Plant breeding programs evaluate varieties in series of field trials across years and locations, referred to as multi-environment trials (METs). These are an essential part of variety evaluation with the key aim of the statistical analysis of these datasets to accurately estimate the variety by environment (VE) effects. It has previously been thought that the number of varieties in common between environments, referred to as “variety connectivity,” was a key driver of the reliability of genetic variance parameter estimation and that this in turn affected the reliability of predictions of VE effects. In this paper we have provided the link between the objectives of this work and those in model-based experimental design. We propose the use of the  The objective of plant breeding is to breed superior plant varieties for various traits of economic importance. Selection of superior varieties is a result of the data analysis from a series of plant variety trials at a number of locations and possibly over several years, which are known as Multi-Environment Trials (METs). The breeding process is a progressive system involving the selection of superior varieties for further rounds of testing, removal of poor performing varieties and the inclusion of new varieties. This leads to datasets with varying levels of balance with respect to the number of varieties in common between environments, a measure known as “varietal connectivity.” Unbalanced datasets are common when the MET spans multiple breeding stages and seasons.Given that most MET datasets are unbalanced and exhibit variance heterogeneity  estimates of genetic variance parameters by proposing yj denote the nj−vector of data for the jth environment, j = 1, …, p. We then let y denote the n−vector of data combined across all environments in the MET, so write y can be written as:Let τ is a vector of fixed effects with associated design matrix X; ug is the vector of random genetic effects with associated design matrix Zg; up is a vector of random non-genetic  effects with associated design matrix Zp and where Gg, Gp and R are variance matrices for ugup and e, respectively. Gg is known as the between environments variance/covariance matrix and is described in later sections. Gp is assumed to be block diagonal given by b is the number of components in up and qi is the number of effects in (length of) upi. R is assumed to be block diagonal, so that Rj = var(ej) is the variance matrix for the residuals for the jth environment. In the LMM of Smith et al.  effects  = Ga ⊗ A where A is the numerator relationship matrix and Ga is a p × p symmetric positive (semi)-definite matrix that will be referred to as the between environment additive genetic variance matrix. In terms of the non-additive effects, it is assumed that var(ue) = Ge ⊗ Im where Ge is a p × p symmetric positive (semi)-definite matrix that will be referred to as the between environment non-additive genetic variance matrix. The variance matrix of the total VE effects  is therefore given by:It is assumed that var variance parameters and let nκ be the associated number of parameters. We use residual maximum likelihood (REML) estimation which requires calculation of the REML scores for the elements of κ. These are given by:The first step in fitting the model in Equation (1) is the estimation of the variance parameters associated with the random effects and residuals. We let P = H−1 − H−1X(X⊤H−1X)−X⊤H−1 with (X⊤H−1X)− being any generalised inverse of (X⊤H−1X). The “dot” notation indicates a derivative so that κ is obtained by equating the scores to zero and will be denoted by where r et al.  which isGg and P are formed using the REML estimate κ. We can then compute a model based reliability  of the fixed effects and empirical best linear unbiased predictions (EBLUPs) of the random effects in Equation (1). In particular, the EBLUPs of the VE effects are given by kk” indicates the kth diagonal element of the associated matrix.where the subscript “gκ are the genetic variance parameters associated with Ga and Ge and ḡκ are the remaining variance parameters, that is, associated with the peripheral random effects and residuals. We partition the full expected information matrix accordingly and write as:An asymptotic variance matrix for the REML estimates of the variance parameters can be obtained as the inverse of the information matrix. This could either be the average information matrix or, more traditionally, the expected information matrix. For the purposes of developing a diagnostic, we use the latter as it does not depend on the data, the elements of which are given in Equation (6). In this paper the interest lies in the estimation of genetic variance parameters, so we partition the variance parameters as The asymptotic variance matrix for Ga and Ge. Other possibilities include compound symmetric and unstructured forms. Irrespective of the form used, the parameters of interest are the variances and covariances in Ga and Ge. The aim in this paper is to develop a diagnostic that reflects the information available to estimate these parameters but which does not require the fitting of the full LMM. In order to achieve this we apply some of the concepts from model-based design in which the aim is to search a design space for a configuration which is optimal in some sense under a pre-specified LMM. The latter includes specification of the terms in the model and also values for the variance parameters. Although the aim here is not to search a design space but rather to assess a particular design (dataset) we can proceed in a similar manner by considering a pre-specified LMM. In order to simplify computations but enable wide applicability we use a LMM that has a relatively simple structure for the non-genetic effects. In terms of the model in Equation (1) we assume that the fixed effects comprise a mean parameter for each environment . The genetic variance matrices, Ga and Ge, are assumed to have unstructured forms with p(p + 1)/2 unique variance parameters in each that are denoted by σajs and σejs, respectively.where the design matrices are given by Finally, we assume that the residual variance matrices are given by gκ comprises σajs and σejs  and ḡκ comprises j = 1, …, p). We then use pre-specified values of these parameters to compute the information matrix in Equation (8) and thence the variance matrix in Equation (9). The chosen variance parameters will be denoted g0κ and 0ḡκ and the resultant variance matrix denoted by and the unknown variance parameters are ngκ is the number of genetic variance parameters which is used as a divisor to provide a scaling for comparisons across models and/or datasets.where the vertical bar represents the determinant and j  as:Although the overall j and ngjκ is the associated number of genetic variance parameters. In the case of models in which information on genetic relatedness is not used we have ngjκ = p, and the parameters are the genetic variance for the environment and all p − 1 genetic covariances with other environments. In models in which the genetic effects are partitioned into additive and non-additive effects we have ngjκ = 2p. To distinguish between these different genetic models we label the diagnostic values as p environments can then be scrutinised in various ways in order to check for “problem” environments with large values, which represent those environments with large variance.where Finally, a further computational simplification can be made in the calculation of This is a reasonable simplification given that the non-genetic variance parameters in the pre-specified LMM are simply the residual variances so that the uncertainty associated with their estimation is likely to be small.dj be the number of varieties in environment j  and define n. The dimensionality of the problem can be reduced by considering a two-stage approximation to the LMM as described in Gogel et al.  to d .We first let l et al. . Given tη denote the full mp × 1 vector of variety mean parameters for individual environments and let ηd be the d × 1 sub-vector corresponding to the VE combinations present in the data. Thus, we can write ηd = Dη where D is a d × mp indicator matrix that selects the appropriate elements. We let j is simply Ωj is the dj × dj diagonal matrix given by rji is the number of plots of variety i in environment j.In the first stage of the two-stage approach, a separate analysis is conducted for each environment in order to obtain predicted variety means and a measure of their uncertainty. In these analyzes the variety effects are regarded as fixed effects. The predicted means are combined across environments to form the data for the second stage analysis. We adopt the notation of Gogel et al.  so let ηp environments can then be written as:The LMM for the second stage combined analysis of the X2 = D(Ip ⊗ 1m). In terms of the variance structures, var(ua) = Ga ⊗ A and var(ue) = Ge ⊗ Im  and var(ξ) = Ω where this is known from the first stage. The variance matrix for the data vector in the second stage is then given by:where Elements of the expected information matrix for the variance parameters in the second stage LMM are then given by:d rather than n. As in the previous section 2.2 we do not actually conduct the two-stage analysis but compute the expected information matrix using Equation (16) for a given choice of variance parameter values. We then form the  variance matrix for the genetic variance parameter estimates using Equation (13) and denote the resultant matrix by where R script file to calculate A β-version of the m = 3, 708 varieties from n = 9, 786 plots corresponding to 40 trials from breeding stages Stage 1 (S1) to Stage 3 (S3) across p = 13 environments sown between 2014 and 2018. The number of varieties per environment ranged from 96 to 1,649 with a median of 105. We note that there are d = 6, 168 variety by environment combinations present in the data, representing a nearly 40% reduction when using a 2-stage approach for computing the diagnostic. The pedigree information comprised 3,959 records and included all the varieties in the MET dataset. The inbreeding coefficients of the latter ranged from 0.750 to 0.998 with a mean of 0.911. The number of varieties in common between pairs of environments . The values of the variance parameters for calculation of the diagnostic were set to:Given that the analysis of the durum data for the purposes of variety selection would proceed using a LMM with the partitioning of the VE effects into additive and non-additive effects, we computed the These values were chosen as being both representative of actual estimates from historical analyzes that are often encountered in practise and of a magnitude that could allow the diagnostic to provide good discrimination between environments. In particular, we have set the additive genetic variance to 80% of the total genetic variance (see Equation 3) for each environment and therefore 20% for the non-additive genetic variance), and a between environments correlation of 0.8 for both additive and non-additive VE effects.The ngκ = 91 representing a four-fold reduction in the number of elements in Given the high percentage of additive genetic variance we also computed the simpler diagnostic, based on a LMM with additive VE effects alone. For this diagnostic To investigate the robustness of the diagnostic we also inspect the In particular, we have set the additive genetic variance to 40% of the total genetic variance (see Equation 3) for each environment and therefore 60% for the non-additive genetic variance, and a between environments