SCN2A gene is co-localized with PCOS in subcutaneous adipose using GWAS-eQTL co-localization analyses. A total of 11 candidate genes were differentially expressed in multiple tissues of the PCOS samples. These findings provide a new understanding of the shared genetic architecture between PCOS and T2D and the underlying molecular genetic mechanism of PCOS.Genome-wide association studies (GWAS) have identified several common variants associated with polycystic ovary syndrome (PCOS). However, the etiology behind PCOS remains incomplete. Available evidence suggests a potential genetic correlation between PCOS and type 2 diabetes (T2D). The publicly available data may provide an opportunity to enhance the understanding of the PCOS etiology. Here, we quantified the polygenic overlap between PCOS and T2D using summary statistics of PCOS and T2D and then identified the novel genetic variants associated with PCOS behind this phenotypic association. A bivariate causal mixture model (MiXeR model) found a moderate genetic overlap between PCOS and T2D . The conditional/conjunctional false discovery rate method identified 11 potential risk variants of PCOS conditional on associations with T2D, 9 of which were novel and 6 of which were jointly associated with two phenotypes. The functional annotation of these genetic variants supports a significant role for genes involved in lipid metabolism, immune response, and the insulin signaling pathway. An expression quantitative trait locus functionality analysis successfully repeated that 5 loci were significantly associated with the expression of candidate genes in many tissues, including the whole blood, subcutaneous adipose, adrenal gland, and cerebellum. We found that  Polycystic ovary syndrome (PCOS) is the most common endocrine disorder in women of reproductive age, with an adulthood prevalence estimated at 5.5–19.9% . ExtensiLeveraging the phenotypic and molecular information of co-morbid PCOS and type 2 diabetes (T2D) may improve the discovery of novel PCOS susceptibility loci. PCOS is the common co-morbidity experienced by adult women with T2D, with its prevalence over 30.0% . Prospec = 0.88) . Signifi = 0.88) . Pleiotr = 0.88) . HoweverIn this study, to determine the shared polygenic architecture between PCOS and T2D with or without the adjustment of body mass index (BMI), we employed a bivariate causal mixture model (MiXeR) based on GWAS summary statistics of T2D and PCOS. Next, we applied the conditional/conjunctional false discovery rate (condFDR/conjFDR) method to identify novel genetic loci associated with PCOS. To explain the genetic variants associated with a gene expression phenotype, we assessed the expression quantitative trait locus (eQTL) functionality of the discovered loci. Finally, we performed a co-localization analysis with eQTL data and a differential gene expression analysis to detect the target genes in different tissues for a set of identified single nucleotide polymorphisms (SNPs).n = 87,943) due to data availability. All PCOS cases were diagnosed based on the National Institute of Health (NIH) criteria or the Rotterdam criteria  consortium. GWAS data from 32 cohorts comprised 74,124 cases and 824,006 controls of European ancestry. The case status was defined by an inclusive T2D diagnosis . With each study, all variants were tested for association with T2D in a regression framework under an additive model of the effects of the risk allele, and the results were merged using fixed-effects meta-analysis with the inverse-variance weighting of log ORs. More details can be found in the supplementary material.The GWAS summary statistics came from the most recently published large-scale GWAS meta-analysis for T2D and PCOS. Data oncriteria . After tTo quantify the polygenic overlap between PCOS and T2D, we performed a MiXeR analysis using the GWAS data on both traits . First, , σβ2) . Second, aligned . Additiop-values by the genomic inflation factor Prior to cond/conj FDR calculation, the data quality control and pre-processing procedures were implemented, as recommended by the authors who developed cond/conj FDR software Pleiofdr . We excl effects .p-values obtained from GWAS summary statistics of one trait for all SNPs and the subsets of SNPs determined by significance levels below the indicated threshold for another trait  plots for one phenotype based on varying levels of association with another phenotype under the null hypothesis to intuitively assess for pleiotropic enrichment of SNP association . Specifip values of the conditional trait. The posterior probability that a given SNP is null (has no association), given that its p-values for that SNP are less than or equal to the observed ones, was calculated. Next, we evaluated the per-SNP condFDR values of the PCOS conditioned on T2D. To further discover the SNP that is associated with PCOS and T2D simultaneously, the conjFDR procedure was further applied. The conjFDR framework is based on condFDR and is determined by the maximum condFDR values for PCOS, given T2D and vice versa. It calculates the posterior probability that a random SNP is null for either trait or both simultaneously, given that the observed p-values for both traits are less than or equal to the given p-values for each trait.To improve the detectability of genetic variants with smaller effect sizes that modulate the PCOS risk, we applied a condFDR statistical method using the GWAS summary statistics of PCOS and T2D without the adjustment of BMI . As an eIn order to include more candidate loci for further analysis, the thresholds of condFDR and conjFDR were both set to 0.05. Manhattan plots were also constructed based on ranking condFDR and conjFDR values to position the PCOS risk loci and the shared genetic risk loci. condFDR and conjFDR analyses were performed after random pruning for all SNPs across 100 iterations by selecting one random SNP per linkage disequilibrium block (defined by http://fuma.ctglab.nl/)  protocol version 1.3, to define the lead SNPs and SNPs having a comparatively high LD (lab.nl/) . SNPs hahttps://genetics.opentargets.org/). Specifically, the information from molecular phenotype quantitative trait locus experiments, chromatin interaction experiments, in silico functional predictions, and the distance between the variant and each gene’s canonical transcription start site was combined and then aggregated by taking the mean weighted-quantile to give an overall V2G score for each SNP–gene pair. The gene with the highest V2G score in a list of genes associated with a given SNP was considered as the mapped gene of it.Gene mapping: two methods were performed to map SNPs to genes. First, positional mapping was performed using the SNP2GENE function of FUMA. Candidate SNPs in each genomic risk locus were assigned to their nearest genes based on functional annotations, namely, the combined annotation dependent depletion (CADD) score, probability of regulatory functionality (RegulomeDB score), and transcription/regulatory effects from chromatin states (the minimum chromatin state) . The CAD0bp bin) . A lower0bp bin)  to perfoeQTL analyses can help identify the associations between genetic variants and their corresponding gene expressions. It also facilitates the isolation of causal genes affecting PCOS. Therefore, we assessed the eQTL functionality of the identified PCOS loci in 49 tissues using publicly available data from the Genotype–Tissue Expression database version 8 (GTEx Release V8) . The fashttps://cran.r-project.org/web/packages/coloc/). COLOC is a Bayesian-based method that produces the posterior probabilities of all possible configurations between two traits by performing an approximate Bayes factor computation  database, we examined whether the nearest/mapped genes and eGenes of pleiotropic SNPs were differentially expressed in PCOS cases. Differential gene expression analyses of the four datasets, including GSE10946 (cumulus cells) , GSE9859A flow diagram presents an overview of the study design .A MiXeR analysis was performed between PCOS and T2D with and without adjustment for BMI. The results were shown as Venn diagrams, providing preliminary evidence of the polygenic overlap between PCOS and T2D . In the ait LDSC . In the ait LDSC , MiXeR erg=0.12 ). In addrg=0.12 .p-values for T2D and vice versa , ADCY5(OMIM 600293), NNT (OMIM 607878), WT1(OMIM 607102), DLG2(OMIM 603583), and FTO (OMIM 610966), respectively. On the other hand, rs1509096 , rs804274 , rs7929660 , rs2432581 , and rs1474758  occur between genes , SEC22A (OMIM 612442), and CTSB (OMIM 116810) using the V2G tool.Considering that the positional mapping of pleiotropic loci using FUMA might not be the best strategy to select the potential causal genes of diseases, the V2G tool was also applied to identify the most probable causal genes having a biological effect on PCOS. By integrating multiple sources of information produced by different experimental methods, eight pleiotropic loci are assigned to eight candidate genes identical to the results generated from the positional mapping method  are likely to affect binding (n = 387) are located in open chromatin state regions  and were defined as eQTL . For exa tissues . Next, wSCN2A gene in the subcutaneous adipose tissue . After applying a more stringent prior  . Specifi tissues .SCN2A gene in the subcutaneous adipose tissue. Furthermore, the differential gene expression analysis found that 11 of all the candidate genes were significantly differentially expressed in PCOS women compared with those of controls. These findings support the importance of abnormal gene expression in shared etiological mechanisms between PCOS and T2D.In recent years, GWAS have identified some unique associations between SNPs and PCOS, but several SNPs with minor genetic effects remain to be identified. By leveraging information on the GWAS summary data from PCOS and T2D, we estimated the polygenic overlap between two phenotypes using MiXeR models and LDSC models. We observed a moderate polygenic overlap between PCOS and T2D, regardless of whether the SNP associations of T2D were adjusted by BMI. Stratified Q–Q plots further support the evidence for the polygenic overlap between PCOS and T2D. In the LDSC model, the estimate of the genetic correlation between PCOS and T2D is significant and positive, similar to the results from the MIXeR model, implying that genes that increase PCOS also increase the risk of diabetes. The cond/conjFDR framework is a powerful method to explore novel genetic susceptibility loci associated with PCOS by integrating two genetically correlated traits. We successfully detected 11 PCOS-associated loci conditional on T2D with a mixture of allelic effect directions. Of those, nine loci were novel, and six loci were jointly associated with PCOS and T2D. These findings strengthen prior genetic evidence . We discFor a pair of traits, a polygenic overlap refers to the fraction of genetic variants affecting both traits simultaneously over the total number of causal variants across the two traits observed regardless of their allelic effect directions. Few studies have estimated the degree of genetic overlap or the number of shared underlying causal variants between PCOS and T2D despite some metrics used to compute the genetic correlation. We reported a moderate polygenic overlap between PCOS and T2D. Using cross-trait LDSC, PCOS shows a moderate positive genetic correlation with T2D, in agreement with previous studies . It shouNEIL2 and CTSB genes. The CTSB gene encodes cathepsin B, a lysosomal cysteine protease. It has been reported that a high activity CTSB