un)predictability should be explicitly considered. Appropriate statistical methods have already been developed to explore the hierarchical nature of individual differences in behavior. Our data suggest both costs of unpredictability, at least in some traits, and some modularity in (un)predictability as a trait. These important questions certainly deserve further study. Our data also shows that patterns of variances differ between traits and likely also between contexts and species. Our results rather suggest that unpredictability does not arise (solely) from organismal errors. Further studies are needed to identify the costs and challenges that make individuals unpredictable in contexts where unpredictability appears beneficial.arad047_suppl_Supplementary_MaterialClick here for additional data file.


Genomic selection (GS) techniques have improved animal breeding by enhancing the prediction accuracy of breeding values, particularly for traits that are difficult to measure and have low heritability, as well as reducing generation intervals. However, the requirement to establish genetic reference populations can limit the application of GS in pig breeds with small populations, especially when small populations make up most of the pig breeds worldwide. We aimed to propose a kinship index based selection (KIS) method, which defines an ideal individual with information on the beneficial genotypes for the target trait. Herein, the metric for assessing selection decisions is a beneficial genotypic similarity between the candidate and the ideal individual; thus, the KIS method can overcome the need for establishing genetic reference groups and continuous phenotype determination. We also performed a robustness test to make the method more aligned with reality. Simulation results revealed that compared to conventional genomic selection methods, the KIS method is feasible, particularly, when the population size is relatively small. China is home to approximately 84 indigenous pig breeds, which are rich in genetic resources and possess a wide range of desirable and undesirable traits. Some of the desirable traits include high meat quality, large litter size, high tolerance to roughage, and disease resistance, while undesirable traits include slow growth, high feed conversion ratio, and low lean meat rate. Examples of typical indigenous pig breeds include Jinhua and Laiwu pigs. To achieve pigs with desirable traits, one approach is to select indigenous breeds with superior performance or to create new breeds (lines) through crossbreeding between Chinese indigenous breeds and intensive breeds such as Duroc, Landrace, and Large White.Before the widespread availability of high-throughput sequencing technology, best linear unbiased prediction (BLUP) was the most commonly used selection method in China, which is based on pedigree information and uses a kinship matrix to estimate breeding values. Despite its widespread use in early animal breeding, the accuracy of BLUP is limited by the quality of the pedigree information and performance test scale .The utilization of genomic prediction, through the correlation of whole-genome genotypes with phenotypes in a large sample of individuals, has been established as a means of enhancing quantitative traits in domesticated animals ,3,4. HowThe advancement of next-generation sequencing technology has greatly increased the volume of pig genomic data, making it possible to utilize this information in breeding studies. The most commonly used method is genomic best linear unbiased prediction (GBLUP), which allows for the direct estimation of an individual’s genetic merit through the construction of a genomic relationship matrix G, instead of a kinship matrix A based on pedigree information. While GBLUP methods assume that all markers have the same genetic variance. In reality, only a few single nucleotide polymorphisms (SNPs) have an impact and are linked to the quantitative trait loci (QTL) affecting the trait, while most SNPs are ineffective ,7,8. In the realm of genetic prediction, Bayesian techniques have gained prominence due to their incorporation of marker effect variance as a predetermined prior distribution. This approach encompasses a range of variations, such as BayesA, BayesB, BayesC, Bayesian Lasso, among others. These methods address limitations in the single-trait genomic best linear unbiased prediction (GBLUP) method by assuming that only a few single nucleotide polymorphisms (SNPs) have significant effects, with the variance of these effects following an inverse chi-squared distribution. This assumption aligns more closely with reality, as only a few SNPs are usually linked to the quantitative trait loci (QTLs) affecting the trait, while most SNPs have no effect. As a result, the BayesB method is often more accurate when few QTLs control genetic variation with large effects ,10,11.Compared to GBLUP methods, Bayesian models have more estimated parameters, leading to increased computational effort and improved prediction accuracy. However, the computational speed and robustness are not as favorable as those of GBLUP ,13. BothIn the case of Chinese pig breeds with small foundation populations and the breeding work being carried out involves both intraspecific selection and the hybridization of locally adapted varieties with introduced varieties to create new hybrid varieties, a genetic reference population may not exist, and the determination of quantitative traits may not be precise due to a lack of highly qualified breeding staff and refined breeding equipment.The main purpose of genomic selection is to estimate genome-wide SNP effects based on a reference population and then to sum the genetic effects of candidate individuals based on the specific alleles they carry. This means first identifying an ideal individual; then the more similar a candidate is to an ideal individual, the higher its breeding value. For this purpose, we propose a kinship index based selection (KIS) method. To validate the efficacy and stability of the KIS method, we performed a series of simulations using the MoBPS R package .In the KIS method, the first step is to identify parental varieties that support the breeding objectives. The second step is to design breeding strategies; common breeding strategies include introgression cross breeding, stepwise cross breeding, and multiple cross breeding. The third step is to design ideal individuals with the desired genotype harboring parental advantageous traits, compare the differences between candidate and ideal individuals, and finally, select and cross the individuals with high similarity with the ideal individual. The visual representation of the process is shown in The establishment of the ideal individual is the core component of the KIS method, and the accuracy of this method is entirely dependent on the quality of the ideal individual. The first step in building the ideal individual is to identify the advantageous traits in the parent breed. Different pig breeds have specific advantages; the advantageous