correlation of 0.4 for both additive and non-additive VE effects. The resultant th Dj(A) . Additioth Dj(A) .As a comparison with the historical measure of variety connectivity the p = 2 environments and label these as Env1 and Env2. Each environment has the same number of varieties (so that d1 = d2), and we vary the number of varieties in common (which is given by c = d − m). We assume the trials in Env1 and Env2 comprise 3 replicates and consider 4 sizes (Tsize) of trial corresponding to different numbers of varieties, namely d1(= d2)= 12, 24, 48 and 96 so that n1(= n2)= 36, 72, 144 and 288.Within the framework of the LMM with independent VE effects it was previously thought that variety connectivity was a key driver of the reliability of variance parameter estimation and that this in turn affected the reliability of predictions of VE effects. We therefore consider a simulation study in which a range of connectivity levels is examined and assess the performance of both variety connectivity and the c = 2, 4…12 (increments of 2). The maximum total number of varieties across Env1 and Env2 is m = 22, corresponding to c = 2. We label these varieties as V1 - V22. We assume that the 12 varieties in Env1 are always V1 - V12. The 12 varieties in Env2 are then V1 - V12 for c = 12; V2 - V13 for c = 11 and so on to V11 - V22 for c = 2. Our focus is on Env1 because this contains the same varieties across all connectivity levels so allows a fair comparison across these levels. The underlying LMM is as in Equation (10) but with independent VE effects alone  so that ngκ = 3. Given the data structure for each value of c and some pre-specified variance parameters, we can compute the diagnostic for Env1 which will be denoted The simulation study for the first trial size (Tsize=12) is described in the following. We consider the connectivity levels tth simulation (t = 1…N) are as follows:In the simulation study, the steps for the ue and residuals e as per the LMM in Equation (10) and for the pre-specified variance parameters g0κ and 0κḡ. In terms of the fixed effects, without loss of generality we choose τ1 = τ2 = 0. Note that we generate 2m genetic effects, where m = 22 which corresponds to the maximum total number of varieties across all connectivity levels. We denote the resultant vector for simulation t by uet. The residuals for simulation t are denoted by et which is a vector of length n1 + n2 = 72.Generate the random genetic effects c, we subset the appropriate 24 elements of uet. We will label the associated vector as uetc. We then form the data vector and fit the LMM as in Equation (10), without the inclusion of pedigree information. We save the REML estimates of the genetic variance parameters, denoting these as For the connectivity level cRepeat step 2. for each value of N = 2, 000 simulations was conducted for each trial size by variety connectivity combination. The simulation based diagnostics and reliabilities were only computed for the LMMs in Step 2. that achieved convergence (with one update if required) and resulted in a positive definite form for the REML estimate of Ge. All models in this paper were fitted using the ASReml-R package , namely:The simulations were conducted in order to obtain two main quantities of interest for each value of c, the reliability for variety k(= 1…12) in Env1 was computed as the square of the sample correlation between the true (generated) effects (element of uetc for the variety and Env1) and the EBLUPs  but with the variance parameters fixed at the value κ0. We then computed the model based reliability for variety k(= 1…12) in Env1 using Equation (7) but because this has been computed with respect to known variance parameters (not REML estimates) we will call it the design based reliability  and those that were present in a single environment only. The MET analysis, compared with separate analyzes of individual environments, has the potential to improve the reliability of predictions for connected varieties through the use of additional data. This is not the case for varieties present in a single environment only. Hence our focus is the connected varieties.c = 2 was particularly low and additionally the results were found to be unreliable. Therefore, in what follows, the results for this connectivity level have been excluded.First we note that the number of simulations in which the model fitting was successful (as defined in section 3.2.1) was strongly related to the number of varieties in common between the two environments see . The numThe relationship between the diagnostic variable .Results displayed in ngκ = 3. The set-up for the study is the same as in section 3.2.1 but we only consider the two larger trial sizes, namely d1(= d2)= 48 and 96 so that n1(= n2)= 144 and 288. Across the range of connectivity levels the total number of varieties required for the simulation was 191  and we label these as V1-V191. The simulation study requires a numerator relationship matrix (A) for these varieties. We therefore chose V1-V191 from the actual lines in Stage 3 (S3) and Stage 4 (S4) in 2018 and 2017 in the durum data and computed A from the associated pedigree information. For the chosen subset of varieties, the inbreeding coefficient ranged from 0.938 to 0.969 with a mean of 0.957.We then extended the simulation study in order to assess the performance of the diagnostic in terms of correlated VE effects. To simplify the simulations, and without loss of generality, we considered the LMM as in Equation (1) but without the non-additive VE effects, so that as in section 3.2.1, For the purposes of both the calculation of c = 2 was reasonable so these results have been included in what follows.As in the independent VE effects study, the number of simulations in which the model fitting was successful was related to the number of varieties in common between the two environments see . HoweverThe results are presented in the same format as in section 3.2.1. The good agreement shown in The mean loss in reliability of the EBLUPs of the additive VE effects for Env1 for those varieties that were present in both environments is well predicted by the diagnostic al sizes  and acroal sizes .We again investigate the robustness to a change in variance parameters. The same low bound values used in the durum example are used and applied to the same set of simulation studies. These results are provided in Despite the fact that Multi-environment trials (METs) are a key aspect of plant breeding, there is little in the literature that addresses the “design” of METs in the sense of determining which trials should be combined. Within the paradigm of the factor analytic linear mixed model (FALMM) approach for MET analysis, the aim of both the design and analysis is the reliable prediction of the variety effects for individual environments (VE effects). These are the baseline predictions which can then be summarised across environments in meaningful ways, for example, using the interaction class approach of Smith et al. .Smith et al.  discuss In this paper we have developed a diagnostic to be applied to a MET dataset prior to analysis in order to assess the likely reliability of genetic variance parameter estimates, both for the dataset across all environments and for individual environments. As in Smith et al.  we use aHistorically, variety connectivity between environments was the measure calculated prior to the conduct of a MET analysis to investigate the likely reliability of genetic variance parameter estimation. This measure is simple to compute and intuitively reasonable but there has been nothing in the literature to validate its use. In our simulation studies it was shown that variety connectivity was only able to predict the reliability of genetic variance parameter estimation across connectivity levels for a given trial size , whereas the new The pre-specified LMM for the The simulation study results suggested that trials with small numbers of varieties will, in general, have larger gururaj.kadkol@dpi.nsw.gov.au.The data analysed in this study is subject to the following licenses/restrictions the datasets presented in this article are not readily available because they are owned by New South Wales Department of Primary Industries and the Grains Research and Development Corporation. Requests to access these datasets should be directed to Gururaj Kadkol, CL, BC, and AS conceived the ideas. CL developed and applied the methodology, prepared the data, conducted the simulation study, and prepared the first draft of the manuscript. All authors contributed to the general discussions, manuscript revision, read, and approved the submitted version.CL and AS were supported by the Grains Research and Development Corporation (GRDC) through the Statistics for the Australian Grains Industry (SAGI) project (UW00009).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The root economics space is a useful framework for plant ecology but is rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory, utilizing genetic variation, high‐throughput phenotyping, and multivariate analyses.2 flux and the open‐source software rhizovisionexplorer to analyze scanned images.We phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high‐throughput method for COWe uncovered substantial variation in specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and structural costs. Multiple linear regression analysis indicated that lateral root tips had the greatest SRR, and the residuals from this model were used as a new trait. Specific root respiration was negatively correlated with plant mass. Network analysis, using a Gaussian graphical model, identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with SRR, SRL, and root branching frequency, and proposed gene candidates.Combining functional phenomics and root economics is a promising approach to improving our understanding of crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems. et al., 232: 5–7Li .See also the Commentary on this article by  A total of 276 hard winter wheat (The 276 wheat lines were grown as two replicates in a single growth chamber (552 plants), and the entire procedure was repeated twice, for a total of four replicates and 1104 plants. Each replicate was treated as a block for an overall experiment with a randomized complete block design. The seedlings were transplanted into the hydroponic grow boxes on 19 June and 4 October 2019. The details of the germination, growth, and sampling procedures are given in the paragraphs that follow.c. 400 µmol m−2 s−1. Each box was filled to the bottom of the lid with a nutrient solution containing 125 µM KH2PO4, 1125 µM KNO3, 500 µM CaCl2, 250 µM MgSO4, 11.5 µM H3BO3, 1.75 µM ZnSO4·7H2O, 2.25 µM MnCl2·4H2O, 0.08 µM CuSO4·5H2O, 0.03 µM (NH4)6Mo7O24·4H2O, and 19.25 µM Fe(III)‐EDTA (C10H12N2NaFeO8). The nutrient solution was continuously aerated with an air pump attached to airstones submerged in each growth box, and the solution pH was maintained between 5.9 and 6.1 by the addition of KOH or HCl throughout the experiment.Seeds were surface‐sterilized in 0.5% sodium hypochlorite (NaOCl) for 10 min, rinsed three times using deionized (DI) water, and pre‐germinated in Petri dishes with filter paper which were placed in darkness at 25°C for 3 d. Uniformly germinated seedlings were selected Fig. , wrapped2/H2O Analyzer   was connected to each laptop to acquire and save the datafile with the appropriate sample name, encoded by the barcode affixed to the cut tube described in the section on growth conditions. Three infrared gas analyzers were used to allow