We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare “clumping-and-thresholding” (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background . For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals. Here, the authors assess performance and limitations to polygenic risk scores in different race/ethnic groups. They find that polygenic risk score performance improves with diverse training data, and a better understanding of varying genetic backgrounds, social and environmental factors, and gene-environment interactions, is needed to enhance PRS performance for all groups. Genome-wide association studies (GWAS) of BP phenotypes, such as systolic BP (SBP), diastolic BP (DBP), pulse pressure (PP), and hypertension, have been conducted in various populations, and have identified hundreds of independent genetic variants associated with BP phenotypes13. Based on such GWAS, Polygenic Risk Scores (PRSs) have been constructed for both hypertension and BP phenotypes. These BP PRSs are associated with BP phenotypes, including rate of BP increase16, and have been shown to predict incident coronary heart disease and stroke22. This demonstrates that BP PRSs may identify at-risk individuals long before they develop elevated BP. Polygenic scores for various health outcomes are now being studied for integration in routine medical care24. A fundamental question is whether and at what performance threshold to adopt a BP PRS clinically, using it to guide early intervention via drug treatment or lifestyle changes for those at greatest genetic risk of progression to hypertension or adverse CVD outcomes. Answering this question requires assessment of research inequities due to lagging PRS performance in non-European populations27. This inequity may translate to healthcare inequity if PRSs trained in European/White populations pass a threshold of clinical utility first, and are of primary benefit to White individuals in identifying patients at risk of future disease26.Hypertension is a major risk factor for cardiovascular disease29, yet genetic studies have been unevenly and inequitably distributed globally, yielding few GWAS of BP in relevant populations. It is therefore important to evaluate PRS performance in multi-ethnic populations, including admixed populations, such as those represented in Trans-Omics in Precision Medicine Initiative (TOPMed)25. Previously, we studied the generalization of PRSs for BP traits from White to Hispanic/Latino and Black Americans30. We showed that, even when utilizing GWAS results from Hispanic/Latino participants to select variants into the PRS or to compute weights, BP PRSs based primarily on GWAS results in White individuals had poor performance in Hispanic/Latino individuals. Furthermore, PRSs based on GWAS results in White participants were only weakly associated with BP phenotypes in Black Americans. Since then, GWAS based on larger and more diverse study populations have been published, and additional methods to take advantage of multiple GWAS in multi-ethnic PRSs have been proposed, including GWAS meta-analysis and weighted-sum approaches. Using GWAS meta-analysis as the basis for PRS has been criticized for potentially obscuring population-specific genetic effects, and presents the further question of choosing a single reference-panel population from which to compute linkage disequilibrium  to select or adjust the marginal SNP effects of independent blocks of SNPs. The recently proposed multiPRS31 and metaGRS32 use a weighted-sum of previously validated and optimized PRSs. In keeping with this general approach, PRS-CSx33 has been developed to compute ancestry-specific PRSs based on GWAS performed on populations of distinct genetic ancestries, followed by summations of the ancestry-specific PRSs.Populations with diverse global ancestries in the US, especially Hispanic/Latino and Black Americans, are at increased risk of elevated BP34. Individuals of diverse backgrounds tend to experience different environments. Cultural environment may influence diet; sociocultural environment may lead to differential social adversity, including effects of economic disadvantage and structural racism35, which may in turn cause or accentuate exposures such as to stress and environmental pollutants, and impose limits on leisure-time physical activity, nutrition options, or access to healthcare. These environmental factors may interact with genetic factors, including unknown or under-researched genetic factors that are unique to or predominant in certain populations36. Thus, while PRSs may have different performance across populations due to genetic architecture factors such as LD structures, PRS associations with their phenotypes may also differ across population groups such as those defined by self-reported race/ethnicity due to various gene-environment interactions. Such environmental exposures may not be