traits of Chinese indigenous pigs are mainly in meat quality, roughage tolerance, and disease resistance, while that of the introduced breeds has better leanness, growth rate, and feed conversion ratio . The benBased on the positional information of the ideal individual, the genotypes of the candidate individuals at these loci were extracted and compared to that of the ideal individual. The results of the comparison are expressed in the form of ordinals of 0, 1, and 2, where 0 represents both disadvantage alleles, 1 represents allelic heterozygosity with half advantageous alleles, and 2 represents advantageous alleles. The sum of the scores for all loci represents the degree of similarity between the candidate individual and the ideal individual. Candidate individuals are ranked according to their similarities to ideal individuals, and those with high similarities will be selected.The historical population consisted of 200 animals, with an equal number of males and females. For each individual, an underlying true genetic value is calculated for each trait. Based on this, phenotypes can be generated . Two quaWe implemented hybridization and pure breeding programs to test the effectiveness of different breeding approaches. During the hybridization process, the first hybrid generation was obtained by mating five sires from population A and 20 dams from population B from generation 10 of their respective populations. Subsequently, 20 females from the first hybrid generation were mated with five males from population B of generation 10. The two generations in the hybridization process were referred to as generations 11 and 12, respectively. Animals in the purebred reproduction breeding process were mated with their contemporaries, and five males and 20 females were selected to maintain the population in each generation. Five generations of the purebred reproduction breeding process were simulated to examine the effectiveness of diverse breeding methods.An animal’s true breeding value was used as an evaluation criterion to evaluate the effectiveness of different breeding methods. The advantageous genotype, as well as, the effect of each QTL was considered in the simulation, therefore, an individual’s true breeding value was calculated as follows:Four selection methods, including KIS, GBLUP, BayesB, and strategy based on true breeding value (TBV), were implemented in the simulation. The effectiveness of each selection method was evaluated by the true breeding values of the simulated individual. In addition, negative control simulation was implemented by replacing the ideal individual with an individual with 600 noneffective loci and random beneficial alleles.Robustness tests were implemented to fit better replicate the real situation, including QTL with different false negative rates, QTL with different false positive rates, QTL with different quantitative gradients, and the scale of foundation population and selection proportion, and simulation of dominant and epistatic effects.QTL for the ideal individual was sorted considering the smallest to greatest effect values, that is, 10%, 20%, and 30% of the QTL were randomly selected for deletion to reconstruct the ideal individual , and the missing QTL of the ideal individual with a low absence rate was contained in the one with high absence rate. The selection and mating of simulated individuals were optimized according to the false negative individual. Nevertheless, the TBV of the mating individuals was calculated using true QTL effects.The advanced KIS method did not consider QTL effects when establishing the ideal individual, therefore, we built an ideal individual containing beneficial genotypes and QTL effects. Other basic settings were as previously described.The pseudo-positive rates were the same as those in the false negative test, which included three gradients. However, pseudo-positive QTLs were adjacent to true QTLs on chromosomes. The false positive QTL did not duplicate true QTL, and beneficial genotypes were assigned randomly. Other basic settings were as previously described.In addition to the combination of 100 and 500, which represent the number of QTLs for two quantitative traits, three other combinations, including, 100–350, 150–400, and 200–500 were considered to investigate the robustness of the KIS method. The number of QTLs for the other three selection methods subsequently changed. Other basic settings were as previously described.In this context, different scales of the foundation population were taken into consideration to detect the robustness of the KIS method, while the number of QTLs was the same as in the previous simulation (100–500). Five different gradients of the foundation population sizes, that is, 2000, 1000, 500, 200, and 50, were utilized to examine if the KIS method was still valid in the disparate scale of the foundation population. The ratio of male to female was half in all the selection methods. Five sires and 20 dams were selected for mating according to the criteria corresponding to different selection methods. Other basic settings were as previously described.For the examination of the selection proportion, the scale of the foundation population was fixed at 200, with an equal number of sires and dams. Five selection proportions: 0.2–0.5, 0.15–0.4, 0.1–0.3, 0.05–0.2, and 0.03–0.1 were simulated. In these combinations, each group of figures represented the proportion of male and female animals in the foundation population. Other basic settings were the same as previously described.In the simulation of dominance and epistatic effects, the impact of each effect on the accuracy of the KIS method was first simulated separately, followed by a simultaneous consideration of both effects on the simulation results. Different QTL gradient settings were employed in the simulation, representing 10%, 20%, and 30% of the total number of QTLs with additive effects.The comparison of the KIS method with conventional breeding methods for determining the breeding values of boars and sows is illustrated in The breeding values corresponding to the KIS method at different deletion rates are shown in Data corresponding to the KIS method with zero absence rate were selected for comparison with general breeding methods; the results were the same as those shown in The results of the pseudo-positive test are shown in The result of the comparison of the KIS method with the other selection methods is presented in The results of the three quantitative gradients regarding the number of different QTL tests are presented in The selection proportion simulation experiment results are presented in Compared to the conventional breeding method, individuals selected through the KIS method exhibited superior performance in terms of true breeding values, with overall higher breeding values than those selected by the conventional method, albeit with a slight reduction in just a few generations. Importantly, individuals selected based solely on true breeding values achieved the highest scores, and all KIS scores