simultaneous respiration measurements in parallel to increase throughput, which reached 25 samples per person h−1, with a team of eight.Ten days after transplanting Fig. , plants SA) Fig. . The cus2, an R  was calculated using Eqn F is the CO2 flux in nmol s−1, P is the pressure in the chamber in kPa, V is the corrected chamber volume in ml, R is the ideal gas law constant in l kPa K−1 mol−1, T is air temperature in K, and dC/dt is the change in CO2 concentration on a molar basis with time (µmol mol−1 s−1). Chamber volume (V) was determined by subtracting the total root volume, estimated using rhizovision explorer, from the chamber volume.In order to calculate the total respiration rate of a root sample from the individual text files containing the time series molar fraction of COC/dt, the slope was used from a linear regression fit to the water‐corrected CO2 concentration provided by the LI‐850 analyzer over the corresponding observation time (20–90 s) using the lm function in R  was set at 20 s. To estimate d−1. Images were analyzed using rhizovision explorer v.2.0.2  at a resolution of 600 dpi. Given the small size of the root systems, which were already clean because they were grown hydroponically, scanning throughput was 15 samples per person h et al., , with thots Fig. .rhizovision explorer in this study are as follows: number of root tips (Tip), number of branching points (BP), branching frequency (BF), total root length (TRL), axial root length (ARL), lateral root length (LRL), average diameter (AvgD), total root volume (TRV), axial root volume (ARV), lateral root volume (LRV), total root surface area (TSA), axial root surface area (ASA), and lateral root surface area (LSA). Branching frequency is determined by the software by dividing the number of branching points by total root length. When root scanning was completed, roots and shoots were dried at 60°C for 3 d before dry weight determination, with a throughput of 70 samples per person h−1. The oven‐dried root mass and root length quantified using rhizovision explorer were used to calculate the specific root respiration (SRR) per unit of root dry mass  and the specific root respiration per unit of root length , respectively.The root traits extracted using Root mass fraction (RMF) was found by calculating the root dry weight as a proportion of the total plant dry weight. Specific root length (SRL) was calculated by dividing root length by the corresponding root dry weight. Lateral : axial root length ratio was calculated by dividing lateral root length by the corresponding axial root length, based on the diameter threshold provided during image analysis, and lateral : axial root volume ratio was calculated by dividing lateral root volume by the corresponding axial root volume. Branching density (BD) was calculated by dividing root tips by axial root length. Root tissue density (RTD) was calculated by dividing root dry weight by root volume, which brought the total number of traits reported in this study to 25.H2) of each trait was calculated based on the equation described by Falconer & Mackay , respectively. The variances were obtained by fitting to a mixed model including genotype as a random effect and block as a fixed effect using the lme4 package  and visualization of outputs were performed on the trait means of the 25 traits using the base function ‘prcomp’ and the R package r) of the traits were estimated to construct a Gaussian graphical model (GGM) for network analysis. Network analysis with a Gaussian graphical model is a more holistic way to capture causality and precursor/product relationships in complex trait networks relative to standard correlation analyses. A GGM provides conditional dependence between two variables after removing the effects of all other variables to avoid spurious correlations , and lateral root tip volume were used as the dependent variables, while the total root respiration was the independent variable. The number of lateral root tips was estimated by subtracting four from the number of root tips supplied by https://triticeaetoolbox.org/wheat/). Data constituting 21 555 SNPs were filtered to exclude markers with missing data greater than 50% and minor allele frequency < 5%, resulting in 16 058 makers that were used in the association analysis. The map positions for the SNP markers used in this study were based on the consensus map developed using a combination of eight mapping populations  in gemma. The 25 traits were grouped into six multi‐trait combinations based on their genetic correlations or their structural and functional relationships  association testing was run for each of the 25 traits using mean phenotypic values, and PC‐GWAS was conducted using each of the first 10 PCs. Multi‐trait (multivariate) GWAS was carried out to increase the power of the association tests and to detect polymorphisms with potentially pleiotropic effects of trait‐associated loci using the multivariate linear mixed effect modeling capabilities of gemma were used to generate Manhattan and quantile–quantile (QQ) plots using the R package qqman  ≥ 3.5 was used for detecting SNPs that are significantly associated with these complex quantitative traits, consistent with the process outlined by Maulana et al. , root dry weight (RDW), total dry weight (TDW), total root respiration (TRR), SRL, lateral : axial root length ratio (L : A_L), ASA, lateral : axial root volume ratio (L : A_V), PC2, PC3, PC4 and PC7 exhibited normal distribution. Near normal distributions were observed for other root traits  explains 14.5% of the variation in total root respiration. Axial root volume, lateral root volume, and lateral root tip volume were all significant explanatory variables . The average specific root respiration rate on a volume basis of lateral root tips was 30.5 and 8.1 times the rates of axial roots and lateral roots, respectively, as determined from comparing slopes in the model . A GWAS based on the first 10 PCs identified 79 SNPs that passed the –log10P of 3.5, and the majority of these detected SNPs were associated with PC1, PC2 or PC9  on chromosome 4D was encoding for four potentially underlying proteolysis genes  on chromosome 1A were associated with processes of DNA transcription regulation  on 1B co‐associated with single‐trait RTD, and another marker (RAC875_c63889_486) on 7A co‐associated with single‐trait SRL  and 7A, as well as 17 candidate genes potentially underlying the top three largest –log 7A Fig. . Five siSignificant marker associations and underlying genes were also detected for branching frequency, multi‐trait biomass, multi‐trait allocation, multi‐trait morphology, all PC‐traits except PC8, and the other single traits Figs .et al.,−1. The use of a bead bath for controlling temperature avoids the risk associated with using a water bath of water entering the respiration chamber, which can result in contamination of the gas analyzer. We observed 8.5‐fold variation for SRR_L and 3.2‐fold variation for SRR_M in the wheat panel. In previous work, root respiration was measured mostly using single root segments  scores.Fig. S2 Manhattan plots of genome‐wide association studies (GWAS) conducted on all traits.Fig. S3 Quantile–quantile (Q–Q) plots for all traits.Table S1 Multiple linear regression model describing relationships between volumes of different root segments and total root respiration.Table S2 Centrality measures for 17 traits from the Gaussian graphical model.Table S3 List of SNPs using a cutoff value set at –log10P = 3.5.Table S4 List of nearest genes underlying SNPs using a cutoff value set at –log10P = 3.5.New Phytologist Central Office.Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries  should be directed to the Click here for additional data file.


Mendelian randomization (MR) can estimate the causal effect for a risk factor on a complex disease using genetic variants as instrument variables (IVs). A variety of generalized MR methods have been proposed to integrate results arising from multiple IVs in order to increase power. One of the methods constructs the genetic score (GS) by a linear combination of the multiple IVs using the multiple regression model, which was applied in medical researches broadly. However, GS-based MR requires individual-level data, which greatly limit its application in clinical research. We propose an alternative method called Mendelian Randomization with Refined Instrumental Variable from Genetic Score (MR-RIVER) to construct a genetic IV by integrating multiple genetic variants based on summarized results, rather than individual data. Compared with inverse-variance weighted (IVW) and generalized summary-data-based Mendelian randomization (GSMR), MR-RIVER maintained the type I error, while possessing more statistical power than the competing methods. MR-RIVER also presented smaller biases and mean squared errors, compared to the IVW and GSMR. We further applied the proposed method to estimate the effects of blood metabolites on educational attainment, by integrating results from several publicly available resources. MR-RIVER provided robust results under different LD prune criteria and identified three metabolites associated with years of schooling and additional 15 metabolites with indirect mediation effects through butyrylcarnitine. MR-RIVER, which extends score-based MR to summarized results in lieu of individual data and incorporates multiple correlated IVs, provided a more accurate and powerful means for the discovery of novel risk factors. Observational studies have long been utilized to detect associations between the exposures of interest and the risk of complex diseases. However, the estimated effects are typically biased and causality cannot be directly inferred because of unobserved confounders or reverse causality . Double-Instrumental variable (IV) analysis provides unbiased causal estimates in the presence of observed and unobserved confounders under certain assumptions . A validACLY and cardiovascular disease which incorporated multiple germline genetic variants (IVs) to construct GS as single IV and further inferred the causal relationship between ACLY inhibitors and the reduced risk of cardiovascular disease (Mendelian randomization (MR), which uses genetic variants as IVs, has emerged recently as a powerful tool to estimate the causal effects of risk factors in observational settings  and has  disease .Thus, we propose an alternative method called Mendelian Randomization with Refined Instrumental Variable from Genetic Score (MR-RIVER)  to constX  and outcome Y  given a set of IVs, denoted by Z =  . The major components of our framework are depicted in bXZ_i, along with standard error se(bXZi), to quantify the association of each Zi with the risk factor X from the traditional single-locus association analysis model, and likewise for bYZ_i and se(bYZi) for each Zi with the outcome Y.We propose a method to infer the causal relationship between risk factor The unified weighted GS incorporating multiple IVs could be estimated by the linear combination of multiple IVs:Zi on X after controlling for the other IVs that derived from multivariable regression. However, in practice, the published-available summarized data were derived from single-variant analysis; it is unlikely to get genetic association estimates from a multivariable regression model in a large independent dataset due to issues of practicality and confidentiality of data sharing on such a large scale. Here, we propose an estimator by borrowing the idea of coefficient decomposition to estimate Where X, Z) follow a multivariate normal