The aim of this study was to estimate genetic parameters for 305-day cumulative milk yield and components, growth, and reproductive traits in Guzerá cattle.The evaluated traits were 305-day first-lactation cumulative yields (kg) of milk (MY305), fat (FY305), protein (PY305), lactose (LY305), and total solids (SY305); age at first calving (AFC) in days; adjusted scrotal perimeter (cm) at the ages of 365 (SP365) and 450 (SP450) days; and adjusted body weight (kg) at the ages of 210 (W210), 365 (W365), and 450 (W450) days. The (co)variance components were estimated using the restricted maximum likelihood method for single-trait, bi-trait and tri-trait analyses. Contemporary groups and additive genetic effects were included in the general mixed model. Maternal genetic and permanent environmental effects were also included for W210.The direct heritability estimates ranged from 0.16 (W210) to 0.32 (MY305). The maternal heritability estimate for W210 was 0.03. Genetic correlation estimates among milk production traits and growth traits ranged from 0.92 to 0.99 and from 0.92 to 0.99, respectively. For milk production and growth traits, the genetic correlations ranged from 0.33 to 0.56. The genetic correlations among AFC and all other traits were negative (−0.43 to −0.27). Scrotal perimeter traits and body weights showed genetic correlations ranging from 0.41 to 0.46, and scrotal perimeter and milk production traits showed genetic correlations ranging from 0.11 to 0.30. The phenotypic correlations were similar in direction (same sign) and lower than the corresponding genetic correlations.These results suggest the viability and potential of joint selection for dairy and beef traits in Guzerá cattle, taking into account reproductive traits. The Guzerá cattle breed is one of the most productive among the Zebu breeds present in Brazil and is widely used in crossbreeding, with the aim of producing animals adapted to tropical climatic conditions. In the last 30 years, in addition to genetic selection for beef production, some breeders have included selection for milk yield in Guzerá cattle to obtain economic benefits from the sale of both meat and milk in dual-purpose production systems ,2.Dual-purpose cattle systems allow a wide variety of production models because they depend on the preferences of the farmer, local market, household consumption, and local environment, resulting in different proportions of incomes generated from sales of meat and milk ,4. FurthGenerally, selection in cattle breeds has allowed large genetic gain that has resulted in beef animals with higher growth rates and dairy cows with higher milk production capacity. In dual-purpose systems, it is important to understand the relationship between growth and milk production traits to evaluate possible genetic antagonisms before defining selection objectives.Furthermore, genetic progress focused on traits of the highest economic value in cattle was accompanied by declines in animal fertility, making low fertility the main reason for cow disposal . TherefoIn Brazilian beef cattle, the most commonly used trait as an indicator of fertility and sexual precocity is the scrotal perimeter (SP), which is easy to measure and exhibits a positive genetic correlation with testicular biometry traits , sperm motility and mass activity and a negative genetic correlation with major and total sperm defects . AdditioAlthough the genetic improvement programs of the Guzerá breed for milk and beef are developed independently, joint genetic evaluation is feasible due to the high genetic association between pedigree datasets of the breeding programs . In addiStudies on the dual purposes of Guzerá cattle have only recently been conducted. It is expected that joint genetic evaluations considering milk and beef traits will contribute to the genetic improvement of the dual-purpose in this breed. In this context, this study aimed to estimate genetic parameters for 305-day cumulative milk yield and milk components, growth, and reproductive traits in Guzerá cattle.Approval of the animal care and use committee was not needed because this study used existing datasets historically collected by the animal breeding program.2).The phenotypic and pedigree data used in this study were obtained from the Zebu Breeds Genealogical Registry Service (SRGRZ), databases of the Brazilian Association of Zebu Cattle (ABCZ), and the National Program for the Improvement of Guzerá for Dairy Purpose (PNMGuL), jointly coordinated by Embrapa Dairy Cattle and the Brazilian Center for the Genetic Improvement of Guzerá  of milk (MY305), fat (FY305), protein (PY305), lactose (LY305), and total solids (SY305); AFC in days; adjusted SP (cm) at the ages of 365 (SP365) and 450 (SP450) days; and adjusted body weight (kg) at the ages of 210 (W210), 365 (W365), and 450 (W450) days.The body weights were adjusted to account for the age of the animal at measurement, age of the dam at birth or weaning, and previous weight, according to the Beef Improvement Federation (BIF) Guidelines . The SP To estimate the nonlinear model parameters, the Gauss-Newton iterative method for nonlinear regression (NLIN) in SAS Software  was usedSPt is the SP at t days of age; A is the estimated SP at maturity; B indicates the proportion of mature testis with an asymptotic size to be obtained after birth ; k is the maturation index, establishing the rate with which SP approaches A; and e is the random error associated with each measurement. The final estimates of the parameters A, B, and k were 40.1638, 2.7315, and 0.0035, respectively.where Contemporary groups (CGs) for MY305, FY305, PY305, LY305, and SY305 were formed by fitting herd, year, and season of calving. For AFC, SP365, SP450, W210, W365, and W450, the CGs were a combination of herd, year, and season of birth. Additionally, for growth traits, sex was also included in the CG. Both calving and birth seasons were defined as dry (April to September) or rainy (October to March). Data that belonged to the CG with fewer than three records were excluded. The number of CGs per trait and the number of animals per CG are shown in The (co)variance components were obtained by the restricted maximum likelihood method (REML) using AIREMLF90 software . The foly is the vector of phenotypes; b is the vector of fixed effects of a CG and covariate; a is the vector of random additive direct genetic effects; m is the vector of random maternal genetic effects; mp is the vector of random maternal permanent environmental effects; Z1, Z2, and Z3 are incidence matrices related to the a, m and mp to y, respectively; and e is the residual vector. The following (co)variance structures were also assumed:where G0a is the additive direct genetic (co)variance matrix; G0m is the maternal genetic (co)variance matrix; A is the numerator relationship matrix; MP0 is the maternal permanent environment (co)variance matrix; Imp is an identity matrix of order equal to the number of dams with progeny; R0 is the residual (co)variance matrix; I is an identity matrix of order equal to the number of animals; and ⊗ is the Kronecker product.in which Single-trait  and bi-trait  analyses were carried out. Specifically, for the correlations between milk production traits  and SP at different ages , tri-trait analyses were performed using W210 as an “anchor” trait. This procedure was performed due to the difference in the number of animals phenotyped in these two groups of traits. Therefore, W210 was used to promote better connections between measured traits since it included a greater number of observations and both sexes. The genetic covariances and correlations presented for W210 were derived from bi-trait analyses.The maternal genetic and permanent environmental effects were adjusted only for W210. The direct maternal genetic covariances were assumed to be zero. The age at calving was considered a linear covariate for MY305, FY305, PY305, LY305, and SY305, and the age of dams at calving was considered a linear covariate for W210, W365, W450, SP365, and