were inferior to these. In addition, the results of the significance test show that there is no significant difference in breeding value among KIS method and conventional breeding methods. The results of the significance tests for breeding values with different selection proportions are shown in In this study, we aimed to assess the effectiveness and stability of the KIS method in a breeding program. The KIS method is distinct from previous selection methods in the following ways: (1) The selection criteria are established by identifying an ideal individual, (2) The measurement of phenotypic traits is not a crucial aspect of the breeding process, and (3) A large reference population is not necessary, making the KIS method appropriate for small pig populations.The effectiveness of genomic selection is dependent on the linkage disequilibrium between causative mutations and the SNP markers  utilized in genomic predictions . The daiUnder almost all the mentioned simulation conditions above, the selection effectiveness of the KIS method was better than that of the GBLUP and BayesB methods but lower than that of the TBV method across all generations. This may be due to the limited sample size in the simulation population, which hinders the full potential of the GBLUP and Bayes methods and reduces their accuracy, this also demonstrates that the KIS method has its own unique features in the case of small population size.In the negative control simulation experiment, the breeding value of F1 was significantly lower than that at other ages. This may be because the F1 generation mainly included heterozygotes, and the heterozygotes were assigned a value of 1 during calculation. In the simulation experiment for false negatives, the performance of individuals selected through the equally weighted KIS method showed no decrease in their true breeding values as the rate of absence increased. However, this result was not in line with expectations as the individuals selected by the equally weighted KIS method were observed to have a higher abundance of favorable gene loci, however, the total of these loci did not yield the highest values in terms of breeding values, in the scenario of low absence rates. This discrepancy could also be due to a truncated selection mating strategy, as the breeding values of offspring produced through different mating combinations are not always positively correlated with the breeding values of their parents. When assigning equal weights, certain QTLs with large effects may not be favored solely based on the number of favorable genes they possess. However, by removing part of QTLs with minor effects, the weights of these large-effect QTLs are amplified, which could help explain this phenomenon. In other words, removing part of the influence of small-effect QTLs through the equal-weighted approach may enable the large-effect QTLs to receive the recognition they deserve. The simulation results for false positives were as anticipated, with a decrease in breeding values as the false positive rate increased. Despite this, the KIS method was generally consistent with both the GBLUP and BayesB methods, even when the false positive rate reached 30%.The patterns observed in the three distinct QTL quantitative gradients were not completely congruent with prior simulations. While the patterns of the breeding values remained unchanged, the results of the significance tests revealed that as the number of QTLs increased, the TBV method demonstrated significant disparities compared to the other methods at earlier generations. For instance, in the first QTL quantitative gradient (350–100), significant differences were only noted in generation G5, while in the third QTL quantitative gradient (500–200), significant disparities appeared as early as generation G1. This may be attributed to the increase in the breeding value of the ideal individual with an increasing number of simulated QTL and the consequent loss of more QTL loci during the transmission hybridization process.In practice, balancing breeding effectiveness with production costs requires controlling the scale of the foundation population within acceptable limits. Results from simulation studies indicated that increasing the size of the foundation population leads to a corresponding increase in breeding effectiveness. However, statistical significance tests of the KIS method revealed little difference between different foundation population scales in generations other than the fifth, where a foundation population size of 100 led to a substantial difference. Additionally, the results of the seed reservation simulations were as expected, with breeding values rising with each generation as reservation rates decreased. These results suggest that breeders using the KIS method have the flexibility to tailor the reservation rate and foundation population scale to meet specific requirement.When dominant and epistatic effects were considered in the simulation, the selection effect of KIS gradually decreased as the proportion of dominant and epistatic effects QTL number increased, probably because the proportion of additive effects gradually decreased when dominant and epistatic effects were taken into account in the design of ideal individuals.When considering only additive effects, we calculated the coefficient of variation corresponding to four different methods within the same generation, and the results are shown in the The simulation study presented in this paper showcases the effectiveness of the KIS method in selecting individuals for breeding. Our model selectively bred individuals from the same generation, under similar environmental conditions, and with a focus on a few key traits. This method has proven to be effective in enhancing genetic gain while minimizing the risk of negative traits being unintentionally selected for. However, it is important to note that our model only considered individuals from the same generation and under similar environmental conditions, without accounting for external factors such as field effects and maternal effects. In the process of hybrid breeding, selection is carried out in the same generation and the same gender, considering only the genotypes and excluding the influence of environmental effects.http://twas.farmgtex.org/) [The effectiveness of the kinship index based selection (KIS) method is limited by the accuracy and coverage of functional genes in pigs. Previous studies have reported on the association of functional genes with traits in pigs, and databases, such as The Farm animal Genotype-Tissue Expression TWAS server (ex.org/)  and PigVex.org/) , exist wex.org/) ,21,22. IThe KIS method solves the problem of phenotypic determination and establishment of genomic reference populations in the breeding process by establishing an ideal individual; it is a breeding method that is easy to implement, has a wide range of applications, and has high accuracy. The application value of the KIS method will continue to improve as research on pig functional genes progresses.