distribution, regressing X on each Zi will yield an estimate of bXZ_i. Without loss of generality, we assume that there is a linear relationship between X and Z. As E(X|Zi) = b0 + bXZiZi, bXZ_i represents the total effect of Zi on X. After adjusting the effect of all the other IVs, the relationship between X and Zi can be expressed as Zi on X under the control of other IVs. Therefore, bXZi can be decomposed into the direct effect and indirect effect via other correlated IVs:Specifically, under the assumption that  is the variance of i. varZ(iZ) and Z_jZ_iρ can be obtained from the public GWAS resources .where X and outcome Y with:We note that Eq. 3 is crucial as it enables us to compute GS defined in Eq. 1 with only summary data, in lieu of individual-level data. With GS as a single IV, we can estimate the association between the risk factor var(Zi) is approximately proportional to 1/var(bYZi); thus, Eq. 4 can be simplified as:As mentioned by The asymptotic standard error for a method :X and Y can be further tested by using the Wald test statistic The association between We stress that, though Eqs.5, 6 resemble the estimator proposed in Two sets of simulation studies were designed to investigate MR-RIVER.Z1, Z2, …, Z6, from a multivariate normal distribution MVN , where Σ is a correlation matrix with an equal correlation structure. We varied the correlation coefficient and set it to be 0, 0.1, 0.3, 0.5, 0.7, and 0.9, corresponding to various scenarios: from the independent case to the highly correlated case. We generated X using the following models:We generated six IVs, The sample size was fixed at 1,000. In addition, we simulated 5,000 additional individuals to provide an external correlation structure for IVs. For each simulation configuration, 2,000 datasets were produced.X on each Zj separately to obtain the summarized effect of Zj on X, and based on these results, we applied Eq. 2 to obtain the estimates of the refined coefficients. The estimated refined coefficients, along with the corresponding standard errors, were compared to those from traditional GWAS summarized results under different correlation structures and effect sizes of Z.We first regressed Xi and Yi denote the exposure and outcome variables of the ith subject, and Zij the jth IV . The data were generated from the following model:Let Σ is the correlation matrix of IVs with an equal correlation structure. We varied the correlation parameter from 0 to 0.9 by 0.1. Each IV explains 0.005 of the variance of X, and we considered J = 5, 10, 15, 20. Moreover, X explained by all IVs, which was set to be 0.025, 0.05, 0.075, and 0.1, while Y explained by X, which was set to be 0.05, 0.1, 0.15, and 0.2. Sample sizes for the IV-exposure association study (N1) and the IV-outcome association study (N2) were set to be 1,000 and 1,500, respectively. In addition, 5,000 (N3) individuals were generated to provide an external correlation structure for genetic variants.where For each parameter configuration, a total of 2,000 datasets were produced. Under all the scenarios examined, MR-RIVER was found to outperform GSMR and IVW by maintaining the Type I error, possessing more statistical power, as well as having smaller biases and mean squared errors.We investigated the accuracy of refined coefficients. With the obtained correlation structure of IVs from the internal analysis set, the estimated refined coefficients  based on the summarized results were in consistent with the corresponding estimates from multivariable regression , suggestAs the key of the approach lies in borrowing the correlation information from public resources, we further evaluated the method by obtaining the correlation structure from the simulated external samples. According to different levels of correlation among IVs, refined coefficients outperformed traditional coefficients obtained from single-locus analysis, especially when the correlations among IVs were relatively high . SimilarWith various strengths of correlations among IVs, MR-RIVER maintained the type I error at the 0.05 level, compared to the IVW (with type I error around 0.04) and GSMR (with the most conservative control of the type I error) . The resbxy from the three MR methods were approximately unbiased, while the biases of the MR-RIVER and IVW estimates were lower than that of the GSMR estimate  were unknown because their chemical identity was not yet determined at the time of analysis. Detailed information of metabolites can be found in Summary results of quantitative trait locus (QTL) analysis of SNPs on corresponding metabolites were obtained from 7,824 European adult individuals  Shin et. SpecifiP-values less than 5 × 10–8. As a result, 9,472 SNPs were selected as IVs, matched with 260 metabolites. Among these, 9,329 SNPs were available in the educational attainment GWAS.We applied the method to explore the causal effect of blood metabolites on educational attainment as depicted in xyb = −0.043, P = 1.08 × 10–7), 1,5-anhydroglucitol  , and homocitrulline . GSMR identified biliverdin , 1,5-AG , and an unknown metabolite, X-12092    . In addi × 10–7) , 6.We performed additional analyses to explore whether the remaining metabolites affected education years through the above-identified candidate metabolites. SNPs associated with the remaining metabolites were treated as IVs to infer potential causal associations between the identified metabolites and remaining metabolites . The resFurther, mediation analysis was used to evaluate potential metabolic regulatory pathways for education years by Sobel test . The 15 We proposed an improved MR approach, MR-RIVER, to combine summarized results of multiple IVs into a single GS and to estimate the unbiased causal effect of a risk factor on an outcome. The publicly accessible summary-level data were obtained from single-locus analyses without consideration of the correlation between IVs. MR-RIVER provides a novel way to refine the effect size of genetic variants account for the correlation based on summary data and makes it efficient to perform summarized data genetic score MR when the correlation between IVs are unignorable. MR-RIVER closely maintains the type I error around the nominal level while it has higher power, lower bias, and smaller variation compared to GSMR and IVW.Genome-wide association studies uses original GWAS summarized results for IV exposure and IV outcome obtained from single-locus analyses and then derives the causal effect by the generalized least-square approach weighted by the variance–covariance matrix to adjust for correlations among IVs . MR-RIVEInterestingly, MR-RIVER and IVW showed similar performance in bias and MSE. If the weights used to aggregate multiple IVs are equal to the original GWAS summary results  analysis of SNPs on metabolites are available at http://metabolomics.helmholtz-muenchen.de/gwas.Publicly available datasets were analyzed in this study. This data can be found here: GWAS summary results for education attainment are available at LL, RZ, YW, and FC contributed the study design. LL, YW, and RZ performed the statistical analysis and interpretation and drafted the manuscript. YW, LL, RZ, YL, XD, SS, HH, YZ, LW, XC, and DC revised the manuscript. RZ, YW, and FC provided the financial support and study supervision. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


ASTN2 locus has been previously implicated in psychiatric and cardiometabolic traits, therefore this study aimed to systematically investigate the genetic architecture of ASTN2 in relation to a wide range of relevant traits. Methods: Baseline questionnaire, assessment and genetic data of 402111 unrelated white British ancestry individuals from the UK Biobank was analysed. Genetic association analyses were conducted using PLINK 1.07, assuming an additive genetic model and adjusting for age, sex, genotyping chip, and population structure. Conditional analyses and linkage disequilibrium assessment were used to determine whether cardiometabolic and psychiatric signals were independent. Results: Associations between genetic variants in the ASTN2 locus and blood pressure, total and central obesity, neuroticism, anhedonia and mood instability were identified. All analyses support the independence of the cardiometabolic traits from the psychiatric traits. In silico analyses provide support for the central obesity signal acting through ASTN2, however most of the other signals are likely acting through other genes in the locus. Conclusions: Our systematic analysis demonstrates that ASTN2 has pleiotropic effects on cardiometabolic and psychiatric traits, rather than contributing to shared pathology.Background: The link between cardiometabolic and psychiatric illness has long been attributed to human behaviour, however recent research highlights shared biological mechanisms. The  Individuals with serious mental illnesses  suffer from physical diseases at a higher frequency than unaffected individuals and have excess mortality to reflect this—the life expectancy is 25 years shorter on average than the general population, largely attributable to cardiovascular disease ,2. Non-gASTN2 locus has been identified as associated with both cardiometabolic and psychiatric traits. Psychiatric associations with ASTN2 single nucleotide polymorphisms (SNPs) include those with bipolar disorder (BPD) [ASTN2 gene encodes astrotactin, a membrane protein [ASTN2 locus might influence cardiometabolic traits is less clear.Through numerous genome-wide association studies (GWAS), the er (BPD) ,6, majorer (BPD) ,8,9, ander (BPD) . Cardiomer (BPD) ,12,13, mer (BPD) ,15,16 aner (BPD) . The AST protein  implicat protein ; thus, wASTN2 locus could contribute to the shared genetic predisposition and potentially shared pathology of cardiometabolic and psychiatric illness, using the UK Biobank dataset. We set out to systematically investigate in primary data whether genetic variation in the https://www.ukbiobank.ac.uk/media/gnkeyh2q/study-rationale.pdf, accessed on 28 July 2021) and for biomarker analysis. DNA extraction enabled genome-wide genotyping of all individuals which was conducted using 2 genotyping platforms, with a large proportion of overlapping SNPs [The UK Biobank study has been described in open-access publications ,21,22. BAnthropometric measurements of height, weight, waist and hip circumferences were used to calculate body mass index  and waist:hip ratio adjusted for BMI . SystolASTN2 locus  build 37, chromosome 9: 118,935,000–120,428,000 (14 October 2020) was selected. Analyses were restricted to unrelated individuals of white British ancestry and SNPs with minor allele frequencies (MAF) < 1% were excluded.Genetic variation in the p value 5.45 × 10−5 for statistical significance. Results were visualised using LocusZoom [Genetic analyses were conducted using PLINK 1.07 , assuminocusZoom  and HaplocusZoom . DescripIn order to assess (a) whether there were multiple signals for each trait and (b) the independence signals between each trait, we carried out conditional analyses. Here we reran all significant analyses including the lead SNP from each significant trait as a covariate. As a complimentary approach, we used Haploview  to visuahttps://www.ebi.ac.uk/gwas/, accessed on 25 April 2021) to search for traits that have previously been associated with the ASTN2 locus and compared these to our own results. We also investigated the effects of lead SNPs on gene expression levels using GTEx data  and the LDexpress tool . Lead SNPs and those with LD > 0.8 R2 and GTEx p < 1 × 10−5 were considered . The most significant SNPs for each gene