gene serves as proapoptotic in mouse ovarian cells, inhibiting the granular cell proliferation via inhibition of the p-Akt and p-ERK1/2 pathways  axis and follicular follicle-stimulating hormone (FSH) resistance . Hyperinetriosis . rs11031etriosis . rs80427pathways . Mendeliith PCOS . StatistFTO gene plays an essential role during the evolution of many reproductive phenotypes, such as ovarian aging. An in vitro model showed that FTO knockdown could induce the faster aging process of granular cells by increasing the total amount of multifunctional N6-methyladenosine, indicating a key effect of FTO in abnormal ovulation processes -dependent demethylase catalyzing the 3-methylthymine in single-stranded DNA and 3-methyluracil and 6-methyladenosine in RNA for repairing and modifying multiple nucleic acids . rs71903rt study . FTO genrocesses . Howeverrocesses . HoweverSCN2A gene is co-localized with PCOS in subcutaneous adipose. A previous study showed that the expression of SCN2A might have positive effects on activating Na + channels in the human nervous system, but the relationship between SCN2A and PCOS is unclear  encodes a member of the ligand-dependent nuclear hormone receptor family of nuclear receptors, regulating adipogenesis through its interaction with several co-activators . PPARG hHPO axis .PCK1 gene. In the liver and kidney, PCK1 encodes the gluconeogenic enzyme (PEPCK-C) that catalyzes the limiting-velocity step of the hepatic gluconeogenic pathway and functions in adipocytes of the glyceroneogenesis pathway  cannot capture the authentic shared genetic architecture if the shared SNPs with mixed effects exist in two traits. Therefore, we introduced the MiXeR tool. The advantage of this tool is that it extends the cross-trait LD score regression by incorporating a causal mixture model, capturing the mixture of the effect directions across shared genetic variants rather than measuring the overall genetic correlation . Second,n = 37,264 versus T2D n = 898,130), underpowering the MiXeR analysis and condFDR/conjFDR. More shared loci between PCOS and T2D are expected to be discovered when larger samples fulfilling the NIH or Rotterdam diagnostic criteria are available in PCOS GWAS. Second, PCOS consists of four phenotypes according to three clinical features: hyperandrogenism , ovulatory dysfunction, and polycystic ovarian morphology (Several limitations need to be acknowledged. First, the self-report samples from the 23andMe database were removed, owing to data availability. Hence, the sample size of PCOS was comparatively small compared to T2D (PCOS rphology . The resIn conclusion, we reported a moderate polygenic overlap between T2D and PCOS, extending the current understanding of the common genetic variants influencing the two diseases. Present results also imply an essential role of BMI in two diseases. More importantly, we successfully improved the identification of pleiotropic genetic variants of PCOS and T2D, including nine novel loci. The results of the eQTL analysis, colocalization analysis, and differential gene expression analysis suggested that most loci are potential regions that regulate PCOS and T2D simultaneously. Our study may provide us with an improved understanding of the potential genetic mechanisms in PCOS.


N = 210,267). We implemented univariable two-sample MR (UVMR) to identify candidate metabolic traits affecting offspring birthweight. We then applied two-sample multivariable MR (MVMR) to jointly estimate the potential direct causal effect for each candidate maternal metabolic trait. In the main analyses, UVMR indicated that higher maternal glucose was related to higher offspring birthweight ), as were maternal glutamine ) and alanine ). In additional analyses, UVMR estimates were broadly consistent when selecting instruments from an independent data source, albeit imprecise for glutamine and alanine, and were attenuated for alanine when using other UVMR methods. MVMR results supported independent effects of these metabolites, with effect estimates consistent with those seen with the UVMR results. Among the remaining 43 metabolic traits, UVMR estimates indicated a null effect for most lipid-related traits and a high degree of uncertainty for other amino acids and ketone bodies. Our findings suggest that maternal gestational glucose and glutamine are causally related to offspring birthweight.Marked physiological changes in pregnancy are essential to support foetal growth; however, evidence on the role of specific maternal metabolic traits from human studies is limited. We integrated Mendelian randomisation (MR) and metabolomics data to probe the effect of 46 maternal metabolic traits on offspring birthweight ( Birthweight is frequently used as a proxy of foetal growth in population studies. Babies born with low or high birthweight are at increased risk of neonatal morbidity and mortality, and birthweight is inversely associated with risk of long-term adverse health outcomes, such as neurodevelopmental disorders and cardiometabolic diseases ,6,7,8,9.During pregnancy, the mother experiences marked physiological changes including substantial changes in maternal metabolism ,11,12. TPrevious observational epidemiological studies have suggested that maternal metabolic traits other than glucose  are associated with birthweight ,25,26,27Mendelian randomisation (MR) is an instrumental variable analysis that uses genetic variants, usually single-nucleotide polymorphisms (SNPs), as instruments to infer causality in the presence of confounding ,30,31. MRecent technological advances in nuclear magnetic resonance (NMR) have enabled the metabolic profiling of large-scale population-based studies, including quantification of glucose, amino acids, ketone bodies, lipids, and many other metabolic traits ,36. ThisUVMR results obtained using SNPs selected from two independent genome-wide association studies (GWAS), i.e., the UK Biobank (UKBB) and Kettunen GWAS, are displayed in p < 0.05 in UVMR); higher maternal alanine ), glutamine ), and glucose ) were associated with higher offspring birthweight. Estimates for ketone bodies were imprecise and had wide confidence intervals (CI). Most lipoprotein-related traits and fatty acids were precisely estimated around the null (Using the SNPs selected from UKBB GWAS (K = 264 SNPs), UVMR of 46 metabolic traits identified three metabolic traits that fit our criteria and were included in MVMR , four metabolic traits were missing from these data and no SNPs were selected for another, leaving 41 out of the 46 metabolic traits. UVMR estimates using SNPs selected from Kettunen GWAS were largely consistent with the UKBB GWAS , althougThe mean F-statistics were calculated for each metabolic trait included in the UVMR analyses, as displayed in When conducting leave-one-out analyses using UKBB-selected SNPs, we did not observe that any single SNP was driving UVMR estimates .We selected putative causal metabolic traits to be included in MVMR: alanine, glucose, glutamine, isoleucine, pyruvate, and 3-hydroxybutyrate. MVMR results including all six maternal traits in a singular model are shown on First, we consider the results using UKBB-selected SNPs. Of the six selected candidate maternal metabolic traits in UVMR, MVMR analyses provided evidence of a direct causal effect on offspring birthweight for glucose ), glutamine ), and possibly alanine ), although estimates for the latter were partly attenuated and less precise in MVMR compared to UVMR. The magnitude of the effects demonstrated was typically slightly larger in UVMR estimates than MVMR estimates, except for pyruvate.We then consider the results using Kettunen-selected SNPs. Of the six selected candidate maternal metabolic traits in UVMR, MVMR analyses provided evidence of a direct causal effect on offspring birthweight for glucose ). The estimated magnitude of effect was slightly attenuated relative to its corresponding UVMR estimate. The precision of estimates for higher maternal alanine ) and higher maternal glutamine ) was reduced in this dataset.The conditional F-statistics were calculated for each of the MVMR selected models to test for the presence of weak instrument bias, The conditional F-statistics were calculated for the MVMR-selected models for both instrument selection approaches . For theThere was strong evidence of between-SNP heterogeneity in both MVMR models, For the six selected metabolites , we compared our main UVMR estimates using IVW to from other UVMR methods  that rely on different assumptions about the genetic instruments . We perfOur findings support a causal effect of some maternal metabolites on offspring birthweight. Taken together, our results confirm a causal effect of higher maternal glucose on offspring birthweight and suggest potential causal effects of higher glutamine. UVMR and MVMR results were supportive of an effect of higher maternal glutamine on higher offspring birthweight when using SNPs selected from UKBB GWAS, although results were less certain when using SNPs selected from Kettunen GWAS due to high imprecision resulting from the small number of SNPs being selected from the latter GWAS. This is in line with findings from a recent MR study probing the effect of 19 amino acids using large-scale data from a cross-platform metabolomics GWAS and from the same birthweight GWAS included in our study . GlutamiIn addition to prioritising putative metabolites influencing foetal growth, our study indicates that maternal lipid traits did not seem to contribute substantially to variations in offspring birthweight in this well-nourished population. This is supported by UVMR results, which estimated a null effect for most of lipid- and lipoprotein-related traits on birthweight. These findings are in line with previous MR studies of conventional clinical chemistry lipid traits  cholesterol, and triglycerides ,33), butTo our knowledge, this is the first study to integrate MR and high-throughput metabolomics data to (de)prioritise putative maternal metabolic factors influencing foetal growth. These findings could contribute to better understanding of key maternal molecular mechanisms influencing foetal growth.One key challenge is determining which risk factors to include in the model. Including all available causal risk factors without careful consideration can be problematic due to the potential for bias due to the inclusion of highly similarly genetically predicted exposures or low power to identify the relevant risk factors when many risk factors which do not have a causal effect are included in the model . We addrOne key limitation of our study is the possibility that horizontal pleiotropy from traits not included in MVMR might have biased our results. The MVMR-IVW method will only provide a consistent estimate if all variants are valid instruments and not affected by unbalanced pleiotropy ,42. GiveAnother limitation of our study is the sample overlap between individuals in the risk factor and outcome datasets in the main analysis. Here, in the main analyses, UKBB participants were included in the SNP–risk factor and SNP–outcome estimates, with approximately ~30% of participants in the birthweight GWAS present in the UKBB metabolic traits GWAS. Selecting instruments in the same sample as the MR analysis may introduce a winner’s curse and overprediction; thus, we performed external validation and selected instruments from an independent dataset to assess the extent of the bias as a sensitivity analysis ,44. SNPsFurthermore, findings from this study should be interpreted bearing in mind that our sample mostly included individuals from European