Introduction: Pelvic organ prolapse (POP) is one contributor to recent increases in sow mortality that have been observed in some populations and environments, leading to financial losses and welfare concerns.Methods: With inconsistent previous reports, the objective here was to investigate the role of genetics on susceptibility to POP, using data on 30,429 purebred sows, of which 14,186 were genotyped (25K), collected from 2012 to 2022 in two US multiplier farms with a high POP incidence of 7.1% among culled and dead sows and ranging from 2% to 4% of all sows present by parity. Given the low incidence of POP for parities 1 and >6, only data from parities 2 to 6 were retained for analyses. Genetic analyses were conducted both across parities, using cull data (culled for POP versus another reason), and by parity, using farrowing data. (culled for POP versus culled for another reason or not culled).Results and Discussion: Estimates of heritability from univariate logit models on the underlying scale were 0.35 Conclusion: The influence of genetics on susceptibility to POP in this population and environment was confirmed and several candidate genes and biological processes were identified that can be targeted to better understand and mitigate the incidence of POP. Pork production has seen an overall increase worldwide and is expected to continue to grow as the global human population reaches new milestones , and inaccurate pedigree information, data on 20,094 sows remained, of which 17,700 were culled during the evaluated period. Of the cull records that remained after quality control, 442 sows had missing sire information due to use of pooled semen for insemination. These sows were, however not removed from the dataset. Of the 20,094 sows, 14,186 sows were genotyped  using a 25K SNP panel and were used for downstream analysis. All animals were imputed up to a 50K panel using FImpute  is the vector of logits of the binary POP phenotypes, either by parity or across parities, b is the vector of fixed effects, a is the vector of random additive genetic effects, e is a vector of random residuals, and X and Z are incidence matrices relating Logit(y) to b and a, respectively. The fixed effect consisted of the combination of herd, year, and quarter (HYQ) of the year of insemination. For the across-parity analysis, the latter referred to the quarter of insemination for the removal parity of the sow and removal parity was added as a fixed effect. In preliminary analyses, total number born was found to be significant (p < 0.001) as a covariate, with an estimated effect of −0.09, but 40% of the sows were culled before farrowing and, therefore, had no litter size information for that parity. Because of this, and to guard against removing genetic variation in susceptibility for POP, total number born was not included in the final model. Genetic relationships were either based on pedigree or genomic relationships. The latter were computed based on the SNP genotypes using calc_grm ; i in parity j, as defined previously; i at SNP n, coded as 0, 1, and 2; n for parity j, where, for bivariate analysis of parities j = k and j = l, k and l, following k and l.To determine whether the genetic control of susceptibility to POP was consistent across parities, bivariate models were fitted to the by-parity data to estimate the genetic correlations of susceptibility to POP between parities. Because bivariate threshold and logistic regression models failed to converge, a bivariate linear mixed model was used for this purpose, noting that linear and threshold models have been shown to yield similar estimates of genetic correlations . To alloThe bivariate analyses were implemented in the Julia for Whole-genome Analysis Software (JWAS) , using aj, or the across-parity analysis, except for the addition of n was (δn = 1) or was not (δn = 0) included in the model in that iteration of the chain, with the prior probability of inclusion (π) estimated using a Bayes-Cπ model (Sus Scrofa 11.1 from every 100th iteration were used to obtain samples of the posterior distributions of the genome-wide and window-based genetic variances and to estimate window-based posterior probabilities of association (WPPA), as described by To identify genomic regions associated with susceptibility to POP, the following univariate marker-based Bayes-B threshold model  was implCπ model . For theGenomic regions associated with susceptibility to POP were identified using the across-parity analysis as 1 Mb windows that explained more than 1% of the genetic variance. The by-parity GWAS were used to confirm the across-parity GWAS results based on the presence of signals in the identified QTL regions in multiple parities. To allow for linkage disequilibrium extending over longer regions, which can result in the location of signals to differ between data sets, signals from the by-parity analyses were allowed to extend 2 Mb to either side of the QTL regions that were identified in the across-parity analyses. Thus, in the by-parity analyses, the signal in each by-parity analysis was quantified as the sum of the genetic variances estimated for the QTL region identified in the across-parity analysis plus the genetic variances estimated for the two 1 Mb windows upstream and downstream from that region, taken as a % of the genome-wide genetic variance.To obtain a better understanding of the QTL regions identified in the across-parity GWAS and their possible biological connections with susceptibility to POP, we identified the SNP that had the highest posterior probability of association PPA,  in each in vivo transcriptomic data for a variety of (patho) physiological states of different porcine tissue types  and then gradually increased to up to 3.18% of all sows present for parity 5. Parity 1 data were not used for analysis because of the low incidence. In the across-parity data used for analyses (parities 2-6), 10.1% of all culls were for POP.In total, 7.1% of the sows that were culled during the evaluated period were removed from the herd because of POP. The incidence of POP over time, as a percentage of sows that died or that were culled, is shown in For the across-parity analysis, the estimate of heritability of susceptibility to POP based on the logistic regression model was 0.21 ± 0.02 when using pedigree relationships and 0.35 ± 0.02 when using genomic relationships. These estimates are on the underlying scale and, based on the 10.1% incidence of culling due to POP, translate to estimates of 0.07 and 0.12, respectively, on the observed scale, following Estimates of genetic correlations of susceptibility to POP between parities were all positive . Posteriπ from the Bayes-Cπ analyses were 0.976 for the across-parity analysis and ranged from 0.983 to 0.992 for the by-parity analysis. These estimates were used in the corresponding Bayes-B analyses. Estimates of heritability based on the Bayes-B analyses were 0.36 ± 0.01 for the across-parity analysis, while estimates for the by-parity analyses are reported in Estimates of STC1 on SSC14 and COL26A1 on SSC3 were identified as potential candidate genes based on genes annotated in the Ensemble genome browser. The pCADD analyses additionally unveiled several potential causal variants associated with susceptibility to POP. Genes ESR1 on SSC1 and TGFβ2 on SSC10 obtained a score between 0 and 10, while SYNE1 on SSC1 and ORAI2 on SSC3 