Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses  with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP . Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding. Since sugarcane is clonally propagated and highly heterozygous, both additive and non-additive genetic effects can be exploited for variety development. Mate-allocation strategies have been successfully used in animal breeding programs to manage inbreeding, preserve genetic diversity, and leverage non-additive genetic effects . NotablyIn this study, we hypothesise that mate-allocation strategies that consider non-additive effects will improve the expected performance of the clones in a sugarcane breeding program when compared to relying solely on additive genetic effects. The aim of this study is to investigate how mate-allocation strategies can enhance the prediction of clonal performance in an elite sugarcane population. First, we establish a pool of parental clones based on their GEBVs, which provide insights into the genetic potential of each parent. Subsequently, we explore two mate-allocation strategies for comparison:The first strategy involves exploiting only additive effects, following a traditional GBLUP model, to maximise the average breeding  value of progeny, resembling conventional mating practices.The second strategy leverages non-additive genetic effects and heterozygosity, in addition to the additive genetic component, through an extended-GBLUP model designed for predicting clonal performance (GPCP) to maximise clonal performance of progeny.This study presents the results of stochastic simulations assessing the expected clonal performance and inbreeding in the next generation under these mate-allocation strategies. To determine the best mating set that optimises expected progeny merit, we utilise integer linear programming (ILP). Our analysis focuses on three commercially important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS) for sugar content, and fibre content (Fibre).This study’s findings hold significant implications for sugarcane breeding programs, highlighting the potential advantages of mate-allocation strategies that capitalise on both additive and non-additive genetic effects. By leveraging genomic predictions for parental selection, breeders can enhance the overall performance of sugarcane cultivars and streamline variety development processes. Moreover, incorporating non-additive effects and heterozygosity into mate-allocation decisions helps to mitigate the potential risks associated with inbreeding depression while preserving genetic diversity. These insights offer valuable guidance for future breeding strategies, fostering sustainable advancements in sugarcane productivity.22.1This study used a 58K SC Affymetrix Axiom SNP array to genotype 3,006 elite sugarcane clones from Sugar Research Australia’s (SRA) breeding program . The popBefore testing the genomic model, the phenotypes were adjusted for experimental and environmental effects across the series, crop, trial, and region to produce the BLUPs. It’s important to note that the BLUPs used in this study were provided by our commercial partner, SRA. SRA employed a robust general linear mixed model to address spatial variations within each trial, treating rows and columns as fixed effects within trials within regions and interaction between trials and crops with spatial variation effects, applying ASREML-R v3 .pu represents random peripheral effects associated with its respective designed matrix ε denotes the residual.Where X is the design matrix, Spatial effects were accounted for by determining an optimal model within each selection trial, following the methodology outlined by r are the genetic variance, error variance and number of replicates per clone within each trial. Furthermore, BLUPs were able to handle small amounts of missing data.A potential concern is that using BLUPs as a response variable may lead to a double penalty for the estimated genetic effects. However, in the first stage of the study, pedigree information was not incorporated, indicating that the phenotypes were not adjusted towards the pedigree before fitting the genomic prediction models. Additionally, the error variances in the FAT trials  were reported to be low due to the high number of replications and large plot size within the region. The degree of shrinkage in the BLUPs was rather small and consistent, given high estimates of broad-sense heritability; The sugarcane Axiom array contained 58,364 probe sets representing 48K single nucleotide polymorphisms (SNPs), which were highly polymorphic. All clones were screened across this array, with strict quality control measures applied. Samples that had a dish quality control (DQC) measure of less than 0.82 or a quality control (QC) call rate of less than 90% were excluded from the analysis. To ensure high-quality results, allele calling was performed using the generated cell intensity files (CEL) with Axiom Analysis Suite’s best practice workflow. Multiallelic markers were called pseudo-diploid genotypes from the array data. 2.2The data analysis in this study consists of two main components: one focusing on simulating the segregation of target traits in progeny and the other on predicting the breeding and clonal values of those progeny. To accurately simulate the recombination process during the formation of gametes passed from parent to progeny, it is essential to have information about marker locations on the genetic map, along with parental marker data. However, there was a challenge. Out of the high-quality 26,086 SNPs, only 4,502 were initially mapped on the available Q208 sugarcane genetic map. This presented a limitation. Despite the existence of high levels of linkage disequilibrium (LD) in sugarcane, a more extensive marker set is essential to pinpoint specific genomic regions linked to these traits . To addr2.2.1To predict the performance of parental crosses, the first step involved simulating the genotypes of phantom progeny. In this process, 70 parents were selected from the overall population based on their GEBVs Figure 1http://population-genetics.uni-giessen.de/~software/), which utilised Plabsoft software to mimic meiosis using a count-location approach  approach. The variance components were estimated using initial data, which included the parental clones selected for crossing. The total population comprised 64,159 clones, with a training population consisting of 2,909 elite clones possessing both phenotypic and genotypic data. The remaining 61,250 clones  were simulated progenies from 1,225 families derived from the top 70 parental clones, with 50 progeny per family Figure 2In matrix notation, the GBLUP and the extended GBLUP model can be represented as follows:m×1 is a vector of BLUPs for TCH, CCS, or Fibre target traits. u corresponds to the additive genetic effects, commonly referred to as breeding values. These breeding values are assumed to follow a normal distribution AG represents the additive genomic relationship matrix and X and y for a specific trait.m = 64,159) and number of SNPs . The elements of The additive genomic relationship matrix, rding to  defined i , and Aijx is an indicator variable for additive effects that takes a value of 0, 1, and 2 if the genotype of the jth individual at SNP i is qq, Qq, or QQ , respectively.with For the GBLUP model (Equation. 1), the mixed model equations (MME) are:The solution of MME (Equation. 3) would be breeding values of clones, GEBVs. For each specific cross, a total of 50 progenies were generated. The average of the top 10% performing progenies was calculated to determine the expected breeding value associated with that cross.d and t are the random vectors of dominance deviation and additive-additive interaction deviation effects. Dominance deviation effects d were distributed as m) and number of SNPs (n). The elements of i , and Dijx is an indicator variable for dominance effects that takes a value of pi0, 2 and (pi − 24) if the jth individual's genotype at SNP i is qq, Qq, or QQ, respectively. This parameterisation of dominance effects ensures orthogonality with additive effects. The additive-additive epistatic deviation effects were assumed to be normally distributed asAAG, calculated using the methodology by AG⊙AG the Hadamard product  of the additive GRM with itself, m being the number of individuals, and tr denotes the trace of the matrix , we used an approach detailed in(In the extended-GBLUP model (Equation. 2), variance . For eactailed in. We calckHet is the average genome-wide heterozygosity for an individual clone k averaged across all SNP markers and H for clone k at the ith SNP, while iq represents the frequencies of the reference and the alternate allele at the ith SNP. The extended-GBLUP model represented by Equation. 2 is theoretically orthogonal to the additive and dominant genetic components. By orthogonality, we imply that there is no covariance between the genetic components; for example, estimates of additive genetic effects remain unbiased even when other genetic components’ effects are present in the model. The additive and dominance GRMs were computed using the GCTA software v.1.93.0b standard algorithm (Where lgorithm . The addlgorithm . MTG2 fiThe extended-GBLUP model (Equation. 2) using Equation 4 to calculate BLUP solutions within a mixed model framework, considering three random effects.ɡ), the predicted random  effects, and the genome-wide heterozygosity effects are summed together. In the context of a specific cross, the expected clonal value is derived by averaging the top 10% performing progenies out of the total of 50 progenies generated. This approach was adopted based on the understanding that only a small percentage of the generated clones would be selected and further advanced for variety development. Breeders can identify and prioritise the most promising candidates for further evaluation and selection by focusing on the top-performing individuals within the progeny set.The solution obtained from Equation 4 encompasses breeding values, dominance deviations, and additive-additive interaction deviation effects. To determine the clonal value (Likelihood ratio tests (LRT) were employed to assess the goodness of fit for the nested GBLUP models. The LRT involved comparing the test statistic, which was calculated as two times the difference between the maximum log-likelihood of the extended GBLUP model and the maximum log-likelihood of the standard GBLUP model. In this comparison, the extended GBLUP model was considered the complete model.p< 0.05 was used to assess whether introducing the heterozygosity as a covariate resulted in a significant improvement of the model’s fit. If the Test statistic > p< 0.05, the model was considered statistically significant. This approach allowed for evaluating model fit and determining the most appropriate model based on statistical significance.A significance threshold of 2.2.3The inbreeding coefficients of the progenies (Equation 5) were estimated by the diagonal element of the additive relationship matrix, pulation :n is the total number of SNPs, i and ix, is coded as 0, 1, and 2, corresponding to the qq, Qq, and QQ genotypes. The coefficient where The mean inbreeding of the progenies resulting from the selected sets of mating pairs was calculated to assess the influence of mate-allocation strategies on inbreeding. This mean inbreeding value was computed and averaged over all repetitions, allowing for an evaluation of the impact of mate-allocation strategies on inbreeding levels.2.3û). The goal was to optimise the additive genetic gain in the next generation. In the second strategy, the best 50 crossing pairs were selected based on the predicted clonal performance  based on their additive value (http://lpsolve.sourceforge.net/5.5/)  approach was employed using the R-lpSolve package (et/5.5/) . ILP is et/5.5/) . Practicmean(û50)-mean(û1225)) was calculated as the difference between the mean breeding value of the selected 50 mating pairs and the mean breeding value of all possible crossing pairs . Similarly, the expected total genetic superiority A total of 50 crosses were ultimately selected based on the ILP optimisation