ancestry from high-income settings, which might limit the generalisability of the findings, and that the NMR platform used in this study is highly enriched for lipoprotein-related traits, whereas many other metabolic pathways are underrepresented. Thus, future research should focus on including more diverse populations and use higher-resolution metabolomics data, allowing for a broader interrogation of maternal metabolic traits. These will become feasible as the availability of large-scale GWASs in individuals of non-European settings and the use of mass spectrometry metabolomics increase. In addition, our findings are exploratory, and further MR studies investigating the mechanisms linking genetic variants to specific candidate metabolic traits  are warranted to assess in depth the credibility of these findings and the plausibility of bias due to horizontal pleiotropy.Importantly, genetic association data for offspring birthweight reflected direct maternal genetic effects  ,46.p < 0.05), which were then taken forward into multivariable MR (MVMR) analyses. MVMR is an extension of UVMR that is suitable to jointly estimate the direct causal effect when considering risk factors that may be correlated, such as metabolic traits. In MVMR, multiple genetic variants that are potentially associated with several measured risk factors of interest are used to simultaneously estimate the direct causal effect of each risk factor on the outcome [Many genetic variants regulate processes that affect multiple metabolites; thus, results from UVMR are likely biased by horizontal pleiotropy. Therefore, we used UVMR to identify candidate metabolic traits : mean = 56, SD: 8) for which metabolic traits were available using linear mixed model (LMM) association method as implemented in BOLT-LMM (v2.3) adjusting for genotype array, fasting time, and sex (hereafter ‘UKBB GWAS’), as previously described ,52,53,54Genetic association data for offspring birthweight were extracted from a recent GWAS meta-analysis including 210,267 European participants, combining results of 41 studies from the EGG consortium and UKBB, with mean age at delivery ranging from 24.5 to 31.5 years (see The NMR platform used in this study includes many lipids and lipoproteins that are known to be closely (i) numerically and/or (ii) biologically related. As an example of (i), concentrations of cholesterol in different lipoprotein particles sum to the concentration of total cholesterol; therefore, total cholesterol is correlated with cholesterol in specific lipoproteins. As an example of (ii), the three glycolysis metabolites  are metabolised via the same biological pathway , and, as a result, are correlated. This can result in variance inflation and biased estimates of effects in both observational and MVMR analyses if an attempt were made to mutually adjust for all or even a subset of them at the same time, unless one has extremely large sample sizes .Therefore, we applied three exclusion criteria to the full list of 249 NMR metabolic traits to reduce redundancy in our UVMR analyses and avoid multicollinearity in our MVMR models .First, we excluded those with multiple measures that reflected the same metabolic entity  or where multiple measures reflected a composite measure of highly related traits . As a result, we excluded 84 derived ratios and three composite fatty-acid measures .Second, among lipoprotein-related traits, we selected measures related to circulating lipid composition , apolipoproteins A1 and B, and total lipids in 14 lipoprotein subclasses , IDL, large, medium, and small LDL, very large, large, medium, and small HDL). It has been demonstrated previously that we cannot conditionally predict different lipid measures within these subclasses in the same MVMR model due to the very high correlation between these traits . This maThird, we checked the genetic correlation across the remaining measures . If two After all exclusions, detailed in We estimated the effects of maternal metabolic traits on offspring birthweight using UVMR and MVMR. Genetic association data for metabolic traits and offspring birthweight were harmonised to reflect the same effect allele using allele frequency information to infer the strand for palindromic SNPs . PalindrWe selected SNPs strongly and independently associated with each of our selected metabolic traits using a threshold of We present UVMR results according to subclasses of metabolic traits .To test instrument strength, we calculated the mean F-statistics across SNPs for each metabolic trait included in the UVMR analyses ,47.We also performed leave-one-out analyses, sequentially omitting the indicated SNP and rerunning the analysis. This may indicate whether the estimated association is being substantially driven by a singular SNP.All metabolic traits that demonstrated statistical evidence of an effect in the UVMR prioritise candidate maternal molecular traits influencing foetal growth. Our findings confirm previous studies on the strong relation between maternal glucose and offspring birthweight, highlight glutamine as a potential causal metabolite, and do not support a relationship between several maternal lipids and offspring birthweight. Identification of specific metabolic traits represents a unique opportunity to understand the relation between the intrauterine environment and offspring growth.


Vicia faba L.) varieties are important and widely used as a source of food and cash source to large number of subsistence farmers. However, their production and productivity is below the world's average partly because of lack of their sustainable performance in the current scenario of climate change. Therefore, the present study was designed to test the effects of seed storage time and to evaluate the performance of 31 faba bean varieties collected from Holeta, Kulumsa and Sinana agricultural research systems, Ethiopia. The study involved germination test and field experiment that was laid out using randomized complete block design (RCBD) and conducted at multiple test locations. Data were collected on qualitative and quantitative traits and analyzed using SAS version 9.0, and MINITAB® Release 19. Accordingly, most of the varieties showed a promising germination rate regardless of their storage duration suggesting their sustainable performance under suitable storage conditions. Most of the qualitative and quantitative traits showed a wide range of variations revealing their stable performance and better chance for further improvement. Analysis of variance also revealed a highly significant (p < 0.001) variation for several of the traits suggesting maintenance of the original diversity that could be important in further selection breeding. Likewise, high genetic advance coupled with high heritability and genotypic coefficient of variation together with wide range of variations in both PCV and GCV observed in several of the quantitative traits suggest their sustainable performance and significance in further effective selection. Moreover, a promising high yielding varieties such as Dida1, Welki, Hachalu, Ashebeka and Obse have been identified for further use. Clustering grouped the varieties into three clusters implying significant amount of genetic variability among them. Overall, the results generated could be used as a baseline information for improving faba bean production and productivity. However, to exploit more and determine the actual performance of the varieties more markers such as molecular markers (DNA based) are recommended.In Ethiopia, faba bean ( In Ethiopian agricultural system, pulses are one of the most valuable crops to smallholder farmers. They serve as source of income, cost-effective protein rich diet and most important ingredient of the country's cultural diet. In addition, they are good source of foreign exchange earnings, next to coffee and sesame , 2. EcolVicia faba L.) is among the most important pulse crops in the country's agricultural system. In terms of total national production, it takes the front line relative to other cultivated grain legumes [Faba bean  were collected from Holeta, Sinana and Kulumsa Agricultural Research Centers, Ethiopia where they were first released and maintained as seed source for further evaluations . The varThe experiment was conducted in 2020 under rain-fed conditions at three test locations: Holeta, and Kulumsa Agricultural Research Centers, and at Elbuko center, Madda Walabu University research field, Ethiopia. Description of the locations is presented under Germination test was conducted at Holeta Research Centre, Highland pulse department using distilled water, clean Petridish, and Whiteman paper. The experiment was laid out using complete random design (CRD) with three replications and ten seeds per sample in each replication.Field experiment was laid out using a randomized complete block design (RCBD) with three replications to systematically offset field heterogeneity. Each variety was represented by 10 plants that was planted on a single row per replication using a spacing of 20 cms between plants and 50 cms between plots . The varGermination data for all study samples were collected after observing the germination of ten treated seeds per sample from the first five days to fourteen days by observing radical and plumule production according to ISTA . The expMorphological data were collected using a total of 28  standard morphological traits following IBPGR, ICRISAT and ICARDA,  Table 3Table 3. The germination % was calculated using the following formula:The seed germination rate was computed following Ellis and Roberts, :n/∑D. n, where n is the number of seeds germinated on day D; D is the number of days counted from the beginning of the test.∑ni is the number of seeds germinated at the time i; ti is the time from the start of the experiment to the ith observation, and k is the time of last germination.Mean germination time (MGT) was calculated following Ranal and Santana :(2)MGT=∑Distribution frequencies of the qualitative traits used were analyzed using Minitab® 18.1. After error variance homogeneity test using Hartley's test (F-max test) , analysim) 9 100% where, GA = genetic advance; m = population mean for the trait considered.Estimation of environmental, genotypic and phenotypic variance components and their coefficients of variations per location and combined over locations were done following the description of Singh and Chaudhary . Broad-st-table at (p − 2) degrees of freedom, where p is the number of populations used in the study, at 5% and 1% level of significance.Pairwise phenotypic and genotypic correlation coefficients were determined by using the variance and covariance components as described in Singh and Chaudhary  and SharD2) between clusters were computed using Statistical Analysis Software (SAS 9.0).Multivariate analyses such as principal components (PC) analysis was conducted for combined and standardized sample means using Minitab® 18.1 . PopulatThe effect of seed storage on germination were measured in terms of percent germination, mean germination time and germination rate, and the result is presented under Performances of the varieties in terms of the qualitative traits considered and their distribution frequency is presented under Four phenotypes have been evaluated regarding pod attitude. In this regard, most of the varieties (71.99%) had erect pod attitude and the remaining showed horizontal (20.00%), mixed (7.85%) and few (0.16%) had pendent attitude. The seed size in several of the varieties (47.96%) were medium while the remaining had smaller (27.96%) and larger (24.09%) seed