consistently measured at scale, i.e., using the same instruments across multiple studies.Environment and lifestyle, as well as gene-by-environment interactions, have been implicated in the BP variability that remains beyond what current PRSs explain38, these constructs also imply differing distributions of continental genetic ancestry and admixture40. Acknowledging both the social and genetic aspects of these identities, we describe them as race/ethnic “backgrounds”. We sought to better understand how PRS performance varies according to race/ethnic background, thereby shedding light on how the use of standard PRS methodologies for BP phenotypes may potentially impact health equity. This approach complements ongoing efforts to refine PRS methodology, as well as scientific initiatives to fund research in diverse genetic backgrounds and admixed populations. Figure We sought to evaluate multi-ethnic performance of BP PRSs constructed using multiple GWAS and methods. Our study population included individuals from multiple diverse backgrounds in several large biobank and consortia studies: Asian or Asian American (Asian), Black or African American/British (Black), Latino or Hispanic American (Hispanic/Latino), and White or European American (White). While these groups correspond to socially-defined racial/ethnic identities, which are social constructs that may shift according to context and change over timeCharacteristics of TOPMed participants, UK Biobank Black participants, MGB Biobank, and All of Us participants are provided in Supplementary Tables PVE for SBP and DBP PRSs across race/ethnic background groups are visualized in Figs. Supplementary Figs. Example comparison of PRS distributions across datasets is provided in Supplementary Figs. Supplementary Figs. We further validated the top-performing PRS (PRS-CSx2) in the All of Us dataset. Supplementary Table p-value with hypertension. Similarly for other outcomes, including type 2 diabetes, chronic kidney disease, coronary artery disease, atrial fibrillation, and heart failure, associations were fairly similar across PRSs. Results were inconsistent in analyses stratified by antihypertensive use. For example, HTN-PRS was associated with multiple outcomes  among medication users, while other PRSs were not. However, both SBP and DBP PRS and their weighted sum were (weakly) associated with chronic kidney disease among non-medication users, while HTN-PRS was not. Given the width of the confidence intervals, we do not have sufficient power to make strong conclusions.We also tested the association of SBP and DBP PRS with prevalent clinical outcomes in All of Us, and compared them with our previously-developed hypertension PRS (HTN-PRS) which combined PRSs based each on a single multi-ethnic SBP, DBP, and hypertension GWAS using clump-and-threshold PRSice methodology, as well with a newly developed weighted sum of the PRS-CSx2 SBP and DBP PRSs. Results are provided in Supplementary Fig. 17, our intent was to develop PRSs that are useful across individuals regardless of both their genetic make-up and their race/ethnic identity, while acknowledging that PRS performance may be related to these factors. In prior work, we considered summary statistics from multi-ethnic GWASs, trained a HTN-PRS summing trait-specific SBP, DBP, and hypertension PRS without weights, and used TOPMed as a primary dataset, and included a longitudinal and incidence analyses. Here, we developed PRSs based on multiple PRS methods and multiple GWAS summary statistics, evaluated them across population strata, and utilized multiple datasets to further assess PRS performance by strata defined by important determinants of BP.We performed an in-depth evaluation of BP PRS associations with BP phenotypes in diverse populations, using varying methodologies reflective of current PRS construction practices, in conjunction with the largest available single-ancestry and multi-ethnic GWAS. This manuscript expands upon prior work in its focus on multi-ethnic, multi-ancestry populations. Similar to our recently published paper about a multi-ethnic HTN-PRSOur hypothesis that multi-ethnic GWAS can yield PRS with superior performance to single-ancestry GWAS and meta-analysis was partially confirmed. For White and, perhaps contrary to expectation, Hispanic participants, the highest-performing single-GWAS PRS was obtained using LDPred2 along with the UKBB + ICBP GWAS consisting solely of European ancestry individuals. For Black and Asian participants, the best-performing single-GWAS PRS was based on the multi-ethnic MVP GWAS. PVE performance differed substantially by race/ethnic background and was lowest in Black participants, corresponding to the smaller GWAS sample size for African continental ancestry. The highest performing PRS for each race/ethnic background, among single- and multi-GWAS PRSs, was the PRS-CSx1, using fixed weights