process. The expected additive gain Δu(=33.12h = 0.18) heritability estimates using the extended-GBLUP model, while CCS and Fibre had relatively high heritability estimates . Notably, estimates of additive genetic variance differed significantly between the GBLUP and the extended-GBLUP models, particularly for TCH. For additive variance, the standard errors for TCH using the GBLUP model were higher compared to the extended model, whereas the standard errors for the other two traits remained the same regardless of the model.The estimates of variance components, heritabilities, and the maximum log-likelihood ratio values obtained from the two described models (Equations 1 and 2) are shown in 2h) estimates for CCS and Fibre were comparable in both GBLUP and extended-GBLUP models. When additive, dominance, epistatic genetic effects, and heterozygosity effects were simultaneously included in the model, the estimates of dominance variance for CCS and Fibre were close to zero. In contrast, the dominance effects explained nearly 4% of the phenotypic variance for TCH (The narrow-sense heritability h2 estimateIncorporating non-additive (dominance and additive-additive interaction) genetic effects and heterozygosity effects resulted in a significant reduction in residual variance for all traits compared to the additive model Figure 33.2The average heterozygosity per clone was calculated based on the heterozygosity across markers. The regression coefficient for heterozygosity in relation to TCH (92.84 ± 11.08) was found to be significant, suggesting that an increase in genome-wide heterozygosity is associated with an increase in average cane yield. However, for CCS and Fibre, the standard error of the regression coefficient was considerably larger compared to the heterozygosity estimates, and the difference was not statistically significant Table 1.3.3The total variation in predicted breeding The average genomic inbreeding coefficient of progenies for TCH indicated that the selected progenies based on the extended-GBLUP model with mate allocation had lower estimates of inbreeding than the additive model Table 2.The expected total genetic superiority of the progeny was higher for all traits when matings were chosen based on clonal performance (GPCP) rather than breeding value (GEBV), providing the offspring with an advantage for TCH, CCS, and Fibre. However, a significant decrease in additive genetic gain was found using the same selection strategy, notably for TCH. There were no major differences in the additive (Δu) and expected genetic (Δg) gain for CCS and Fibre in a specific mate-allocation strategy. The rankings of crossing pairs varied significantly depending on whether the selection was based on GEBVs or GPCPs. For example, we observed that out of the selected 50 crossing pairs, there was an overlap of only approximately six pairs between the mate-allocation strategies for TCH. This limited overlap can be attributed to the higher epistatic effects associated with this trait. This indicates that different parents were selected to optimise the total additive  value.4Pinus taeda L.) tree breeding.Our simulation study demonstrates that mate-allocation strategies that account for non-additive genetic effects can improve progeny performance in the next generation. Considering non-additive genetic effects in mating decisions would likely lead to breeding higher-performing varieties. We observed a substantial improvement in the average expected progeny value of selected crossing pairs, with increases of 57%, 12%, and 16% for TCH, CCS, and Fibre, respectively, when non-additive and heterozygosity effects were exploited. These results aligned with other clonal crops and outbred species . For insAs sugarcane breeding programs progress, inbreeding and a reduction in genetic diversity pose risks of inbreeding depression. It is crucial to carefully select and allocate mates to strike a balance between genetic gain and the negative consequences of increased inbreeding. 1 descendants are unique  than selection based solely on breeding value, which is consistent with previous studies . Among tUnder the assumption of Hardy-Weinberg equilibrium, the total genetic variance was partitioned into additive, dominance, and epistatic variance. Although the model is theoretically orthogonal, our results demonstrate a reduction in additive variance for all traits when non-additive genetic effects are included in the model. These results are consistent with those obtained when applying the natural and orthogonal interaction (NOIA) model to the same population of clones where the Hardy-Weinberg equilibrium condition was relaxed . High liIt is important to acknowledge that mate-allocation strategies used in this study consider truncation selection and planned mating based on additive or total genetic performance. It might result in the selection of close relatives. This focus on selecting the top-performing lines offers short-term genetic progress; however, it poses a risk of limiting long-term genetic gain due to high rates of population inbreeding. To balance long- and short-term gain, the sugarcane breeding scheme can be viewed as having two interconnected goals: recurrent GS for population improvement and the development of varieties for immediate use. Recurrent GS focuses on allele substitution effects, which control and increase the frequency of favourable alleles in the population over time and can be primarily driven for larger genetic gain in the long term. On the other hand, the variety development pipeline utilises non-additive and heterozygosity effects to improve the phenotypic performance of market-ready clones, prioritising short-term goals. However, it is important to note that preselecting clones based on GEBVs might restrict the opportunity to select alternative clones that might potentially generate offspring with higher overall genetic value in specific matings. It is crucial to consider that mating, which benefits from non-additive effects, can only increase progeny performance during its implementation, and the benefits resulting from specific combining abilities cannot be accumulated over multiple generations. Therefore, exploring innovative methods for optimizing genetic contributions  can be advantageous to balance short-term gain and preservation of long-term genetic diversity.Our results are limited to a specific population and focus on a single trait. Consequently, the proposed approach could lead to the selection of different parental lines when targeting different traits. In practice, however, expanding the approach to multiple-trait selection is preferable. A simple extension would be to use a selection index that includes multiple traits and then consider the selection index as a new target trait for the existing single-trait approach. Another possible modification is directly implementing multi-trait genomic prediction models and evaluating selection lines using an appropriate selection index.Furthermore, in order to approximate a high-complexity genome, we used simplified assumptions in our simulation schemes. However, genetics in auto-polyploid species is more complicated than in diploid species since more than two alleles may occur at the same locus. As a result, there are additional phases and recombination, and preferential pairing can vary. In addition, there is limited theoretical and experimental information on recombination and segregation in high-ploidy species. And determining which alleles co-occur on each homologous copy gets increasingly challenging as ploidy increases. Furthermore, a reference genome is essential for phasing genotypes in heterozygous polyploids like sugarcane. Unfortunately, the sugarcane community does not have access to the complete reference sequence, which did not allow for leverage of genome-wide phased haplotypes. Nevertheless, the pseudo-diploid markers are a rough approximation of what we used in our study because the majority of the markers in the SNP array are single/low-dose markers. Based on genotype allele count , the software we used to simulate the progenies assumes that the input (marker) data is in the correct gametic phase.We acknowledge the limitations of our research, particularly the simplifications inherent in using a basic diploid parametrisation and the challenges in representing sugarcane’s complex genomic and meiotic characteristics. The availability of the Affymetrix SNP array, with its considerable number of single-dose SNP markers, has facilitated our study. Still, it is important to recognise that this approach may not entirely capture the genetic complexity of sugarcane. Furthermore, the small size of our training population and the impact of environmental variations on genotypic values should be taken into account when selecting cross combinations. Therefore, conducting field trials to validate our findings would be a valuable next step. Additionally, further research is required to fully understand the benefits of mate-allocation methods in a larger context.5In conclusion, our simulation study demonstrates that implementing genomic mate-allocation strategies that consider non-additive genetic effects holds promise as a feasible and effective method for enhancing the performance of clonal offspring in sugarcane breeding programs. Across all traits evaluated, the inclusion of non-additive effects in mate-allocation strategies yielded favourable outcomes, resulting in a substantial 57% improvement in cane yield performance compared to strategies focused solely on additive effects. Moreover, when crossing pairs were selected to leverage non-additive and heterozygosity effects, the average inbreeding coefficient of the progeny was significantly reduced, particularly in cases where the target trait was TCH. This reduction in the inbreeding coefficient helps to preserve long-term genetic gains and maintain the overall genetic diversity within the population.One notable advantage of mate-allocation methods that account for non-additive genetic effects and heterozygosity is their relative ease of implementation compared to other approaches described in the literature. By directly estimating the performance of progeny in the subsequent generation, these methods provide a practical and efficient means of incorporating non-additive effects into breeding operations. This becomes especially valuable when breeding operations involve time-consuming processes such as establishing segregating populations and conducting field evaluations, emphasising the importance of systematic planning in cross-selection. Although genomic mate-allocation contributes to the overall improvement of offspring performance in our simulation study, it is crucial to note that it does come at the cost of reduced expected additive genetic gain, particularly in the case of cane yield. Therefore, future breeding programs will face the interesting challenge of balancing long-term gains derived from selection on additive effects and the short-term development of high-performing varieties. We emphasize that these findings are derived from simulation experiment, and their real-world applicability should be considered in light of the simulation-based nature of this study,https://github.com/SimmiSudhir/Linkage-Disequilibrium-Algorithm. Further inquiries can be directed to the corresponding author.The datasets presented in this article are not readily available due to confidentiality agreements. The modified marker data set and genetic map for sugarcane are made available on GitHub at this link SY: Data curation, Formal Analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. ER: Supervision, Writing – review & editing. XW: Formal Analysis, Investigation, Project administration, Writing – review & editing. OP: Supervision, Writing – review & editing. VH: Software, Writing – review & editing. LH: Supervision, Writing – review & editing. FA: Project administration, Writing – review & editing. ED: Data curation, Formal Analysis, Writing – review & editing. KA: Resources, Writing – review & editing. KV-F: Supervision, Writing – review & editing. BH: Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Writing – review & editing, Methodology.