SP450.The variance components obtained by single-trait analysis for the evaluated traits are provided in The direct heritability estimates ranged from 0.16 (W210) to 0.32 (MY305). The maternal heritability estimate for W210 was 0.03 (0.004). The genetic correlations among all traits evaluated were favorable. Genetic correlations among milk production traits (milk yield and milk components) were positive and high, ranging from 0.92 (FY305×LY305) to 0.99 (MY305×LY305). For W210, W365, and W450, the genetic correlations were also positive and high, ranging from 0.92 (W210×W365 and W210×W450) to 0.99 (W365×W450). When comparing milk production traits and growth traits, the estimated genetic correlations were moderate, ranging from 0.33 (SY305×W210) to 0.56 .Regarding reproductive traits, the genetic correlations among AFC and all other traits evaluated were negative, presenting values ranging from −0.31 (AFC×FY305) to −0.27 (AFC×SY305) for milk production traits and from −0.43 (AFC×W450) to −0.39 (AFC×W210) for growth traits. Body weights and SP measured at different ages showed positive genetic correlations, ranging from 0.41 (W210×SP450) to 0.46 (W450×SP365). The genetic correlations between SP365 and AFC and between SP450 and AFC were −0.36 and −0.40, respectively. Low-to-moderate genetic correlations were estimated between SP at different ages and milk production traits, ranging from 0.11 (PY305×SP450) to 0.30 (MY305×SP365).Phenotypic correlation estimates among all traits were similar in direction and smaller than their corresponding genetic correlations. Overall, the phenotypic and residual correlations were similar in direction and magnitude, except between milk production and growth traits. Among milk production traits, the phenotypic correlations were high and positive, ranging from 0.86 (MY305×PY305) to 0.98 (PY305× LY305). All traits presented negative and low-to-moderate phenotypic correlation estimates with AFC. For correlations with milk production traits, values ranged from −0.17 (AFC× SY305) to −0.07 (AFC×MY305), and values ranged from −0.36 (AFC×W365) to −0.27 (AFC×W210) for correlations with growth traits. Phenotypic correlations of AFC with SP365 and SP450 were equal to −0.07 and −0.08, respectively.Considering growth traits and SP measured at different ages, the phenotypic correlation estimates were positive, presenting moderate magnitudes with values from 0.39 (W210× SP450) to 0.55 (W365×SP365). Growth and SP traits presented positive and low-to-moderate phenotypic correlation estimates with milk production traits, ranging from 0.01 (FY305×W210) to 0.18 . The phenotypic correlation between SP365 and SP450 was positive and high (0.88).The residual correlation estimates are shown in This study estimated genetic associations among milk yield, milk components, growth traits, and reproductive traits in Guzerá cattle. To the best of our knowledge, the study by Brito et al  is the oOverall, heritabilities were moderate 0.16 to 0.32), indicating that all traits studied could respond to selection with lesser or greater intensity and could achieve a satisfactory genetic progress rate in the breeding program of Guzerá cattle. The heritability estimate for MY305 (0.32) was similar to that reported by Brito et al  (0.29),  to 0.32,Regarding growth traits, the heritability estimates were moderate (0.16 to 0.24). They corroborated the estimates reported by Gama et al , but theAnimal breeding strategies are determined by the relative importance of traits and the genetic correlation between traits. With regard to the dual-purpose focus, there have been few studies that have addressed genetic relationships between dairy and beef traits. The genetic correlations between MY305 and 305-day cumulative milk components were high (0.92 to 0.99). No studies have reported genetic correlations between MY305 and 305-day cumulative milk components in Guzerá cattle. A high genetic correlation indicates a strong genetic association among these traits, i.e., selection for higher MY305 promotes increased 305-day cumulative milk components.Among growth traits, the genetic correlations were high (0.92 to 0.99), emphasizing that most genes controlling weight at a given age are the same at other ages, making it possible to select animals at younger ages. The genetic correlations among growth traits found in the present study corroborate the literature on cattle breeds ,20,21.The genetic correlation estimates between AFC and all evaluated traits were low to moderate and favorable. Between AFC and MY305, the genetic correlation was equal to −0.28. Although of moderate magnitude, this result suggests that females selected for MY305 would exhibit improved sexual precocity. To the best of our knowledge, no other studies on other Zebu cattle breeds have reported genetic correlations between AFC and MY305. Genetic selection involving milk yield and AFC should be performed with caution to avoid an increasing risk of dystocia in younger cows, mainly those with high genetic advantage for milk yield . The genGenetic correlations between AFC and growth traits were low but favorable (−0.43 and −0.39). In turn, genetic correlations between AFC and SP at different ages  were −0.36 and −0.40, respectively, which are values consistent with those reported in the literature for beef cattle ,21. The Regarding SP, the genetic correlations with MY305 and 305-day cumulative milk components (0.11 to 0.30) were low to moderate, suggesting little genetic association among these traits. Thus, selection for MY305 and milk components will not increase SP but will not cause harm either. Some studies reported genetic correlations among SP and growth traits, with estimates ranging from 0.30 to 0.78 ,21. In tThe genetic correlations between milk production and growth traits were moderate and favorable (0.33 to 0.56) and were highest among W365 and milk production traits , ranging from 0.44 to 0.56. These results suggest that selection for MY305 and milk components would also result in animals with a higher weight at one year of age. Few studies have evaluated genetic relationships between milk and growth traits in cattle, but our results suggest the existence of pleiotropic effects among these traits. The estimates found in the current study are slightly higher than those reported by Gama et al  and BritSpecifically, for milk components genetically correlated with AFC, growth traits, and SP traits, the standard errors of the estimates were high. This can be explained by data-related differences among the traits, with milk components having a much lower number of observations and fewer animals with phenotypes for all traits evaluated. The standard errors were slightly higher for genetic correlations between milk components and SP traits, possibly due to the difference in the number of animals recorded for the two groups of traits. Thus, the genetic correlation estimates among these traits should be interpreted with caution.The phenotypic correlation estimates exhibited a similar trend to the corresponding genetic correlation estimates; however, they generally had lower magnitudes than the genetic correlations. Phenotypic correlation estimates among milk, growth, and reproductive traits were smaller in magnitude than genetic correlation estimates in other studies involving genetic parameter estimates in dual-purpose breeds ,25. A phThe confidence of breeders in the genetic evaluation results of the National Program for the Improvement of Guzerá for Dairy Purpose has increased the number of herds and animals participating in this program, as well as the use of this breed in dual-purpose production systems . The nonAll evaluated traits presented moderate heritability estimates and the possibility of responding to selection in the Guzerá breed.Furthermore, the favorable genetic correlations among all evaluated traits suggest the possibility of joint genetic selection for milk and beef production in Guzerá cattle, with a favorable correlated response for reproductive traits.