sizes. In addition, most of the varieties (55.97%) had flattened seed shape and the remaining had mixed (28.01%) and round (16.02%) seed shapes. With regards to seed coat color, larger number of the varieties (63.98%) had brown seed color followed by yellow (28.01%) and green (8.01%) colors.BmWPP) and seed production efficiency (SPE) revealed the widest ranges with range units of 4367.34 and 3443.97, and average mean performance values of 4561.39 ± 22.12 and 69.15 ± 2.76, in that order, followed by seed yield per plot (SYPP) , and thousand seed weight (TSW) . On the other hand, three traits such as, pod width (PoW) (0.5), number of branches per plant (NBPP) (0.82), and number of seeds per pod (NSPPo) (0.85) showed minimum range with average mean performance values of 1.46 ± 0.02, 1.37 ± 0.04, and 2.67 ± 0.07, respectively.Summary of the ranges and the means together with their standard errors, obtained on the basis of quantitative traits data combined over the three experimental locations, are shown under p < 0.001) variation among the treatments (varieties). Similarly, most of the traits  showed a significant variation over the three test locations (environment). However, only eight of the total traits revealed a significant variation for treatment (varieties)-environment interactions. A high coefficient of genetic determination (R2) was recorded for all of the traits except seed production efficiency (SPE) (0.59), with the highest score being 0.92 in number of seeds per pod (NSPPo) and days to flowering (DTF). Coefficient of variations (CV) is within the acceptable range for most of the traits considered with the lowest being 2.65 in days to flowering (DTF) and the highest being 21.38 in harvest index (HI).ANOVA, computed using data combined over the three experimental locations, is presented under δ2p) and genotypic (δ2g) variance components of the traits considered is presented under δ2p and δ2g variance estimates showed a wide range of variations in the traits considered. The minimum δ2p and δ2g (each 0.02) were recorded in pod width (PoW) and the maximum were (δ2p = 1072149.48 and δ2g = 964311.30) recorded in biomass weight per plot (BmWPP). Similarly, both phenotypic (PCV) and genotypic (GCV) coefficients of variations showed a wide range of variations in the traits considered. In this regard, PCV ranged from 2.77 in days to maturity (DTM) to 33.85 in seed production efficiency (SPE). Likewise, GCV score ranged from 2.42 in days to maturity (DTM) to 26.80 in seed yield per plot (SYPP). Half of the traits considered showed medium to high (>10%) PCV and GCV estimates. Among these, four traits such as seed production efficiency (SPE), biomass weight per plot (BmWPP), seed yield per plot (SYPP), and harvest index (HI) revealed a higher (>20%) PCV and GCV estimates. One trait, economic growth rate (EGR), scored higher PCV with no eventual higher GCV. The genotype-environment interaction coefficient of variation (GECV) score is detectable in most of the traits considered and showed a significant wider range of variations (0.00 in harvest index (HI) and biomass weight per plot (BmWPP) to 8.02 in seed production efficiency (SPE)).Estimate of the phenotypic (SPE) with PCV score slightly higher than the corresponding GCV score in all the traits considered.In general, the difference between GCV and PCV scores in each of the traits considered is moderate (the highest being 9.88 in 2%) revealed a wide range of variations (29.33 in leaf length (LL) to 92.61% in economic growth rate (EGR)) among all the traits considered. According to Singh [Estimates of heritability and genetic advance for the traits considered is presented under to Singh ; high hePoW) to 1914.75 in biomass weight per plot (BmWPP). Similarly, considerably higher genetic advance as percent of mean (GAM) was recorded in seed yield per plot (SYPP) (52.36%), biomass weight per plot (BmWPP) (41.98%), harvest index (HI) (39.68%) and economic growth rate (EGR) (38.98%).Genetic advance under selection (GA) refers to the improvement of characters in genotypic value for the new population compared with the base population under one cycle of selection at a given selection intensity . In thisThe extents of pair-wise genotypic  and phenotypic  correlation coefficients between the traits considered is presented under SYPP), a direct reflection of seed yield per hectare, showed a highly significant (p < 0.001) genotypic and phenotypic association with the remaining 13 and 16 traits, respectively.Yield and yield related traits, an important aspect in food crops, had a significant association with most of the traits considered. For example, seed yield per plot (DTF), leaf length (LL) and internode length (IL) with factor loadings of 0.32 (the first two) and 0.31. The second PC axis accounted for 24.00% of the total variation and differentiated the varieties largely on the bases of seed filling period (SFP) (0.30), number of seeds per pod (NSPPo) and leaf area (LA) (each 0.29), number of seeds per plant (NSPP) (0.28), and seed yield per plot (SYPP) (0.27). The third and fourth PCs axis contributed 11.00% and 7.00% of the total variations, respectively with high contributing factor loadings from harvest index , number of seeds per plant , seed producing efficiency (SPE) and leaf width (LW) (each −0.33), height to the first podding node (PHFPo), plant height (PH) (each −0.31). The fifth and sixth PCs axis each contributed almost the same variations  and had high factor loadings from pod width (PoW) (0.54), pod length (PoL) (0.43), leaf width (LW) (−0.36), and leaf area (LA) (−0.35)  and clustering were conducted using pooled standardized data of the 22 quantitative traits. Accordingly, the first six principal axes  in PCA accounted for 86.00% of the total variation . The fir (−0.35) .SYPP), number of seeds per plant (NSPP), biomass weight per plant (BwPP), plant height (PH) and thousand seed weight (TSW). Moreover, there exist a strong positive correlation among the remaining traits except the association between days to flowering (DTF) versus inter-node length (IL), and leaf length (LL)  .PCA score plot revealed that the entire varieties were grouped roughly into three groups excluding the local check that appeared alone. The grouping pattern weakly followed the year of release or pedigree of their ancestral line. Thus, the result showed that several varieties released at different times were clustered together and varieties released during the same years were placed under different groups . LikewisI) contained the largest number  of varieties which were sub-divided into three sub-clusters . The second cluster (II) contained ten varieties which were again sub-clustered into three groups . The third cluster contained only six varieties which appeared in two sub-clusters. One variety, Welki appeared as monophyletic  revealed roughly three major clusters . The firphyletic .vs III had the largest inter-cluster distance (91.27) followed by I vs III (49.34) and I vs II (20.14). on the other hand, varieties in cluster II and III had the largest intra-cluster distance (each 4.28) as compared to varieties in cluster I, which had intra-cluster distance of 2.89  have been tested at multiple locations and implications of the results obtained are presented below:Life processes of a given seed is partly dependent on the storage conditions and thus, there is no defined demarcation of life-time for a given seed . Seed deThe present study revealed a varied performance, expressed as frequency distribution, in most of the qualitative traits considered. Similar result has been reported by Thomas et al., . Such wip < 0.001) variation where Dida1 showed a good yield performance with the highest average seed yield of 2459.73 kg/ha, followed by Welki (2394.48 kg/ha), Hachalu (2243.92 kg/ha), Ashebeka (2224.72 kg/ha) and Obse (2114.94 kg/ha). The result is in line with the reports of Yirga and Zinabu [Yield and yield related trait's performance are very essential and several breeding attempts are directed towards the improvement and maintenance of those traits. In this regard, all the tested varieties revealed a statistically significant , biomass weight per plot (BmWPP), seed yield per plot (SYPP), and harvest index (HI) are very important.Estimate of genetic advance (GA) is important to improve genotypic value of a given character in the base population under one cycle of selection at a given selection intensity. However, it is more feasible if coupled with heritability and other variance components . Thus, hPairwise correlation coefficient analysis determines the magnitude and degree of relationship between two traits. The association could be due to genotypic (linkage between genes) or pleiotropic gene effect, or due to environmental correlation, or both , 48. WitPrincipal component analysis (PCA) is important in understanding the sources of variation among the study samples and to find out the characters which accounted more to the total variation. In the present study, the first six principal components  accounted nearly 86.00% of the total variations and score plot distinguished the varieties into three clusters implying significant amount of genetic variability among the tested varieties and thus good opportunity in maintaining the varieties and using them for targeted breeding programs. Several of the traits considered contributed for the largest variation suggesting that selection based on these morphological traits may be effective. Likewise, biplot and loading plot graphs demonstrated a positive association among the traits and varieties showing their relative importance in improving the varieties.D2 = 91.27, and 20.14, respectively) indicating that faba bean varieties in each cluster are still maintained sufficient variability and thus further selection between the clusters could bring high genetic gain for the character of interest.Cluster analysis has a power to tell us how samples are genetically similar to each other or different from each other. In this regard, the tested varieties were grouped into three major clusters. However, the clusters had a considerable inter-cluster distance (the maximum and minimum being Current faba bean production in Ethiopia is very much less than the actual potential of the country. One of the reasons is lack of quality seeds with sustainable performance under varying conditions. In this regard, the present study generated a baseline information that could be used to conduct sustainable faba bean breeding and improvement. The wide range of variation in all the qualitative traits considered signals the importance of those traits for selection breeding. Similarly, the significant variations in most of the quantitative traits considered could be exploitable in improvement of the crop at large. The high genetic advance coupled with heritability and genotypic coefficient of variation observed in some of the traits could reveal their high importance for selection breeding. This is because larger extents of heritability coupled with genetic advance and genotypic coefficient of variations is most useful to indicate the amount of genetic improvement that would result from selection of individual genotypes. Grouping of the genotypes showed high difference among the clusters suggesting large difference in seed performance, morphological traits and yield or in genetic background between aged seeded varieties and the recent ones. However, further research involving advanced molecular markers needs to be carried out to clearly indicate the extents of genetic diversity in the varieties and thus, their performance.


