Genetic associations have been quantified using a number of statistical measures.Entropy-based mutual information may be one of the more direct ways ofestimating the association, in the sense that it does not depend on theparametrization. For this purpose, both the entropy and conditional entropy ofthe phenotype distribution should be obtained. Quantitative traits, however, donot usually allow an exact evaluation of entropy. The estimation of entropyneeds a probability density function, which can be approximated by kerneldensity estimation. We have investigated the proper sequence of procedures forcombining the kernel density estimation and entropy estimation with aprobability density function in order to calculate mutual information. Genotypesand their interactions were constructed to set the conditions for conditionalentropy. Extensive simulation data created using three types of generatingfunctions were analyzed using two different kernels as well as two types ofmultifactor dimensionality reduction and another probability densityapproximation method called m-spacing. The statistical power in terms of correctdetection rates was compared. Using kernels was found to be most useful when thetrait distributions were more complex than simple normal or gamma distributions.A full-scale genomic dataset was explored to identify associations using the 2-horal glucose tolerance test results and γ-glutamyl transpeptidase levelsas phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs)and interacting SNP pairs associated with these phenotypes were found and listedwith empirical p-values. Over the past decades, genetic association studies have been conducted to identifygenetic variants associated with various traits or diseases –3. GenetMultifactor dimensionality reduction (MDR) has been successfully used as a genomicassociation measurement method . It can Entropy-based methods of analyzing genomic associations have emerged as anotherstream of research . AccordiHere, we propose another way of analyzing genomic associations for quantitativetraits based on the kernel density estimation (KDE). KDE estimates a distributionfunction by summing kernels over the domain, or the observed data points. Kernelsare designed to be normalized and non-negative functions, symmetric around each datapoint . MI woulJT with the kernel when thevariable is transformed. MI between the genotype and the quantitative phenotype is investigated to establish agenomic association. Measuring MI requires estimating the entropy and conditionalentropy. To estimate them for a quantitative trait, the probability density function(pdf) needs to be estimated first. KDE has been adopted to estimate the pdf fordistributions with or without a boundary effect. f(x), is known, the entropy,H, is defined in the integral form of the pdf as below,which is also called the differential entropy [When the probability density function, entropy .MI is defined as the difference between the entropy of one set and thatconditioned by the other set, where two sets are interchangeable. MI canquantify the association between two sets , which, H(P) is the amount of informationcontained in the phenotype distribution [H(P|G) measures theamount of information still necessary to describe the phenotype distributionwhen the genotype is known. Equivalently, it is the amount of information thatthe phenotype distribution does not share with the genotype. Therefore, MI in, where ribution . The conf(x)from the data [Xi}denote the set of random samples drawn from a distribution with densityf. Then, the entropy H can be estimated asfollows.To estimate the entropy in , we firsthe data . Let {Xif . For that purpose, KDE can be used toestimate f. A simple and known function K,called a kernel, may be defined around each data point and summed for theestimation of a pdf, as shown below [The estimation of entropy now becomes equivalent to the estimation ofwn below .K(u) should be non-negative and symmetricfor our purpose, while satisfying the normalization condition. The requirementfor a kernel function that it should be normalized in the range of its argumentalso ensures the normalization of the pdf [x, the pdf is determined by the sum ofn individual kernel functions whose centers are atx = Xj. Thewidth of a kernel function is controlled by the bandwidthh.Here  the pdf . It shouSome phenotype distributions have distinct boundaries. For example, let usexamine the phenotype of γ-GTP levels, as shown in x space, whose range is. The different Jacobian between Here, the kernel is symmetric in ln x space, the estimated pdf fits better, asshown in x space, and the shape will be dependent onthe data point Xj, around which thekernels are estimated.Transforming back to Several types of kernel functions have been proposed that satisfy the symmetricand non-negative conditions imposed for our purpose . Among tThe indicator function, 1(..), is used. Meanwhile, the Gaussian kernel has about5% lower efficiency, which means it requires 5% more data pointsto achieve the same error level as the Epanechnikov kernel. However, theGaussian kernel is widely used because of its mathematical convenience. It hasthe form given below.The Epanechnikov kernel is also more advantageous for computation due to itsrelative simplicity, which becomes an important factor with genomic datacontaining an extensive set of genotypes . We examh should be determined to make arguments forkernel functions. It also plays the role of a weight factor for the sum ofkernels at each point. The value of bandwidth can be deduced by setting thederivative of the asymptotic mean integrated squared error with respect to thebandwidth as zero [σ̂, and a constant specific to the kernelused [n is the number of datapoints.As can be seen in  as zero . Howevernelused . Itsexpν = 2 and C2=2.34, 1.06for the kernels in The bandwidth in P, can be estimated as follows.Combining P|G=g}. Letg indicate each genotype and d be used forthe order of genomic interaction. Because each SNP has three different forms, d-order interacting SNPs should have3d possible genotypes. The conditionalentropy can now be obtained by summing the above KDE calculations on eachsubset, weighted by the subset size, as below.For computing the conditional entropy, the phenotype set needs to be dividedaccording to the corresponding genotypes, represented as{i-th phenotype data in subset{P|G=g} by We denote the If statistical significance of the obtained MI is required, the p-value isestimated by random permutation of the trait values among samples to make theresultant dataset satisfy the null hypothesis. The maximum MI value of allgenotype combinations from this dataset would form a single point of the nulldistribution of MI constructed by repeated random permutations. Counting thenumber of points in this null distribution that are larger than or equal to theobserved MI would give the desired empirical p-value.tij,were tabulated for each of the nine possible genotype combinations of twointeracting SNPs, along with specified values of the minor allele frequency(MAF) and heritability. To generate quantitative values, we took the penetranceas the mean of the distribution from which the trait value was sampled. Threetypes of distributions were considered. The first type was a normaldistribution, as given below.The application of KDE to a genomic association study and its performance wereexamined with a simulated dataset. Simulated data were generated based on theVelez models , which aAnother was a gamma distribution, shown below.tij, was used in thedistribution functions above, while σ remained a free parameter. Whenthe penetrance, tij, was larger orsmaller than the overall average value, the class of the samples for thegenotype ij was assigned as high or low risk, respectively. Tosimulate various situations, three distinct values of σ, , were assigned for high- and low-risk subgroups, independently establishingnine different cases. To further investigate the trait distribution, a thirdtype of trait value sampling was done from a mixed form as shown below, withα set to 0.2.It should be noted how the penetrance,tij was found to be larger thanthe overall average . There were also nine combinationsof σ. The number of SNPs was taken as 20 with a single causal pair and400 samples. The Velez model has seven heritability values, each of which hasfive different penetrance tables for two different MAFs. For each of those 70models, along with nine σ combinations, 100 simulations were conducted,yielding 70×90×100 files for the three distribution schemes,respectively. In all, for each of the seven heritability values, simulateddatasets generated from 