obtained a pCADD score greater than 10. On SSC12, pCADD analysis revealed GGA3, SMIM5, and ENSSSCG00000034198 as genes with scores of 23, 20, and 23, respectively.Through extensive candidate gene and pCADD analyses, several candidate genes were identified, as presented in The GSEA analysis revealed several significant features that were enriched in the ranked 1 Mb windows, and these are listed in This study is the first to provide a detailed analysis of the genetic basis of pelvic organ prolapse in pigs. Using different types of analyses, this study clearly establishes a substantial role of genetics in susceptibility to POP and identifies several biological pathways that underlie the genetic basis of susceptibility to POP.versus environmental influences on a trait and thus become pivotal in not only finding associated genomic regions but also developing selection strategies to reduce the prevalence of POP in sow herds. In contrast to our results, the only other previous study on the genetics of susceptibility to POP reported very low heritability estimates, indicating that environment and management are the major contributing factors for POP , careful record collection is suggested to enable separate analysis of each diagnosis.Genomic analysis revealed several genomic regions that are associated with susceptibility to POP in the across-parity analysis, which included sows that were culled in parities 2 to 6 . We usedTo further strengthen our understanding of these associations, we also performed GWAS using the by-parity data. Comparing the resulting Manhattan plots in ESR1), the Spectrin Repeat Containing Nuclear Envelope Protein 1 gene (SYNE1), and the Coiled-coil Domain Containing 170 gene (CCDC170). In goats, SYNE1 has been reported to be differentially expressed between animals with low and high litter yields and has been associated with biological processes such as ovarian follicle development and the ovulation cycle process  and other forms of estrogen (estrone and 17β-estradiol) were reported in sows with high versus low POP risk , RAS P21 Protein Activator 4B (RASA4B), and ORAI Calcium Release-Activated Calcium Modulator 2 (ORAI2). Collagen is the most abundant protein in vertebrates and is a major component of the extracellular matrix. Collagen is essential in providing strength and supportive functions to the pelvic floor , which one study found to be a major candidate gene associated with calcium levels in blood in pigs  gene, which is a member of the Transforming Growth Factor β (TGF-β) latent complex and has been reported to bind Fibulin-5, which regulates elastic fibre assembly  and number of teats  in pigs.assembly . In humaassembly . Interesassembly . Use of β2 (TGFβ2) gene, which belongs to the Transforming Growth Factor β superfamily and plays several important roles in uterine development and function based on studies in mice and human models  gene, which was identified through the pCADD pipeline with a score of 23. This gene has been reported to play a role in cell migration and, more importantly, in regulating the levels of β1 integrins (SMIM5) gene, with scores of 23 and 20, respectively. The SMIM5 gene has been reported to encode structural proteins in skeletal muscle and has been found to be associated with marbling in cattle  gene, which codes for a glycoprotein that plays an important role in calcium/phosphorus homeostasis. STC1 gene as an implantation marker in pigs, suggesting a strong biological role of this gene for uterine receptivity during implantation. In mice and sheep, studies have also reported a similar role of STC1 gene in implantation  was observed, which overlaps with our finding from the candidate gene analysis that suggests a potential role of SYNE1 gene in the identified region on SSC1. Genomic regions associated with POP were also enriched for the Interleukin (IL) Receptor 1 binding GO term . The GSEA analyses revealed several terms that were enriched for susceptibility to POP across the two investigated annotation libraries, as reported in This study provides multiple lines of evidence that susceptibility to POP is partially determined by genetics in the evaluated population and environment. We estimated moderate heritabilities in both the across- (35%) and by-parity analyses, moderately high estimates of genetic correlations of susceptibility to POP in the by-parity analysis, which confirms a similar genetic basis for POP across parities, and evidence of genomic regions associated with susceptibility to POP. The latter revealed several genomic regions to be associated with susceptibility to POP, in particular on chromosomes 1, 3, 7, 10, 12, and 14. Several candidate genes that could contribute to sow susceptibility to POP were identified, as well as various biological processes and pathways. These results suggest that, for this population and for the environment that prevailed for the evaluated sows, genetic selection can be used to reduce the incidence of POP. This, however, must be validated for other populations and environments, including investigation of possible genotype by environment interactions. In addition, knowledge of these identified biological pathways can be used to develop targeted management recommendations or interventions to reduce the risk of POP. Thus, results from this study provide important leads for potential solutions to a major industry-wide issue.


Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.Soybean ( Soybean is one of the most important dual-use leguminous crops and is the main source of protein ~40%) and oil ~20%) for food . Soybean0% for fo0% and oiCurrent remarkable progress in high throughput genotyping techniques has provided breeders and geneticists with a unique opportunity to have access to thousands of single nucleotide polymorphisms (SNPs) in a time and cost-effective manner ,11. One With the availability of affordable next-generation technologies, researchers are now able to capture much of the genetic variation in a given genome and generate large numbers of genomic sequences and genetic properties even in large plant populations ,23. The This study aimed to (1) investigate the genetic structure of soybean seed composition traits; (2) conduct a comparative analysis between FarmCPU, a well-known conventional GWAS method, and SVR-mediated GWAS for detecting genomic regions associated with soybean seed composition traits; and (3) identify genes and QTL co-localized with the detected MTAs for soybean seed composition traits. The identified MTAs in this study can be used in different soybean breeding programs for selecting value-added genotypes through the simultaneous selection of all the target seed quality traits at early growth stages.The phenotypic evaluations and collecting process of the soybean yield for the tested panel are explained in detail in . After aThe linear correlation coefficients (r) estimated among yield and the target seed traits revealed a significant negative correlation (−0.67) between seed protein and oil concentrations . In addi2) of alleles using 17,958 high-quality SNPs. The minimum number of SNPs (403) was found on chromosome 11, and the maximum number of SNPs (1780) was found on chromosome 18. The average number of SNPs across all the 20 soybean chromosomes was 898, with a mean density of 0.12 cM for every single SNP across the genome.Out of 250 soybean genotypes, 23 of them