B. juncea introgression lines (ILs) generated from B. carinata with increased productivity and adaptability. Forty ILs were crossed with their respective B. juncea recipient parents to generate introgression line hybrids (ILHs) and the common tester (SEJ 8) was used to generate test hybrids (THs). Mid-parent heterosis in ILHs and standard heterosis in THs were calculated for eight yield and yield-related traits. Heterotic genomic regions were dissected using ten ILs with significant mid-parent heterosis in ILHs and standard heterosis in THs for seed yield. A high level of heterosis for seed yield was contributed by 1000 seed weight (13.48%) in D31_ILHs and by total siliquae/plant (14.01%) and siliqua length (10.56%) in PM30_ILHs. The heterotic ILs of DRMRIJ 31 and Pusa Mustard 30 were examined using polymorphic SNPs between the parents, and a total of 254 and 335 introgressed heterotic segments were identified, respectively. This investigation discovered potential genes, viz., PUB10, glutathione S transferase, TT4, SGT, FLA3, AP2/ERF, SANT4, MYB, and UDP-glucosyl transferase 73B3 that were previously reported to regulate yield-related traits. The heterozygosity of the FLA3 gene significantly improved siliqua length and seeds per siliqua in ILHs of Pusa Mustard 30. This research proved that interspecific hybridization is an effective means of increasing the diversity of cultivated species by introducing new genetic variants and improving the level of heterosis.Interspecific hybridization resulted in the creation of  B. rapa , B. nigra , and B. oleracea  are diploids; whereas, B. juncea , B. napus , and B. carinata  are digenomic tetraploids, which evolved in nature following hybridization between the constituent diploid species [The rapeseed-mustard group comprises six agriculturally important crops, out of which gra 2n = 6, BB, anBrassica, B. juncea (Indian mustard) is predominantly cultivated in India and accounts for more than 90% of its acreage [B. juncea, being a pivotal crop in the edible oil sector, needs to be genetically improved for productivity traits to meet the edible oil requirements and, hence, the improvement of seed yield and oil content in B. juncea remains a major priority. Current varietal development programmes are ineffective in increasing productivity to the desired level [Among the different species of the genus  acreage ,4. With ed level . Hybridsed level . Therefoed level . B. juncea has a narrow gene pool because more than 90% of present-day varieties in India are either derived directly from the cultivar Varuna or its derivatives [B. juncea, the genetic base of the working germplasm needs to be broadened. Interspecific hybridization is a powerful tool for targeted crop improvement, broadening the genetic base of a species, introgression of favorable adaptive alleles between species, and the exploitation of heterosis [B. napus having introgression segments from Chinese semi-winter type with natural spring B. napus and reported heterosis for seed yield in hybrids. Qian et al. [B. rapa can be a valuable gene pool for increasing biomass in B. napus. Interspecific hybridization between B. juncea and B. carinata holds promise for introgressing desirable traits from one species to another. Inter-subgenomic heterosis was also observed when B. rapa and B. carinata-derived B. juncea introgression lines were crossed with B. juncea genotypes [B. carinata (BBCC) is endowed with many agronomically desirable traits such as resistance/tolerance to abiotic stresses (drought and heat); and biotic stresses, viz., aphid, Sclerotinia rot, white rust, Alternaria blight, blackleg, and powdery mildew [B. carinata to B. rapa [B. napus [B. juncea [B. oleracea to exploit heterosis in hybrids between B. napus and B. oleracea; and suggested that the C genome of B. oleracea has potential to improve the seed yield in hybrid cultivars.ivatives . A narroivatives . To expleterosis . It brineterosis . Effortseterosis  developen et al.  concludeenotypes . Moreovey mildew . Success B. rapa ,17, B. nB. napus , and B. . juncea ,19,20,21. juncea  used B. B. juncea with favorable adaptive alleles of B. carinata, resulting in the development of morphologically and cytogenetically stable B. carinata-derived B. juncea ILs (2n = 18II). Using these lines, we have successfully demonstrated higher yield heterosis and drought tolerance in a set of 105 hybrids [B. carinata-derived B. juncea ILs. Hence, the present study ensures the first use of ILs derived from the interspecific cross between B. carinata accession BC-4 and B. juncea genotypes (DRMRIJ 31 and Pusa Mustard 30) for the identification of heterotic genomic segments and underlying candidate genes introgressed from B. carinata accession BC-4 to B. juncea genotypes.Therefore, interspecific hybridization was used at the Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi to enrich the genome of  hybrids . These oObservations on ILs, ILHs, and THs for yield and yield-contributing traits were recorded for both the genetic backgrounds. An analysis of variance revealed the significance of the mean sum of squares due to treatments for all of the traits, except for harvest index in test hybrids generated in the genetic background of Pusa Mustard 30 . Both seSeed yield in PM30_ILs ranged from 1.77–2.83 t/ha with an average of 2.38 t/ha, and the mean seed yield of PM30_ILs and PM30_ILHs was found to be higher than the seed yield of Pusa Mustard 30 (2.03 t/ha). Similar results were observed for mean seed yield (t/ha) in the genetic background of DRMRIJ 31. Total siliquae per plant and 1000 seed weight exhibited higher values for D31_ILs than the recipient parent DRMRIJ 31. Similarly, PM30_ILs were superior to Pusa Mustard 30 for seeds per siliqua, total siliquae on main shoot, total siliquae per plant, oil content, and harvest index . In geneMid-parent heterosis was calculated for introgression line hybrids (ILHs) generated by crossing ILs with their respective recipient parent; and for the set of test hybrids created by crossing ILs with standard tester SEJ 8, standard heterosis was calculated over respective check hybrids. Recipient parents, viz., DRMRIJ 31 and Pusa Mustard 30 were crossed with SEJ 8 to generate check hybrids (CHs). Wide ranges for mid-parent heterosis over introgression lines and respective genetic backgrounds, and standard heterosis over respective check hybrids were observed . The higB. carinata-derived B. juncea introgression lines. For all the traits, hybrids with significant mid-parent heterosis and standard heterosis over the respective check hybrids are presented in A significant difference between the mid-parental value and ILHs or THs and CHs indicates the presence of heterotic loci in the parental line(s). In the present investigation, both experiments were conducted to establish the presence of heterotic genomic segments in the Brassica carinata Acc. BC-4). Polymorphic 5157 SNPs between DRMRIJ 31 and 4893 between Pusa Mustard 30 and BC-4 were identified. These polymorphic markers were used to genotype introgression lines in order to identify genomic segments introgressed from alien donor species and responsible for improved hybrid performance. Graphical genotypes for each introgression line were generated using the Graphical GenoType 2.0 software (B. carinata and 57.3% of it from B. juncea (DRMRIJ 31), with an average heterozygosity of 5%. Among these lines, D31_IL136 had the highest number of introgressed donor genome segments (169), followed by D31_IL94 (109), and the maximum number of introgressed segments (117) were observed on the B07 chromosome (B. carinata and 64.3% from B. juncea (Pusa Mustard 30), with an observed heterozygosity of 6.2%. PM30_IL177 (108) had the most introgressed segments, followed by PM30_IL181 (99), and chromosome B08 had higher introgressed segments as compared to other chromosomes  was used for the identification of Single Nucleotide Polymorphisms (SNPs) between introgression lines and their donor parent  in D31_ILs, were also reported in previous studies to regulate seed size —was attributed to improved 1000 seed weight (g) and harvest index (%). Additionally, these hybrids were also superior to their respective D31_ILs for 1000 seed weight (g) and harvest index (%) . D31_IL1cc. BC-4 . Identifcc. BC-4  and harvcc. BC-4 , and a feed size .FLA3 gene  was identified in most of the heterotic PM30_ILs. This gene was previously reported to be involved in the regulation of siliquae development and siliqua length, and is expected to contribute to the superior performance of PM30_ILHs in this study ; whereas hybrids, viz., PM30_ILH180, PM30_ILH187, and PM30_ILH190 outperformed by increasing siliquae length (cm) and the number of seeds per siliqua a,b. A tois study . AP2/ERF, SANT4, MYB, zinc finger domain, Protein kinases, glutathione s transferase, and homeobox domains were common among heterotic ILs developed in both the genetic backgrounds  in the genetic backgrounds of cultivars DRMRIJ 31 and Pusa Mustard 30. Wei et al. [B. carinata and B. rapa sub-genomes into the B. juncea genome resulted in improvements for thousand seed weight and seeds per siliqua, but the performance of these traits in the new B. juncea remained inferior. However, inter-subgenomic hybrids between the introgressed Brassica line and the recipient B. juncea parent showed considerable potential for heterosis. Interspecific hybridization proved to be the most prominent approach for expanding the genetic variation in cultivated cultivars of diploid and polyploid species ,24. Amono biotic  and abioo biotic ,23 streso biotic ; therefoi et al.  demonstrB. carinata caused desirable trait-specific variations among ILs. Results suggested that the use of biparental mating among F2 plants and selfing thereafter has potentially increased the variation among introgression lines for yield-related traits. Moreover, Schelfhout et al. [B. juncea and B. napus followed by selfing led to enhanced genetic diversity in canola quality progeny of both species without loss of donor alleles, whereas repeated backcrossing generally associated with loss of donor parent alleles. Hybrids generated between ILs and their respective genetic backgrounds (ILHs) expressed higher heterosis for yield and its contributing traits. The superiority of introgression line hybrids as compared to introgression lines indicates that heterotic segments (heterozygous introgressed segments) were responsible for the expression of this heterosis [B. juncea backgrounds that had higher main shoot length and total siliqua on the main shoot as compared to normal B. juncea genotypes. Further, higher heterosis was reported in hybrids generated between these chromosomal substitution lines