and tissue were reported. In addition, we investigated the predicted functional effects of all SNPS meeting the significance threshold for any trait using Ensembl Variant Effect Predictor (VEP) .We used the GWAS catalogue (The characteristics of the cohort are presented in p = 2.02 × 10−5), DBP  p = 3.56 × 10−6), BMI  p = 1.92 × 10−8) and WHRadjBMI  p = 4.52 × 10−9). In conditional analyses, where the primary SNP from each analysis was included as a covariate, no secondary signals were observed for SBP , anhedonia  1.12 (1.06–1.18) p = 3.52 × 10−5) and mood instability  p = 7.42 × 10−7). Of note, rs13284474 is both the lead SNP for primary mood instability and tertiary BMI signals. Significant associations E–G were p = 1.28 × 10−5, p = 4.03 × 10−5, p = 1.46 × 10−5 and rs4836751-A, 1.03 (1.02–1.05), p = 2.52 × 10−5, respectively, Conditional analyses identified secondary (but not tertiary) signals for neuroticism  The SNPs significantly associated with blood pressure traits or BMI demonstrated consistent effect directions on the other cardiovascular/metabolic traits , so the SNPs significantly associated with anhedonia, neuroticism or mood instability demonstrated consistent effect directions on the other psychological traits , so the Considering the LD between the lead SNPs for all trait signals , it appeConditional analyses were also used to assess whether the cardiometabolic signals were independent or overlapped with those for mental health traits. Conditional analyses demonstrated that the lead SNP for SBP remained significant after adjustment for any other primary SNPs C–G, suggAdjusting neuroticism for the primary signals for SBP/DBP, BMI, WHRadjBMI or anhedonia had no impact on the associations D–G, whilASTN2 locus are presented in https://www.nhs.uk/conditions/metabolic-syndrome/, accessed on 28 July 2021) is noteworthy: because of the growing evidence of firstly, a bi-directional relationship between the brain and metabolic systems [Robustly reported associations with the  systems  and seco systems   [ASTN2 and plasma triglycerides which further suggested the possible influence on neuronal pathways and insulin sensitivity, contributing to obesity and T2D [With regards to cardiometabolic health, previous literature again demonstrates several associations between 85234-G)  is consi and T2D . The novLD Express and GTEX were used to identify SNPs with genotype-specific effects on gene expression levels in relevant tissues. Results of protein-encoding genes are presented in p value to <0.05 meant an additional 3 proxies for the WHRadjBMI lead SNP were significantly associated with ASTN2 levels, suggesting that this result is not due to different sample sizes and power for the different tissues. There is limited evidence for ASTN2 being the gene through which the lead SNPs (or their LD proxies) have their effects: a proxy (r2 = 0.92) for the WHRdajBMI lead SNP demonstrated reduced levels of ASTN2 expression with the WHRadjBMI-decreasing allele in subcutaneous adipose tissue A, and a The secondary signal for DBP influences TLR4 in whole blood, with the DBP-increasing allele (rs34432054) associated with reduced levels of TLR4. Likewise, the primary signal for BMI influences TLR4 in whole blood, with the BMI-decreasing allele (rs55654527) being associated with increased levels of TLR4. The primary signal for neuroticism (rs579017) also influences TLR4 in whole blood, with the neuroticism-increasing allele being associated with increased levels of TLR4. Assessment of all SNPs with significant effects on at least one trait for predicted effects on the genes or proteins demonstrated no moderate or severe consequences.ASTN2 locus might contribute to shared mechanisms underlying CMD and SMI. We identified associations between genetic variation in ASTN2 and cardiometabolic traits  as well as traits of relevance to SMI . We demonstrated that there is consistency in effects between CMD traits and between SMI traits, but that the relationship between CMD and SMI traits is complex and inconsistent within the locus. This candidate gene study sought to determine whether the ASTN2 is found on chromosome 9 and encodes a transmembrane protein expressed primarily in the brain  with neuroticism, anhedonia, and mood instability might suggest common mechanisms underlying these traits. Indeed, our results agree with numerous studies demonstrating the association between mood instability and neuroticism, which is reassuring, given that mood instability has been described as a “distinctive feature of neuroticism” [he brain , largely neurons . Thus itoticism” . Prior soticism” ,45,47,48ASTN2 being associated with the same allele as reduced central fat accumulation (WHRadjBMI) in adipose tissue is less easy to interpret. The ASTN2 locus has been associated with triglyceride levels [astn2 knockout models have been developed. Mice lacking a brinp1, a member of the same protein family as astn2, which is located nearby astn2 and has potentially overlapping functions has been investigated [ASTN2 being the gene through which the WHRadjBMI locus acts is reasonable but requires further investigation.The reduced levels of e levels  and there levels , but thee levels ,44,45 anstigated . These mstigated . TherefoASTN2 expression effects for neuroticism, mood instability, blood pressure and BMI might imply that this is not the functional gene. However, as maturation of the ASTN2 protein requires cleavage of a signal peptide in the endoplasmic reticulum [TRIM32 and TLR4  can prompt an anhedonia-like phenotype in a manner dependent upon gut microbiota and leptin signalling [ASTN2 locus appears to be divided, with one part having consistent direction of effects on CMD and SMI and the other part having opposite effects on CMD and SMI in white British ancestry individuals, further investigation of this locus is warrantedIt is much harder to draw conclusions on the connection between cardiometabolic and psychiatric health in this locus. Most of our data do not seem to support shared mechanistic contribution to both psychiatric and cardiovascular health. Except for BMI, none of the cardiometabolic lead SNPs are also significant in the psychiatric traits, and conditional analyses as well as the LD structure do not suggest coinheritance of psychiatric and cardiometabolic traits. However, the BMI association could have a behavioural explanation, whereby gnalling . There aThis work was based on the UK Biobank cohort, which is one of the largest studies to date with genetic data as well as a wide range of cardiometabolic, psychological and psychiatric phenotyping. Limitations of the cohort include its bias towards healthier and wealthier individuals . PsychiaASTN2 locus in mental and physical health. Expanding this study to include high psychiatric and/or cardiometabolic risk and/or prevalence populations would be of value, as would extending the study to additional ancestry groups with different LD patterns. In particular, haplotype analyses in a diverse ancestry individuals could elucidate the impact of the consistent/divergent parts of the locus. Additional work in experimental models to better define the role of ASTN2 and effects of reduced levels, particularly in neuronal and adipose tissue would be of interest.Future work is required to fully understand the role of the ASTN2 locus. However, we do provide evidence for SNPs in this locus acting through ASTN2 to influence central fat accumulation as well as risk of anhedonia. Whilst we cannot exclude the possibility of a behavioural explanation , the av


Some of these improved germplasms have resulted in varieties for farmer's field. We conclude the review by pointing out the importance of collaborative interdisciplinary translational research to alleviate the effects of climate change on wheat production in South Asia.Developing climate-resilient wheat is a priority for South Asia since the effect of climate change will be pronounced on the major crops that are staple to the region. South Asia must produce >400 million metric tons (MMT) of wheat by 2050 to meet the demand. However, the current average yield <3 t/ha is not sufficient to meet the requirement. In this review, we are addressing how pre-breeding methods in wheat can address the gap in grain yield as well as reduce the bottleneck of genetic diversity. Physiological pre-breeding which incorporates screening of diverse germplasm from gene banks for physiological and agronomic traits, the strategic crossing of complementary traits, high throughput phenotyping, molecular markers-based generation advancement, genomic prediction, and validation of high-value heat and drought tolerant lines to South Asia can help to alleviate the drastic effect of climate change on wheat production. There are several gene banks, if utilized well, can play a major role in breeding for climate-resilient wheat. CIMMYT’s wheat physiological pre-breeding has delivered several hundred lines  Even though India had a record wheat production of 106 MMT in 2019-20, the Asian countries must cumulatively produce > 400 million tons of wheat by 2050. When compared to the highest productivity obtained in some of the Asian countries -Saudi Arabia , China  and Uzbekistan -, South Asian countries still need to increase the productivity of wheat through modern breeding approaches and agronomic practices [In Asia, wheat is the second largest crop after rice and these two crops ensure food security of almost 57% of the world's population and livelihood of 80% of smallholder farmers of the world . The protons), and India’s production is nearly 14% of the total wheat in the world [In India, wheat is the second major crop and staple food after rice in terms of land allocation, production and daily dietary intake. In 1950-51, the total area under wheat was 9.75 million ha, with an average yield of 0.66 t/ha; total production was 6.46 million metric tons (MMT) and 34% of the area was irrigated [Interestingly, similar to Bangladesh, and other countries in Africa -8, the dThe potential for agricultural extensification in the form of land expansion for wheat is very limited in S Asia, so cultivating climate resilient high yielding wheat can be the best way to enhance production to meet the growing demand. Climate resilient wheat is becoming more important for India due to global warming; the winter in India is becoming shorter and warmer, which has already generated threats for sustainable wheat production . Therefo22) and the variability of climate with the potential to increase the occurrence of abiotic stresses such as heat, drought, waterlogging, and salinity [Current trends in population growth suggest that global food production is unlikely to satisfy future demand under predicted climate change scenarios unless rates of crop improvement are accelerated. In order to maintain food security in the face of these challenges, a holistic approach that includes stress-tolerant germplasm, sustainable crop and natural resource management, and sound policy interventions will be needed . In Eurosalinity . Accordisalinity  where tha large reduction in grain yield are in India and Pakistan [cts wheat production and net revenues negatively [Global wheat production is estimated to fall by on average 6% for each °C of further temperature increase and become more variable over space and time -20. SimuPakistan . CroppinPakistan . In PakiPakistan , 24. In gatively . Raisinggatively . It was gatively . Similargatively .Delivering climate ready wheat will require the integration of different disciplines; pre-breeding and genomics assisted