10 models (five penetrance tables and two MAFs), withnine variations in high- and low-risk samplings from the three types ofdistributions were considered.The high-risk term in H(P) andH(P|G) values, as definedin In tij in that equation ispenetrance, which should be smaller than 1, the resultant gamma distributionwould have a shape parameter, k, smaller than the scaleparameter, θ, in most cases because of the used σ values. Thiscondition results in a quite skewed gamma distribution, as intended, giving riseto the boundary effect. KDE, as designed, showed consistency and betterperformance than m-spacing, QMDR, and GMDR, with the exception mentioned above,regardless of the distribution shapes.KDE is also a non-parametric method, as is m-spacing. With a symmetricdistributions in To examine the type I error rate, the same process used to build the simulationdataset was adopted to construct the null dataset, except that no causal pairswere intended. With the null dataset, the empirical p-value was evaluated bypermuting the phenotype part 1,000 times. The p-value evaluation was repeatedwith the entire null dataset. Counting the number of instances in which thep-value obtained turned out to be smaller than the significance level, which wastaken as 0.05, indicates the type I error. A genome-wide dataset from the KARE project  was inveThe γ-GTP distribution was found to be skewed enough to be regarded asthe gamma distribution. Therefore, an analysis was performed by KDE with theboundary effect considered. We investigated genomic associations with quantitative traits, including genomicinteractions. Entropy-based MI can measure the association strength if the entropyof the trait could be estimated both by itself and as conditioned on the genotypes.We estimated entropy through KDE.We explored and compared two types of kernel functions for KDE. The Epanechnikovkernel involves a far lower computational burden than the Gaussian kernel, but itwas found to be as powerful as the Gaussian kernel for the genomic association task.There are several other kernels whose efficiencies lie between the Epanechnikov andGaussian kernels, but under the non-negativity and symmetry constraint, their shapesare quite similar, especially in that their extents are limited by the indicatorfunction, unlike the Gaussian kernel. Therefore, the two kernels investigated maylie at two extremes in terms of efficiency and how they are defined. Other kernelsare expected to provide similar results.When the dataset is made from a skewed distribution with a crowded boundary, using asymmetric kernel inherently leads to an extended tail outside the supported range. Aconsequence is an incorrect estimation of the association. The real data forγ-GTP, which we reported in the present analysis, may not be correctlyanalyzed with a usual symmetric kernel. We suggested defining a transformed argumentin the kernel to confine the sum of the kernel functions within the supported range.Through these tactics, the hit ratios were found to be stable and superior to thosefrom other methods.The proposed method can be extended to multivariate phenotype traits, while m-spacingis intrinsically a univariate method. Multivariate traits should be the naturalextension of this paper. When the real data are expected to be more complex, beyonda dichotomous classification, our method in this paper would therefore be alegitimate candidate. Phenotypes with more than one threshold can be found, one ofwhich is the OGTT-2h phenotype analyzed here. Simultaneous associations of SNPs werefound with the phenotypes that have been suggested to have OGTT-2h-related traits asa prognostic factor. Therefore, these SNP findings may provide additional evidencefor the reported pathways. This might be a benefit of analyzing quantitative traitsin their original form.


Caenorhabditis elegans is a premier toxicology model that has revolutionized our understanding of cellular responses to environmental pollutants and boasts robust genomic resources and high levels of genetic variation across the species. In this study, we performed dose-response analysis across 23 environmental toxicants using eight C. elegans strains representative of species-wide genetic diversity. We observed substantial variation in EC10 estimates and slope parameter estimates of dose-response curves of different strains, demonstrating that genetic background is a significant driver of differential toxicant susceptibility. We also showed that, across all toxicants, at least one C. elegans strain exhibited a significantly different EC10 or slope estimate compared to the reference strain, N2 (PD1074), indicating that population-wide differences among strains are necessary to understand responses to toxicants. Moreover, we quantified the heritability of responses  to each toxicant exposure and observed a correlation between the exposure closest to the species-agnostic EC10 estimate and the exposure that exhibited the most heritable response. At least 20% of the variance in susceptibility to at least one exposure level of each compound was explained by genetic differences among the eight C. elegans strains. Taken together, these results provide robust evidence that heritable genetic variation explains differential susceptibility across an array of environmental pollutants and that genetically diverse C. elegans strains should be deployed to aid high-throughput toxicological screening efforts.The genetic variability of toxicant responses among indisviduals in humans and mammalian models requires practically untenable sample sizes to create comprehensive chemical hazard risk evaluations. To address this need, tractable model systems enable reproducible and efficient experimental workflows to collect high-replication measurements of exposure cohorts.  Over 350,000 chemicals are currently registered for use and production globally, of which tens of thousands are either confidential or ambiguously described . This stCaenorhabditis elegans is a free-living nematode that can be cheaply reared in large samples in a matter of days, vastly accelerating the pace and scale at which hazard risk evaluations can be performed compared to most vertebrate models. Furthermore, studies using C. elegans provide data from whole animals with intact neuromuscular, digestive, and sensory systems unlike popular in vitro systems. C. elegans is a powerful toxicology model that unites toxicologists with molecular geneticists so that expertise in routes of chemical exposure, internal dosage-specific effects, tissue distribution, and chemical metabolism is combined with expertise in DNA damage, oxidative and osmotic stress, and regulation of apoptosis and necrosis , sulfotransferase enzymes (SULTs), and ATP-binding cassette (ABC) transporters  to translate a hazard’s point of departure (POD) between species with distinct exposure routes and pharmacokinetic and pharmacodynamic capacities . POD calariation  and, by ariation . QuantifC. elegans representative of species-wide genetic diversity. We used a high-throughput imaging platform to assay development after exposing arrested first larval stage animals to each toxicant in a dose-dependent manner and used custom software   are available from the  (CeNDR) . Isolati (CeNDR) . The NGMelow see .2.2.E. coli as a nematode food source for all assays in this study. In brief, we streaked a frozen stock of HB101 E. coli onto a 10 cm Luria-Bertani (LB) agar plate and incubated it overnight at 37 °C. The following morning, we transferred a single bacterial colony into a culture tube that contained 5 ml of 1x Horvitz Super Broth (HSB). We then incubated that starter culture and a negative control (1X HSB without bacteria) for 18 h at 37 °C with shaking at 180 rpm. We then measured the OD600 value of the starter culture with a spectrophotometer , calculated how much of the 18-h starter culture was needed to inoculate a one liter culture at an OD600 value of 0.001, and used it to inoculate 14 4 L flasks that each contained one liter of pre-warmed 1x HSB. We grew those 14 cultures for 15 h at 37 °C with shaking at 180 rpm until they were in the early stationary growth phase  or water depending on the toxicant’s solubility. The exact sources, catalog numbers, stock concentrations, and preparation notes for each of the toxicants are provided . Followi2.4.E. coli suspended in K medium at an optical density at 600 nm (OD600) of 100  model, to capture animals with more abnormal body sizes caused by extreme toxicant responses. We used R/easyXpress . Our high-throughput imaging platform produced thousands of images across each experimental block. It is unwieldy to manually curate