were eliminated because of the high level of missing data, and a total of 17,958 high-quality SNPs were archived from a total of 40,712 SNPs from 227 soybean genotypes and mapped on 20 soybean chromosomes. For the tested association panel, pairwise linkage disequilibrium (LD) between SNPs was calculated based on the correlation coefficient , Glyma.14G035100 (GO:0009888), Glyma.15G107800 (GO:0016926), Glyma.15G109300 (GO:0009658), and Glyma.19G068300 (GO:0010099), were detected as the strong candidate genes governing seed protein, which encode amino acid transport, tissue development, protein desumoylation, chloroplast organization, and regulation of photomorphogenesis, respectively (Glyma.13G187100 (GO:0008168), Glyma.16G133500 (GO:0009697), and Glyma.16G133600 (GO:0016887) were identified as the strong candidate genes underlying soybean seed oil, which encode methyltransferase activity, salicylic acid biosynthetic process, and ATPase activity, respectively (Glyma.02G113600 (GO:0042631), Glyma.02G115400 (GO:0006007), Glyma.02G240400 (GO:0005986), Glyma.04G131700 (GO:0010182), and Glyma.04G228300 (GO:0005982) were selected as the strong candidate genes associated with 100-seed weight, which encode cellular response to water deprivation, glucose catabolic process, sucrose biosynthetic process, sugar mediated signaling pathway, and starch metabolic process, respectively  had the highest allelic effect compared to other identified peak SNPs A. Five cectively . For soyectively B. Based ectively B. The peectively C. The caectively C.Improving soybean seed composition traits has been an important criterion in most soybean breeding programs . It has GWAS is currently considered as the most common way to discover MTAs for complex traits of interest . Howeverp-values from single SNP analyses [There are probable reasons why SVR was able to better detect the genomic regions associated with a trait of interest than FarmCPU. One of the important reasons is the ability of SVR to estimate significance levels for identifying SNP–trait associations using variable importance methods instead of the statistical methods used in conventional GWAS . Variablanalyses ,76,77. Tanalyses .In this study, SVR-mediated GWAS detected MTAs that were co-localized with two QTL directly related to soybean seed protein ,55. MostGlyma.05G186700, Glyma.14G035100, Glyma.15G107800, Glyma.15G109300, and Glyma.19G068300) were detected via SVR-mediated GWAS, which seems to have a direct influence in seed protein concentration. As an example, Glyma.05G186700 encodes amino acid transport, which plays an important role in distributing essential nitrogen for plant growth and development [Glyma.14G035100 encodes tissue development which depends on the nitrogen distribution as encoded by Glyma.05G186700. Glyma.15G107800 and Glyma.15G109300 were other candidate genes for seed protein, which encode protein desumoylation and chloroplast organization, respectively. Those genes play important roles in maintaining energy production sites for supplying the required energy for storing seed compositions [Glyma.13G187100, Glyma.16G133500, and Glyma.16G133600) were found to be strong candidate genes for soybean seed oil. Glyma.13G187100 encodes methyltransferase activity, which plays a vital role in regulating tocopherols, an important component in the stability of soybean seed oil [Glyma.16G133500, which encodes salicylic acid biosynthetic process. Salicylic acid regulates the nitrate reductase activity in the plant, which plays an important role in increasing the protein and decreasing the oil percentages in seed [Glyma.02G113600, Glyma.02G115400, Glyma.02G240400, Glyma.04G131700, and Glyma.04G228300 were selected as the strong candidate genes governing the trait. The candidate genes seem to be involved in glucose metabolism, specifically sugar catabolic processes. The breakdown of sugars is essential for providing energy during seed maturation and development. This gene may play a role in regulating the balance between glucose and other sugar molecules in the seed, contributing to the overall 100-seed weight. During the soybean seed maturity stages, the glucose level decreases significantly, while the levels of sucrose, sugar, and starch increase in the full mature soybean seed yield [Glyma.02G113600 encodes the glucose catabolic process responsible for breaking down the glucose to produce the primary sources of energy for the cellular production of ATP [Several candidate genes related to seed protein , as one of the well-known error control methods [The GWAS panel, which consisted of 250 soybean genotypes, was grown in field conditions at the University of Guelph, Ridgetown campus in four environments (two locations × two years) in 2018–2019 at Ridgetown  and Palmyra , Ontario, Canada. The tested genotypes were derived from the core soybean germplasm, Ridgetown soybean breeding programs, that have been used for genetic studies and cultivar development activities. Field experiments were conducted using randomized complete block designs (RCBDs), with two replications in each tested environment. Each phenotypic plot consisted of five rows, which were 4.2 m long each, and the seeding rate was 50–57 seeds per m methods ,97,98, w−1) was measured by harvesting three middle rows of each plot and adjusted based on days to maturity and 13% seed moisture. The total percentage of oil and protein in soybean seeds was measured via near-infrared reflectance (NIR) using a DA 7250 NIR analyzer  on a dry weight basis. The used instrument was calibrated based on Perten Instruments [https://github.com/MohsenYN/Available-Datasets (accessed on 15 July 2023)). In order to estimate the average phenotype of the tested traits, the best linear unbiased prediction (BLUP) was used for each soybean genotype [lme4 [Y stands for the trait of interest as a function of an intercept μ; μ is equal to the overall mean (fixed); x stands for the vector of block effects; z is the vector of the genotype effects (random), in which z ~ N; i stands for the vector of random GxE interaction effects; and 2E). A, B, and C represent the incidence matrices of x, z, and i effects, respectively.Soybean seed yield ) of each tested trait was calculated based on the following equation:® 2.0 fluorometer . The genotyping-by-sequencing (GBS) step was performed using ApeKI [For extracting DNA, the collected trifoliate leaf tissues from the first rep of phenotyping plots at the Ridgetown location were freeze-dried for 72 h using a Savant ModulyoD Thermoquest . DNA of each soybean genotype was extracted using NucleoSpin Plant II kit , followed by a quality check through a Qubitng ApeKI  as one ong ApeKI , consideThe population structure analysis for the tested 227 soybean genotypes was conducted from a total of 17,958 high-quality SNPs using fastSTRUCTURE . For thiiY represents the observation on the ith sample; Ci1, Ci2, …, itC is equal to the genotypes of the t pseudo-QTNs; D1, D2, D3, …, Dt stands for the corresponding effect for the pseudo-QTNs; ijM is equal to the genotype of the jth SNPs and ith sample; jK stands for the corresponding effect of the jth SNPs; iU is the total genetic effect of the ith sample; and ie is the residual.Different GWAS methods may provide different results based on the population diversity, number of SNPs, and statistical power linked with each method . TherefoY stands for the output, W