breeding can contribute to the more efficient development of climate resilient crops , 30. Hig31).Pre-breeding is the introgression of desirable genes from exotic germplasm into genetic backgrounds readily usable by breeders with minimum linkage drag. Pre-breeding activities use promising landraces, wild relatives, and popular cultivars to transfer the desired alleles or traits into the elite background -36. Wher3.13.1.1were phenotyped for several agronomic traits in the hot desert of Sonora, Mexico to develop diversity and trait panels [DArTseq markers [. Among them, many were utilized for pre-breeding and breeding. A recent study revealed that in the bread wheat breeding program 20% of the lines were primary synthetic derived and well represented in international nurseries [Wheat holdings at CIMMYT gene bank comprise of 150,000 seed samples from more than 100 countries; the largest unified collection in the world for a single crop. In 2012 and 2013, about 70,000 accessions of wheat t panels . A recen markers . CIMMYT urseries .3.1.2the management of plant genetic resources. Here a total of 1762 crop species were conserved, it houses the second largest gene bank in the world with a total of 446,636 accessions collections which includes 5,034 Released Varieties and 4,316 Genetic Stocks apart from the exploration collections and introductions. In Vitro Gene bank holds 1902 collections and Cryogene bank with 11839 accessions.The NBPGR was established by the Indian Society of Genetics and Plant Breeding in a meeting in 1941 to conserve germplasm collection in India. Dr. B.P. Pal working at the Indian Agriculture Research Institute (IARI) approached the Imperial Council of Agricultural Research to set up a unit for assembly of global germplasm under phytosanitary conditions in India. The division of Botany at IARI, now known as Division of Genetics, started a separate scheme for ‘Plant Introduction’ under the leadership of Dr. Harbhajan Singh in 1970. Government of India upgraded it to an independent institute named as ‘National Bureau of Plant Introduction’ in August 1976 which was renamed as ‘National Bureau of Plant Genetic Resources’ (NBPGR) in January 1977. NBPGR is a nodal organization in India for NBPGR holds 34542 wheat accessions including 2304 collections of 55 wild species, 14702 exotic collections from different countries, 585 landraces, 338 genetic stocks and 428 released varieties from India. These had been assembled from different agro-ecological regions of India, and from 99 countries, mainly from South America and North America [Triticum aestivum, T. durum and T. dicoccum were screened sequentially at multiple disease hotspots, during the 2011–14 crop seasons, carrying only resistant accessions to the next step of evaluation a core set and to develop a trait-specific reference sets were assembled and detailed characterization reports were available for these sets [In India, a comprehensive germplasm evaluation and characterization study was conducted for wheat accessions conserved in the Indian National Gene bank to identify sources of resistance/tolerance to different biotic and abiotic stresses. Gene bank accessions comprising three species of wheat ese sets . These a3.1.3This was founded in 1983 and holds 155 species of crop plants. Several wheat accessions from this gene bank are also available at CIMMYT gene bank. Recent studies used the availability of Iranian wheat landraces at CIMMYT for genomic prediction and deployment of them into breeding , 45.3.1.4http://www.isaaa.org/kc/cropbiotechupdate/article/default.asp?ID=9982). As far as we know, other south Asian countries do not have a gene bank that holds several thousand wheat accessions.Bangladesh wanted to establish gene bank for several species and efforts started as early as 2012 (3.23.2.1focuses on dissecting complex traits -like heat or drought adaptation- into component traits -like deep rooting and storage of reserve carbohydrates- and making complementary crosses to increase the grain yield under abiotic stress conditions [2). Most of the complex traits cannot be dissected into simple molecular markers since the percentage of variation explained by any of the many molecular markers identified is not close to the heritability estimates. However, screening advanced generations using high throughput methods and field based phenotyping have proven to improve the selection for high yielding lines [The physiological pre-breeding approach nditions , 47. Thinditions , 49 and ng lines .Trait based pre-breeding for heat stress tolerance is based on the conceptual model for heat stress tolerance; where,Grain yield = LI × RUE × HI (1) a:b, carotenoids, antioxidants, CO2 fixation traits (Rubisco specificity and rubisco activase), spike photosynthesis, membrane thermostability, and respiration. Traits related to HI include pollen sterility, floret survival, grain filling, and stem carbohydrate storage and remobilization. LI is related to rapid growth cover and stay green. WUE is related to root traits and transpiration regulated by plant signaling [LI is light interception, RUE is radiation use efficiency, and HI is harvest index . In shorignaling .Yield under drought can be also be shown using conceptual models,Grain yield = WU × WUE × HI (2)dependent on water uptake by roots. Main traits of importance to improve drought stress tolerance includes leaf morphology, leaf rolling, wax, pubescence, spike/awn photosynthesis, harvest index, biomass, stem carbohydrates, early vigor and ground cover, length of coleoptile, seed size, leaf water content, canopy temperature, and root system [Where WU is water use, WUE is water use efficiency, and HI is harvest index. Here, water use is t system , 51, 52.t system .3.2.2e.g. Flowering time, plant height), whereas complex traits are governed by many QTL and the G × E are high for them compared to simple traits. Use of markers in pre-breeding involves understanding the genetic basis of traits, marker discovery and development of usable markers, integration of the trait into elite lines through marker assisted selection or back crossing, pyramiding traits through genomic prediction or trait-based stacking. Among above mentioned traits used in pre-breeding canopy temperature and stay green has been widely studied and genetic basis was explored [have resulted in their use in pre-breeding. Major genes of phenology traits are not routinely used in pre-breeding since they are easy to measure in many lines. However, complex traits which are difficult to measure are high priorities for the development of markers are use in selection of segregating generations. The most common marker that is used in selection is for thousand grain weight in CIMMYT, since it is a high heritability trait and KASP markers are available [Use of markers for breeding can be classified into two groups based on the genetic structure of traits (1) simple traits and (2) complex traits . Simple explored , 55. Recvailable -58. For vailable .3.2.3Wheat has a huge genome (16Gb) among crop plants with similarities between sub-genomes and abundant repetitive elements (85% of the genome). Recent advances in wheat genome sequencing may influence the development of high yielding lines under abiotic stresses. It may help to identify novel alleles and gene cloning in wheat for important traits. A pan-genome analysis has shown introgressions from wild wheat for adapting to diverse environments . In addi3.2.4et al., 2020). Several genetic and physiological approaches are followed in pre-breeding. The germplasm mainly used are synthetics and landraces, which are crossed and backcrossesd to widely adapted elite lines. The resulting generations are screened based on different traits and markers to select the best fixed lines for specific traits that are associated with high yield under abiotic stress . High throughput phenotyping for canopy temperature and Normalized Difference Vegetation Index (NDVI), genomic prediction and selection for grain yield, screening for rust resistance; yellow and brown rust resistance are routinely conducted in pre-breeding. Pre-breeding also involves several of the initial steps of a breeding program.Pre-breeding involves stacking different traits and alleles that are complementary to increase the desired traits in elite germplasm, and thereby grain yield (Liu Stress Adapted Trait Yield Nurseries (SATYNs) (Table 1). Each alternate SATYNs are for drought or heat stress e.g. 1, 3, 5, and 7 SATYNs have germplasm that has drought stress tolerance when compared to checks, whereas lines in 2, 4, 6, and 8th SATYNs have heat stress tolerance. Up to now, 272 lines were distributed to 5 countries in south Asia and 29 locations through SATYNs. However, if we consider the locations around the world 499 collaborators received the SATYNs (Table 2). These special nurseries have lines developed by the crossing of synthetics or a landrace with high value allele/traits into an elite background. They are grown by the national and private breeders in several countries in South Asia; India, Pakistan, Nepal, Bangladesh, Iran, and Afghanistan. Even though the main purpose of these SATYN are to provide semi-elite germplasm for use as parents to develop heat /drought tolerance varieties, some of the lines from physiological pre-breeding have resulted in direct release as varieties in west of India (Table 3). These releases have a Mexican landrace or a synthetic wheat in their pedigree.CIMMYT’s physiology pre-breeding populates special nurseries of spring wheat that are tolerant to heat, and drought stress called 4needs to be complemented through pre-breeding efforts. However, the huge number of germplasms present in the gene bank and linkage drag currently limit the utilization of these resources. One approach to overcome that bottleneck is through physiological pre-breeding , where complementary crosses are made to improve the grain yield under drought and heat stress conditions using carefully selected genetic resources to reduce linkage drag [Pre-breeding is important in a crop like wheat which provides 20% of calories and proteins to the world population. Any major catastrophe will jeopardize the food supply of the >7 billion population. The genetic bottle neck created by the continuous use of working collections of advanced lines age drag , 47. Theage drag . Both apage drag .S1.A recent study has shown the environment in Cd. Obregon where CIMMYT has it major research station is very important to develop lines for South Asia . The sucbenefit farmers (Table 4). They are specifically suited to Northwestern plain zone (NWPZ), central zone (CZ), peninsular zone (PZ), and northwest zone (NW) [http://www.hedwic.org/) that facilitate global coordination of wheat research to adapt to a future with more severe weather extremes, specifically heat and drought, are timely.In India, the main challenges of wheat productivity enhancement are climate change, sinking land and water resources, lack of pre-breeding efforts, in addition to the major breeding constraints of narrow genetic base, lack of phenotyping methods . Howeverone (NW) . In addione (NW) . Therefovia the International Wheat Improvement Network (IWIN), coordinated for more than half a century by the International Maize and Wheat Improvement Center.HeDWIC delivers new technologies to wheat breeders worldwide