each individual well image to assess the quality of animal measurement data. Therefore, we took several steps to clean the raw data using heuristics indicative of high-quality animal measurements suitable for downstream analysis.control wells was greater than 0.6  of the number of animals in than 0.6 . Experimthan 0.6 .We then reduced the data to wells containing between five and thirty animals, under the null hypothesis that the number of animals is an approximation of the expected number of embryos originally titered into wells (approximately 30). This filtering step screened for two problematic features of well images in our experiment. First, given that our analysis relied on well median animal length measurements, we excluded wells with fewer than five animals to reduce sampling error. Second, insoluble compounds or bacterial clumps were often identified as animals by CellProfiler  and woulAfter the previous two data processing steps, we removed statistical outlier measurements within each concentration for each strain for every toxicant to reduce the likelihood that statistical outliers influence dose-response curve fits.Next, we removed measurements from all exposures of each toxicant that were no longer represented in at least 80% of the independent assays because of previous data filtering steps, or had fewer than 10 measurements per strain.median_wormlength_um ~ Metadata_Experiment + bleach using the lm function in base R. We then extracted the residuals from this linear model for each exposure and subtracted normalized phenotype measurements in each exposure from the mean normalized phenotype in control conditions. These normalized phenotype measurements were used in all downstream statistical analyses.Finally, we normalized the data by (1) regressing variation attributable to assay and technical replicate effects and (2) normalizing these extracted residual values with respect to the average control phenotype. For each compound, we estimated a linear model using the raw phenotype measurement as the response variable and both assay and technical replicate identity as explanatory variables following the formula 2.7.post hoc test, filtered to only comparisons to control exposures, and determined the lowest exposure that exhibited a significantly different phenotypic response as distinguished by an adjusted p-value less than 0.05. This analysis was performed on all phenotype measurements, as well as for each strain individually to determine if genetic background differences explain differences in LOAEL for each toxicant.We determined the lowest observed adverse effect level (LOAEL) for each compound by performing a one-way analysis of variance using the normalized phenotype measurements as a response variable and toxicant dosage as an explanatory variable. We then performed a Tukey 2.8.R/drc  calculated for each strain-specific dose-response model’s e parameter estimates are significantly different. We quantified the relative slope steepness of dose-response models estimated for each strain within each compound using the drc::compParm function, which uses a z-test to compare means of each b parameter estimate. Results shown are filtered to just comparisons against N2 dose-response parameters  and residual variance explained by other factors (EV). We extracted the among strain variance (GV) and the residual variance (EV) from the model and calculated broad-sense heritability (2H) with the equation 2H = G+VE).VG / (V We estimated the 2H using the lme4 (v1.1.27.1) R package to fit a linear mixed-effects model to the normalized phenotype data with strain as a random effect. Genetic variance (GV) can be partitioned into additive (AV) and non-additive (NAV) variance components. Additive genetic variance is the amount of genetic variance that can be explained by the discrete collection of variants that differ in a specific population. Narrow-sense heritability (2h) is defined as the ratio of additive genetic variance over the total phenotypic variance (PV), i.e., 2h = A / VPV. We generated a genotype matrix using the genomatrix profile of NemaScan, a GWAS analysis pipeline (https://www.elegansvariation.org/data/release/latest). We then calculated 2h using the sommer (v4.1.5) R package by calculating the variance-covariance matrix (AM) from this genotype matrix using the sommer::A.mat function. We estimated AV using the linear mixed-effects model function sommer::mmer with strain as a random effect and AM as the covariance matrix. We then estimated 2h and its standard error using the sommer::vpredict function.Phenotypic variance can be partitioned into variance caused by genetic differences or genetic variance  for each compound (adjp < 0.05).We performed dose-response assessments using a microscopy-based high-throughput phenotyping assay  for devecompound . We thencompound . Out of b) and effective concentration (e) parameters of each dose-response model were estimated using strain as a covariate, allowing us to extract strain-specific dose-response parameters. Undefined EC10 estimates (estimates greater than the maximum exposure) were observed for at least one strain from two compounds (chlorfenapyr and manganese(II) chloride). Additionally, we observed virtually uniform responses and high within-strain phenotypic variance across the dose-response curves of deltamethrin and malathion across all strains. We speculate that this high variance is in part driven by insoluble particles in culture wells that interfered with reliable inference of animal lengths and have consequently excluded these four compounds from further dose-response analyses . We observed substantial variation in effective concentration between toxicants within classes of chemicals  but not across strains  (adjp ≤ 0.003). EC10 estimates for propoxur were not significantly different from aldicarb, nor were the estimates for methomyl compared to chlorpyrifos  but EC10 estimates for all other compounds within the insecticide class were significantly different . EC10 estimates for lead(II) nitrate were significantly different from all other tested metals . EC10 estimates for arsenic trioxide were significantly different from all tested metals , except nickel chloride . EC10 estimates for all other metals were not significantly different from each other . These results suggest that susceptibility to different toxicants in C. elegans is quite variable both between and within chemical classes.Dose-response models using strain as a covariate explained significantly more variation than those models without the strain covariate for the other 21 compounds  . All funadjp < 0.05) with paraquat and propoxur being the exceptions (p < 0.05), suggesting that diverse C. elegans strains are not equally likely to be susceptible or resistant with respect to the commonly used reference strain N2.Most differences in EC10 were explained by differences among compounds of different classes. However, variation in EC10 estimates caused by genetic differences among strains were pervasive . In ordeceptions . Twenty-b) estimates for each dose-response model varied substantially as well but followed different patterns than those estimates observed for EC10 (p < 0.001) but not across strains . Slope estimates for pyraclostrobin were significantly lower than all other fungicides . Slope estimates for 2,4-D were significantly lower than those estimates for the other two herbicides . Among insecticides, the only slope estimates that were not significantly different from each other were methomyl and aldicarb . Slope estimates for nickel chloride were significantly different from all other metals .Strain-specific slope (for EC10 . We agaip = 0.041).We next compared the relative steepness of dose-response slope estimates compared to the N2 reference strain, analogously to our EC10 relative potency analysis  based on empirical data. We demonstrated that EC10 estimates and slope parameters vary significantly between genetically distinct C. elegans strains and that, in fact, the N2 reference strain exhibits a significantly different dose-response profile than at least one other strain with respect to every toxicant we assessed. Additionally, strain-agnostic EC10 estimates are correlated with, but generally lower than, the exposure at which we observed the largest additive genetic variance. These observations suggest that previous analyses of toxicity in C. elegans might suffer from “genetic blindspots” in that significant intrinsic drivers of population-level toxicity are being systematically ignored, which then masks a source of complexity in toxicant susceptibility. For example, we observed that the strains ECA396 