Pyrus spp.) is one of the most common fruit crops grown in temperate regions worldwide. Genetic enhancement of fruit quality is a fundamental goal of pear breeding programs. The genetic control of pear fruit quality traits is highly quantitative, and development of high-density genetic maps can facilitate fine-mapping of quantitative trait loci (QTLs) and gene identification. Bin-mapping is a powerful method of constructing high-resolution genetic maps from large-scale genotyping datasets. We performed whole-genome sequencing of pear cultivars ‘Niitaka’ and ‘Hongxiangsu’ and their 176 F1 progeny to identify genome-wide single-nucleotide polymorphism (SNP) markers for constructing a high-density bin-map of pear. This analysis yielded a total of 1.93 million SNPs and a genetic bin-map of 3190 markers spanning 1358.5 cM, with an average adjacent interval of 0.43 cM. This bin-map, along with other high-density genetic maps in pear, improved the reference genome assembly from 75.5 to 83.7% by re-anchoring the scaffolds. A quantitative genetic analysis identified 148 QTLs for 18 fruit-related traits; among them, QTLs for stone cell content, several key monosaccharides, and fruit pulp acids were identified for the first time in pear. A gene expression analysis of six pear cultivars identified 399 candidates in the identified QTL regions, which showed expression specific to fruit developmental stages in pear. Finally, we confirmed the function of PbrtMT1, a tonoplast monosaccharide transporter-related gene responsible for the enhancement of fructose accumulation in pear fruit on linkage group 16, in a transient transformation experiment. This study provides genomic and genetic resources as well as potential candidate genes for fruit quality improvement in pear.Pear ( Pyrus spp.) is an important temperate fruit crop with a planting area of >1.38 million hectares and 23.9 million tons of global production in 2019 . Although pear cultivation can be traced back around 3000 years 401]).PbrtMT1. S.L.Z. provided experimental support. J.W. designed and managed the research. M.F.Q. and L.T.L. should be considered joint first authors.M.F.Q. and L.T.L. carried out data analysis, prepared the figures, and drafted the manuscript. A.K. and J.S. provided support for data analysis, interpretation of results, and manuscript writing. M.Y.S. and M.Y.Z. helped revise the manuscript. B.B., J.Y.Z., X. Zhang, S.W.L., W.L.W., and K.J.Q. helped collect samples and collected the phenotypic data. J.P.N. and J.M.L. performed the transient transformation experiments to verify the function of The datasets have been submitted to NCBI-SRA database with the BioProject ID PRJNA846875.The authors declare no conflict of interests.Horticulture Research online.supp_data_uhac141Click here for additional data file.


Juglans regia L.), which is grown extensively worldwide, is an important economic tree fruit due to its horticultural, medicinal, and material value. The quality of the walnut fruit is related to the selection of traits such as thinner shells, larger filling rates, and better taste, which is very important for breeding in China. The complex quantitative fruit-related traits are influenced by a variety of physiological and environmental factors, which can vary widely between walnut genotypes.Understanding the underlying genetic mechanisms that drive phenotypic variations is essential for enhancing the efficacy of crop improvement. Persian walnut  using 10.9 Mb of high-quality single-nucleotide polymorphisms (SNPs) and 10 agronomic traits to explore the underlying genetic basis of the walnut fruit. Several candidate genes are proposed to be involved in walnut characteristics, including In addition to providing considerable available genetic resources for walnut trees, this study revealed the underlying genetic basis involved in important walnut agronomic traits, particularly shell thickness, as well as providing clues for the improvement of genetic breeding and domestication in other perennial economic crops.The online version contains supplementary material available at 10.1186/s12870-022-03824-1. A comprehensive elucidation of the underlying mechanisms of crop domestication processes that drive phenotypic evolution is essential for enhancing the efficacy and feasibility of genetic level crop improvement. However, relevant studies have been more focused on annual crops, such as cereals , soybeanJuglans, comprising approximately 20 perennial species extensively distributed across the planet [Juglans regia L.) is one of the oldest food sources known, as well as the most commercially significant Juglans species cultivated for nut production [https://www.fao.org/faostat/zh/#search/walnut). China is presently viewed as one of the primary walnut diversity centers, providing abundant germplasm sources for walnut cultivating and breeding. Thus, as a leading walnut producer, the quality of walnut fruit is related to the selection of traits such as thinner shells, larger filling rates, and improved taste, which is very important for its breeding and procreation in China [There are four sections  in the genus e planet . Owning oduction . It is aoduction . The hisoduction . Accordiin China , 8, 9.Investigations into the improvement of genetic walnut breeding have a long developmental timeline. As a preliminary breeding approach, morphological research has provided directions for the selection of cultivars that are suited to specific growing conditions . CorrelaJ. regia through de novo assembly from short reads opened and subsequently extended the genomic era for studies on walnut evolution and enhancements in genetic breeding. With the assistance of up-to-date long-read and Hi-C sequencing technologies, higher-quality reference genomes of the J. regia have since been published [To date, thanks to the improvement of next-generation sequencing (NGS) technologies, it is possible to detect the genome-wide variations in a considerably short time, which makes single-nucleotide polymorphism (SNP) markers an ideal choice. The release of the first reference genome of ublished . These hJ. regia 700 K SNP array developed from the resequencing data (27 founders) of the Walnut Improvement Program (WIP) of California University, USA, has enormously promoted the research on the genetic level and development of walnut breeding [Against this background, genome-wide association studies (GWAS) as an ideal alternative tool can capitalize on linkage disequilibrium (LD) in diverse populations to single out target quantitative trait loci. Compared with traditional linkage mapping methods, GWAS has several advantages, as it takes on natural variation and has a higher genetic resolution by exploiting historical recombination . The J. breeding . Using tbreeding , 8, 9, nbreeding , and watbreeding . A few abreeding .Nevertheless, due to the technical limitations of the SNP array, although the associated loci can be identified, it is typically difficult to determine the causative genes and variations responsible for given traits. It is still unknown how genomes and genes change during the cultivation of the long-lived perennial walnut crop. Conversely, in addition to the few existing studies that explored limited association gene loci focused on specific given traits, the genetic basis of various important agronomic traits remains for researchers to decipher. For example, in addition to the edible fruit and the usual fruit-related traits, the walnut shell, which is discarded after the walnut is consumed, is an excellent industrial raw material . Walnut Thus, in this study, for the first time, we investigated the genetic control of the cellulose content of walnut shells. Firstly, a total of 101 Persian walnut cultivars, distributed across Guizhou Province , were collected for whole-genome resequencing. Meanwhile, the shell thickness, combined with nine other fruit-related traits , were measured for the follow-up GWAS. Lastly, the expression levels of candidate genes corresponding to different tissues and development stages in walnut were further verified through transcriptomic analysis and qRT-PCR experiments. The aims of this study were (1) to understand the genetic structures and relationships of the walnut populations under study and their linkage disequilibria, and (2) to identify correlations between molecular markers and traits using GWAS to aid with marker-assisted breeding. In addition to providing considerable available genetic resources for walnut trees, this study revealed the underlying genetic foundations involved in important agronomic traits, particularly walnut shell thickness, in addition to offering insights into the improvement of genetic breeding and domestication for other perennial commercial crops.This study was approved by the Chinese government and carried out according to the laws of the People’s Republic of China. All participants had a license approval letter from the College of Life Sciences, Northwest University. From 2020 to 2022, when the green husk cracked in September, 101 Persian walnut accessions in 12 genotypes were sampled in Guizhou Province, China, and investigated. The accessions sampled in the same location were regarded as belonging to one genotype category. The number of trees for each genotype ranged from five to twelve (Table S− 2 mm), while an electronic balance (METTLER TOLEDO ME104) was employed to evaluate the SW and KW (accuracy of 10− 1 g). Each accession within five biologically replicates was used to measure these traits, and the standard deviation (SD) of these repetitions was calculated (Table Shttp://factominer.free.fr/) was used to conduct the PCA . Additionally, the NJ  and UPGMA (unweighted pair-group method with arithmetic means) methods were employed to perform the cluster analysis as a function of the Euclidean distances of these studied traits.Subsequently, eight quantitative traits of interest that described the walnut fruits were measured, namely, longitudinal diameter (LD), cross diameter (CD), side diameter (SD), single-nut weight (SW), kernel weight (KW), shell thickness (ST), fat content (FC), and protein content (PC). Specifically, the LD, CD, and SD were measured using an electronic Vernier calliper  using the Burrows–Wheeler alignment maximal exact match (BWA-MEM) algorithm, with parameters -T 20 -k 30 [p-value of Fisher’s exact test, and mapping quality, respectively. PLINK [FASTP software was adopted to filter the raw data using default parameters. Subsequent to pruning, the clean reads were aligned to the chromosome-level reference genome  was set from 1 to 5. The optimal K value was inferred according to the maximum marginal likelihood value according to minimum cross-validation (CV) errors.To explore the phylogenetic relationships of the studied walnut accessions, we constructed a neighbor-joining (NJ) tree using MEGA software , in whicr2. The visualization of the results was performed using the Plot_MultiPop.pl script package in POPLDDECAY.Linkage disequilibrium (LD) is the nonrandom association of alleles at different loci. To evaluate the LD patterns among the studied accessions, LD decay was computed using POPLDDECAY software . Nonrandy = Xα + Zβ + Wμ + e, where y is the phenotypic trait, X is the indicator matrix of fixed effects, Z is the indicator matrix of SNPs, W is a matrix for random effects, and e is the random residual subjected to e ≈ . Candidate SNPs were initially determined by the 5% significance