Ovis aries) is one of the important livestock with diverse phenotypic traits. However, little is known about the molecular mechanism of diverse phenotypic traits in domestic sheep. Using the genome-wide high-density SNP data (600K) in 253 samples from 13 populations, we conducted the tests of selective sweeps  associated with several important phenotypic traits . We identified strong selective signatures in previously reported  and novel genes associated with the traits, such as CERS6, BTG1, RYR3, SLC6A4, NNAT and OGT for fat deposition in the tails, FOXO4 for fertility, PTCH1 and EMX2 for ear size, and RMI1 and SCD5 for body size. Further gene annotation analysis showed that these genes were identified to be the most probable genes accounting for the diverse phenotypic traits. Our results provide novel insights into the genetic mechanisms underlying the traits and also new genetic markers for genetic improvement in sheep and other livestock.Sheep ( Ovis aries) is an excellent model species for investigating the genetic basis of diverse phenotypic traits under the effects of genetic drift, natural and artificial selection factors  < 0.00001. Consequently, the final data after filtering contained various sets of SNPs and individuals in the comparison tests, such as 506,350 SNPs and 24 individuals (9 Kazakh Edilbai sheep vs 15 Celle Black Sheep) for the trait of tail shape, 485,747 SNPs and 55 individuals (36 Merino sheep vs 19 Polled Merino sheep) for horn morphology, 514,795 SNPs and 30 individuals (15 Hu sheep vs 15 Tan sheep) for fertility, 509,580 SNPs and 30 individuals (15 Celle Black Sheep vs 15 Tan sheep) for coat-color pigmentation, 529,338 SNPs and 30 individuals (15 Caucasian sheep vs 15 Altamurana sheep) for wool fineness, 519,650 SNPs and 20 individuals (11 Jill Wagner sheep vs 9 Kazakh Edilbai sheep) for ear size, 483,150 SNPs and 70 individuals (34 Meat Lacaune sheep vs 36 Dairy Lacaune sheep) for meat production, 528,576 SNPs and 20 individuals (9 Kazakh Edilbai sheep vs 11 Shetland sheep) for body size, 471,257 SNPs and 31 individuals (16 East Friesian sheep vs 15 Caucasian sheep) for milk production . For each chromosome, we averaged the XP-CLR scores per window across non-overlapping 10 kb windows. We selected the top 0.05% of these windows as the putative selective regions.To identify the genomic signatures of selection between pairwise populations of contrasting these phenotypes in domestic sheep, we calculated the T values  for eachT values . We tookCERS6, BTG1, RYR3, T, SLC6A4, NNAT and OGT)   had been reported to be involved in regulating meat production such as intramuscular fat, drip loss, marbling score, meat traceability and longissimus muscle in the meat-type vs non-meat-type sheep   were ide-tailed) . In the in sheep . In the  females . In addir breeds . The fivol sheep . The genrs sheep . The elepe sheep . The genody size . The genpe sheep .CERS6, BTG1, NNAT and OGT for fat deposition in the tail of sheep, FOXO4 for fertility, PTCH1 and EMX2 for ear size, and SCD5 for body size. As a negative regulator of β-oxidation, the expression of CERS6 was significantly increased in subcutaneous fat of obese subjects with type 2 diabetes  pathway (EMX2 gene is highly expressed in mouse inner ear, with the role of activating early hair cell development (SCD5 gene is linked to the regulator of sterol regulatory element-binding proteins involved in the development of body size (In particular, we detected novel genes with functions associated with specific traits in sheep, such as diabetes . The BTGmyocytes . The gen obesity . The genposition . The FOXogenesis . The PTC pathway . The EMXelopment . The SCDody size . Taken tPDGFD and BMP15, which are associated with fat deposition in the tails of sheep (BMP15, NCOA1 and NF1 for Wadi, Icelandic and Finnsheep, respectively (Noteworthy, we did not detect previously reported important functional genes associated with specific traits, for example, of sheep  and littof sheep , respectectively . In addiectively . FurtherIn conclusion, we detected some novel and previously reported functional genes associated with particular phenotypic traits under strong and long-term artificial selection. Nevertheless, associations between these genes detected in two tests and the specific traits should be worthy of further exploration in future investigations. These findings contribute to understanding of the genomic consequences of artificial selection in the genomes of domestic sheep.


In dairy cattle, genomic selection has been implemented successfully for purebred populations, but, to date, genomic estimated breeding values (GEBV) for crossbred cows are rarely available, although they are valuable for rotational crossbreeding schemes that are promoted as efficient strategies. An attractive approach to provide GEBV for crossbreds is to use estimated marker effects from the genetic evaluation of purebreds. The effects of each marker allele in crossbreds can depend on the breed of origin of the allele (BOA), thus applying marker effects based on BOA could result in more accurate GEBV than applying only proportional contribution of the purebreds. Application of BOA models in rotational crossbreeding requires methods for detecting BOA, but the existing methods have not been developed for rotational crossbreeding. Therefore, the aims of this study were to develop and test methods for detecting BOA in a rotational crossbreeding system, and to investigate methods for calculating GEBV for crossbred cows using estimated marker effects from purebreds.For detecting BOA in crossbred cows from rotational crossbreeding for which pedigree is recorded, we developed the AllOr method based on the comparison of haplotypes in overlapping windows. To calculate the GEBV of crossbred cows, two models were compared: a BOA model where marker effects estimated from purebreds are combined based on the detected BOA; and a breed proportion model where marker effects are combined based on estimated breed proportions. The methods were tested on simulated data that mimic the first four generations of rotational crossbreeding between Holstein, Jersey and Red Dairy Cattle. The AllOr method detected BOA correctly for 99.6% of the marker alleles across the four crossbred generations. The reliability of GEBV was higher with the BOA model than with the breed proportion model for the four generations of crossbreeding, with the largest difference observed in the first generation.In rotational crossbreeding for which pedigree is recorded, BOA can be accurately detected using the AllOr method. Combining marker effects estimated from purebreds to predict the breeding value of crossbreds based on BOA is a promising approach to provide GEBV for crossbred dairy cows. Crossbreeding is common practice in many livestock production systems, where production animals have parents from different breeds, lines or populations. Three-breed rotational crossbreeding systems have been recommended for dairy cattle , 2. In rThe genomic evaluation of crossbred animals and across populations may lead to challenges that are not experienced when it is performed within uniform populations . GenomicFor the pedigree-based genetic evaluation of crossbred animals, García-Cortés and Toro  presenteModels with BOA specific effects, hereafter denoted as “BOA models”, have been tested on both simulated and empirical data from terminal crossbreeding production systems , 10, 11.Models that account for BOA in crossbred animals rely on the accurate detection of BOA in the genotypes of crossbred animals. For F1 crosses, the genotype of the crossbred animal phased to the maternal and paternal haplotypes includes this information since the parents are purebred , 12. In Estimating local ancestry is relevant in other fields than for the calculation of GEBV of crossbreds, for example for investigating population structure . MethodsTherefore, the aims of this study were two-fold. The first aim was to develop a method for BOA assignment in crossbred dairy cows, which accounted for the available pedigree information. The second aim was to investigate if combining marker effects estimated from purebred genomic evaluations, based on estimated BOA, would result in more accurate GEBV than assuming constant breed proportions throughout the genome. The accuracy of BOA detection and reliability of GEBV were assessed on simulated data that mimicked rotational crossbreeding in dairy cattle.Here, first we present a method for assigning BOA for crossbred animals, where one parent is known to be purebred and the other parent can be crossbred. Second, we present methods for the calculation of GEBV of crossbred cows, where the estimated marker effects from purebred genomic evaluations are combined based on (1) the detected BOA or (2) the breed proportions estimated from the genotypes, but in both cases the records on crossbreds are not considered. Finally, we describe an application of these methods on simulated data that mimic the first four generations in a rotational crossbreeding dairy cattle program.Allele Origin). It was designed to detect BOA in genotypes of crossbred animals from medium-density single nucleotide polymorphism (SNP) chips, where the sire is known and of a purebred known breed, as in typical rotational crossbreeding. The dam can be a purebred of a known breed or a crossbred of a number of known breeds. The genotypes of representative samples of all contributing pure breeds are required. In order to make use of the known relationships, pedigree information that connects the crossbred cows to genotyped purebred ancestors needs to be included. The method also requires breed information on each of the crossbred animals, i.e. what is the breed of the sire and which breeds are contributing to the dam, such that the program can limit the breed assignments to the possible breeds. Furthermore, the input genotypes of both the purebred and crossbred animals should be phased to two haplotypes for each animal, and these need to be complete, i.e. genotypes need to be imputed to fill in the missing ones. Phasing can be performed alongside imputation of genotypes by existing software, such as Beagle [Marker alleles of the crossbred animals were assigned to breed of origin with a new method that we name AllOr  of markers. The windows overlap, with a predefined number of shifted (NS) markers between rounds, giving an overlap of WL-NS markers. For the last window of a chromosome, the last window length (LWL) is LWL > WL-NS and LWL ≤ WL. In order to have markers at the end of chromosome included in more than one window, WL*0.2/NS. The additional rounds at the beginning consider the WL-NS, WL-NS*2, …,WL-NS*LWL-NS, LWL-NS*2, …, LWL-NS*The comparison of haplotypes is performed in multiple rounds for each chromosome, each round considering a predefined window length . This means that a recombination which results in a shift from one breed to another in the haplotype cannot be detected at a finer scale than NS, and the accuracy of assignment is therefore expected to increase with smaller NS. However, the number of rounds and therefore running time are proportional to 1/NS. The optimal WL may depend on marker density, linkage disequilibrium and number of generations of crossbreeding.The most appropriate values for WL = 100, one allele is allowed to mismatch in order to consider that the haplotypes are matched.Within each window, the two haplotypes are assumed to originate as a whole from pure breeds and are matched with haplotypes from purebred animals. Haplotypes are considered as matching if 99% or more of the marker alleles are matched, meaning that, for example for . Given that there are matching haplotypes, Because all the sires are purebred, one of the two haplotypes originates from Because the windows overlap, each