and the original lines. In another recent effort, Zhang et al. [B. juncea through the introgression of the B. rapa genome.The wide variation observed among ILs for all traits reported in this study, as compared to their respective genetic backgrounds, suggests that interspecific hybridization can be useful for increasing variability among ILs . The phet et al.  reportedeterosis . Gupta eeterosis  developeg et al.  expanded2 [2 and DH [1 [B. carinata-derived B. juncea introgression lines were used in the present study for the identification of heterotic genomic segments.Studies on heterosis require a different kind of population as compared to conventional QTL mapping. The population required for the identification of heterotic loci and heterotic effects should be derived from elite hybrids with a strong heterozygous advantage over parents . Heterot2 , immorta2 and DH , RILBC1 nd DH [1 , and thend DH [1 ,34,35,36nd DH [1 ,38. TherB. carinata introgressed segments was higher and the percent introgression was lower, indicating that the size of the introgressed segments was small but the number of introgressed segments was quite high in each IL. This is primarily due to biparental mating in the F2 generation followed by selection in each filial generation for the development of ILs, which led to the breakage of undesirable linkage and reduced the size of introgressed segments. Quezada-Martinez et al. [B. carinata and B. juncea, and higher chances of recombination between A and C sub-genomes [B. juncea. Less introgression into “A” sub-genome of B. juncea is, therefore, anticipated due to directional phenotypic selection towards B. juncea. “A” sub-genome of B. juncea contributes maximally to the seed yield [In general, the average number of z et al.  also sug-genomes ,41. Intred yield , and anyB. carinata accession BC-4 and respective genetic backgrounds) were used to identify 254 and 335 heterotic genomic segments  and hybridized with respective genetic backgrounds to generate Introgression Line Hybrids (ILHs), and with a common tester to generate Test Hybrids (THs). Previous studies have identified heterotic loci on the basis of mid-parent heterosis (33–35) or heterosis over check hybrids in test cross populations . Howeversegments  from D31eterosis .B. carinata accession BC-4 into the background of B. juncea were identified, and representative markers from each segment were used for candidate gene analysis. The flanking sequences to the polymorphic SNPs were determined using BLAST with the reference genome, and 1000 base pairs upstream and downstream to the position of the SNPs were taken for annotation with the Arabidopsis genome for identification of candidate genes. To identify candidate genes, a similar approach was followed by Cai et al. [B. napus. Two hundred and seventy-three candidate genes in heterotic segments of D31_ILs and 352 in heterotic segments of PM30_ILs were observed , SANT 4, CYP72A13, zinc finger/ring type, Glycoside hydrolase family 9, potassium transporter, UDP-glucosyl transferase 73B3, and 3-ketoacyl-acyl carrier protein synthase III were reported in heterotic PM30_ILs  followed by biparental mating in the F2 generation, and continuous selection and selfing thereafter, has led to the development of B. carinata-derived B. juncea introgression lines (ILs). Morphological resemblance to the respective B. juncea genotype was preferred while selecting single plants in segregating generations along with valuable transgressive segregants. This effort has resulted in the development of more than 200 lines. From this set, we included 40 ILs carrying B. carinata genomic segments in the genetic background of two B. juncea genotypes, viz., DRMRIJ 31  and Pusa Mustard 30  for the present study. Using these lines, 40 Introgression Line Hybrids (ILHs) were generated by hybridizing the above-mentioned ILs and their respective genetic backgrounds. SEJ 8, a synthetic B. juncea genotype already known as a good combiner, was used as a tester to hybridize with all the 40 ILs to generate Test Hybrids (THs) and two Check Hybrids (CHs), viz., DRMRIJ 31x SEJ 8 and Pusa Mustard 30 × SEJ 8, thus, generating a total of 40 THs and two CHs. Hybrids were generated and their parents were selfed during the rabi 2020–2021 season at the Experimental Field of the Division of Genetics, ICAR-IARI, New Delhi. Interspecific hybridization between rabi 2021–2022 season. Each entry consisted of two rows of 2.5 m length (average 40 plants/plot) with a row-to-row and plant-to-plant spacing of 45 and 15 cm, respectively. Recommended agronomical practices and plant protection measures were adopted for raising a healthy crop.Two different experiments were designed to evaluate the ILs, ILHs, THs, CHs, and their respective parents. In the first experiment, 40 ILHs, 40 ILs, and their respective genetic backgrounds were evaluated for the expression of heterosis, if any, and the identification of genomic segments responsible for its expression. To validate the reported heterosis and establish it again in a different genetic background, 40 THs and two CHs were compared in the second experiment. Both the experiments were conducted in a randomised complete block design (RCBD) with three replications at the experimental plot of Genetics Division, ICAR-IARI, New Delhi in the Observations were recorded on nine seed yield and yield-related traits, viz., total siliquae on the main shoot, total siliquae per plant, siliqua length (cm), seeds per siliqua, seed yield per plant (g), biological yield per plant (g), seed yield (t/ha), 1000 seed weight (g), and oil content (%). Seed yield per plant and biological yield per plant were used to calculate harvest index using a standard formula . Observations were recorded on five randomly selected competitive plants and averaged from every plot in each replication, except for seed yield (t/ha), where observations were recorded on the plot basis. Mean phenotypic values for all the studied traits were computed from three replications and further used for statistical analysis. An analysis of variance for randomized complete block design was performed for all the studied traits using the method of Panse and Sukhatme . The folMid-Parent Heterosis (MPH) and Standard Heterosis (SH) were calculated using the standard equation, MPH (%) = (ILH—MP)/MP × 100, and SH (%) = (TH—CH)/CH × 100, where MP is mid-parental value, TH is the value of test hybrid and CH is the value of check hybrid . The sigB. juncea var. varuna UDSC Var 1.1 [DNA was isolated from young leaves of ILs and their parents following the CTAB (Cetyl Trimethyl Ammonium Bromide) modified method suggested by Saghai-Maroof et al.  and used Var 1.1 . The per Var 1.1 . Polymor Var 1.1 . The sof Var 1.1 .B. carinata accession BC-4, expressing both significant mid-parent heterosis for seed yield in ILHs and significant standard heterosis for seed yield in THs over respective CHs were considered heterotic. Sequence flanking to SNPs, 1000bp upstream and downstream, from these heterotic genomic segments were mapped to B. juncea reference genome using the BLAST tool . Candidate genes were identified using the annotation of the Arabidopsis genome and physical positions of SNPs in the reference B. juncea genome [It is assumed here that heterosis arises due to the effect of heterozygous loci; therefore, loci giving significant differences between hybrids (heterozygote) and the mean of two parents (homozygotes) were considered to be heterotic . Signifia genome .B. carinata has generated sufficient genetic variation in B. juncea accessions DRMRIJ 31 and Pusa Mustard 30, which in some cases do not provide any significant gain in ILs over the B. juncea genetic backgrounds intended to improve. Significant positive heterosis for seed yield (t/ha), expressed in ILHs and THs, indicates that homozygous introgressed segments from B. carinata after attaining heterozygosity in hybrids expressed higher heterosis. Marker analysis of ILs, on the other hand, also confirmed the presence of B. carinata segments, and the superiority or inferiority of these ILs to their respective genetic backgrounds could be attributed to introgressed segments. Introgression lines expressing higher heterosis for seed yield in ILHs and THs enabled the identification of heterotic segments and their association with yield-related traits. Candidate gene analysis revealed the presence of important transcription factors and genes which were reported to regulate critical yield related traits, viz., thousand seed weight (g), siliqua length (cm), seeds per siliqua, and oil content (%). The present study justifies the importance of interspecific hybridization in generating novel genetic variability, and its usefulness in improving the level of heterosis. Heterotic ILs, identified in this study, can be converted into cytoplasmic male sterile lines and/or fertility restorer lines for the development of commercial hybrids. Furthermore, candidate genes are envisaged for validation for their precise role in the regulation of yield and yield-related traits before their deployment through MAS or genomic selection.Introgression of genomic segments from