and MY16 are significantly more sensitive than other strains across more toxicants than expected by chance. The susceptibility profiles of these strains underscore the need to assess hazard risk across individuals that are intrinsically susceptible or resistant to understand the implications of dose-response endpoints. Because our high-throughput assay only reports the magnitude of developmental delay over one generation as a trait, it remains unknown whether the resistance that we observed in these strains, or for a given toxicant more broadly, extend to other toxicity endpoints . The toxicants in our study belong to classes of chemicals with documented effects on all these organ systems, so the identification of putatively resistant genetic backgrounds could represent fertile ground for the discovery of novel pathways that potentiate well characterized stress responses.One goal of dose-response analysis is to identify a point of departure (POD) for exposure to a certain compound (C. elegans strains. Furthermore, we show empirical support for the notion that toxic endpoints derived in experimental studies from one genetic background cannot be neatly translated across genetically diverse individuals. These findings build upon similar analyses conducted using human cell lines derived from the 1000 Genomes Project (C. elegans and other mammalian systems (An open question in toxicogenomics is the degree to which variation in human disease and development can be explained by our chemical environment, and whether these contributions exceed those from genetic differences among individuals. Our study suggests that for any given compound, we can find a dosage for which at least 20% of the variation in developmental delay can be explained by genetic differences between  Project , which r systems , high-thSupTab2SupTab3SupTab1SupTab5SupTab4SupTab6SupFig1SupFig2SupFig3SupFig4SupFig5SupFigs6-30


Lactoria cornuta through ontogeny (across nearly an order of magnitude in standard length) and in a high‐throughput fashion, using high‐resolution microCT data and segmentation algorithms to characterize the hundreds of scutes that cover each individual. We show that carapace growth is canalized with little variability across individuals: rather than continually adding scutes to enlarge the carapace surface, the number of scutes is surprisingly constant, with scutes increasing in volume, thickness, and especially width with age. As cowfish and their scutes grow, scutes become comparatively thinner, with the scutes at the edges (weak points in a boxy architecture) being some of the thickest and most reinforced in younger animals and thinning most slowly across ontogeny. In contrast, smaller scutes with more variable curvature were found in the limited areas of more complex topology . Measurements of Gaussian and mean curvature illustrate that cowfish are essentially tessellated boxes throughout life: predominantly zero curvature surfaces comprised of mostly flat scutes, and with scutes with sharp bends used sparingly to form box edges. Since growth of a curved, tiled surface with a fixed number of tiles would require tile restructuring to accommodate the surface's changing radius of curvature, our results therefore illustrate a previously unappreciated advantage of the odd boxfish morphology: by having predominantly flat surfaces, it is the box‐like body form that in fact permits a relatively straightforward growth system of this tessellated architecture . Our characterization of the ontogeny and maintenance of the carapace tessellation provides insights into the potentially conflicting mechanical, geometric, and developmental constraints of this species but also perspectives into natural strategies for constructing mutable tiled architectures.Biological armors derive their mechanical integrity in part from their geometric architectures, often involving tessellations: individual structural elements tiled together to form surface shells. The carapace of boxfish, for example, is composed of mineralized polygonal plates, called scutes, arranged in a complex geometric pattern and nearly completely encasing the body. In contrast to artificial armors, the boxfish exoskeleton grows with the fish; the relationship between the tessellation and the gross structure of the armor is therefore critical to sustained protection throughout growth. To clarify whether or how the boxfish tessellation is maintained or altered with age, we quantify architectural aspects of the tessellated carapace of the longhorn cowfish  Lactoria cornuta through ontogeny, using high‐resolution microCT data and segmentation algorithms to characterize the hundreds of scutes that cover each individual.The carapace of boxfish is composed of mineralized polygonal plates, called scutes, arranged in a complex geometric pattern and nearly completely encasing the body. To clarify whether or how this armor is maintained or altered with age, we quantify architectural aspects of the carapace of the longhorn cowfish  The individual specimens examined varied by the factor of 8.5x in size, ranging from a standard length  of 1.7–13.62 cm  with an RX Solutions EASYTOM μCT scanner (RX Solutions). Scans for all samples were performed with helix scan sample rotation, at 100 kV source voltage, 150 μA source current, 15 W, and 333 ms exposure. A beam hardening correction algorithm was applied during image reconstruction.2.3CT images were visualized and processed with AmiraZIBEdition. For segmentation of the carapace and individual scutes, since the close association of scutes did not allow a purely automatic threshold‐based segmentation, we adapted the semi‐automatic seed‐based segmentation workflow published by Baum et al. . This apFrom the region adjacency graph (RAG), we then generated a surface using the “Create Surface Field from Spatial Graph Attribute” module, producing a clean representation of the segmented tessellation.2.4The carapace surface area (SA) of each boxfish was computed in AmiraZIBEdition from the generated surface with the “Surface Area Volume” module.2.5x, y, and z coordinates of each boxfish separately. The first principal component always represented the anteroposterior axis, the second one the dorsoventral axis, and the third one the laterolateral axis. The absolute distance between the minimum and maximum values along each principal component was used to represent length, height, and width, respectively.If not specified otherwise, we used the software R version 4.1.2  was reported and the “confint” function used to calculate its 95% confidence interval.2.6The number of scutes was regressed on surface area using a generalized linear model with a Poisson distribution and a log‐link function as implemented in the “glm” function. The “confint” function was used to calculate the 95% confidence interval of the regression slope.2.7number of neighbors: number of neighboring scutes, evaluated on the RAG, as described above. Secondly, we quantified various scute dimensions, such as (2) scute volume: number of voxels × volume of a voxel; (3) scute plane‐based area (PBA): the projected area of a scute, calculated for each scute by placing nodes in the centers of triangles/quads that surround the scute's vertex in the RAG. If the scute vertex was not completely surrounded by triangles or quads , then all plane‐based measures were set to −1000 to remove that scute from this variable's analysis. The least‐squares plane for the triangle/quad center nodes was calculated and that plane translated to contain the scute center, then the triangle/quad center nodes were projected onto the plane. PBA was calculated as the area of the polygon created by the projected nodes; (4) scute thickness: shortest dimension of a cuboid enclosing the scute. This measure captures the principal dimension of the scute orthogonal to the carapace surface; (5) scute width: largest principal dimension of the cuboid, perpendicular to the thickness dimension  mean surface curvature: the mean of the two principal curvature values of the surface field at the location of the scute's center. The relative frequency of the number of neighbors was plotted for each specimen, which were ordered by increasing size to assess scaling effects. Here and below, we always used SA as a size indicator. All raw data can be found in Table The “Create Tesserae Statistics” module in AmiraZIBEdition was used to extract and characterize a variety of variables describing the morphology of scutes and their local environment. Input to the module were the label field containing the segmentation of the scutes, the RAG derived from this label field, and a surface