threshold  through the correlation results. In addition, the Bonferroni correction method was employed to correct the p-value via the multiple hypothesis test, to reduce the probability of false positives. Candidate SNPs were initially determined by the 5% significance threshold (the −log10p ≥ 5) through the correlation results. The Q–Q and Manhattan plots were constructed to better interpret the association results using the R package “CMplot” (https://github.com/YinLiLin/R-CMplot).Before carrying out the GWAS, the outlier values of the phenotypic data were removed. GWAS was performed using GEMMA , which aTo locate the candidate genes, LD blocks around the most correlated loci were estimated using LDBLOCKSHOW software . Each phJrPXC1 was identified as a significant candidate gene that mediated the thickness of walnut shells, which encoded a leucine-rich repeat protein kinase PXC1 (see results). A total of 17 walnut transcriptome datasets obtained from previous studies [JrPXC1 gene in different tissues including immature fruit, pistillate flower, embryo, somatic embryo, vegetative bud, callus exterior, catkins, hull cortex, hull immature, hull peel, young leaves, and roots  was investigated  from J. regia trees growing in Xi’an, Shaanxi Province (at Northwest University campus) from May to July at three development stages  (Table Shttps://www.primer3plus.com/index.html) according to the sequence of the target gene in the reference genome. After 1:5 dilution using RNase-free water, synthetic cDNA was employed as the qRT-PCR template. The qRT-PCR was performed using a Bio-Rad CFX96 Touch Real-Time PCR system and BioTeKe 2 × Plus SYBR real-time PCR mixture. For the internal reference gene, walnut β-actin was used as an internal reference gene.To verify the expression pattern of ) Table S. Using tFor the phenotypes, the frequency distribution histogram of phenotypic measurement results indicated that seven traits  of the 101 walnut individuals presented a normal distribution, while the other three phenotypic data  showed a skewed normal distribution pattern Figs. . The valFor the genotypes, a total of ~ 906.1 Gb clean reads were generated from whole-genome resequencing with an average read depth of 13.80× for each accession. The mean Q30 (sequencing error rate < 0.05%) ranged from 90.92 to 93.18% across all studied individuals, with an overall mean Q30 of 92.03%. The GC ranged from 37.72 to 40.61%, whereas the mean GC was 38.87% , while 76 individuals (orange) were classified into Cluster II, which corresponded with the results of ADMIXTURE analysis  were initially considered as candidate loci accounting for the studied phenotypic traits , cellular components (113 genes), and biological processes (112 genes) Fig. S. For theCombined with the results of gene annotation, for FR, the JreChr03G11738 and JreChr11G10011 genes, located on chromosomes 3 and 11, were considered as the relevant candidate genes  between the genes and significance peak signals of the targeted SNP within the LD blocks, 49 genes were finally selected as candidate genes that accounted for the variation in ST.To determine the most important target traits for walnut shell thickness, we conducted a relatively deep investigation to explore its underlying genetic basis. The results of GWAS revealed that the most significant SNPs associated with shell thickness (ST) were located on chromosomes 1, 2, 3, 5, 6, 9, 10, 11, 13, and 14 , “JreChr02G11142” , “JreChr02G11216” , “JreChr03G11591” , “JreChr03G11591” , “JreChr05G10768” , “JreChr05G11239” , “JreChr06G10266” , “JreChr09G10847” , “JreChr10G11611” , “JreChr11G10209” , “JreChr11G10535” , “JreChr13G10891” , and “JreChr14G11517” . Among these, CYP86B1 and CYP707A2 were found to belong to the cytochrome P450 family, which have been reported to regulate plant growth and development. Furthermore, GAMYB (belonging to the MYB family) regulates the traits related to fruit shape, plant resistance, and sex determination.Furthermore, among these candidate genes, combined with the results of annotation and enrichment analysis, 14 genes with specific functions were selected as the most important candidates associated with the ST  was considered to be one of the important candidate genes associated with walnut shell thickness , consisting of five domains including Myb_DNA-binding, SANT superfamily, Myb_DNA-bind_6, SANT, and SANT_TRF, was also gathered into the same clade with homologous genes in C. illinoinensis of the phylogenetic tree  with a significant SNP signal ) , which encodes a leucine-rich repeat protein kinase, which was significantly correlated with the walnut shell thickness , followed by the USA and Iran. Due to the climatic diversity and geographical differences in China, there are wide differences in yield, quality, and protection against abiotic and biotic stresses among different walnut varieties . The preComplex quantitative fruit-related traits are impacted by a variety of physiological and environmental factors that vary widely between walnut genotypes. Genetic and agronomic improvements have significantly increased plant yield potentials. Therefore, it is critical to genetically establish correlations between these traits . Indeed,Conversely, selected natural walnut populations must possess a sufficiently high genetic diversity, as selecting the correct populations is critical for successful association mapping. In this study, a relatively high phenotypic variation was observed in a number of specific fruit-related traits such as SW, KW, and ST Table S, with thJ. regia 700 K SNP array) also revealed a significant positive correlation between these traits [In addition to significant variations in phenotype, correlations also existed between these studied accessions Fig. . Notablye traits . This reGenetic structure analysis using different approaches  classified our panel into two main groups Figs. , and S5.r2 = 0.2, 3.8 kb and 7.1 kb) observed in our sample collection was most likely due to the open-pollinated origin of our materials, as well as the high levels of genomic recombination in local walnut populations, comparable to those found in other outcrossing woody crops such as grapevine  , the candidate IAA16 (auxin indole-3-acetic acid 16) gene located on chromosome 7  gene on chromosome 11 was found to be involved in LD, SD, and FI  . ERG3 (elicitor-responsive protein 3) encodes a small C2-domain protein, which is a common Cafficking . Most smfficking . Further content . The fat content .Moreover, the JreChr03G12405 gene located on chromosome 3 was annotated as “zinc finger BED domain-containing protein DAYSLEEPER-like”, containing four conserved domains: “Streccoc_I_II”, “ZnF_BED”, “DUF4413”, and “Dimer_Tnp_hAT”. The gene was significantly related to the SD and KW (Table J. regia [+-dependent protein kinase 11) is involved with dehydration responses and has been identified in various plants [2+ in the cytoplasm may trigger a signal for stomatal closure to reduce further water loss. The expression of CKP genes is rapidly induced by drought and high-salt stress, as well as the plant hormone abscisic acid (ABA) [The kernel and signal weights were significantly correlated and typically controlled by three candidate genes: WAKL8 on chromosome 4, CKP11 on chromosome 5, and CSLG3 on chromosome 11 (Table J. regia . Being tJ. regia . Furthers plants . Increasid (ABA) . CSLG3 (id (ABA) . In summIn addition, MYB1, MYB106, and 4CL2 were identified as being responsive to KW (Table Vaccinium corymbosum [In terms of the protein content (PC) trait, JreChr03G10302 ABC1K8), JreChr07G11142 (ABCB21), and JreChr07G11162 (ABC1) loci, coding for the ABC transporter, were considered to be important genes. Marrano et al. found that the ABC transporter was significantly correlated with the leafing date via the 700 K SNP array walnut GWAS panel , JreChr0. A previrymbosum , confirmThe walnut fruit consists of a kernel, pellicle, shell, and husk. The embryo that forms the kernel initially becomes apparent approximately 7 weeks after pollination and increases in size within the husk cavity until late July . During Walnut shells need to remain intact during harvest and storage  to exclude dirt, insects, moisture, or other contaminants. Shell thickness and suture strength were found to be significantly correlated with broken kernels and insect damage, whereby kernel breakage can result in increased microbial damage and a decreased antioxidant capacity . ResearcJ. regia 700 K SNP array, Sideli et al. preliminarily explored candidate genes associated with walnut shell suture strength [To date, only a few studies have focused on walnut shell thickness. Using the strength . In thisstrength , plays astrength . Sideli strength . In thisGAMYB, located on chromosome 13, was considered to be another important candidate gene related to the walnut shell thickness. The MYB gene family was identified as playing important roles contributing to the fruit-related traits in our study Table D. HoweveJrPXC1 gene, which also encodes a leucine-rich repeat protein kinase (LRR) involved in secondary cell-wall formation in xylem fibers [Arabidopsis thaliana. A soybean orthologous PXC1 (GmLRK1) was shown to be involved in the development of the cell-wall architecture, where the mutant of this gene induced a defect in leaf cell elongation [JrPXC1 gene was closer to that in Prunus salicina , was significantly correlated with the shell thickness , CD (Cross diameter), SD (Side diameter), FI (Fruit index), SW (Single weight), NW (Nut weight), FR (Filling rate), ST (Shell thickness), FC (Fat content), PC (Protein content), respectively. Figure S2. The distribution of SNPs on 16 chromosomes mapped to reference genome. Figure S3. The distribution of InDels on 16 chromosomes mapped to reference genome. Figure S4. The population structure of 101 walnut accessions from K=2 to K=4 by using the software Admixture v1.3. Figure S5. Principal component analysis (PCA) of 101 walnut accessions. PCA analysis based on (A) PC1 (6.75%) and PC2 (3.65%), (B) PC2 (3.65%) and PC3 (2.85%), and (C) PC1 (6.75%) and PC3 (2.85%), respectively. Figure S6. Cluster analysis on phenotypic traits of 101 walnut accessions. Clustering based on methods of (A) Neighbor-joining (NJ) and (B) unweighted pair group method with arithmetic mean (UPGMA). Figure S7. The Manhattan plot of the 10 phenotypic association results and the corresponding Q-Q plot. The results are calculated by the GEMMA software, and the grey dotted line represents the significant threshold. (A). CD; (B). FC; (C). FI; (D). FR; (E). LD; (F). NW; (G). PC; (H). SD; (I). ST; (J). SW. Figure S8. (A) The GO analysis of associated genes in shell thickness. (B) The KEGG enrichment analysis of associated genes in shell thickness. Figure S9. The protein sequence alignment of the PXC1 gene homologs in 11 plants.Additional file 2: Supplementary Table S1. Overview of Illumina sequencing data produced and alignment to the reference genome assembly. Supplementary Table S2. The statistics of ten phenotypic traits in 101 walnut accessions used in this study. Supplementary Table S3. The information and annotation of SNPs detected in 101 walnut accessions. Supplementary Table S4. Transcriptome data corresponds to the full name of the organization. Supplementary Table S5. Sample information for qRT-PCR expression levels of JrPXC1 gene between walnut shell and husk. Supplementary Table S6. The phenotypes with significant SNP information obtained by GWAS.