The capacity of plants to resist abiotic stresses is of great importance to agricultural, ecological and environmental sustainability, but little is known about its genetic underpinnings. Existing genetic tools can identify individual genetic variants mediating biochemical, physiological, and cellular defenses, but fail to chart an overall genetic atlas behind stress resistance. We view stress response as an eco-evo-devo process by which plants adaptively respond to stress through complex interactions of developmental canalization, phenotypic plasticity, and phenotypic integration. As such, we define and quantify stress response as the developmental change of adaptive traits from stress-free to stress-exposed environments. We integrate composite functional mapping and evolutionary game theory to reconstruct omnigenic, information-flow interaction networks for stress response. Using desert-adapted Euphrates poplar as an example, we infer salt resistance-related genome-wide interactome networks and trace the roadmap of how each SNP acts and interacts with any other possible SNPs to mediate salt resistance. We characterize the previously unknown regulatory mechanisms driving trait variation; i.e. the significance of a SNP may be due to the promotion of positive regulators, whereas the insignificance of a SNP may result from the inhibition of negative regulators. The regulator-regulatee interactions detected are not only experimentally validated by two complementary experiments, but also biologically interpreted by their encoded protein–protein interactions. Our eco-evo-devo model of genetic interactome networks provides an approach to interrogate the genetic architecture of stress response and informs precise gene editing for improving plants’ capacity to live in stress environments. Stresses, such as soil salination and drought, are one of the most significant land crises that threaten global ecosystems and the productivity use of natural resources , 11. Oneet al.’s [Traditional reverse genetic strategies for mapping stress-adapted traits have two major drawbacks. First, current GWAS attempt to map individual environment-sensitive biochemical and physiological traits of stress-adapted species expressed in single environments, but fail to quantify and dissect how plants balance stress defense and growth performance, which forms a unified strategy allowing plants to respond to stressed environments . At preset al.’s  compositet al.’s , 75, coFSecond, existing GWAS analyze genotype–phenotype relationships at individual markers once at a time, without considering a global landscape of the genetic architecture underlying complex traits that are thought to involve all genes the organism may possibly carry throughout its genome . The juset al. [Network reconstruction is based on the data measured on each sample, but in GWAS, the genetic effect of a SNP is a population concept, rather than one that is reflected by individual samples, making it difficult to infer SNP-SNP interaction networks. This issue has been overcome by implementing functional mapping to estimate genetic effects as a time-varying curve. Thus, by treating time points as individual samples, genotype–phenotype data in GWAS are converted into a data format of genetic effects appropriate for reconstructing networks . Based oet al.  reconstrPopulus euphratica Oliver). As the only tree species that can grow and reproduce in aridic deserts, Euphrates poplar has strong salt resistance [In this article, we integrate eco-evo-devo, coFunMap, and Wu and Jiang’s network model to formulate a unified framework for reconstructing omnigenic multilayer networks that mediate stress response. To validate the biological relevance of this framework, we analyze genetic data from two complementary (mapping and association) populations of Euphrates poplar  before it reaches a certain high dimension . Modules can be detected by clustering all SNPs into distinct groups according to their time-varying genetic effect patterns. SNPs within a group, i.e. module, form a subnetwork, which is linked to some extent with other subnetworks. We classify 272 719 SNPs into 66 modules using BIC minimization . Each moWe further divide each module into its distinct submodules, each composed of a reduced number of SNPs. Then we divide each submodule from every module into its distinct sub-submodules, each with the number of SNPs further reduced. This dendritic clustering procedure is repeated until the number of SNPs contained within a unit reaches a manipulatable size determined by Dunbar’s law. Through KEGG analysis, the overall biological function of each module can be explained by different submodules. Similarly, the overall biological function of each submodule is explained by different sub-submodules. At the end, we elucidate a coarse-grained and fine-grained classification taxonomy for the genetic network of salt resistance.After all SNPs are clustered into hierarchical structure , we reconstruct genetic networks among modules, submodules, sub-submodules, and so on to form a multilayer interactome graph. At the top (first-layer) of the graph is a 66-node inter-module network . Nodes iMultilayer interactome networks provide a detailed framework to dissect salt resistance at different levels of resolution from collective to individual effects. Links in a network can be outgoing if a node affects a second node or incoming if a node is affected by a second node. While outgoing links are associated with leaders, incoming links are associated with workers. Thus, the relative number of outgoing and incoming links can be used to evaluate the position of a node. In the first-layer network, we identify modules M32, M54, M66, M16, and M21 as primary leaders not only with many more outgoing links than average, but also with a minimum number of incoming links , which pWe reconstruct the second-layer submodule networks for each of the 66 modules, in which individual SNP-based networks at the third layer are inferred. Modules M43 (containing 2 QTLs) and M57 (containing 4 QTLs) form 143- and 303-node submodule second-layer networks, respectively , we can characterize how each QTL exerts its impact on the plastic response of shoot growth to salt stress. Q154935 in SM138_43 and Q28567 in SM11_57 each have a small independent effect; i.e. these QTLs do not affect salt resistance when they are in isolation . HoweverDifferent from the above-mentioned QTLs, the observed effects of Q152323 in SM69_43 and Q141650 in SM249_57, though significant, are lower than their independent effects because they are substantially down-regulated by other loci. For example, in the SNP network of submodule SM69_43, Q154935 (located at gene LOC105111422) is negatively regulated by regulator S212042 (located at gene LOC105115006). The existence of such an interaction