across background groups trained in the MGB Biobank). While we did not report results for PRS based on meta-analysis of the four GWAS contributing to this manuscript due to differences in outcome scales, multi-PRS approaches provide a useful alternative as they do not depend on scaling of the outcome in individual GWAS. It is a topic of future work to meta-analyze summary statistics from different GWAS by potentially rescaling the effect sizes to bring them to the same scale.42. Secondary analyses suggest that relative PVE performance across race/ethnic background groups may also be driven in part by differences in BP distributions, covariate distributions and effects, and age-dependent BP trajectories.Importantly for clinical adoption of PRSs for BP and other important phenotypes, current PRS performance differs by race/ethnic background in a systematic way. Achieved performance largely tracks available sample size from underlying GWAS, again emphasizing the need for additional recruitment of GWAS participants from under-represented groups. This is in line with other reports25. PRS distribution changes by genetic ancestry highlight the difficulty in using PRS to determine disease risk in admixed individuals.PRS distribution diverges by race/ethnic background. These effects are attributable to differential patterns of allele distribution (frequency and LD) across the groups with respect to the fixed PRS estimated allelic effect weights, and can potentially bias PRS associations and interpretations, and their interpretation in multi-ethnic studies. These ancestry-specific distributional effects have been consistently noted before39.Consistent with expectation, PRS based on the largest GWAS, UKBB + ICBP, trained in individuals of European ancestry, outperformed other PRS and achieved the highest PVE performance overall, when evaluated on White participants in the TOPMed test cohort. Surprisingly, performance of the UKBB + ICBP PRS in Hispanic/Latino participants was roughly equivalent, perhaps due to large proportion of European ancestry in Hispanic/Latino admixed populationsPerhaps more surprisingly, the performance of the UKBB + ICBP PRS was also relatively high among Asian and Black participants, compared with other alternatives, exceeding the performance of BBJ (Japanese population), and roughly equaling the performance of MVP (multi-ethnic population). This suggests the PRS performance benefits to both the target ancestry and other ancestries of large-sample underlying GWAS analyzing, here about ~700 K individuals, from a common continental ancestry. Nonetheless, for all groups, performance of multi-GWAS PRS exceeded that of the best-performing based single-ancestry GWAS. However, current methodology to borrow strength across GWAS of different continental ancestries is not capable of making up the difference and equalizing performance for under-represented background groups. This points to the likely persistence of performance inequalities across race/ethnic backgrounds, unless funding is allocated to equalize representation of continental ancestries.43. Indeed, PRS performance varied by age, sex, and obesity strata, affirming the need to account for these factors as well as other sources of heterogeneity by cohort when evaluating and utilizing PRS to predict phenotypes that have strong age and other clinical and environmental dependencies. In supplemental analyses, the above-described stratified PRS performance patterns were seen more clearly in White and Hispanic backgrounds than in Asian and Black backgrounds, again suggesting a role for more complex environmental and GxE effects.To better understand the role of underlying phenotypic variance and GxE effects in quantifying PRS performance, we studied PRS performance by strata of important BP determinants in the validation dataset All of Us. This follows reports by others demonstrating that PRS performance may differ across population segments defined by environmental and clinical factors44 for genetic analyses involving diverse ancestries and reported social racial groupings. Specifically, we accounted for differential intercepts and heteroskedasticity by reported race/ethnic background. We tested the predictive capability of each of these PRS in the portion of the TOPMed-BP dataset that did not overlap with the training GWAS, and finally validated the top performing PRSs in a the All of Us, multi-ethnic, validation study. In secondary analysis we evaluated PRS associations with prevalent clinical outcomes in All of Us, with further stratification by antihypertensive medication use. While this analysis is useful in that it provides evidence for the potential use of BP PRSs in capturing risk that relates to specific outcomes, it is limited in that we did not evaluate associations with incident outcomes. Also, in future work, phenotypes and study samples should be further