We identified marker-trait associations for key faba bean agronomic traits and genomic signatures of selection within a global germplasm collection.Vicia faba L.) is a high-protein grain legume crop with great potential for sustainable protein production. However, little is known about the genetics underlying trait diversity. In this study, we used 21,345 high-quality SNP markers to genetically characterize 2678 faba bean genotypes. We performed genome-wide association studies of key agronomic traits using a seven-parent-MAGIC population and detected 238 significant marker-trait associations linked to 12 traits of agronomic importance. Sixty-five of these were stable across multiple environments. Using a non-redundant diversity panel of 685 accessions from 52 countries, we identified three subpopulations differentiated by geographical origin and 33 genomic regions subjected to strong diversifying selection between subpopulations. We found that SNP markers associated with the differentiation of northern and southern accessions explained a significant proportion of agronomic trait variance in the seven-parent-MAGIC population, suggesting that some of these traits were targets of selection during breeding. Our findings point to genomic regions associated with important agronomic traits and selection, facilitating faba bean genomics-based breeding.Faba bean (The online version contains supplementary material available at 10.1007/s00122-023-04360-8. Vicia faba L.) is an important cool-season grain legume (pulse) crop grown worldwide for its high seed-protein content that is of great interest for the production of animal feed and food for human consumption. Faba bean has a high yield potential and an average crude protein content of 29%. In addition, it is one of the most efficient nitrogen fixers and is grown with little or no applied inorganic nitrogen fertilizer , chickpea (Cicer arietinum L.), field pea (Pisum sativum L.), cowpea (Vigna unguiculata L.), and lentil (Lens culinaris Medik.) , rice (Oryza sativa L.), alfalfa (Medicago sativa L.), and soybean (Glycine max (L.) Merr.) by comparing subgroups with different geographical origins  analyze the genetic diversity, population structure, and linkage disequilibrium (LD) of a global faba bean panel of 2678 accessions using high-quality SNP data; (2) use a mapping population to identify markers associated with key agronomic traits; and (3) select a large, non-redundant diversity panel to study faba bean genetic diversity and inter-population selection signatures. Understanding the genetic diversity and structure of these accessions lays a foundation for future genome-wide association studies (GWAS) or genomic selection (GS) and will aid in the utilization of these materials in future faba bean breeding programs.The data studied consist of 2682 faba bean accessions belonging to eight different international panels developed in different projects. The panels are referred to, respectively, as: EUCLEG, Göttingen Winter Bean (GWB), four-way-cross, seven-parent-MAGIC, Northern Faba (NORFAB), ProFaba, Reading Spring Bean Panel (RSBP) and Virtual Irish Centre for Crop Improvement (VICCI). The panels differed in size, type, allelic diversity included, and crossing strategies Table . ExtensiThe seven-parent-MAGIC lines were grown in field trials at two locations in Denmark during 2020 and 2021. The first location was Sejet Plant Breeding, Sejet  and the second Nordic Seed, Dyngby . The experimental design for field trials was an alpha design with three replicates. Plants were grown in plots made up of 6 rows with 14–15 seeds sown per row. Each plot contained two entries and therefore consisted of two inbred lines, each contributing three rows. To minimize neighbor effects, every other plot between the 6-rowed plots consisted of commercial cultivars; Kontu at both field trials during 2020 (Sej20 and Dyn20) and Taifun and Daisy at both field trials during 2021 (Sej21 and Dyn21). For the field trials at Sejet, the sowing dates were 9 April 2020 and 19 April 2021, and the harvest dates were 10–12 September 2020 and 31 August to 1 September 2021. For field trials at Dyngby, the sowing dates were 2 April 2020 and 8 April 2021, and the harvest dates were 24 August 2020 and 21 August 2021.Trials were rain-fed and treated with herbicides and insecticides. Furthermore, the field trials in Dyngby had fertilizer (NPK 0-8-23) applied. More details on the treatments can be seen in Supplementary Table 1.Botrytis fabae), rust (caused by Uromyces viciae-fabae), and downy mildew (caused by Peronospora viciae); herbicide damage; branching; plant height; number of ovules per plant; sterile tillers per plant; lodging; maturation date; earliness, end, and duration of flowering; thousand grain weight (TGW); and seed area, length, and width. The description of each trait and scoring methods appears in Supplementary File 2.To minimize border effects, all traits were scored in the middle row of the three rows per inbred line. Plants were phenotyped for the following 17 traits: disease susceptibility to chocolate spot  for the EUCLEG panel, a NucleoSpin Plant II kit (Macherey–Nagel) for the seven-parent-MAGIC and NORFAB panels, and a DNeasy 96 Plant Kit  for the remaining panels. DNA quality was assessed on agarose gel electrophoresis, while concentration was assessed using a Quant-iT PicoGreen dsDNA Assay Kit  following the manufacturer’s guidelines.Vicia faba reference sequence  at position 1,574,527,093 by the faba bean genome consortium to facilitate data analysis.Functional annotation was done using eggNOG-mapper v. 2.7.2 with the eggNOG eukaryotic database  were available, we modeled, in the R package cobs, genetic position as a smooth, strongly monotonic function of physical position, and then, using this function, we estimated genetic positions for all SNPs  between accessions were calculated as the fraction of shared alleles by applying the following equation to the VCF file using a custom R-script :1\documeWhen two samples showed GI ≥ 94%, the sample with the largest proportion of genetic missingness or the least information in terms of geographic origin (diversity panel) was removed. The threshold was set so that we excluded most accessions that we knew were present in duplicates and avoided discarding too many genetically close lines.The site-frequency spectrums were based on the panel-wise polymorphic SNPs Table . The altK ranging from 2 to 20  filter at 1% was applied, leaving 21,116 markers. The software ADMIXTURE was run with Principal component analysis (PCA) was performed on all accessions across panels and within the diversity panel using the markers that passed a minor allele frequency (MAF) threshold ≥ 1%, that is 21,077 and 21,116 markers, respectively. All PCAs in this study were made by using PLINK v. 1.9 setting the number of principal components (PCs) to the number of samples  between pairs of subpopulations as identified by ADMIXTURE. For this purpose, the ‘–weir-fst-pop’ in VCFtools v. 0.1.16 was used  chromosome-wise for each pairwise combination of markers  and a smooth curve was fitted using the loess function with a 10% smoothing span in R. There were 5000 bins plotted for the seven-parent-MAGIC and four-way-cross populations where the LD decayed slowly, whereas 1000 bins were plotted for the remaining populations. The LD decay was estimated per panel as the point where the fitted curve reached half of its maximum value.Linkage disequilibrium (LD) was estimated individually for each panel using PLINK v. 1.9 to compute the squared correlation coefficients  in the kth replication, µ is the overall trait mean, Gi is the genetic effect of the ith line, Ej is the environmental effect of the jth environment, Gi × Ej denotes the genotype environment interaction of the ith line in the jth environment, R(E)jk is the effect of the kth replicate within the jth environment, and p value > 0.05, inclusion of the effect was not considered to improve the model. To extract best linear unbiased estimators (BLUEs) for each trait, statistically insignificant terms are excluded from Eq. Gi and Phenotypic data analyses were performed for each trait in individual field trials and for all environments (envs) combined, using the lme4 package in R  using the SimpleM method  method integrated in the GAPIT v. 3 library in R with a MAF filter of 5% ; n is the total number of observations.The phenotypic variance explained (PVE) by SNPs were estimated for all traits as proposed by Martinez et al.  Eq. 4)::4):4\docmentclass2pt{minimPulses Pretzel (https://pulses.plantinformatics.io/). In addition, the Medicago truncatula genome  and its annotation was loaded, with the flowering time genes from Yeoh et al. , together with sequence-based markers  were mapped to the faba bean genome and loaded into h et al.  defined.After quality control, a total of 2,682 accessions from eight different panels were genotyped for 21,345 high-quality SNP markers. Since 6 accessions appeared twice by name within a panel, we checked for genetic identity between these expected duplicates using a threshold of ≥ 94% (Eq. Ho) which was considerably lower than the expected heterozygosity (He) .Most inbred panels showed average observed heterozygosity equal to or below 0.06. An exception was the EUCLEG panel, which comprised a group of accessions with higher heterozygosity. The outcrossing VICCI panel showed higher average heterozygosity than the other panels. As expected, all inbred panels had an average observed heterozygosity (π). The nucleotide diversity was highest for the broad diversity panels, EUCLEG, ProFaba and NORFAB (0.32), whereas the remaining panels, which were all established from a limited number of founder lines, had lower π-values (0.26–0.30)  and calculated nucleotide diversities  Table A.Fig. 1G).For the EUCLEG, NORFAB, and ProFaba panels, LD dropped to half of its maximum at values close to the average distance between SNPs—that is, 681.7 Kbp, 676.6 Kbp, and 672.9 Kbp, respectively. LD decayed over larger distances for the GWB (1.4 Mbp), VICCI (4.8 Mbp), and RSBP (7.8 Mbp) panels, consistent with the lower number of recombinations represented in the respective panels. The seven-parent-MAGIC and four-way-cross panels showed much larger LD blocks with average decay values of 68.4 Mbp and 77.6 Mbp, respectively . Additionally, we found that the replicate variance, the environmental variance, and the GxE interaction were significant (p < 0.05) for all traits, except susceptibility to downy mildew as measured in percentage (Supplementary File 4). Seed traits showed relatively little environmental influence and high heritabilities of 0.96–0.98 . When including data from multiple environments, we found low heritabilities for disease resistance to chocolate spot as measured in percentage (0.21) and rust (0.28–0.30) (Supplementary File 4). When considering environments separately, however, heritabilities above 0.50 could be found for at least one of the environments scored for these traits (Supplementary File 4). Because of this, and the significant GxE interactions for almost all traits, we performed GWAS for each environment separately and by using the BLUEs of combined environments.To get a better understanding of the genetic basis of key agronomic traits in faba bean, the seven-parent-MAGIC panel was phenotyped for a wide range of traits during the two years of field trials at two locations in Denmark . We identified 238 (177 unique) markers associated with statistically significant signals for the following traits: earliness of flowering; plant height; lodging; sterile tillers; seed length, width, and area; TGW; herbicide damage; and susceptibility to chocolate spot, rust, and downy mildew (Supplementary File 5). Manhattan plots for multi-environmental traits—that is, susceptibility to chocolate spot, rust, and downy mildew; plant height; lodging; earliness of flowering; TGW; seed area, length, and width—are shown in Fig. Of the 238 marker-trait associations, 230 originated from multi-environmental traits. Of these, 65 were stable across all environments and the associated markers explained between 0.03% (TGW) and 21.8% (seed width) of the overall trait variation Table . AlthougFor the disease susceptibility traits, 58 