is the weight for each high-dimensional input variable (c). The lower and upper borderlines are created as SVR is based on creating a set of hyperplanes used in regression problems . This alCaret package [For SVR-mediated GWAS, the scaled method (0–100) was used for estimating the importance of each SNP associated with traits of interest. In order to implement the SVR method in GWAS, a five-fold cross-validation strategy with ten repetitions was applied to estimate the variable importance of each SNP . Still,  package  in R sofG. max William 82 reference gene models 2.0 in SoyBase (https://www.soybase.org (accessed on 15 July 2023)) on 150 k bp flanking regions of each MTA, identified using LD decay distance (https://www.soybase.org (accessed on 15 July 2023)) were used as three criteria to detect the most relevant over-represented QTL and genes with a trait of interest.One of the most common ways to verify QTL and candidate genes co-localized with MTAs detected using the tested GWAS methods is to investigate the functional annotation of candidate genes. The potential genes and QTL were retrieved based on the distance . PreviouGlyma.16G133500, that could potentially break the negative correlation between seed oil and protein concentrations. Further investigation of the identified candidate genes and their differential gene expressions could lead to the development of gene-specific markers for marker-assisted selection (MAS) in soybean breeding programs. Overall, the results highlight the potential of advanced computational methods for improving the accuracy and efficiency of identifying MTAs and developing new soybean varieties with desired seed composition traits.This study suggests that improved GWAS methods, such as SVR-mediated GWAS, can enhance the understanding of the genetic basis of seed composition traits in soybeans. This understanding can be used by food-grade soybean breeders to develop more reliable and efficient cultivars. The study also identified a candidate gene,


High genetic correlations were found in most months, and genetic gain also showed a slight influence on the time series. Using a consistent ranking of family breeding values, optimal slash pine families were selected, obtaining a promising and reliable predictive ability based on multispectral+VIs (MV) alone or on the combination of pedigree and MV. The highest predictive value, ranging from 0.52 to 0.56, was found in July. The methods described in this paper provide new approaches for phenotypic selection (PS) using high-throughput multispectral unmanned aerial vehicle (UAV) technology, which could potentially be used to reduce the generation time for conifer species and increase the genetic granularity independent of sequencing or pedigrees.Genomic selection (GS) is an option for plant domestication that offers high efficiency in improving genetics. However, GS is often not feasible for long-lived tree species with large and complex genomes. In this paper, we investigated UAV multispectral imagery in time series to evaluate genetic variation in tree growth and developed a new predictive approach that is independent of sequencing or pedigrees based on multispectral imagery plus vegetation indices (VIs) for slash pine. Results show that temporal factors have a strong influence on the  HoweverThere are two important kernel functions that have been used primarily in GS, including the Gaussian kernel (GK) and the genomic best linear unbiased predictor (GB) . GB is aRecently, phenomic selection (PS) has emerged to address these issues by using high-dimensional secondary traits (HDSTs)  spectra or hyperspectral imaging) instead of SNPs to estimate the realized genomic relationship matrix (kinship matrix) between individuals, taking advantage of algorithms and workflows developed for GS . PS was UAV-based remote sensing has been greatly facilitated for data acquisition by advances in sensor technology, which has the potential to increase fieldwork efficiency with less time to collect spatial information than ground-based spectroscopy and to cover large areas while maintaining accuracy and resolution.UAVs can acquire various types of data, including spectral, structural, thermal, and feature data, which have been widely used in plant science to estimate various traits . For exaIn addition, UAV-based imagery also provides a high-precision, high-throughput method for field-based multitemporal phenotyping data collection in the context of plant breeding. This allows for the provision of dynamic information on plant growth and performance . For exaPinus elliottii). However, previous studies did not consider using the multispectral as an indicator to predict the genetic parameter. Here, we further combined this methodology with multitemporal growth and multispectral data in a slash pine breeding plantation to evaluate the potential of linking high-throughput phenotyping with growth parameters to perform phenomic selection.Although there are achievements in growth trait detection and leaf physiological prediction for plant breeding based on UAV-based imagery, no research has been found on the use of multitemporal HDSTs to perform phenomic selection of growth traits in slash pine. In previous studies , we deveSlash pine is a typical conifer with a large, uncharacterized, and complex genome, and the reference genome of slash pine is still unavailable; therefore, genetic studies of slash pine are mainly based on the transcriptome .Therefore, we used slash pine breeding populations as model materials to evaluate a novel approach for low-cost, high-throughput phenomic selection of growth trait-based multispectral images. Our objectives were to 1) estimate genetic variation in growth traits in time series using UAV multispectral imagery; 2) evaluate the predictive ability of the GB and GK models using time series multispectral data for phenomic selection; and 3) develop new predictive selection approaches that are independent of sequencing or pedigrees in trees, especially in conifer breeding programs.22.1The study was conducted on a slash pine population in a national forest farm in Anhui, China; details can be found in 2.2Flights were performed monthly in 2021 (at the age of 8 years) using DJI Phantom 4 Multispectral , which has 1 RGB camera and 5 wavelengths . This UAV is equipped with an RTK system that can reduce the horizontal and vertical positioning errors to 0.03 m and 0.06 m, respectively. The output images from each multispectral camera are in TIF format with a resolution of 1600×1300 pixels.Flights were conducted at a fixed height of 35 m above ground level during a sunny and less windy day in each month to ensure high accuracy requirements and to reduce any systematic bias caused by environmental factors. A standard reflectance panel was used during each flight to improve the consistency of the spectral data. The operation was set to 80% overlap between images and a forward speed of 5 m/s during the flights. The original images were normalized to adjust the data and align the spectral information across the images. The total area covered was 4.5 ha and the duration of each flight was 1 hour. During the Covid-19, the field trip was strictly restricted in February 2021, so data were not available. To validate the accuracy of tree height and crown area (CA) measurement by UAV images, the ground truth data of tree height and CA were