elaborated with specific, pre-defined, relevant exclusion and inclusion criteria to yield useful insights for future application in clinical care.This study has several notable strengths. We have attempted to characterize the performance of BP PRS developed based on largest available GWAS summary statistics and modern analytic methods for studies involving multiple ancestries and reported backgrounds. We specifically compared performance of PRS by self-reported race/ethnic background, rather than calculated genetic ancestry because at a time when PRS are coming closer to clinical use, we focus on investigating whether PRS perpetuate a socially constructed racial bias and highlight equity issues for BP PRS related to these social identities. Specifically, the use of race/ethnic background groups reflects potential clinical application, where PRS may be trained and adopted based on reported background rather than more complex models of genetic ancestry and admixture patterns. We identified and produced PRS from a variety of large-scale GWAS in diverse populations to enhance PRS to reflect the unique genetic architecture of BP in the various genetic ancestries represented in the reported backgrounds. We followed standard practices for clumping and thresholding based PRS construction as well as newer Bayesian model-based PRS, and investigated a few approaches to address multi-ancestry populations, including multi-ancestry GWAS and weighted and unweighted PRS summations, where we used the independent MGB Biobank dataset to estimate summation weights. However, we note that the MGB Biobank is limited in being a biobank population, that tends to have more measures related to hospital-based encounters . Thus, both BP phenotypes and medication data are “noisy”, and better PRS combination weights could potentially be developed. We also investigated the effect of background-specific vs multi-ancestry LD reference panels. We followed TOPMed best practicesThis work highlights challenges to address in developing PRSs for diverse populations. First, consistent with other work, these results underline the clear need to recruit to genetic studies individuals who, combined, fully represent the diversity of the underlying population, including oversampling to achieve adequate power. Second, the results imply a continued need to evaluate and improve methods to account for the ancestry-dependent distribution of PRS. Consequences on PRS include bias, miscalibration, and loss of precision. To better correct these biases, it would be useful to compare conditioning on PCs to other methods, e.g. those based on proportion ancestry for admixed populations. Third, more data collection and methodology are needed to develop PRSs that are useful in diverse populations by appropriately accounting for environmental factors that may vary in distribution by background group and may be involved in GxE interactions, in addition to genetic ancestry distributions. Because race/ethnic background tend to be a proxy for social adversity and racial discrimination, as well as additional environment and lifestyle factors, training and calibration of PRS by reported race/ethnicity could induce bias or perpetuate discrimination, including in individuals identifying with one group but who are relative outliers in their genetic ancestral make-up, or lead to situations where GxE effects driven by group-specific exposures are interpreted as immutable ancestry-specific genetic effects. On the other hand, such grouping may be useful for prediction and early intervention precisely because typical exposures across the lifespan, that modify genetic effects, may be more similar within socially-constructed groups. Ideally risk models would disaggregate objective properties of genetic ancestry  from social adversity and other environmental factors that vary by background group.In summary, we found that combining multiple PRSs based on different GWAS is useful, and PRS-CSx software that computes ancestry-specific PRS, which are then combined, leads to strong PRSs that perform well across race/ethnic background strata. Our work also suggests that to improve PRS performance across race/ethnic backgrounds requires understanding all factors driving performance: not only PRS characteristics driven by population structure, but also cohort effects, differing distributions of age and other covariates by race/ethnic background, as well as GxE effects, including those that are due to genetic or environmental factors more prevalent in certain backgrounds. Construction and evaluation of BP PRS risk models for clinical use may need to appropriately account for all these factors to avoid perpetuating research and healthcare inequity.This analysis used multiple datasets. The primary evaluation dataset is the TOPMed-BP, which was used for developing some of the PRSs, assessing PRSs, and study of