between Q154935 and S212042 is verified by pathway analysis, which suggests that LOC105111422 encodes FACT complex subunit SSRP1 protein HMG that interacts with peptidyl-prolyl cis-trans isomerase CYP57 protein AT4G33060 encoded by LOC105115006 to determine plant response to stress  Fig. 4BFig. 4B. From multilayer interactome networks, we can further reveal the underlying mechanisms of why a SNP is insignificant using coFunMap. We choose two QTL-absent modules M9 and M60 to reconstruct four-layer genetic networks . As can Although S135419 also is up-regulated by SNP S36791 (at gene LOC105128220), this up-regulation is not large enough to compensate for the down-regulation of S105553. A pathway analysis suggests that S135419-S105553 and S135419-S36791 interactions detected in SNP networks can be well explained by the interactions of putative pre-mRNA-splicing factor ATP-dependent RNA helicase DHX16 protein (XP_010436393.1), coded by S135419, with eukaryotic translation initiation factor 4E-1 protein (XP_010434584.1), coded by S105553, and with DEAD-box ATP-dependent RNA helicase 5 protein (XP_010478619.1), coded by S36791, respectively .In the SNP network of sub-submodule SSM4_18_9, S96522 is up-regulated by S62702, and simultaneously down-regulated by S56846 to a larger extent. The up-regulation and down-regulation are almost equal, making the net effect of S96522 smaller than its independent effect . The ideThrough a detailed effect component analysis, we find that all insignificant SNPs can be broadly classified into three categories. In the first category, SNPs have large intrinsic capacity for independent genetic effects, but they are not significantly expressed because of strong negative regulation from other loci. The second category includes those SNPs with small independent effects, which receive both strong positive and negative regulation from different loci. The cancellation of positive and negative regulation does not contribute to increase net genetic effects. In the third category, SNPs have small independent effects and do not receive any regulation or are slightly regulated in either positive or negative direction by other loci, in which case no genetic effects can be observed. For the first two categories, insignificant SNPs can be made to be significant for salt resistance by inhibiting the genetic expression of negative regulators and/or promoting the genetic expression of positive regulators through genetic manipulations. Thus, missing heritability hidden within these types of SNPs can be recovered and utilized for practical genetics. Taken together, reconstructing fine-grained SNP genetic networks at the SNP level enables the reconstruction of the roadmap describing how any genetic locus affects salt resistance, through direct or indirect pathways.Populus chromosomes  as a positive regulator . One population includes a panel of wild trees sampled from the species’ natural distribution (GWAS), whereas the other is a full-sib family derived from two wild trees (genetic mapping). Thus, the two experiments are complementary in genetic dissection and can be used to validate the findings of each other. The GWAS population has 121 tree genotypes, genotyped by 6 978 931 SNPs, of which 272 719, passing the tests of Hardy–Weinberg equilibrium and minor allele-segregation, are used for the subsequent analysis. Using fastStructure software [1 hybrids, from which a high-density linkage map of length 4575 cM was reconstructed from 8305 SNPs [We apply two experimental populations of Euphrates poplar to validate our models. A detailed establishment procedure of these two populations were described in Wang et al.  and Zhensoftware , we find305 SNPs .Both populations have been clonally propagated, having the same genotype planted with three or more clonal replicates in salt-free medium (control) and salt-exposed medium (containing 0.1% NaCl) (stress). Shoot heights were measured for the GWAS population once every 20 days during the 120 days of culture, totaling six evenly-spaced measurement time points. The total lengths of all adventitious roots were measured for the mapping population, starting 13 days after the buds were cultured in the tubes, followed by repeated measurements at a frequency of once every 5 days until there was no space in the tubes for growth (day 78). The change in the phenotype of the same genotype from control to stress conditions reflects the capacity of this genotype to exert salt resistance.xi = (ix(t1), …, ix(Tt)) and yi = (iy(t1), …, iy(Tt)) denote a vector of phenotypic values for a trait measured at time schedule  under control (X) and stress (Y) conditions for the ith individual from a population, respectively. The time-varying differences of trait values for the ith individual between two conditions are computed as.Let i responds to salt stress over the time course. The z vector represents adaptive changes in stress response during ontogenetic development. We implement Sang et al.’s [s = 1, …, m, where m is the total number of SNPs from a population.where the size and sign of each element at a specific time point reflect the degree and pattern of how individual et al.’s  coFunMapet al. [s on stress response at time t, s assuming that it is isolated, s that results from the impact of other SNP Sun et al.  introducet al. , 57 to dWe incorporate LASSO-based variable selection , 67, 85 m SNPs into different modules based on the similarity of the temporal pattern of their genetic effects. Each module represents a distinct network community. Through this clustering, we can ultimately reconstruct multiple interconnected smaller networks from any number of SNPs. Thus, the model can overcome the curse of network dimensionality.When Equation (2) contains tens of thousands of dimensions, it cannot be readily solved. According to developmental modularity theory, we divide a large-scale network into multiple distinct network communities. Components within a network community are connected more closely with each other than those from other communities. Communities can be detected by using functional clustering , 70 to cWe thank members at Beijing Forestry University Center for Computational Biology for their contributions to data collection. This work is supported by Beijing Forestry University Research Fund.FL derived the model, analyzed the data, and wrote the code. TD, PJ, ZY, AD, and SX participated in data analysis. CG introduced game theory. RW conceived and supervised the study and wrote the manuscript within inputs from CG.https://github.com/CCBBeijing/PPMultilayerNetwork/tree/main/Euphrates and can also be requested from the corresponding author.All data and code are publicly available at The authors declare no competing financial interests.Horticulture Research  online.Web_Material_uhac135Click here for additional data file.