marker-trait associations were identified, of which 22 were stable across environments . All stable markers had a minor effect on trait variation (PVE(%) < 10%). Broader peaks were found for susceptibility to rust at Chr1L 609,902,557–635,636,923 (~ 26 Mbp) and for susceptibility to downy mildew at the following genomic locations: Chr2 26,807,439–42,451,531 (~ 16 Mbp) and 839,256,282–880,296,875 (~ 41 Mbp) . Given the high number of accessions, the first two PCs explained a noticeable share of the overall genetic variance (10.1%). We found that the plot showed a clear panel structure. Most obvious was the four-way-cross accessions, which formed a tight cluster clearly separated from the remaining panels. Additionally, the GWB accessions formed a tight cluster, suggesting relatively large genetic differences between winter and spring varieties.To investigate the genetic characteristics of the eight panels, a PCA plot was generated based on the 2678 studied accessions Fig. A. Given To establish a diversity panel of inbred lines, we removed the populations that were outbred (VICCI) or generated from a limited number of founders . This left us with 787 combined accessions from the EUCLEG, NORFAB, and ProFaba panels Fig. B. The acPassport information for the diversity panel is included in Supplementary File 1. The lines have a wide range of geographic origins representing 52 countries. In addition, they exhibit large seed variation, with seeds ranging widely in their size, color, and morphology, as exemplified in Fig. K ranging from 2 to 20. After plotting the average CV error as a function of K, we found that the local minimum was reached at K = 15, but that the relative reduction of the CV error when going from K to K + 1 was significantly smaller (less than 1%) after K = 4 . With this in mind, and for interpretation reasons, we considered the best value of K to be between 2 and 4. The optimal number of K was chosen as the value where genetic subpopulations reflected geographic subpopulations to the highest degree. At K = 3, we found a clear correlation between the coarse geographic origin of accessions and their ancestral proportions . For the geographic groups, “North” covers Northern and Central Europe, Canada and Russia; “South” includes Southern Europe, South America, Africa and Australia; “Middle East” represents the Middle East; and “Asia” predominantly covers Central and East Asia. Based on membership coefficients, accessions were assigned to a subpopulation (SP). A PCA analysis of the genotypes separated accessions from different SPs by using the first two PCs. PC1 distinguished SP1 from SP2 and SP3, whereas PC2 further distinguished SP2 and SP3 , Finland (24), Germany (18), and Great Britain (12).SP2 was made up of 304 accessions, of which 161 had a known geographic origin. The vast majority (133) was associated with the geographical group “South”. Of these accessions, 66 originate in Spain, but SP2 also includes most South American and African lines, as well as 24 Middle Eastern lines.The smallest subgroup is SP3. It consists of 49 accessions, where the vast majority (46) have a geographic origin in Central and East Asia, predominantly China (23) and Afghanistan (12).The remaining 31 accessions were considered admixed and were therefore not assigned to any population.FST values. SP2 was closely related to both SP1 and SP3, showing overall FST values of 0.06 and 0.07, respectively. SP1 and SP3 showed the highest degree of genetic differentiation with an FST value of 0.12 (Table Ho = 0.03), expected heterozygosity (He = 0.26) and nucleotide diversity (π = 0.26) than the remaining SPs  .The genome-wide genetic differentiation between the three subpopulations was quantified by calculating pairwise 12 Table . These r12 Table C. AMOVA Ps Table . To ensuq-values < 0.05, which show a substantial to decisive probability (0.89–1.00) of being under diversifying selection. The number of outliers detected by the other two methods were higher, with pcadapt identifying 339 significant outliers  and Ohana finding 1596 SNPs with a likelihood ratio ≥ 2. Although the overlap between the methods was small, five markers were identified by all methods, giving rise to a confident set of markers pointing to direct targets of diversifying selection. In total, 35 markers were considered outliers by at least two of the three methods . We found that the 35 selection markers showed extreme differentiation between subpopulations, as compared to 35 randomly chosen markers . The vast majority of outlier SNPs, including two of the five high-confidence SNPs (AX-416737096 and AX-416745027), were related to divergence of SP3 from SP1 and SP2.To get a better understanding of the characteristics of the outlier SNPs, we visualized their segregation between subpopulations Fig. B and quaFST values of these markers were especially large for SP1 versus SP2 when compared to the background signal (0.52–0.71), reflecting what could be patterns of selection during breeding in Nordic environments , we looked at correlations between genetic values of the traits (as used in GWAS) . Traits related to seed size were correlated with the following four types of traits that showed no correlations with each other: end of flowering (negative), susceptibility to rust (negative), sterile tillers (positive), and lodging (positive). Additionally, susceptibility to chocolate spot had a positive correlation with sterile tillers and lodging. Plant height and susceptibility to downy mildew show no correlation with any of the other measured traits . From the results, it seems likely that multiple traits might have been co-selected during breeding for different market types or environments. This was further supported by the geographically distinct SPs having different proportions of allele frequencies for the 65 stable QTLs MTAs identified in GWAS .Using 21,345 genome-wide high-quality SNPs, we performed genetic analyses on a large collection of faba bean germplasm. Our results revealed genetic diversity reflecting the underlying panel structure. Most strikingly, GWB, a population derived from 11 winter-type founders, was clearly genetically distinguishable from the remaining panels. As the remaining panels predominantly consisted of spring-type germplasms, this suggests that winter-type and spring-type cultivars are highly genetically distinct. A similar distinction between winter and spring-types has been described in Chinese germplasm . This pattern is expected because of the ascertainment bias of the Axiom SNP array, which is caused by using only 12 individuals for SNP discovery, with preference given to alleles of intermediate frequency with a high polymorphism information content  and the outbreeding population . The nucleotide diversity in the combined diversity panel (n = 685) was 0.31. These values are similar to those reported using SNP data in inbred panels of maize, where values between 0.27 and 0.39 have been estimated  scored in multiple environments. Markers here explained between 0.1 and 15.8% of phenotypic variation.Earlier studies have identified several stable QTLs associated with seed size on chromosomes 2, 4, 5, and 6 in faba bean . In this study, we detected six QTLs that were stable across three Danish environments for plant height. The stable markers individually explained between 0.2 and 10.8% of phenotypic variation.Lotus japonicus  of the five flowering time genes in the identified region of Medicago truncatula chromosome 7 to have putative orthologs in the corresponding region on faba bean chromosome 5 .None of our stable flowering-related QTLs were estimated to explain a large proportion (> 10%) of the trait variation. On the contrary, our findings suggest a relatively polygenic nature of flowering, with multiple QTLs specific to environments. A major stable flowering time QTL was previously found on chromosome 5  has previously been reported on chromosomes 3 and 6, respectively , chocolate spot (caused by Botrytis fabae), and downy mildew (caused by Peronospora viciae). Due to environmental and economic reasons, breeding for disease resistance is preferred over treating crops with fungicides  for downy mildew, where we found very strong peaks on chromosome 2, at positions 26,807,439–42,451,531 bp and 839,256,282–880,296,875. This is of great interest, as no QTLs for this trait have, to our knowledge, yet been published for faba bean. Similar to recent studies, we found that chromosome 1 harbors QTLs associated with resistance to chocolate spot . Although some accessions were present in duplicates because of their inclusion in more than one of the initial project-based diversity panels, many were also found to be genetically redundant within these panels. In general, earlier studies have reported that germplasm collections both within and between genebanks suffer from the presence of genetically redundant lines, which do not contribute to genetic diversity and complicates the genetic analyses  and Central and East Asian accessions (SP3) as genetically distinct subpopulations with southern accessions (SP2) located in between. This is also demonstrated by very few accessions showing a high degree of admixture between SP1 and SP3 and close to no geographical overlap between SP1 and SP3. Geographically, our findings fit well with the proposed routes of migration for faba bean cultivation, suggesting that different routes radiated from the Middle East (SP2). One progressed eastwards to Asia (SP3), whereas two different routes are proposed for the European cultivation—One toward the Iberian Peninsula (SP2) via the Mediterranean coast of Africa, and a second toward Northern Europe (SP1) via the Mediterranean regions of Southern Europe (SP2)  Cubero .Consistent with our findings, previous studies have reported that Asian, or specifically Chinese, germplasm is highly distinct from other germplasm , we speculate that it might be a consequence of SP3 mostly originating from two countries (China and Afghanistan), thereby representing what might be expected to be a low effective population size compared to the remaining subpopulations.The level of genetic diversity was lowest for SP3, which includes most of the Central and East Asian accessions. This is in contrast to the findings published by Zong et al. , where AAMOVA results revealed a higher genetic diversity within than between the three subpopulations. This is in agreement with what has earlier been found for faba bean  were identified to be under selection by at least two of the three outlier detection methods. In general, there was low agreement between the results of the different methods, most likely due to the different assumptions and estimation methods of the models. This