locus is present in multiple windows. In order to obtain one assignment for each allele at each locus, we calculate the average ,, and locus *WL and the alleles at WL*0.8 markers, i.e. if it has the same length as the shortest window considered in step (1). In the same manner, for loci WL*0.8 markers from the end of the chromosome.For alleles that could not be assigned in step (1), step (2) assigns them based on neighboring assigned loci. For locus  For homozygous loci, which allele is the maternal allele and which one is the paternal allele are not relevant. Thus, one of the alleles at homozygous loci that are still unassigned after steps (1) and (2) is assigned to The alleles that are still unassigned at this point cannot be assigned to a definite breed of origin. Instead, the output of AllOr includes information about which breeds are possible based on information from the assignment process, if available, otherwise from the input to the program. Probabilities of BOA are given based on the following rules applied successively: (1) if the allele could not be assigned because of several non-zero NS = 5. Three values of WL, 100, 150 and 200, were tested on one replicate of the simulated data (as explained later), and based on the results obtained, WL = 100 was used for further analysis.AllOr was implemented in a Fortran program to test the method on simulated data for which breed of origin of alleles was known. The program runs on one chromosome at a time. It was not optimized for speed, thus improvement regarding speed could be made. All the results presented here are from runs with We used two genomic prediction models that are adapted for prediction of crossbreds: (1) the breed of origin model (BOM), where marker effects estimated from the evaluation of purebreds are combined based on assigned BOA; and (2) the breed proportion model (BPM), where marker effects estimated from the evaluation of purebreds are combined using BBR, i.e. the estimated contributions of pure breeds to a crossbred genotype are used as weights.The GEBV for animal ,1 is a vector of 1 s; The BPM model is based on the methods presented by VanRaden et al. . BBR wasThe GEBV for animal For assessing the BOA assignment using the AllOr method and the prediction reliability of the prediction models for crossbred cows, we simulated genotypes, breeding values, and phenotypes of three dairy cattle breeds, Holstein (HOL), Jersey (JER) and Red Dairy Cattle (RDC), and their crosses in four generations of a rotational crossbreeding system as explained below. The simulations were performed with self-written Julia  scripts,Among the available 13,324 SNPs, 250 were selected at random from those that had a minor allele frequency (MAF) within a 0.01 to 0.30 range, with MAF being calculated from the averages of allele frequencies computed separately for each breed at G0. These 250 SNPs were assigned to be QTL and were then excluded from the final set of SNPs before the analyses. Allele substitution effects of the QTL were assumed to be identical across the breeds, and were sampled from the standard normal distribution. These substitution effects were then multiplied by the number of reference alleles  at each locus to compute true breeding values (BV) for purebred and crossbred animals. Since allele frequencies of QTL differed among breeds, the genetic variances (variances of true BV) also differed among breeds. The substitution effects of QTL were scaled such that the mean genetic variance across the three pure breeds was 100 in G0. A residual simulated from a normal distribution n et al. .Within each pure breed, genotypes from a set of animals were selected as reference genotypes for estimating marker effects for BBR estimation and for comparison of haplotypes to detect BOA. The reference set consisted of purebred bulls from generations G0, G1, G2 and G3 and 10% of the cows from the same generations, resulting in 1200 animals from each breed. Although the true phase of the simulated genotypes was known, in order to make the testing of the AllOr method and the prediction models more realistic and to include possible phasing errors, the genotypes were phased using Fimpute  includinMarker effects for GEBV were estimated for each pure breed separately using phenotypes and genotypes of the purebred cows from generations G0, G1, G2 and G3, with 8000 individuals from each breed. A SNP-BLUP model for the estimation of marker effects was used: package , given tAll the steps in the simulation were repeated 10 times. Although the simulations used the same set of base populations, and therefore, the number of SNPs that could be selected as QTL was fixed, the selection of SNPs as QTL was independent across replicates resulting in limited overlap between QTL in different replicates. Similarly, the animals at G0 were randomly assigned as males and females for each replicate of simulated data.WL on one replicate over the five chromosomes showed that 99.6% of the alleles were correctly assigned using WL = 100, but 99.2% and 98.8% using WL = 150 and WL = 200, respectively. The number of incorrectly assigned alleles was also larger with longer window lengths, i.e. 0.3% and 0.5% using WL = 150 and 200 respectively, compared to 0.2% using WL = 100.The detection of BOA for chromosome 1 that harbored 3270 markers took around ten hours using the AllOr program for the 8400 simulated crossbred animals. Investigation of different values of Figure Across the ten replicates and four types of crosses, BOA could be correctly assigned for 99.6% of the alleles Table , 0.2% weThe estimated values of GBC summed to 100% and the only correction to calculate BBR was for CRS_G1 for which the RDC contribution was estimated to be negative in some cases. Table Reliability of GEBV was highest for CRS_G1 (F1 crosses), both with BOM and BPM, and lowest for CRS_G4  for the 8400 simulated crossbred animals. However, there are many possibilities to increase speed, for example by saving the information about matching haplotypes across overlapping windows, constructing a haplotype library of unique purebred haplotypes in order to avoid comparing the crossbred haplotype with the same common haplotype many times, and parallel computing. We did an additional trial with ChromoPainter  and the WL in AllOr, in which the windows should be sufficiently short to ensure that recombination between breeds is rare within each window. With more generations of crossbreeding in the ancestry of the crossbred animals, WL should be shorter, but shorter haplotypes are more likely to exist in more than one breed [WL, i.e. 200, 150 and 100 markers, tested in this study, did not show more errors with shorter windows. The WL values tested here were within the range, or smaller, than those tested by Vandenplas et al. [WL because the number of rounds in the first step of AllOr only increases marginally with longer WL, but the number of comparison within rounds increases with longer WL. Figure WL markers from either end of the chromosome, cannot be detected with AllOr, although this is partially compensated by including extra rounds with a shorter WL at the ends of the chromosomes. The performance of any haplotype-based assignment method will be poorest at the chromosome extremes because the number of possible matching haplotypes is smaller when only one direction can be considered.The overlapping window approach adopted in AllOr is similar to that applied in the LAMP software . Howeverion rate , and it ne breed , which cs et al.  (150–350s et al.  for F1 aAllOr, like Chromopainter  and the In spite of our effort to simulate a realistic situation, applying AllOr on real genotypes creates additional challenges. For example, genotype errors were not included in the simulations. Allowing some mismatches in the comparison of haplotypes, here a 1% mismatch was set, should allow assignment in many cases in spite of sporadic genotyping errors. In addition, potential errors in pedigree information and the presence of only a few or no genotyped ancestors could further complicate BOA assignment for individual animals. The BOA program of Vandenplas et al.  showed aComparison of GEBV with parent averages was only possible for CRS_G1 because, for the other groups, both parents were not purebred. Higher reliability of genomic predictions than of parent averages Table  was expeThe reliability of GEBV obtained with both BOM and BPM was highest for CRS_G1, i.e. the F1 crosses Table  and loweOur results indicate that BOM is superior to BPM for the prediction of genetic merit of crossbred cows for the first generations of rotational crossbreeding between HOL, JER and RDC. In CRS_G1 and CRS_G2, all heterozygous loci had alleles from two different breeds. For CRS_G3 and CRS_G4 75% and 87.5%, respectively, of the loci were expected to have alleles from different breeds. Among those loci, BOA was relevant for the heterozygous loci. Assuming correct BOA and correct BBR for the F1 crosses, the SNP estimates obtained with BOM and BPM are the same for homozygous loci but differ by half the difference in allele effects from the two breeds for heterozygous loci. To what extent the marker effects differ between breeds is therefore an important factor to determine if BOA should be accounted for as shown by Ibánẽz-Escriche et al. . In CRS_In addition to accounting for the origin of the two alleles at heterozygous loci, BOM also accounts for variation in breed proportions across the genome, contrary to BPM. The breed proportions were constant throughout the genomes for CRS_G1 but varied for other crosses, depending on local ancestry, which should benefit BOM compared to BPM. However, the difference in the reliability of the models was largest for CRS_G1, which indicates that other factors, such as the similarity of the breeds in question, were more important in this case.Errors in the detected BOA will impair the reliability of prediction with BOA models , 12, espWhen training is done separately in different purebred populations, we cannot detect genetic variation that exists only between breeds and not within breeds. This variation is therefore not included in the estimated marker effects and we cannot predict this part with the BOM or BPM models presented here. In particular, when an allele at a QTL is fixed in one breed, its effect is not estimated in this breed, and because it is common to all animals of the breed, it is included in the intercept. Thus, BOM cannot determine if a crossbred animal has markers connected to the allele and the effect of the allele will be included according to the proportion of the breed via the intercept,  systems , 10, 11,It is well established in terminal crossbreeding systems that the genetic correlation between crossbred performance and purebred performance is lower than 1 –35. The The benefits of using the method of VanRaden et al.  and the In the presence of reliable pedigree information and extensive genotyping of purebred sires, marker alleles of crossbred cows from rotational crossbreeding can be detected accurately using the AllOr method. Taking BOA into account can facilitate genomic prediction of crossbred cows by using marker effects estimated from the genomic evaluation of purebreds, and our results indicate an advantage of BOM over BPM. The BOM model could be an interesting option for providing GEBV for crossbred dairy cows without having to establish a large reference set of crossbreds. Thus, it could contribute to more efficient dairy farming, where the benefits of the genomic prediction of cows and crossbreeding can be used simultaneously.