The solution to this problem is to identify all causal genetic variants and to measure their individual contributions4. Here we report a graph pangenome of tomato constructed by precisely cataloguing more than 19 million variants from 838 genomes, including 32 new reference-level genome assemblies. This graph pangenome was used for genome-wide association study analyses and heritability estimation of 20,323 gene-expression and metabolite traits. The average estimated trait heritability is 0.41 compared with 0.33 when using the single linear reference genome. This 24% increase in estimated heritability is largely due to resolving incomplete linkage disequilibrium through the inclusion of additional causal structural variants identified using the graph pangenome. Moreover, by resolving allelic and locus heterogeneity, structural variants improve the power to identify genetic factors underlying agronomically important traits leading to, for example, the identification of two new genes potentially contributing to soluble solid content. The newly identified structural variants will facilitate genetic improvement of tomato through both marker-assisted selection and genomic selection. Our study advances the understanding of the heritability of complex traits and demonstrates the power of the graph pangenome in crop breeding.Missing heritability in genome-wide association studies defines a major problem in genetic analyses of complex biological traits A precise catalogue of more than 19 million variants from 838 tomato genomes, including 32 new reference-level genome assemblies, advances the understanding of the heritability of complex traits and demonstrates the power of the graph pangenome in crop breeding. The resolution of missing heritability is hindered by several factors, including incomplete detection of causal genomic variants, particularly structural variants (SVs), which leads to estimation bias caused by incomplete linkage disequilibrium (LD) between genetic markers and causal variants, as well as genetic heterogeneity of causal variants, which reduces the statistical power of GWAS10. To overcome these bottlenecks, an exhaustive and precise catalogue of genetic variants is required.Missing heritability—the discrepancy between heritability estimates from family-based genetic studies and the variance explained by all of the significant variants in genome-wide association studies (GWAS)12. A pangenome comprising multiple reference genomes may more fully represent species-wide genetic diversity and, as such, retains non-reference information15. However, it is challenging to incorporate coordinates of non-reference sequences into existing analysis pipelines16. Recently, graph-based structures have been used to integrate all genetic variants into a single genome graph, enabling thorough and accurate identification of genomic variants as well as data integration19. Recent studies have demonstrated the superiority of using graph pangenomes as references in identification of SVs with short reads22. Here we report the construction of a variant-based graph pangenome of tomato (Solanum lycopersicum), an important fruit crop and a model system for plant biology and breeding. We demonstrate its use in capturing missing heritability in GWAS, providing insights into a classical genetics problem and facilitating genomic breeding . Moreover, SL5.0 contains 19.3 Mb more sequences than SL4.0 (801.8 Mb versus 782.5 Mb), with 43 contigs (99.8% of the assembly) ordered and oriented on the 12 chromosomes  using high-fidelity (HiFi) long reads and high-throughput chromosome conformation capture (Hi-C) long-range scaffolding  accessions, eight cherry tomato  accessions and eight accessions from S. pimpinellifolium   , were integrated into a variation graph. Complex SVs were not specifically considered when constructing the graph pangenome  and small insertions and deletions  identified from the 31 accessions with HiFi reads, as well as SVs (>50 bp) from all 131 accessions with long reads  of 0.966 for SNPs, 0.941 for indels and 0.840 for SVs in the graph pangenome using 10× sequencing data, significantly better than those in the linear genome   (Supplementary Table ly) Fig. . Given tper Fig. .33, whereas only 6,812 (88.2%) were detected using SL5.0 as the reference. Notably, the linear genome yields only 20% of the SVs called by the graph pangenome, indicating the high efficiency in detecting SVs using the graph pangenome. In summary, TGG1.1 represents one of the most comprehensive and accurate maps of tomato genome variation to date.Next, we genotyped genetic variants of 332 tomato accessions by mapping their Illumina sequences onto TGG1.1, resulting in a callset designated TGG1.1-332 that comprises 6,971,059 SNPs, 657,549 indels and 54,838 SVs. We also mapped these sequences against the linear genome SL5.0 and identified variants in a callset designated SL5.0-332 comprising 7,317,844 SNPs, 447,098 indels and 11,397 SVs. We found that SNPs that were uniquely identified by the linear reference were physically closer to their neighbouring SVs than SNPs uniquely identified by the graph pangenome Fig. , consist34 to estimate the variant heritability of 20,323 molecular traits, comprising 19,353 expression traits and 970 metabolite traits, from fruits of the 332 tomato accessions35. First, we analysed each category of genetic variants individually . The average heritability estimated using the graph pangenome is higher than that using the linear reference genome for all three categories  between the traits, <0.20) . We used the composite model to estimate the average heritability explained by SNPs, indels and SVs from TGG1.1-332, finding that SVs contribute the largest proportion of overall heritability   with adjacent (50 kb on either side) SNPs and indels , but only small fractions  are in complete LD (R2 = 1)  Fig. , indicatrong LD R > 0.7 wi 1) Fig. .Solyc03G002957, which encodes a protein that interacts with phosphoinositides. To evaluate the effects of cis-variants on gene expression, we partitioned genetic variants into six categories, namely cis-variants (50 kb on either side of the gene) and trans-variants of SNPs, indels and SVs from the linear and graph pangenome callset, respectively. We found that total heritability estimated from SL5.0-332 is 0.54 (s.d. = 0.32). By contrast, total heritability estimated from TGG1.1-332 is 0.75 (s.d. = 0.51), to which cis- and trans-SVs jointly contribute the largest proportions, 0.41 (s.d. = 0.34) and 0.28 (s.d. = 0.10), respectively    Fig.  and expl2. One of the important sources is allelic heterogeneity , a widespread phenomenon in complex traits that tends to impair the power of GWAS38. To assess the potential effect of allelic heterogeneity on GWAS in tomato, we analysed the effects of variants in cis-regions (within 50 kb on either side of genes) on their corresponding gene expression . Using a single-locus mixed linear model (MLM)39 on the TGG1.1-332 callset, we detected cis-expression quantitative trait loci (eQTLs) for 1,179 genes. Although the average estimated heritability of the expression of these genes is 0.62, the average heritability explained by leading significant variants is only 0.27 , the average estimated heritability increases to 0.37, therefore capturing an additional 0.10 of heritability  and gene expression, motivated by the assumption that SVs are likely to be causative. Using the least absolute shrinkage and selection operator (LASSO)41, a multilocus regression model, we found that the expression of 1,787 out of the 19,353 genes is affected by at least two significantly associated SVs . Compared with MLM, LASSO uniquely detected 1,249 cis-SV eQTLs, indicating its greater power in resolving allelic heterogeneity , most of which are probably multiallelic SVs43, could not be adequately addressed in this study. Thus, it is probable that allelic heterogeneity may be even more prevalent than estimated here.Multilocus models have the potential to resolve allelic heterogeneity, but only small numbers of variants can be analysed simultaneously, limiting their applications in GWASity Fig. . The cisSolyc03G001472, which encodes a protein of unknown function. The cis-heritability of this gene is 0.24 (s.d. = 0.18), contributing 52% of the total heritability. There are 646 SNPs, 46 indels and three SVs within the gene-proximal region, none of which are significantly associated with its expression when applying the MLM. Considering that the three SVs explain approximately half of the cis-heritability , we applied the LASSO model to the three SVs, and found that two of them show significant association with gene expression, one with minor allele frequency (MAF) of 0.017 (sv3_42936717) and the other with MAF of 0.032 (sv3_42954617)  Fig. . The exp44. In theory, the LASSO model could be used to resolve locus heterogeneity  but, in practice, this is not feasible owing to the large number of genome-wide markers. An alternative approach is to focus on a network of genes potentially involved in regulating specific traits. The ‘omnigenic model’ postulates that all expressed genes may be involved in the regulation of complex traits45; however, only genes with largeeffects can be detected with a limited sample size. For gene expression, we derived a co-expression network formed by 99 modules, including 17,592 genes, using weighted correlation network analysis (WGCNA)46 , an important tomato fruit-quality trait, with heritability ranging from 0.07 to 1.00  constitute the candidate dataset for evaluating the effect of locus heterogeneity on flavonoid content. We performed association analyses between the cis-SV set and the 38 metabolites using LASSO and identified 16 SVs surrounding nine genes associated with 31 metabolites  and six enzyme-coding genes. In particular, Gene Ontology analysis shows that there are two transcription factors and two enzymes involved in the flavonoid biosynthetic process (Supplementary Table The nine genes affecting the 31 flavonoids consist of three genes with transcription factor activity (including the previously reported gene 47. Significant genetic variants identified in GWAS are promising candidate markers for marker-assisted selection (MAS) in breeding. As a proof-of-concept study taking advantage of the added value of the graph pangenome to tomato breeding, we took fruit soluble solids content (SSC), an important yield and flavour trait, as a breeding target.Optimal use of the extensive genome variants is expected to facilitate a paradigm shift in crop improvement30, Lin5 on chromosome 9 and SSC11.1 on chromosome 11. To detect variants that potentially cause locus heterogeneity, we developed a universal pipeline by analysing SSC and gene expression simultaneously using WGCNA and identified a module containing 103 genes that are probably related to SSC. SVs in the proximal regions of these genes contribute 0.33 (s.d. = 0.21) to SSC heritability, comprising 52.9% of total heritability . Using LASSO, we identified cis-SV eQTLs in 25 genes among these module genes. Three SVs  in physical proximity to the corresponding genes  are significantly associated with SSC   significantly affect the expression of their nearby genes (42) Fig. . Solyc02449 Fig. , we did 449 Fig. . These rP = 0.032), the accuracy  of genomic selection using SVs is higher than that using SNPs   Fig. . This deP = 0.693)  Fig. . As SVs 93) Fig. , this paCas9 for all predicted genes and released them in a web-based database (http://solomics.agis.org.cn/tomato). This database also provides tools to search the comprehensive catalogue of SNPs, indels and SVs, and to design kompetitive allele-specific PCR (KASP) markers, which can benefit the tomato research and breeding communities.Genetic variants identified from the graph pangenome will facilitate transgenic and/or genome editing-based breeding. To improve primer design in genome editing, we designed sgRNA primers with the protospacer