representing the carapace shape. The following variables were computed: Firstly, (1) 3/2, PBA by dividing with SA, and thickness and width by dividing with SA1/2, respectively.The scaling of the scute dimensions with boxfish size was analyzed by linearly regressing the median of the scute dimensions on SA across the 13 specimens. The variables were natural log‐transformed prior to analysis and the same R functions as explained for the carapace dimensions were used. For the purpose of investigating the variability of scute dimensions within boxfishes, we also performed boxplots of normalized dimension values ordered by increasing specimen size. Volume was normalized by dividing with SA1/2.The trend of the median and the variability of the curvature variables were also analyzed by normalization and the generation of boxplots ordered by specimen size. For this purpose, Gaussian surface curvature was multiplied with SA and mean surface curvature multiplied with SAThe variables above capture scute properties, but with no positional determinants or relationship to anatomical location. To compare different body regions , four specimens of different sizes were chosen as examples, and their scutes were color‐coded in AmiraZIBEdition according to all scute parameters.33.1Boxfish carapace length scaled with positive allometry with regard to SA Figure . The sloScute number did not vary significantly with SA  , followed by scutes with five (~19% to 24%) and seven neighbors (~11% to 14%). Very few scutes had four (~7% to 10%), or three neighbors (~3% to 5%) Figure . Scutes 3.3Scute volume scaled with negative allometry , but isometric scaling  could not be excluded as the true relationship since it was included in the 95% confidence interval  indicates whether a surface is saddle‐like (negative), or sphere‐like (positive). If the Gaussian curvature is zero either the surface is flat at this point  or only one principal curvature is zero, as in a long ridge or valley. These two cases of zero Gaussian curvature can only be distinguished by looking at a second metric, the mean curvature , which represents the relationship between convexity and concavity of the principal curvatures of a surface. Sphere‐like surface regions possess large absolute values, whereas all other regions, flat and saddle‐shaped, tend toward zero. Therefore, in long ridges or valleys, where Gaussian curvature is zero, the mean curvature will be either positive for a convex curvature or negative for a concave curvature.Our measures of normalized curvature allowed comparison of the general carapace shape across individuals, ignoring the effect of size. For example, in all specimens, on the lateral, ventral, and dorsal sides, the normalized Gaussian curvature of the scutes was positive but close to zero in all specimens Figure . The regWith regard to normalized mean curvature, individuals were fairly similar in being evenly covered by scutes with a relative mean curvature close to zero  curvature shape, with larger positive curvatures only along its edges. As observed by previous authors  curvature, rather only local curvature: individual scutes are predominantly either relatively flat or exhibit localized bends to form carapace edges Figures , 11. CorL. cornuta permits a canalized developmental system where an armor with a fixed number of scutes and limited scute shapes can develop through a comparatively simple accretionary growth strategy , over a large range of body size, while maintaining armor integrity. Future work will further clarify constructional constraints in the development and evolution of this tessellated armor across the diverse polygonal body cross sections of boxfishes (Ostracioidea; Bartol et al., Our findings demonstrate how the combined effects of body geometry and tessellation can provide a biological solution for the “problem” of growing a tiled surface. The boxy body morphology of the longhorn cowfish Figure S1Click here for additional data file.Table S1Click here for additional data file.DataS 1Click here for additional data file.


The spread of infectious diseases in populations is controlled by the susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection), and recoverability (propensity to recover/die) of individuals. Estimating genetic risk factors for these three underlying host epidemiological traits can help reduce disease spread through genetic control strategies. Previous studies have identified important ‘disease resistance single nucleotide polymorphisms (SNPs)’, but how these affect the underlying traits is an unresolved question. Recent advances in computational statistics make it now possible to estimate the effects of SNPs on host traits from epidemic data . However, little is known about how to effectively design disease transmission experiments or field studies to maximise the precision with which these effects can be estimated.SIRE-PC.In this paper, we develop and validate analytical expressions for the precision of the estimates of SNP effects on the three above host traits for a disease transmission experiment with one or more non-interacting contact groups. Maximising these expressions leads to three distinct ‘experimental’ designs, each specifying a different set of ideal SNP genotype compositions across groups: (a) appropriate for a single contact-group, (b) a multi-group design termed “pure”, and (c) a multi-group design termed “mixed”, where ‘pure’ and ‘mixed’ refer to groupings that consist of individuals with uniformly the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than estimates for infectivity. Whereas the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferred because it uses information from naturally-occurring rather than artificial infections. The same design principles apply to estimates of the epidemiological impact of other categorical fixed effects, such as breed, line, family, sex, or vaccination status. Estimation of SNP effect precisions from a given experimental setup is implemented in an online software tool Methodology was developed to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables that underlie host susceptibility, infectivity and recoverability. Designs that maximize the precision of estimates were derived.The online version contains supplementary material available at 10.1186/s12711-022-00747-1. Infectious disease constitutes one of the biggest threats to sustainable livestock and aquaculture production, global food security, and human health. Over the last decades, genome-wide association studies (GWAS), together with high-density sequencing and other ‘omics’ technologies, have facilitated enormous breakthroughs in disease genetics, with the number of genetic loci that have been identified to be associated with disease resistance increasing at a rapid rate –6. AccorThe most effective way to reduce infectious disease prevalence in a population is to reduce the individuals’ susceptibility to infection or their ability to transmit infections, once infected. Yet, remarkably little is known about the role of previously identified ‘resistance’ loci in infectious disease transmission, because in most studies, disease resistance refers to the resistance of an infected animal to develop disease or other side-effects from infection (e.g. performance reduction or death), rather than to resistance to becoming infected or transmitting the infection –10. Hencsusceptibility, infectivity, and recoverability [susceptibility is defined as the relative risk of an uninfected individual becoming infected when exposed to a typical infectious individual or to infectious material excreted from such an individual, infectivity is the propensity of an infected individual to transmit infection to a typical (average) susceptible individual, and recoverability is the propensity of an infected individual to recover or die [Epidemiological models are widely used to identify risk factors for disease transmission in populations and to assess the impact of control measures on these. Particularly relevant for genetically heterogeneous populations are compartmental models, in which individuals are classified as, for example, susceptible to infection (S), infected and infectious (I), or recovered/removed (dead) (R) . These erability –17. In ar or die , 18, 19.Conceptually, genetic improvement in any or all three of these underlying epidemiological host traits is expected to reduce disease spread within and across populations. Indeed, recent advances in treating infection partly as an indirect genetic effect (IGE) have pointed to far greater responses to selection than had previously been expected , 21. Thi. infection, disease, or