helped us limit the selection signatures to a few highly confident markers that show strong differentiation between the different subpopulations. Most (26) of these markers were associated with differentiation of SP3 from SP1 and SP2, whereas only 6 markers (from four genetic regions) were associated with SP1 differentiation from SP2 and SP3. This further supports the differentiation of northern (SP1) and asian germplasm (SP3), with the southern germplasm (SP2) being located somewhere in between. Especially interesting were five selection markers that were identified by all three methods. These markers, representing three regions at chromosome 1S , show very strong selection signatures and have very likely played an important role in the geographical differentiation of faba bean.To couple the selection signatures with their associated traits, we took advantage of the seven-parent-MAGIC panel, where we tested the amount of trait variance that markers under selections could explain compared to random markers. Interestingly, we mainly found selection markers associated with the differentiation of northern (SP1) versus southern (SP2) germplasm to explain a significant proportion of trait variances. With a key influence of the strongly differentiated region at chromosome 1S position 17.4–18.1 Mbp, the selection signatures of northern and southern accessions explained variance related to disease resistance, end of flowering, seed size, plant height, and lodging. This is in line with studies of selection in other domesticated crops such as chickpea  at this stage. With comprehensive phenotyping, the better mapping resolution of the diversity panel could help to clarify this question in future studies.This study provides valuable insights into the genetic diversity, geographical differentiation and the underlying genomic regions of key agronomic traits in faba bean. Genome-wide association studies in a MAGIC population provided high-confidence candidate genomic regions associated with seed size, flowering time, plant height, lodging and disease resistance to downy mildew, rust and chocolate spot. Our identified QTLs confirmed both previous studies and provided novel QTLs for key agronomic traits in faba bean. However, the extent of LD in the MAGIC population complicated candidate gene discovery.Genetic analysis of a large sample of global faba bean germplasm allowed establishment of a non-redundant faba bean diversity panel representing 52 countries. Accessions in the diversity panel could be divided into three subpopulations, which showed clear genetic divergence related to their geographical origin. The largest genetic differentiation was observed between SP1, which mostly consisted of Northern European accessions, and SP3 comprising lines from Central and East Asia, predominantly China. The latter also showed lower genetic diversity than the remaining subpopulations. In addition to its role in describing global diversity in faba bean, the diversity panel constitutes a valuable resource for future breeding and high-resolution gene mapping, including candidate gene discovery for the wide genomic regions covered by the QTLs identified in the MAGIC population.Supplementary Figure 1. Heterozygosity of panels. Histograms showing the heterozygosity of genotypes within each panel. The average observed (Ho) and expected (He) levels of heterozygosity are indicated by green and red lines, respectively. (PDF 1132 KB)Supplementary Figure 2. Histograms for GWAS traits. The distributions of raw phenotypes are plotted for all traits and environments. Abbreviations: cs, chocolate spot; dm, downy mildew; Dyn20, Dyngby 2020; Dyn21, Dyngby 2021; dur, duration; ear, earliness; Sej20, Sejet 2020; Sej21, Sejet 2021; susc., susceptibility; TGW, Thousand grain weight. (PDF 2160 KB)Supplementary Figure 3. Trait variance. Proportion of phenotypic variance of traits explained by residual variance (VarRes), replication variance (VarRep), genotype x environment variance (VarGxE), environmental variance (VarE), and genetic variance (VarG). A) All traits scored in multi-environmental field trials. B) Traits scored in one environment only. Abbreviations: cha, character; susc., susceptibility; TGW, Thousand grain weight. (PDF 489 KB)Supplementary Figure 4. QQ-plots for all GWAS results. The plots show the observed distribution of p-values of markers tested for association in GWAS plotted against the expected distribution of p-values if no associated loci are found. Abbreviations: cha, character; Dyn20, Dyngby 2020; Dyn21, Dyngby 2021; envs., environments; Sej20, Sejet 2020; Sej21, Sejet 2021; TGW, Thousand grain weight. (PDF 3159 KB)Supplementary Figure 5. Additional Manhattan plots. Manhattan plots for GWAS of herbicide damage, branching, number of ovules, number of sterile tillers per plant, maturation date, end of flowering, and duration of flowering. The green line indicates the SimpleM-corrected threshold for significance. Abbreviations: Dyn20, Dyngby 2020; Dyn21, Dyngby 2021. (PDF 902 KB)Supplementary Figure 6. ADMIXTURE results. A) Cross-validation error of ADMIXTURE with K = 2 to K = 20. The bars display standard errors associated with repeating the CV 10 times for each value of K. B) ADMIXTURE proportions at K = 15 where the CV error is minimized. C–D) ADMIXTURE plots at K = 4. Each vertical bar represents a single accession colored by its ancestry proportions. Accessions are grouped according to their subpopulation membership (C) or by their geographic origin (D). E) Principal component analysis (PCA) based on genotypes. The ADMIXTURE subpopulations at K = 4 are represented by colors and geographic origin is represented by shapes. (PDF 1131 KB)Supplementary Figure 7. Distribution of average genome-wise nucleotide-diversity (π) values of 1000 subsets (n = 49) of SP1 (A) and SP2 (B). The horizontal green line displays the π value for SP3 (n = 49). (PDF 484 KB)Supplementary Figure 8. Genome-wide distribution of FST values for pairs of subpopulations. The FST values of each SNP throughout a chromosome are displayed as grey dots. The green dots report the 35 SNPs under selection identified in the outlier scans. The numbers next to the green dots serve as a marker code: 1: AX-416824401, 2: AX-416760427, 3: AX-416791399, 4: AX-416723873, 5: AX-416737096, 6: AX-416776470, 7: AX-181492359, 8: AX-416745027, 9: AX-416819371, 10: AX-416741889, 11: AX-181188041, 12: AX-181482613, 13: AX-416771656, 14: AX-416765862, 15: AX-181440418, 16: AX-181487950, 17: AX-181175939, 18: AX-181486832, 19: AX-181194098, 20: AX-416747475, 21: AX-416778737, 22: AX-416724016, 23: AX-416761735, 24: AX-181496895, 25: AX-416722420, 26: AX-181165197, 27: AX-416775196, 28: AX-416783057, 29: AX-416763147, 30: AX-416779502, 31: AX-416767699, 32: AX-181158030, 33: AX-181497981, 34: AX-416738786, 35: AX-181155942. Markers in LD group 1 are highlighted in blue. (PDF 747 KB)Supplementary Figure 9. Segregation of 35 random markers. Each row shows the segregation pattern of one of 35 random markers. Each vertical line represents an accession and is colored by genotype for a specific marker. Genotype coloring scheme is as follows: green, reference homozygote; pink, heterozygote; blue, alternative homozygote. (PDF 621 KB)Supplementary Figure 10. Genetic correlations between traits significantly explained by markers associated with North (SP1) versus South (SP2) differentiation. Green asterisk indicates statistical significance of correlation coefficients using a Bonferroni-corrected threshold of p < 0.05. Abbreviations: cha, character; cs, chocolate spot; dm, downy mildew; Dyn20, Dyngby 2020; Dyn21, Dyngby 2021; envs., environments; Sej20, Sejet 2020; Sej21, Sejet 2021; susc., susceptibility; TGW, Thousand grain weight. (PDF 350 KB)Supplementary Figure 11. The subpopulation specific allele frequencies of the 65 stable QTLs identified in GWAS. Alleles associated with larger trait values in the GWAS population were defined as ‘positive’ (blue), whereas those associated with lower trait values were defined as ‘negative’ (pink). Asterisks indicate statistical significance of differential allele frequencies between subpopulations when corrected for multiple testing (Bonferroni correction) at p < 0.05 (*), p < 0.01 (**) and p < 0.005 significance levels. Abbreviations: cha, character; Dyn20, Dyngby 2020; Dyn21, Dyngby 2021; envs., environments; Sej20, Sejet 2020; Sej21, Sejet 2021; TGW, Thousand grain weight. (PDF 1028 KB)Supplementary Figure 12. Synteny for QTLs related to seed size. A) Mapped QTLs for seed area, seed width, seed length and TGW on chromosome 2  and zoomed region (right side). Markers defining the QTL interval on chromosome 2 reported for seed weight in Khazaei et al. 2014 are shown in blue. B) Mapped QTLs for seed area, seed width, seed length and TGW on chromosome 4  and zoomed region (right side). Markers defining the QTL interval on chromosome 4 reported for seed weight in Khazaei et al. 2014 are shown in blue. (PDF 74 KB)Supplementary Figure 13. Synteny for QTLs related to flowering. A) A region on Medicago truncatula chromosome 7 containing five flowering time genes, defined in Yeoh et al. 2013 . The complete Medicago truncatula chromosome 7 projected across to faba bean chromosome 5 (axes 2 and 3). Green lines indicate gene mappings by sequence from Medicago truncatula to faba bean. The earliness of flowering QTLs from the present study are indicated by colored diamonds . The QTL from Catt et al. 2017 identified in the Icarus x Ascot genetic map (axis 4) is projected against faba bean chromosome 5 via mapped markers . B) Zoomed in view of the syntenic context around the spring1 locus in Medicago truncatula chromosome 7 (left axis) and corresponding region of faba bean chromosome 7 (right axis). The plot features 5 flowering time genes in Medicago truncatula  located on chromosome 7. (PDF 73 KB)Supplementary File 1: Passport information on individual accessions(XLSX 631 KB)Supplementary File 2: Trait descriptions (XLSX 15 KB)Supplementary File 3: SNP markers and their positions (XLSX 809 KB)Supplementary File 4: ANOVA results of the seven-parent-MAGIC population (XLSX 27 KB)Supplementary File 5: Significant marker-trait associations identified by GWAS (XLSX 93 KB)Supplementary File 6: Individual results of all three methods of outlier detection (XLSX 3005 KB)Supplementary File 7: Correlations between genetic values of all traits (XLSX 29 KB)Supplementary File 8: Seven-parent-MAGIC phenotypes used for GWAS (TXT 133 KB)Supplementary File 9: Raw Phenotype Scores from Field Trials (PDF 7208 KB)Supplementary Note 1. Pretzel Instructions (CSV 1567 KB)Supplementary file24 (DOCX 13 KB)Below is the link to the electronic supplementary material.