adjacent motif of The state-of-the-art graph pangenome presented here incorporates genetic variants from a wide range of tomato germplasms. The inclusion of biodiversity from non-reference accessions will serve as an important platform for next-generation genomic studies and genome-assisted breeding. In particular, using the resources offered by the graph pangenome highlights the importance of SVs in capturing missing heritability by addressing incomplete LD, allelic heterogeneity and locus heterogeneity.49. We believe that these problems will be addressed in the future through the development of tools that can generate a unified pangenome graph and annotation graph, reinforced by the greater availability of population-level reference-grade genome assemblies.Here we used both read mapping and assembly-based methods to detect SVs and genotype SVs in a population using short reads using a graph-based method. One limitation is that complex SVs—for example, segmental duplications, tandem repeats and CNVs—are not specifically considered in our current pipeline. Another limitation is that only SVs present in the graph could be genotyped, and the accuracy of SV genotyping is still lower than that for SNPs and indels. Methods based on high-quality genome assemblies are superior for identifying highly complex SVsP values43. The power of these tools can probably be improved by incorporating SVs. Moreover, we have demonstrated the importance of locus heterogeneity. However, we recognize that our solution to use the LASSO is suboptimal, because it is not yet computationally feasible to consider all genetic markers at once. Ideally, multilocus tools will be developed that consider more markers. Furthermore, when it becomes feasible to genotype complex SVs, it will be necessary to develop tools that, for example, allow for multiallelic variants, and can use these variants to capture additional missing heritability and improve the accuracy of MAS and genomic selection.Some statistical tools exist that consider allelic heterogeneity, although these tools often fail to detect causal variants without high marginal 52 and potential misassemblies were corrected using the GALA pipeline53  and sequenced on the PacBio Sequel II platform to generate HiFi reads. Primary assemblies were generated from three assemblers peline53 . For the55) and PRAM56 pipelines. RNA evidence was collected by aligning RNA-sequencing (RNA-seq) reads to the repeat-masked assembly using HISAT2 (v.2.10.2)57 and assembling them to transcripts with StringTie (v.1.3.0)58. TACO (v.0.7.3)59 was applied to merge stringtie gtf (--filter-splice-juncs). Ab initio gene prediction was performed using SNAP (v.2006-07-28)60 and AUGUSTUS (v.3.3.3)61. SNAP was trained for two rounds, and AUGUSTUS prediction was performed using the ‘tomato’ model. Proteins from SwissProt (Viridiplantae) (https://www.uniprot.org) and three Solanum species (S. lycopersicum cv. Heinz 1706 ITAG4.0 (ref. 23), Solanum pimpinellifolium LA2093 (ref. 25) and Solanum tuberosum DM (v.6.1)62 were integrated, with redundant sequences removed using CD-HIT (v.4.6)63 with the parameter ‘-c 0.99’. Non-redundant proteins were used for homology-based prediction using BRAKER (v.2.1.4)64 and GeneMark (v.4.3.8)65. Only integrated gene models with AED values of <0.5 were retained. Furthermore, new gene models were predicted using PRAM.Protein-coding genes were predicted for each genome assembly using the MAKER2 (ref. 66) with the parameters ‘-a -k 19 -O 5,56 -E 4,1 -B 5 -z 400,50 -r 2k --eqx --secondary=no’. DeepVariant (v.1.0.0) with the pretrained PacBio mode (--model_type PACBIO) was then used for variant calling of each accession, and all individual variants were merged using glnexus_cli from DeepVariant (v.0.9.0). Finally, variants that met all of the following criteria were retained: (1) total sequencing depth from 400 to 1,500; (2) quality score ≥ 20; (3) biallelic variants; (4) length ≤ 50 bp for indels.The HiFi reads were first mapped to SL5.0 using minimap2 67, SVIM (v.1.2.0)68, CuteSV (v.1.0.10)69 and PBSV (v.2.4.0) (https://github.com/PacificBiosciences/pbsv) with the default parameters were used for variant calling in each accession. We retained variants with a ‘pass’ flag and a read depth of at least three. Deletions ranging from 51 bp to 100 kb in length, and insertions ranging from 51 bp to 20 kb in length were retained. To identify SVs from the 45 genome assemblies, Assemblytics70 was applied to the genome alignments generated using MUMmer (v.4.0)71 with the default parameters. For the 31 accessions with SVs from the five callers, we merged all SVs shorter than 100 kb using SURVIVOR (v.1.0.6)8 using a maximum allowed distance of 1 kb, reporting only calls supported by at least two callers and where the callers agreed regarding the type of variant. SVs longer than 100 kb detected by Assemblytics were retained. As the publicly available SVs from 100 tomatoes were identified using a different version of the reference genome (SL4.0), we transformed the coordinates to SL5.0 using the LiftOver software according to the instructions provided on the UCSC website .To detect SVs using HiFi reads from the 31 accessions, we mapped HiFi reads to SL5.0 using NGLMR (v.0.2.7)https://github.com/vgteam/giraffe-sv-paper/blob/master/scripts/sv). The variation graph toolkit (vg) pipeline19 was used for the construction of TGG1.0, with SNPs and indels called from the HiFi reads. The vg pipeline was also used for variant calling with short reads. To obtain genotypes of variants in TGG1.0, the GBWT index was created using the greedy path-cover algorithm and 32 paths, and the default minimizer length of 29 was chosen in the minimizer index with a window size of 11. Short reads from 706 tomato accessions (>6×) were mapped to TGG1.0 with Giraffe19 and SNPs and indels were called using DeepVariant with the NGS model. These SNPs and indels were filtered as recommended. Non-redundant SVs, SNPs and indels from both the 31 accessions with HiFi reads, the 100 accessions with ONT long reads and the 706 accessions with short reads were integrated into TGG1.1. Genotypes of SVs for the 706 accessions were called by Paragraph18 using the default parameters.SVs from the 31 accessions with HiFi reads and previously identified SVs from the 100 tomatoes were merged, and redundant SVs were removed according to instructions provided on GitHub . All of the coding sequences from the 45 accessions25 and the previous pangenome31 were next mapped to the graph using minigraph. Coding sequences with more than 90% coverage and sequence identity and overlapping with the SL5.0 gene models were discarded. For genes mapped to the backbone without any protein-coding gene annotation, we selected the longest one if annotated in more than one accession. For genes that were not mapped on the backbone of the graph, we removed redundant genes using CD-HIT with the parameter ‘-l 0.9’ and only genes from the accession with the lowest distance from SL5.0 were retained. Finally, the gene sets mapped to the backbone and the graph were merged, and redundant genes were removed using CD-HIT with the parameter ‘-l 0.9’.To determine the coordinates of genes from non-SL5.0 assemblies, we calculated the distance of each accession from SL5.0 using Mash (v.2.2)74 for all 51,155 gene models in the graph pangenome. RNA-seq data from a total of 332 accessions  were quantified as transcripts per million (TPM). Genes with TPM values of >0.5 were defined as expressed. Only genes expressed in at least 100 accessions were retained for the downstream analyses. Raw expression levels were normalized with quantile–quantile normalization. To remove potential batch effects and confounding factors affecting gene expression, the probabilistic estimation of expression residuals method75 was applied with the top four factors as covariates. For metabolites with missing values in <100 accessions, the mean value of two replicates was used. Raw metabolite values were transformed using the ternary logarithm and then normalized using quantile–quantile normalization.To quantify the expression of all genes, we used Kallisto (v.0.46.2)76 was used to estimate the proportion of phenotypic variance explained by genetic variants. The genetic variants were first pruned to exclude nearby SNPs in perfect LD using LDAK-thin with parameters ‘--window-prune 0.98 and --window-kb 100’. When computing the kinship matrix, it is necessary to specify the power parameter alpha, which determines the expected relationship between per-variant heritability (hj2) and MAF (fj). Specifically, it is assumed that E[hj2] is proportional to [fj(1 − fj)]. By trying multiple values between −1 and 0, we found that alpha = −0.5 fits best under most scenarios, indicating a tendency for per-variant heritability to decrease with lower MAF. Principal component analysis was performed using PLINK (v.2.0)77 using SNPs and indels from TGG1.1-332, and the first four principal components were used as covariates when estimating heritability. For partitioning contributions to heritability by different types of genetic variants, we derived the kinship for each variant category and estimated the heritability using a composite model with multiple kinship matrices. For all estimations with LDAK-thin, we added the parameter ‘--constrain YES’ to ensure no negative estimates of heritability .The LDAK-thin modelWe identified the coordinates of seven anchor dots that represent the seven categories as described in Supplementary Table 39. After pruning using PLINK (v.2.0) with the parameter ‘-indep-pairwise’ set to ‘50 5 0.2’, the pruned SNPs were used for the kinship matrix . For SNPs and indels, the pruned dataset  was used. The first four principal components were used as covariates in the model. A Bonferroni-derived threshold  was used as the significance threshold. For the LASSO model, the best linear unbiased prediction (BLUP) value estimate from LDAK (obtained from the composite model) was used as the response variable (new phenotype) for each trait, and the significance of genetic variants was assessed using the lassopv package41. The significance threshold of LASSO was determined by 1/number of SVs and the false-discovery rate at the threshold was estimated on the basis of permutations.For the MLM, we used the leave-one-chromosome-out method and the mixed model implemented in GCTAR2 of two adjacent variants was >0.20 and the physical distance was <1 Mb. Clusters containing more than three significant variants were considered as candidate QTLs. For eQTL classification, cis-eQTLs were inferred if the leading significant variants were <50 kb from the transcription start sites or transcription end sites of the corresponding genes; otherwise, they were considered to be trans-eQTLs.Significant variants were grouped into the same cluster if the correlation coefficient 46 was applied to the prefiltered expression data from 332 accessions to reconstruct gene modules exhibiting different expression patterns. Based on the criterion of approximate scale-free topology, the number nine was chosen as the proper soft-thresholding power for a signed network. Similar expression profiles were merged to the same module with a minimum module size set to 10 and the dissimilarity set to 0.15.WGCNA78 package was used for genomic prediction of metabolites. SNPs and indels with positive weight were used to calculate the kinship matrix with the A.mat function implemented in rrBLUP. The prediction accuracy was obtained by a five-fold cross-validation with five repetitions. As the kinship matrix was calculated from genomic data, the method is also called genomic best linear unbiased prediction.The rrBLUPFurther information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-022-04808-9.Supplementary InformationSupplementary Methods and Results are provided within Supplementary Notes 1–9 and Supplementary Figs. 1–20.Reporting SummaryPeer Review FileSupplementary Tables 1–16.