survival status, or measures of pathogen load, immune response, or performance after infection challenge), the three epidemiological host traits have the clear advantage that their role in disease spread is fully specified by epidemiological models. However, until recently, estimation of genetic effects for these traits has proven challenging, as they need to be inferred from observable disease phenotypes. Fortunately, recent advances in computational statistics now enable genetic effects for host traits to be estimated from longitudinal disease records of individuals [Compared to conventional disease resistance traits used in most GWAS . Our objectives were: (1) to derive analytical expressions for the precision of estimates of the effects of a SNP on the three underlying epidemiological host traits; for tractability, these expressions assume a best-case scenario  and, therefore, represent upper bounds for precision of estimates from real data; (2) to use these insights to develop optimal designs of disease transmission experiments that aim at estimating the effects of a single SNP of interest on host susceptibility, infectivity, and recoverability; (3) to validate the analytical expressions and designs for a range of realistic data scenarios, e.g. the inclusion of group effects, other fixed effects, and residual noise, and cases when only the deaths of individuals are recorded and infection times are unknown; and (4) to present an easy-to-use online software tool to assist in the construction of a suitable design for a disease transmission experiment.Although this study focuses on the estimation of SNP effects underlying disease transmission, the developed methodology and optimal design principles also apply to investigating the effects of other categorical variables  in the seeders and contacts, respectively, and AA minus the proportion of BB individuals.For each group the genotypic makeup for the target SNP is characterised by the following key quantities: mentclass2pt{minim. who infects who) are known [Previous studies have shown that the effects of SNPs and other genetic effects for the epidemiological parameters can be inferred from a wide range of available data that can be routinely collected from disease transmission experiments , 30, 31.re known .Based on these concepts, for a given disease and epidemiological data from a fixed number of genotyped animals, the optimal experimental design is determined by finding how the numbers of seeders  allele over the B (A) allele, whereas absence of dominance is represented by a value of 0) [The SNP contribution to the traits for individual ue of 0) .The model in Eqs. –3 contaiGiven data from a disease transmission experiment, inference can be used to estimate model parameters. Assuming an uninformative flat prior, posterior estimates and associated uncertainties can be captured by a multivariate probability distribution called the likelihood. The precision for each parameter is characterised by the posterior standard deviation (SD) in the corresponding marginalised likelihood.For validation, analytically derived SDs were compared against inferred values from simulated epidemic and genetic data for different experimental designs , and AA minus BB) in the seeders and contacts, respectively. Note that The parameters are defined as follows:  in Eqs.  and 5)  \documenAn important point to take from Eq.  is that Inversion of the Fisher information matrix defined in Eq.  leads toThese rather unwieldy expressions reflect a complex confounding between estimating susceptibility and infectivity SNP effects. They show that precisions of parameter estimates depend not only on the number of seeders and contacts, but also on the genetic composition of each group for the SNP.AA and BB contact individuals .In spite of their apparent complexity, a number of important design lessons can be drawn from Eqs.  and 7)::7): (1) Equivalent analytical expressions for recoverability can be derived  the optimal design depends on a trade-off in precision between different parameter estimates (a given design that estimates one parameter as precisely as possible may be less precise for other parameters in the model); and (2) practical considerations often restrict what can be implemented . First, agreement between the analytical curves (dashed lines) and the simulation-based results (crosses), .Lastly, since precision is expected to scale as the square root of the total number of individuals, the SDs for an experiment with 1000 individuals are expected to be a factor √10 = 3.2 times smaller than those for an experiment containing 100 individuals. This can be seen in Fig. We now consider optimising the proportions of seeder and contact individuals in the single contact group design for maximum precision. Figure In contrast, the SD in the SNP effect for infectivity, entclass1pt{minimarelies on differences in the genetic makeup  between the seeders and contacts,Figure 2 in Eq. , with thA and B alleles can be explained intuitively as follows. Suppose BB individuals and AA individuals. Because susceptible individuals become infected as the epidemic progresses, the infected population becomes more and more a mixture of AA and BB individuals. Thus, a comparison of how quicklyAA compared to BB individuals, and hence of the A compared to the B alleleThe reason that differences in the genetic makeup between the seeders and contacts provide information about the relative infectivity of contact population varies between designs; thus we vary BB contacts and so no information regarding their susceptibility. Therefore, to attain a reasonable precision for BB individuals in the contact population, as quoted in Fig. Figure entclass1pt{minimaAA and BB individuals and, therefore, no information regarding the dominance relationship between the A and B alleles is provided. Introduction of AB individuals into the seeders and contacts can inform The single contact group design contains only AA and BB across those groups. Such an approach is appealing because it allows conclusions to be easily drawn directly from the data. For example, if epidemics progress significantly faster in groups that contain AA seeders , this provides direct evidence that allele A confers greater infectivity than allele B (largely regardless of their relative susceptibility). Similarly, the relative susceptibility of allele A compared to B can be found by comparing the relative epidemic speeds of groups where the infection was initiated by the same seeder genotypes, but the genotypes of the contact individuals differ . Unlike the pure design, the mixed design does not rely on a large number of seeders .The so-called “mixed design” uses replicates of the design illustrated in Fig. BB and 85% AA in the contact population of group 1 and AA and BB in the contact population of group 2.Results for the mixed design are shown in Fig. r of Eq.  is neglir of Eq.  simplifigreater probability that infections are initiated by AA individuals in group 1, simply because there are more of them. Likewise, in group 2, BB individuals cause most of the infections. Consequently, if the epidemic in group 1 proceeds more quickly than in group 2, it is tempting to conclude that the A allele confers greater infectivity than B. However, caution is required due to potential confounding between infectivity and susceptibility . Fortunately, this confounding is broken because for each group the relative rate at which AA and BB individuals become infected gives direct evidence for differences in susceptibility (irrespective of infectivity), so allowing precise estimation of both An intuitive explanation of how this experimental design works is as follows. As with most disease transmission experiments, when an infection occurs it is not known from which individual that infection originates. However, a key feature of the mixed design is that there is a Figure AB individuals are present in the mixed design, it cannot be used to provide information regarding dominance. Therefore, for completeness, we also include a mixed design with dominance estimation, as illustrated in Fig. Again, if no For comparison, the precision of SNP effects from the five optimal designs in Fig. AA and BB genotypes can simply be replaced by “vaccinated” and “unvaccinated” classifications  in Eq.  showed tentclass1pt{minima. without group, fixed or residual effects in Eqs.  