Combining inbred line panels is a useful strategy to identify candidate genes associated with root traits in maize and improve the efficiency of the genomic prediction in maize breeding programs. The identification of genomic regions associated with root traits and the genomic prediction of untested genotypes can increase the rate of genetic gain in maize breeding programs targeting roots traits. Here, we combined two maize association panels with different genetic backgrounds to identify single nucleotide polymorphisms (SNPs) associated with root traits, and used a genome-wide association study (GWAS) and to assess the potential of genomic prediction for these traits in maize. For this, we evaluated 377 lines from the Ames panel and 302 from the Backcrossed Germplasm Enhancement of Maize (BGEM) panel in a combined panel of 679 lines. The lines were genotyped with 232 460 SNPs, and four root traits were collected from 14-day-old seedlings. We identified 30 SNPs significantly associated with root traits in the combined panel, whereas only two and six SNPs were detected in the Ames and BGEM panels, respectively. Those 38 SNPs were in linkage disequilibrium with 35 candidate genes. In addition, we found higher prediction accuracy in the combined panel than in the Ames or BGEM panel. We conclude that combining association panels appears to be a useful strategy to identify candidate genes associated with root traits in maize and improve the efficiency of genomic prediction. Maize is one the most important cereal crops, with a wide geographic distribution . It has Root-related traits are difficult and laborious to measure, and thus the identification of genes underlying root traits may help maize breeders to use marker-assisted selection for highly efficient root systems and, consequently, for developing maize varieties that are more efficient in nutrient use and tolerant to drought. Genome-wide association study (GWAS) is a powerful tool for gene mapping in plants and animals, and has been widely used for genetic dissection of complex quantitative traits in many major crop species , grain yIn the past, breeders selected individuals based on phenotypic selection and pedigree information. With advances in molecular genetics, genomic prediction has become a promising method to shorten the breeding cycle, reduce the costs per cycle, and save labor costs  methods, have led to a considerable reduction in costs for genotyping a large number of individuals with high density single nucleotide polymorphism (SNP) markers . Hence, per se and in the combined panel using different sets of training and validation populations and several genetic models for root traits in maize.In our study, we combined two datasets from previous GWAS studies with a different genetic background to perform GWAS and genomic selection using several scenarios. Our hypothesis was that combining the genomic data from different backgrounds increases the power of detecting polymorphisms associated with maize seedling root traits, and improving the prediction accuracy across different datasets is possible. Thus, our objectives were to: (i) identify the genomic regions associated with root traits in maize in a combined panel including two association panels and assess the effect of combining datasets in GWAS, and (ii) assess the potential of genomic prediction in two maize inbred line panels 1F1) population was developed using exotic maize landraces as donors and two expired Plant Variety Protection lines, PHZ51 and PHB47, as recurrent parents, within the Germplasm Enhancement of Maize (GEM) project. BC1F1 plants were crossed with the inducer hybrids RWS9×RWK-76 to produce haploid plants , logistic prior, and five cross-validations. To identify the K that explained the population structure most, we followed the recommendation of K that maximizes the log-marginal likelihood of the entire dataset.The model-based program fastStructure (r2) between vectors of SNP alleles following r2 value was set to be 0.2. The TASSEL 5.0 package was run with a 50 kb sliding window, which determines the width of the window on one side of the start, and LD was calculated for sites within the window for sites surrounding the current site using 232 460 SNP markers evenly distributed across all chromosomes  between pairs of SNPs for each chromosome was determined using squared Pearson correlation coefficients  correlation matrix from SNP genotypes, and a correlation matrix for all markers with the corresponding eigenvalues for each SNP locus (Meff_G). The effective number of independent tests is calculated, and applied similarly to the Bonferroni correction method (α/Meff_G). We set the α-threshold at 0.05. The significant SNPs were obtained by comparing the P-value from FarmCPU analysis and the SimpleM threshold. The LD decay distance (r2=0.2) for each chromosome was used to determine the size of the chromosome segment containing a significant SNP, to search for candidate genes. The MaizeGDB database was used to find linked candidate genes for each SNP based on the maize B73 RefGen_V2 genome, and to find matches with B73 RefGen_V4. With the gene ID, Ensembl Biomart (Best unbiased prediction (BLUP) estimates of lateral root length, primary root length, total number of roots, and total root length were used for GWAS in each panel and in the Combined panel. Although GWAS has been previously conducted in the Ames  were calculated for 11 scenarios . Accuracy=Xβ+Zα+ε, where y is a vector of phenotypes; β is a vector of non-genetic fixed effects; X is an incidence matrix for the fixed effects; α is a vector of random regression coefficients of all marker effects; Z is an n×k genotypic matrix of markers; and ε is a vector of residuals. The four models are explained in detail elsewhere  in the R package rrBLUP , and Bayectively .The traits followed a normal distribution with a slight skew to the left for LRL and to the right for PRL. Considerable variation was observed among lines for all five root morphological traits in both the Ames and the BGEM panel . HoweverLinkage disequilibrium among the 679 maize lines in the Combined panel was measured for all 232 460 markers across the 10 chromosomes. The average LD decay was 9 kb, and was similar across the 10 chromosomes, except for chromosome 4, which showed the slowest decay of approximately 23 kb . The ranK was observed when K=2 for both panels, and the 377 inbred lines from Ames and 302 DH lines from BGEM were clustered into two subpopulations each (K=4), and the population stratification was consistent with their association panel (BGEM and/or Ames) and heterotic groups in maize. Thus, the first subpopulation comprised 314 inbred lines, mainly NSS from the Ames panel, and some tropical, popcorn, unclassified lines, and Mo17; the second subpopulation comprised 65 mostly SS lines from the Ames panel including B73; and the third and fourth subpopulations comprised 169 PHB47-derived lines and 131 PHZ51-derived lines, respectively, all of them from the BGEM panel. The maize inbred lines PHB47 and PHZ51 are known lines that belong to SS and NSS heterotic groups, respectively. Some BGEM-DH lines were misgrouped into the opposite heterotic group because of high donor (exotic) parent contributions of 60%  SNPs associated with TNR in the Ames panel and six SNPs significantly (P<1.76 × 10−6) associated with root traits in the BGEM panel: one with TRL, two with LRL, and three with