measured by randomly selected 100 trees in July of 2021, with the high accuracy of RTK system, the UAV-based tree height and CA have a high correlation with the ground truth, with the R2 value higher than 0.85 .2.3In this study, the image processing methodology employed a series of steps to extract essential information from the original multispectral images of the plantation. The initial data processing involved the use of DJI Terra software  to generate multispectral orthomosaics and dense 3D point clouds of the entire plantation. These orthomosaic images, along with the 3D point clouds, served as the basis for further analysis.lidR package version 4.0.0  using a point-to-grid algorithm. The resolution of both the DTM and the DSM was set at 0.5 m, ensuring a high level of detail in the representation of terrain and surface objects. The difference between the DSM and the DTM provided the canopy height models (CHM), which indicate the height of vegetation above the ground surface.dalponte2016 function with specific criteria, including a minimum height threshold of 2.6 m and a maximum crown diameter of 2.5 m. This step allowed for the identification and delineation of individual trees within the study area.Using the CHM, individual trees were detected using the For each detected individual tree, tree-level attributes such as tree height and crown area were manually labeled. In addition, relevant family, site, and block information was associated with each tree to improve the accuracy and context of the tree-level data.To represent the spatial extent of individual tree canopies, tree crown polygons were generated from the manually labeled crown areas using the raster package . FinallySimilar to previous studies , fifteen2.4Estimates of genetic parameters for slash pine growth traits in each month of the year were collected by fitting a general multiple mixed linear model using restricted maximum likelihood (REML); details can be found in . A briefwhere 2h for trait Where where Where 2.5q and h are the median of the Euclidean squared distance and the bandwidth parameter affecting the covariance decay rate between genotypes, respectively. Specifically, for each month, we randomly divided the data into an 80% training set for model training and a 20% validation set for model validation. To evaluate model stability, the data were randomly divided 100 times for model training.We performed two important GS methods, including GB and GK kernels , to compng kernel, where q2.6The PBWAS in our study was conducted according to the principles of GWAS (Genome-Wide Association Study) methodology. We considered each temporal month as a chromosome, and a genome-wide association analysis (GWAS) was applied to detect the multispectral and VIs (20 variables) related to the growth traits at each temporal month level. Thresholds of P< 10^-3 were used as the significance level to identify associations between variables and traits. While a typical threshold for GWAS is usually around 10^-4 to 10^-5, we chose a relatively lower threshold given the smaller size and scope of our study compared to traditional GWAS studies with larger genomic datasets. The lower threshold allowed us to identify potentially meaningful associations between the multispectral and VIs traits and tree growth traits in the context of our specific study using spectral data.BGGE package  and the combination of MV and pedigree (MV+P) using the GB and GK models is shown in 3.5-3 in these 11 months  were found by 2h, selected families with high genetic gains for both tree height and CA at different selection ratios. In addition, the best month of selection for tree height and CA was also found in our study.Tree height and CA differences were detected among families and increased during spring  and summer  with the increase of tree growth characteristics. However, with the limitation of low quality of RGB camera, there are some tree height and CA do not extract correctly. which occurred that the tree height and CA for some trees from January to May have a trend of decreasing. The  in July . ModeratMost multispectral and VIs in this paper have a large positive or negative genetic correlation with tree height and CA in different months, and multispectral and VIs have been shown to have a strong correlation with plant photosynthetic status, which has the potential to be used in plant phenomics approaches. We used GWAS methodology to reveal the significant VIs and spectra associated with growth traits at different growth times, which we call PBWAS. The results demonstrated the effectiveness of combining phenomic information with UAV imagery to characterize growth differentiation at different growth times in slash pine. We identified relevant VI phenotypic associations for tree height and CA in several months. These associations were inconsistent across months for tree height and CA, as reported by In discussing the limitations of our study, it is important to acknowledge the lack of a propagation of error analysis in our current manuscript. We recognize the importance of such an analysis in determining the reliability and applicability of our findings. However, several factors prevented us from including a comprehensive error propagation analysis in this study. First, the limitations of our experimental design and data collection process, including sample size limitations and measurement precision, may have affected the feasibility of conducting a robust error propagation analysis. Second, the selected data analysis methods, which rely on model-based estimation and prediction, have inherent limitations with respect to error propagation. Finally, given the scope and objectives of this study, we faced time and resource constraints, as well as limitations in data availability. As a result, a comprehensive analysis of error propagation was beyond the scope of this study.5With the development of UAV technology, the collection of multispectral or NIR spectra has greatly increased and conversely decreased in cost. In this paper, we use this technology to reinforce the advantages of using the PS approach in Scots pine to estimate the ability of PS used in conifers independent of sequencing or pedigrees. The heritable variation of growth traits in time series was evaluated, temporal growth strongly influenced the genetic variation of growth traits, and the optimal breeding selection time for tree growth traits was suggested. Two types of GS kernels, including GB and GK, showed satisfactory prediction ability based on the tree growth traits at different months using the pedigree and MV instead of genomic markers, indicating that with high-throughput UAV imagery, phenomic selection using multispectral and VIs was possible and reliable. Our study provides insight into the spectral processes reflecting phenotypic differentiation  in a time series of UAV technology. Our new PS approach in slash pine bridges the gap between high-dimensional secondary traits  and individual phenotypes.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.YL designed the study, conducted the experiment and wrote the manuscript. XY, LT, LW, and LX revised the manuscript. JJ supported the data collection and field experiments and supervised the experiments. QL supervised experiments and performed revisions of the manuscript. All authors contributed to the article and approved the submitted version.