Fig. . This ca in Eqs. , and for in Eqs. , 11), a, a\docum in Eqs. . Note thDetermining the appropriate experimental design is achieved by adjusting the input values, subject to any practical/logistic limitations , with the aim of minimising the SD in the SNP effects. To facilitate this process, the tool includes the optimal experimental designs in Fig. . [There has been increasing acknowledgment within the livestock genetics community that the spread of infectious disease in populations may not only depend on the genetic susceptibility of individuals to infection but also on their genetic infectivity and recoverability , 31, 32..  showed t. , 28–30, This study provides analytical expressions for the precision of estimated SNP substitution and dominance effects associated with host susceptibility, infectivity, and recoverability, which have been implemented in an online software tool to assist with the design of transmission experiments and for statistical power analyses in experimental studies. To make the derivations tractable, the calculations were shown for a best-case scenario, in which non-SNP contributions were ignored and infection and recovery times were assumed known. Nonetheless, the derived expressions were found to be in strong agreement with numerical results obtained by performing inference on data from simulated epidemics that account for a range of complications and confounding that are likely to be present in real data. The parameter that was found to be most difficult to precisely estimate was From these analyses, three types of optimal designs emerged. The first of these considered just a single contact group. It was found that, in principle at least, it is possible to infer entclass1pt{minimaentclass1pt{minimaFor a fixed total number of individuals, both the pure and mixed designs were found to be similar in terms of their precision for estimating SNP effects  infections in turbot fish [To the best of our knowledge, to date relatively few animal disease transmission experiments have been conducted to specifically estimate host genetic effects on epidemiological traits , 34, 35.nditions . That exbot fish , 38.. [Many previous studies have investigated the effects of vaccines on disease transmission in farmed animals . In this case, allocation of seeders and contacts according to their SNP genotype is not possible (because the genotype composition will be different for each SNP). So how should experiments be optimally designed in this case? An analysis based on considering an arbitrary SNP that is in Hardy–Weinberg equilibrium , with frnoted in ). ConseqWe now consider the possibilities and additional complications that arise when considering field data . As with the GWAS discussion above, here we do not have the luxury of being able to choose the composition of groups in terms of SNP genotypes. Nevertheless, the analytical expression in Eq.  provide When investigating vaccination effects, presence of some groups with a high vaccination rate and others with a low (or no) vaccination rate, would naturally lend itself to something akin to the optimal mixed design proposed in this paper. Hence, we would expect such experimental vaccination designs to be highly informative not only about susceptibility and recoverability effects, but also about infectivity.It should be mentioned that analysis of real-world data comes with additional complications: (1) proper accounting for related individuals; (2) the fact that groups are not entirely closed (e.g. cows in different fields may share milking facilities); and (3) not all individuals start in the susceptible state, especially for endemic diseases. Tackling these problems will require further development of the approaches outlined in this paper.In this paper, epidemics were modelled using SIR dynamics, but it is important to point out that the results are equally applicable to diseases for which individuals do not recover (i.e. the SI model). In these cases, estimates of SNP effects on susceptibility and infectivity can be used in selective breeding programs to reduce disease prevalence. Although more complicated compartmental models were not investigated, e.g. the inclusion of an exposed (infected but not infectious) state, the basic idea of accentuating differences between contact groups for the factor under study (e.g. by ensuring large differences in the SNP genotypic composition in the mixed and pure designs) to increase variation in epidemic speed (which in turn provides evidence for variation in infectivity), is expected to remain valid.. insects).Table Although SNPs with large effects on disease resistance have been identified , 22, 42,The aim of this paper was to identify optimal designs for disease transmission experiments to estimate the effects of a particular SNP of interest (or other factors) on the susceptibility, infectivity, and recoverability of individuals. It was found that while the precision of estimates of susceptibility and recoverability effect were relatively insensitive to the design for a given total number of contact individuals , infectivity was not . In particular, to precisely estimate genetic effects on infectivity, a so-called “mixed” design was identified, which specifies the optimal proportions of different genotypes in the contact populations of different groups. Replication of this basic design was found to be effective at reducing confounding that can arise from group effects. An easy-to-use software tool accompanying this paper was developed to aid experimental design by providing estimates for the precision of parameter estimates. The results shown here illustrate that such estimates are reliable and robust to noise and to a range of potential confounding factors that are likely present in real-world disease systems.Additional file 1: Provides details on how inference was performed on simulated datasets to generate results against which the analytical expressions could be compared.Additional file 2: Derivation of the observed Fisher information matrix [n matrix –47. DescAdditional file 3: Inversion of the observed Fisher information matrix. Description: This shows how the observed Fisher information matrix in Eq. (x in Eq.  is inverx in Eq.  and 7)..AdditionAdditional file 4: Derivation of standard deviations for recoverability SNP effect. Description: The standard deviation for the SNP effect on recoverability ar is derived.Additional file 5: Derivation of standard deviations in the dominance parameters Δg, Δf and Δr.Additional file 6: Fig. S1. Shows how the probability of epidemic extinction varies as a function of the number of seeder individuals Nseed for different basic reproductive ratios R0.Additional file 7: Further details for the single contact group design. Description: Provides analytical expressions for the standard deviations in the SNP effect parameters ag, af and ar for the single contact group design.Additional file 8: Fig. S2. Impact of changing R0. In additional file 8, we numerically investigate how changing R0 affects the fraction of infected contacts ϕ and the optimal design choices outlined in Fig. Additional file 9: Fig. S3. Further details for the “pure” design. Description: Provides analytical expressions for the standard deviations in the SNP effect parameters ag, af and ar and dominance parameters Δg, Δf and Δr for the “pure” design.Additional file 10: Fig. S4. Further details for the “mixed” design. Description: Provides analytical expressions for the standard deviations in the SNP effect parameters ag, af and ar and dominance parameters Δg, Δf and Δr for the “mixed” design.Additional file 11: Impact of realistic model/data scenarios on design. Description: Considering the optimal designs in Fig.  Fig. S5. Impact of sources of model/data variation on optimal design for infectivity. Fig. S6. Impact of sources of model/data variation on optimal design for susceptibility. Fig. S7. Impact of sources of model/data variation on optimal design for recoverability. Fig. S8. Impact of residual contributions.Additional file 12: Fig. S9. Partitioning contributions to the SD of SNP effects. Description: Figure Additional file 13: Fig. S10. Design replication. Description: Investigates how the reduction in precision when incorporating group effects can be moderated by means of design replication (that is repeating the same basic designs in Fig. Additional file 14: Fig. S11. Figure S11 investigates the addition of a single large fixed effect with elements in the design matrix X set in such a way as to give a certain degree of correlation with the SNP.Additional file 15: Rather than defining the proportions of genotypes in the seeder and contact populations, we consider the case in which individual genotypes are randomly allocated with A allele having frequency p, assuming Hardy-Weinberg equilibrium. Fig. S12. Precision estimates for the SNP parameters with random genotype allocation.