TNR (P<3.73 × 10−6) associated SNPs with PRL (10), LRL (7), TNR (3), and TRL (10)  . Those 3TRL (10) . CandidaThe traits LRL and TRL shared common SNPs. SNP S8_3886189 is linked to gene Zm00001d008285. It codes for a PQ-loop repeat family protein/transmembrane family protein, identified in the BGEM panel. In the Combined panel, we found various candidate genes. SNP S8_19122996 is linked to Zm00001d008782, which encodes protein MARD1 (mediator of ABA-regulated dormancy); S6_83217874 is linked to Zm00001d036386, coding for a Transducin/WD40 repeat-like superfamily protein; and S6_107885930 is linked to gene Zm00001d037109, encoding a Smr  domain containing protein. SNP S3_164391691 was found in the Combined panel and it was linked to the gene Zm00001d042434, coding for a member of the Late embryogenesis abundant (LEA) hydroxyproline-rich glycoprotein family. This SNP was associated with LRL, TNR, and TRL.The GEBVs in the Combined panel showed higher prediction accuracies than when each panel, BGEM or Ames, was analysed separately for all tested root traits and genetic models . The accComparing the prediction accuracies among traits, generally PRL showed a lower accuracy among all traits whereas LRL showed the highest prediction accuracy. The highest accuracy found was 0.74 for LRL in the Combined panel using BayesC, and the lowest accuracy found was −0.15 for LRL in the BSSS Ames→BGEM scenario using BayesC. For TRL, the highest accuracy was 0.72 in the Combined panel using BayesC. For TNR, the highest accuracy was 0.61 in the Combined panel using RR-BLUP. Concerning PRL, the highest accuracy found was 0.60 in the NSSS Combined scenario using RR-BLUP.1F1) among 66 tropical maize landraces and expired-PVP temperate lines, and had about 20% of donor  parent contribution  SNP associated with LRL in the Combined panel. It belongs to the protein kinase superfamily. The identification of molecular markers associated with root traits enables marker-assisted introgression into elite germplasm and increased genetic gain in the breeding programs. Most candidate genes identified in our study were associated with root growth and development in maize. The gene Zm00001d028967 found for primary root length belongs to the ADP-ribosylation factor (ARF) family, which regulates different cellular processes such as cell growth and cell proliferation . JinchenIn the last 15 years, genomic prediction has become an important tool to predict whether untested genotypes will increase breeding efficiency and the genetic gain in maize breeding programs . This alSeveral factors can affect the accuracy of genomic selection, such as training population size, genetic relationship, heritability, marker density, and statistical models (Different statistical approaches have been used to determine breeding values from genomic prediction models (The main goal of breeding is to select the best genotypes in early generations, saving time and money. GWAS and genomic prediction are powerful approaches that can be integrated in a breeding program to identify molecular markers associated to target traits and estimate GEBVs of individuals to save resources predicting and selecting untested genotypes. Recent progress in developing new DNA sequencing and marker technologies along with other -omics approaches will produce a large amount of data. Combining large datasets will be routine in breeding programs enabling geneticists to analyse data gathered from different experiments to identify biological causes for phenotypic variation in complex traits more reliably. As well, a standardized method for root phenotyping is important to expand this approach across the community and over time. However, statistical methods need to improve with computational speed to handle the continuously increasing sample size and marker density. In conclusion, combining results across studies is useful, even with different genetic backgrounds, to improve the prediction accuracy and detect polymorphisms associated with maize seedling root traits. One approach to handle these large datasets, as shown in this study, is to assign individuals to subpopulations by population structure analysis. The genes found can be further studied to help understand the genetic basis of root development and to manipulate root architecture.The following supplementary data are available at Table S1. The 679 maize lines comprising the Combined panel, pedigree, breeding program, heterotic group, and subpopulation by fasStructure.erac236_suppl_Supplementary_Table_1Click here for additional data file.


CMPK2 (chromosome 11) and ASB16 (chromosome 19) both being implicated in immunological processes. A sequence analysis of the chromosomal regions revealed rs208894039 and rs109521151 polymorphisms as having significant influence on susceptibility to the disease. Specific genotypes were significantly more likely to be affected by BDD and developed chronic lesions. Our study provides an insight into the genomic background for a genetic predisposition related to the pathogenesis of BDD. Results might be implemented in cattle-breeding programs and could pave the way for the establishment of a BDD prescreening test.Bovine digital dermatitis (BDD) is an infectious disease of the hoof in cattle with multifactorial etiology and a polygenic influence on susceptibility. With our study, we identified genomic regions with the impact on occurrence and development of BDD. We used 5,040 genotyped animals with phenotype information based on the M-stage system for genome-wide association. Significant associations for single-nucleotide polymorphisms were found near genes  Treponema spp. is highly probable , first described over 40 years ago , is an iprobable .The disease has been reported as a worldwide problem in dairy cows and beef cattle . In addiAll cows considered for this study belonged to the Holstein breed, the worldwide dominating dairy breed, and were from commercial herds in northeastern Germany. Phenotypes were collected as scores based on visual inspection and thus completely non-invasive. Cows were not specifically restrained while scoring but rather the possible occurrence of BDD lesions was scored while cows were milked, according to the usual daily routine of a commercial dairy farm. This daily routine was completely unchanged for the scoring and not altered in any way, for example, timewise or by the order of animals for scoring. SNP array genotypes were available for all cows included in the study and had been based on blood samples taken for projects of the respective herdbook association. Thus, the results of SNP array genotyping were available from a central database. Likewise, DNA samples that had been stored as retention samples after SNP array genotyping were available from the two laboratories that had carried out the genotyping with the consent of the two herdbook associations involved, namely, RinderAllianz GmbH and RBB Rinderproduktion Berlin-Brandenburg GmbH. The collection of samples was approved by the Lower Saxony State Office for Consumer Protection and Food Safety (33.19-42502-05-17A196) according to §8a Abs. 1 Nr. 2 of the German Animal Protection Law.Scoring of phenotypes was conducted in two batches. The first batch consisted of seven large dairy herds visited between October 2015 and April 2016. Herds were selected based on the type of the milking parlor. External rotary parlors were preferred to enable BDD assessment from