An allele score-based Mendelian randomization leveraging individual-level data from UK Biobank participants  further examined the effects of EAA on kidney function.Previous studies have reported cross-sectional associations between measures of epigenetic age acceleration (EAA) and kidney function phenotypes. However, the temporal and potentially causal relationships between these variables remain unclear. We conducted a bidirectional two-sample Mendelian randomization study of EAA and kidney function. Genetic instruments for EAA and estimate glomerular filtration rate (eGFR) were identified from previous genome-wide association study (GWAS) meta-analyses of European-ancestry participants. Causal effects of EAA on kidney function and kidney function on EAA were assessed through summary-based Mendelian randomization utilizing data from the CKDGen GWAS meta-analysis of log-transformed estimated glomerular filtration rate , findings which were strongly supported by allele-based Mendelian randomization study (both P < 0.001). Summary-based Mendelian randomization identified 24% increased odds of CKD with each 5-unit increase in IEAA (P = 0.05), with consistent findings observed in allele score-based analysis (P = 0.07). Reverse-direction Mendelian randomization identified potentially causal effects of decreased kidney function on HannumAge acceleration (HannumAA), GrimAA, and PhenoAge acceleration (PhenoAA), conferring 3.14, 1.99, and 2.88 year decreases in HanumAA, GrimAA, and PhenoAA, respectively  with each 1-unit increase in log-eGFR.Using summary-based Mendelian randomization, we found that each 5 year increase in intrinsic EAA (IEAA) and GrimAge acceleration (GrimAA) was associated with − 0.01 and − 0.02 unit decreases in log-eGFR, respectively (This study supports bidirectional causal relationships between EAA and kidney function, pointing to potential prevention and therapeutic strategies.The online version contains supplementary material available at 10.1186/s13148-023-01476-y. Chronic kidney disease (CKD) is an important determinant of morbidity and all-cause mortality worldwide . DispropIn recent years, epigenetic age has become the gold standard measure of biological aging , 7. Basen = 34,710) and kidney function , as well as individual-level genotype and kidney function data from 433,462 White British UK Biobank participants, to carry-out the first MR study of EAA and kidney function phenotypes  helps to support causal inference by leveraging genetic variants as instrumental variables for an exposure, with random genotype allocation mimicking intervention allocation in randomized controlled trials (RCTs) . For comP = 0.02), and marginally associated with a higher CKD risk (P = 0.051). The estimated effects were equivalent to a 0.01 decrease in log-transformed eGFR and a 24% increase in CKD risk per 5 year increase in IEAA. These causal estimates were based on the MR-Egger method, since significant directional pleiotropy was determined by the Rücker’s model-selection framework. However, these associations did not attain significance after FDR correction. After further excluding SNPs with relatively weak IEAA associations but potential pleiotropic effects, causal estimates remained consistent (Table P = 0.09). Based on the IVW method, each 5 year increase in GrimAA was associated with an 0.02 decrease in log-transformed eGFR. This is supported by a series of sensitivity analyses using a conservative genetic instrument  and remained significant after False Discovery Rate (FDR) correction . The causal estimates for genetically predicted HannumAA and PhenoAA on kidney function outcomes remained nonsignificant. It should be noted that the estimates of association represent the difference in eGFR or hazard ratio for CKD per standard deviation increase in transformed allele scores, which explain relatively small proportions of the variation in their corresponding EAA measures. As expected, the presented effect sizes are small and do not reflect the magnitude of association between EAA itself and kidney function.In the allele score-based MR analyses of UK Biobank data, each standard deviation increase in IEAA and GrimAA allele score  was significantly associated with a lower eGFR , 1.99 year decrease in GrimAA (P = 0.05), and 2.88 year decrease in PhenoAA (P = 0.02). The association with HannumAA and PhenoAA remained significant after FDR correction. Notably, using the eGFRcr + BUN genetic instrument, which was comprised of only 45 SNPS, elevated.Results from the reverse-direction summary MR analyses demonstrated a significant causal association between genetic predisposition to decreased kidney function and increased EAA Table , Fig. 3.P = 0.03]. Non-significant, but directionally consistent estimates between the eGFRcr + BUN genetic instrument and both GrimAA and PhenoAA and were observed. There was no causal association observed between kidney function and IEAA. Sensitivity analyses using conservative genetic instruments yielded generally consistent results with the main analysis. Sensitivity analyses using MR-Egger, weighted median and MR-PRESSO showed similar effect estimation with a few exceptions to MR-Egger, which has low statistical power in the absence of horizontal pleiotropy (Egger intercept P > 0.1)  = − 3.24 − 6.15, − 0.34), 5, − 0.34In light of the lack of significant associations from our summary-level MR of EAA on kidney function, we assessed the statistical power of this two-sample analysis. Our MR power analysis indicated that all EAA measures, except for IEAA, had insufficient power to detect less than a 15–25% increased odds of CKD per 1 SD increase in EAA  that achieved genome-wide significant associations (P = 5 × 10–8) with EAA in GWAS meta-analysis, with exclusion of SNPs robustly associated with hypertension, blood pressure, diabetes mellitus, cholesterol-lowering medications, body mass index (BMI), obesity, and smoking  with log-transformed serum creatinine-based eGFR in the large-scale CKDGen GWAS meta-analysis conducted in a predominantly ancestrally European population. From there, SNPs further demonstrating weak or no associations with cystatin C-based eGFR or blood urea nitrogen (BUN) were removed, separately, leaving 140 SNPs as the basis for a creatinine-based eGFR plus cystatin-C-based eGFR (eGFRcr + eGFRcys) genetic instrument and 47 SNPs for a creatine-based eGFR plus BUN (eGFRcr + BUN) genetic instrument, respectively. After performing additional filtering steps including a confirmative clumping, removal of palindromic SNPs with intermediate allele frequency, and Steiger filtering, 133 and 45 SNPs remained in the respective eGFRcr + eGFRcys and eGFRcr + BUN genetic instruments for kidney function  them. As a result, rs1598856, a SNP in genetic instrument for HannumAA, was found in strong association with eGFR at genome-wide significance in GWAS studies. This SNP was excluded by the PhenoScanner filter in the sensitivity analysis as shown in Supplemental Table S2, and the removal of this SNP did not significantly affect the results. No additional SNPs were identified.To ensure the validity of bidirectional MR, genetic instruments for exposures in this bidirectional analysis, namely EAA and kidney function, were examined to make sure they were independent from each other. First, instrumental SNPs for exposures in the bidirectional analysis were examined, as suggested by Smith et al. . We idenN = 567,460 for eGFR; N = 480,698 for CKD, including 41,395 CKD cases). Summary statistics for kidney function traits, including log-transformed eGFR and incident CKD (eGFR < 60 ml/min per 1.73 m2), were downloaded from the CKDGen Consortium public domain website (https://ckdgen.imbi.uni-freiburg.de) and used as outcome data. To investigate the effects of our kidney function genetic instruments on the EAA outcomes of interest, we used summary statistics from the most-recent large-scale GWAS meta-analyses of EAA , which were downloaded from https://datashare.is.ed.ac.uk/handle/10283/3645 [To investigate the effects of our EAA genetic instruments on kidney function outcomes, we used summary statistics from the CKDGen Consortium  between Cochran’s Q statistic for the IVW method and Rücker’s Q’ for the MR-Egger method (with respect to a P < 0.1) [I2 statistic [P < 0.05 was considered nominally significant. To account for multiple testing of four EAA measures, FDR correction was performed with an adjusted P < 0.05 considered significant. Original P-values were displayed in tables and figures throughout this study with footnotes indicating their significance after the FDR correction.For summary-level MR, the Rücker model-selection framework , 43 was P < 0.1) , 43. ForP < 0.1)  and I2 statistic , 48, restatistic  and Mendtatistic , were petatistic . A detaitatistic  and MR-Ptatistic  packagesAs a complement to summary-based MR analyses, individual-level allele score-based MR analyses were conducted by applying genetic instruments derived from the EAA GWAS meta-analysis to the UK Biobank using publicly available imputed genotype and phenotype data . For eacR2) for genetic instruments on exposure, true causal effect sizes, and a type-I error rate level (α = 0.05). Sample sizes were based on the CKDGen GWAS study. The average R2 for genetic instruments was estimated for each EAA based on the EAA GWAS meta-analysis [Statistical power analysis of MR was performed using the method proposed by Brion et al. . Brieflyanalysis . True caanalysis . The uniMultivariable MR and mediation analysis were applied to investigate whether the lymphocyte count mediates the associations of kidney function (approximated by eGFR) and HannumAA. The genetic instrument of lymphocyte counts was derived by extracting GWAS summary statistics of the Blood Cell Consortium  within tAdditional file 1: Fig. S1. Sensitivity analyses of summary-based MR using a conservative genetic instrument from genetically predicted EAA to eGFR and CKD. Fig. S2. Single-SNP and leave-one-out analyses for the causal estimates from genetically predicted IEAA to eGFR and CKD. Fig. S3. Single-SNP and leave-one-out analyses for the causal estimates from genetically predicted GrimAA to eGFR and CKD. Fig. S4. Single-SNP and leave-one-out analyses for the causal estimates from the sensitivity analysis using a conservative genetic instrument from genetically predicted IEAA to eGFR and CKD. Fig. S5. Single-SNP and leave-one-out analyses for the causal estimates from the sensitivity analysis using a conservative genetic instrument from genetically predicted GrimAA to eGFR and CKD. Table S1. Summary of lead SNPs for the genetic instrument of IEAA. Table S2. Summary of lead SNPs for the genetic instrument of HannumAA. Table S3. Summary of lead SNPs for the genetic instrument of GrimAA. Table S4. Summary of lead SNPs for the genetic instrument of PhenoAA. Table S5. Summary of lead SNPs for the genetic instrument of kidney function based on serum creatinine eGFR refined by serum cystatin eGFR. Table S6. Summary of lead SNPs for the genetic instrument of kidney function based on serum creatinine eGFR refined by BUN. Table S7. Findings of summary-based MR from genetically predicted EAA to eGFR and CKD based on the primary method and pleiotropy or outlier-robust methods. Table S8. Findings of summary-based MR sensitivity analysis using conservative genetic instruments from genetically predicted EAA to eGFR and CKD based on the primary method and pleiotropy or outline-robust methods. Table S9. Findings of summary-based MR from genetically predicted eGFR to EAA based on the primary method and pleiotropy or outlier-robust methods. Table S10. Findings of summary-based MR sensitivity analysis using conservative genetic instruments from genetically predicted eGFR to EAA based on the primary method and pleiotropy or outline-robust methods. Table S11. Multivariable MR and mediation analysis of lymphocyte count on the association of kidney function with HannumAA. Table S12. Power analysis of the two-sample MR using the Brion et al. method.