We later discuss the necessity to update the current GEA models to predict both regional- and local- or micro-habitat–based adaptation with mechanistic ecophysiological climate indices and cutting-edge GWAS-type genetic association models. Furthermore, to account for polygenic evolutionary adaptation, we encourage the community to start gathering genomic estimated adaptive values (GEAVs) for genomic prediction (GP) and multi-dimensional machine learning (ML) models. The latter two should ideally be weighted by de novo GWAS-based GEA estimates and optimized for a scalable marker subset. We end the review by envisioning avenues to make adaptation inferences more robust through the merging of high-resolution data sources, such as environmental remote sensing and summary statistics of the genomic site frequency spectrum, with the epigenetic molecular functionality responsible for plastic inheritance in the wild. Ultimately, we believe that coupling evolutionary adaptive predictions with innovations in ecological genomics such as GEA will help capture hidden genetic adaptations to abiotic stresses based on crop germplasm resources to assist responses to climate change.Leveraging innovative tools to speed up prebreeding and discovery of genotypic sources of adaptation from landraces, crop wild relatives, and orphan crops is a key prerequisite to accelerate genetic gain of abiotic stress tolerance in annual crops such as legumes and cereals, many of which are still orphan species despite advances in major row crops. Here, we review a novel, interdisciplinary approach to combine ecological climate data with evolutionary genomics under the paradigm of a new field of study: genome–environment associations (GEAs). We first exemplify how GEA utilizes Letter to Karl Freiesleben, June 1799.“I shall endeavor to find out how nature’s forces act upon one another, and in what manner the geographic environment exerts its influence on animals and plants. In short, I must find out about the harmony in nature” Alexander von Humboldt— Crop wild relatives (CWR) and landraces are well known for providing new alleles for plant breeding . They alVigna subterranea), chickpea (Cicer arietinum), cowpea (V. unguiculata), grass pea (Lathyrus sativus), groundnut (Arachis hypogaea), marama bean (Tylosema esculentum) (Lupinus mutabilis) (Phaseolus acutifolius) , sorghum (Sorghum bicolor), and finger millet (Eleusine coracana) or pearl and proso millets . Many of these crops have interesting drought-tolerance traits and some capacity to grow in compacted soils. Cowpeas, groundnuts, and lesser known cereals are already traditional food sources and biocultural components for vulnerable areas, especially in Sub-Saharan Africa and parts of Asia and Latin America  , tarwi (tabilis) , and tepifolius)  constitu America .Yet, despite their tolerance to drought and heat plus their high nutritional quality, the utilization of these orphan crops is limited partly because of the poor characterization of their genetic background . TherefoStill, a major question to harness crop prebreeding for climate change pressures is whether there is enough heritable variation in traits associated with tolerance to abiotic stress. In this context, genomic characterizations of reference collections comprising CWR, landraces, and orphan crops that span contrasting habitats offer a straightforward scenario to identify natural standing adaptation to abiotic pressures .The spirit behind this novel approach is to detect genomic regions that correlate with habitat heterogeneity as an indication of the natural selection imprint to environmental gradients . Since tGlycine  WCR; or For orphan crops, rather than nondomesticated natural plant species, fewer GEA studies have been undertaken. Although still limited to the easier-to-grow annual grain species compared to perennial and root/tuber crops, GEA is starting to make important contributions to genetic analysis of landraces and WCR germplasm, often richly represented in the world’s major gene banks for crop species. The analytical pipeline for GEA studies is shown in Historically, based on the technological improvements in sequencing and SNP detection and as a means to improve GEA, the field has moved from the candidate gene approach  into fulin situ ecological georeferencing of accessions, and by 2) revealing the genomic architecture of adaptation via cutting-edge predictive models  using georeferencing and statistical downscaling  and machine learning (ML) , capableOverall, CWR and landraces undeniably harbor unique adaptations to abiotic stresses, rarely present in the cultivated and improved genepools . HoweverGP works either on the basis of shared relatedness  or on the basis of linkage disequilibrium (LD) between the SNP marker loci and the genetic variants that underlie phenotypic variation . The relThe predictive ability of GP models may be further biased depending on whether the entire SNP set is used, or the most predictive SNP data are chosen after the GWAS-type analysis , the latde novo GWAS-based GEA estimates gathered from other (or even the same) panels (Better approaches to be implemented and reported include 1) weighted GP models using ) panels  and 2) o) panels . GP can ) panels . GEAVs c) panels  would be) panels , high-th) panels , and ML-) panels .Next-generation GEA models and GEAVs will allow the use of “exotic” parents for targeted predictive prebreeding in crop species. They offer feasible methodologies to trace the sources of abiotic stress adaptation and tolerance targeting crop resources in low-income countries, which are also the most vulnerable to climate change. Here, we have discussed strategies to implement the latest-generation predictive e.g.,  and genoModern GEA approaches will allow further studies of evolutionary conservatism, parallelism, and convergence in the genetic architecture of adaptation to various types of abiotic stresses across a wide range of environments, landraces, and wild accessions . ApproacHow genetic diversity and genomic divergence arise and are shaped based on ecological pressures is one of the main questions in molecular evolution  and has ST statistic (A). Once the potential confounding demographic patterns have been accounted for, it is then feasible to disentangle genuine signatures of environmental adaptation (B) from spurious concurring genetic drift due to genomic constraining features. Finally, these combined summary statistics  can ultimately redound in prebreeding efforts aiming to introgress exotic adaptive variation into elite lines (C), for instance, via backcrossing (BC) schemes for abiotic and biotic stresses.Therefore, GEA efforts can be enhanced by exploring SNP density and statistics of site frequency spectra  in associated vs. nonassociated regions . Figure ad hoc multiple hypotheses  to which scale epigenomic marks  regulatand sRNA , and mayeritance , eventuaeritance .e.g. GEAVs), as well as the identification of underlying factors that may facilitate or constrain future adaptive responses to changing climate.Ultimately, GEA is empowering the understanding on how plant genomes interact with their environment while shaping adaptive phenotypes. Such mechanistic insights of the genome functionality in the wild promise leveraging the characterization of landraces and CWR to assist prebreeding efforts through multi-dimensional adaptive scores (Lastly, GEA studies offer new possibilities to efficiently unlock crop diversity for climate adaptation . Unexplo