the outside of the milking carousel. Six of the herds had external rotary parlors, while one farm was equipped with a side-by-side milking parlor. Herds were selected for phenotyping from a pool of 56 herds participating in a scientific project covering a broad range of phenotypic traits for examinations of feasibility of genomic selection based on individual cow phenotypes. In these herds, 20,000 cows, focusing on young cows in their first lactation, had been genotyped based on a protocol consisting of random sampling. Cows were drawn to be included in the study before phenotyping started.The second batch of data consisted of cows in a further set of six large dairy farms visited between April 2018 and July 2018. Participating herds in this case had enrolled in a genotyping program organized by their herdbook association with the goal of obtaining genomic breeding values for their livestock. The genotyping program was organized in such a way that young stock and cows during first lactation were genotyped by birth year cohorts without any exemptions within birth year. Thus, no selection was practiced. Depending on the time when the herd had enrolled, genotypes were available at least for first lactation and partly also for cows in second lactation.Every farm in both batches of data was visited three times by a team of three trained persons at intervals of 3 weeks. The team leader and third author is a veterinarian specifically trained for assessing BDD status and was present during all farm visits. All cows present at the time of milking were examined in each herd. As cows in a commercial dairy herd may enter or leave the herd based on herd management, not all cows could be assessed during all three visits. The study group included only cows that were inspected at least twice, with a clear majority of 81.7% being scored three times. After phenotype collection, the resulting data were merged with the genotypes available thus forming the data set useable for analysis. By mere coincidence, after merging phenotypes with genotypes, the first and the second batch of data resulted in 2,520 cows each.Every cow received a score for the M-stage   and a scvia the bioinformatics tool FImpute version 2.2  and the Zentrum für molekulare Diagnostik ZMD at the Institute of Veterinary Medicine . The majority of all cows in batches 1 and 2 were genotyped using the Illumina EuroG10K BeadChip versions 4 and 5 . A subset of 516 animals within batch 1 was analyzed using the Illumina BovineSNP50 BeadChip versions 1 and 2. Quality control was set as call rate >90% and minor allele frequencies >1%. Imputation from the 10-K SNP chip information to the 50-K variant sion 2.2  with an PR is the probability of occurrence, yijkl is the vector of phenotypic observations coded as binary traits across 2 or 3 observations, φ is the probit link function, μ is the overall mean, herdi is the fixed effect of the farm , parityj is the fixed effect for parity (primipar or multipar), DIMk is the fixed effect for lactation stage grouped into classes , and animall is the random additive genetic effect of animals . For random additive genetic effects, the covariance structure is given by the matrix G. G was set up comprising all cows with phenotypes and genotypes and their genotyped sires as available from the central database. To control for multiple testing, the false discovery rate (FDR) was used , φ is the probit link function, μ is the overall mean, herdi is the fixed effect of the farm , parityj is the fixed effect for parity (primipar or multipar), DIMk is the fixed effect for the lactation stage grouped into classes , and haplotypel is the fixed effect for haplotype .In a first run of haplotype analysis, all individuals homozygous for a given haplotype formed one effect class while the vast majority of animals were assigned to a class “heterozygous” as they showed some form of heterozygosity for the haplotype. After analysis of traits TBIN, TBINA, and TCHRONA, the results provided an indication on the status of haplotypes being favorable, intermediate, or unfavorable. In a second run, for each allele, the status of each individual was defined based on whether the haplotype allele occurred twice (homozygous), once (heterozygous), or not at all . Only haplotype alleles appearing in at least 10 animals were included in further analysis. The impact of each haplotype allele was estimated applying the SAS procedure GLIMMIX under the following threshold model:p-values of significance as well as the occurrence of homozygous and heterozygous individuals, the number of homozygous individuals (N ≥ 5), and a clear differentiation between LSM estimates of homozygous and heterozygous animals.Haplotype alleles that would warrant further analysis were chosen on the basis of the For position 90,100,118 , three haplotypes were identified with one being unfavorable in the homozygous status and two being favorable. For each of the three haplotypes, sires being heterozygous were identified with one sire exhibiting haplotype 669/701 (both haplotypes favorable), another sire showing 701/164 haplotypes (favorable/unfavorable), and a further sire with 669/164 haplotypes (favorable/unfavorable). For all three sires, two homozygous daughters for each haplotype were selected for sequencing of the target gene. For position 44,597,888 , two haplotypes were identified with one being favorable and one being unfavorable in the homozygous status. For five heterozygous sires, a total of 11 homozygous daughters were sequenced for the target gene.CMPK2) and rs109521151 (ASB16) was carried out using fluorescence resonance energy transfer (FRET) for 2,485 (CMPK2) and 2,471 (ASB16) animals from batch 1 with phenotype information regarding the established binary BDD traits.High-throughput examination of rs208894039 (Since in the genome database Ensembl (Genome assembly ARS-UCD1.2) , A and GPhenotype data were collected from cows in a total of 13 large dairy herds. Initially, seven herds were included in the study and phenotyped in 2015 and 2016, and these data are denoted as batch 1. In 2018, cows in further six herds were phenotyped forming batch 2 of the data. Herds included in batch 1 are numbered from one to seven, while second batch herds are numbered from 8 to 13. Differences between herds appear to be of high magnitude when considering infectious cases only as indicated by means for TBINA. Herds 3, 4, 10, and 12 exhibit rather low numbers of M2 lesions and in herds 8 and 9 infectious cases are even scarcer. In contrast, herds 2 and 13 show rather high values for infectious cases. Phenotypes, TBIN and TCHRONA, however, show that in all herds BDD is abundant. It should be noted that phenotypes TBIN and TCHRONA are closely related, since many M4 cases exhibit chronic signs, that is, proliferation and/or hyperkeratotic lesions.p-values were found for any chromosomal region and phenotype TBINA (data not shown). Preliminary analyses (not shown) for this phenotype applying simpler approaches for GWAS neglecting co-variances between animals arising from genetic relationships, yielded p-values that differed among each other but provided no indications for any chromosomal region with a suspected effect on the trait. It has to be concluded that given the data shown in Results for GWAS are presented in p-value of 4.23 is obtained for HAPMAP60356-rs29024194 at position 90,100,118. Other SNPs around this position show -log10 p-values between 2.0 and 3.0. For BTA19, a total of 11 SNPs show chromosome-wise significant FDR values; among them, two SNPs show a Bonferroni-corrected genome-wide significance for trait TCHRONA   , which lpathways , especiapathways , this geASB16    is locat0019658) . In addi0019658) . ConsideCMPK2. This distinct position with rs208894039 is not included on the Illumina Bovine BeadChip used in this study. Sequencing of ASB16 was performed analogously and uncovered a clear variation between samples at genomic position 43,995,946 (BTA19) within exon 5. This position has dbSNP number rs109521151. The alternative G allele of this SNP causes a missense variation leading to incorporation of arginine instead of histidine at amino acid position 447 of ASB16 protein.The two chromosomal regions identified in the GWAS and the respective candidate genes were subsequently used as a basis for a haplotype analysis and subsequent identification of animals for sequencing of the candidate genes. This analysis revealed a remarkable difference between samples at genomic position 90,083,819 (BTA11), which lies 307 bp downstream of CMPK2 at position 90,083,819 that was identified after sequencing of this genetic region. Apart from the genotype, as fixed effects in the model herd, parity, and classes for the lactation stage were included. For TBIN, substantial differences exist between LSMeans (LSM) of the three genotypes. WT/WT animals show lowest values for the status = 1 of being diseased while MT/MT animals show highest values. Heterozygous animals appear to react similar to MT/MT animals, thus pointing to an incomplete dominance of the G allele for the disease status. Differences comparing MT/MT with WT/WT and WT/WT with Het are highly significant (p < 0.0001). Comparisons of MT/MT against Het were not significant. For TBINA, including only M2 lesions for status = 1 (diseased), none of the comparisons for the three genotypes were significant. In the bottom part of q = 0.24). More than half of the animals possess the advantageous genotype WT/WT. However, only one copy of the MT allele drastically changes susceptibility to BDD suggesting a dominant effect of the alternative allele over the reference allele.Genotypes for rs208894039 and rs109521151 were the results from a FRET analysis of batch 1, with a total of N = 2,485 of evaluable genotypes among the 2,520 total number of samples. ASB16, N = 2,471 animals had evaluable genotypes. For phenotype TBIN, drastic differences can be observed between MT/MT and WT/WT genotypes, with WT/WT genotypes being advantageous. MT/MT animals are less frequent, with an allele frequency of p (MT) = 0.128. Heterozygous animals exhibit a diseases status of almost intermediate incidence when compared to MT/MT and WT/WT animals. Contrast of MT/MT vs. WT/WT animals was significant with p = 0.0011. Comparing WT/WT animals to heterozygous animals yielded significant differences with p < 0.0001. Contrasts between MT/MT and heterozygous animals were not significant. The results for phenotype TBINA follow the same pattern as shown for TBIN; however, all comparisons between genotypes were not significant. For TCHRONA, that is, when contrasting chronic proliferative cases vs. all other animals, elevated values for the status of phenotype equal to 1 can be found for MT/MT animals, while WT/WT animals show substantially lower values and heterozygous animals have intermediate values. Contrasts between MT/MT and WT/WT animals as well as WT/WT and heterozygous animals were highly significant (p < 0.001), while contrasts between MT/MT and heterozygous animals were significant at a level of p = 0.03. Again, for phenotype TCHRONA, a similar pattern was observed in comparison to phenotype TBIN. In summary of the results in CMPK2 variant and the ASB16 variant is WT (see ASB16) is shown to have a dominant effect and rs208894039 (CMPK2) a modulatory effect.In s WT see , 3. Thiss WT see  and thuss WT see  and TCHRs WT see , were orGenome-wide association studies using SNP data and appropriate phenotype information have been successfully applied to identify candidate genes and further to detect causal variants for complex traits in cattle . For ourTreponema spp. making herds susceptible to endemic BDD , a potent bacterial inflammatory agent, and also by IFN (interferon)  inflammasome complex, which leads to the production of the pro-inflammatory cytokine IL-1β (interleukin 1β) in activated macrophages  . IFN is erferon) . CMPK2 e in mice  and huma in mice . BDD in  in mice . In our  in mice  in dairye  overall better coping with disease. Database search  box family . Membersproteins . So far,proteins  can be iproteins . The conproteins . The bovproteins  localizeproteins . For exaproteins . In addiproteins , TFBIND proteins , and Conproteins ) revealeedictSNP  and EnseedictSNP ) for theedictSNP . Yet, alASB16 wild-type and CMPK2 wild-type causes a favorable immune response, which is reflected in the lower susceptibility to the disease (ASB16 is present in the mutant variant, it is always associated with a trend toward high disease susceptibility for all traits, regardless of which genotype is present with respect to CMPK2 (ASB16 therefore appears to have a greater impact on susceptibility to BDD than the variant status of CMPK2. As a part of an ubiquitin ligase complex, the ASB16 protein is involved in the degradation of a large number of target proteins and thus may be involved in the regulation of a broad spectrum of cellular pathways (Even though the exact role of the two genes in the regulation of inflammatory processes is still largely unclear, it may be hypothesized that the advantageous genotype combination of  disease . Since tto CMPK2 . The genpathways . Studiespathways . In perspathways , or whetLASP1 on BTA19 and DAB2IP on BTA11 (To our knowledge, our study appears to be the largest GWAS on the genomic background of BDD susceptibility according to the number of animals with phenotypes and genotypes and at the same time considering repeated observations. A trained team focusing solely on BDD with the team leader being present at all farm visits collected all observations. The results from GWAS for BDD in dairy cattle are limited in total and hampered by either small numbers, and/or not accounting for the etiology by using repeated observations, or imprecise phenotypes. However, some analogies on the identification of the chromosomal regions as shown in this study do exist. Thus, among many other regions, chromosomal regions on BTA11 at 90 Mbp and on BTA19 at 30 Mbp and further positions have already been identified to influence BDD . A studyon BTA11 , two genCMPK2 and ASB16 could contribute to the use of more animal-specific immunomodulatory therapies in the future. This would improve animal welfare and allow farmers to be proactive and prevent reservoirs of infection in their farms instead of only reacting to advanced lesions. Nonetheless, since the etiology of BDD is considered multifactorial, genetic screening will just be one part of an integrated prevention programs. The abolishment of unsanitary conditions along with an improved claw and health management on farms are indispensable.In conclusion, this study represents one step further to determine genetic predispositions influencing the pathogenesis of BDD. As recently has been shown, genetic selection can aid substantially in the eradication of infectious diseases in livestock . Our res