genetic ancestry-related patterns in PRSs and their associations. All PRSs were standardized according to means and standard deviations (SDs) estimated in the TOPMed-BP dataset. Second, we used the Mass Genetic Brigham (MGB) Biobank to train weights for multi-PRS combinations (weighted sums of PRSs). Third, we used Black individuals from the UK Biobank to complement TOPMed individuals self-reported as Black, because the available sample size was small after eliminating overlap with one of the GWAS summary statistics used. Finally, we used the All of Us dataset for final validation of PRS performance and effect size estimation in an independent dataset of diverse individuals from the U.S. All of Us validation was applied only on the highest performing PRSs in the main evaluation dataset. UK Biobank, MGB Biobank, and All of Us methods are provided in Supplementary Note 45 based on data downloaded from dbGaP. For each cohort, we generally used BP phenotypes, covariates, and medication data from the first available exam, to maximize the sample size. We studied SBP and DBP. As accepted elsewhere in genetic studies of BP phenotypes, SBP and DBP values were raised by 15 mmHg and 10 mmHg, respectively, in individuals using antihypertensive medications46.The TOPMed-BP dataset includes 62,501 individuals from 15 parent population-based studies. Individuals are categorized into four race/ethnic “background” identity groups, based on self- or study-identification. We used BP phenotypes that were harmonized by the TOPMed Data Coordinating Center (DCC), according to its published methodology47 freeze 8 release. The TOPMed Data Coordinating Center constructed a kinship matrix estimating recent genetic relatedness, the corresponding sparse kinship matrix, where values were set to zero when the genetic relationship was estimated to be more distant than 4th degree relatedness, as well as providing genetic principal components (PCs), using the PC-Relate algorithm48. Information about genome sequencing, allele calling, and quality control in TOPMed is provided in https://www.nhlbiwgs.org/topmed-whole-genome-sequencing-methods-freeze-8. In Supplementary Note 49 and compute ancestry-specific allele frequencies.We used genetic data from whole genome sequencing via the TOPMed programTable We excluded from consideration SNPs with MAF < 0.01 in the TOPMed-BP dataset and/or in the discovery GWAS  and alleles  to match those in the TOPMed data (build hg38). Then, we applied PRSice using data from each of the GWAS in Table p-value thresholds of 5 × 10−8, 1 × 10−5, and 1 × 10−2 for SNP inclusion. For clumping, we used the combined TOPMed-BP dataset as a reference panel for Linkage Disequilibrium (LD), and set clumping parameters to R2 = 0.1 and distance = 1000 Kb, so that if a variant was selected into the PRS, other variants with LD higher than 0.1 R2 and within 1000 Kb were removed. For each GWAS, this resulted in three PRSice PRS, one for each p-value threshold. We also used LDPred2-Auto to train PRSs. For computational feasibility in LDPred2 we were required to limit the number of SNPs available to approximately one million, so we selected only SNPs appearing in HapMap53. Once weights were computed using LDPred2, we constructed the PRS using PRSice2. In a sensitivity analysis, we also stratified TOPMed-BP individuals by race/ethnic background to create separate background-specific reference panels for computing LD, and re-computed PRSice2 and LDPred2 PRSs. Similarly, in secondary analysis we evaluated the potential value in using LD reference panel that closely resemble the study population used in GWAS. To facilitate comparing effect sizes across background-specific and multi-ethnic analyses, each PRS was scaled and centered to have mean 0 and variance 1 in the TOPMed-BP dataset, computed using all individuals. Measures such as p-values and variance explained are not affected by rescaling.We constructed PRSs using the clump-and-threshold method implemented in PRSice2 software version 2.3.1.e1), MVP, (PRS2), BBJ (PRS3), and COGENT (PRS4) GWAS; after centering and scaling each component PRS in the TOPMed-BP dataset, simple PRS sum = PRS1 + PRS2 + PRS3 + PRS4. Similarly, we have weighted PRS sum = w1PRS1 + w2PRS2 +w3PRS3 + w4PRS4. Weights were trained using the MGB Biobank, as described below. To limit the number of possible combinations, we always generated these sums using GWAS-specific PRSs that were constructed with the same approach . In addition, we used the PRS-CSx software version v1.0.0 to train ancestry-specific effect sizes and create ancestry-specific PRS, and combined them as sums in the same way. PRS-CSx takes summary statistics from multiple GWAS. Each GWAS is assigned an ancestry and a reference panel that matches this ancestry; we used the provided UK Biobank reference panels. Because the software only accommodates one GWAS per ancestry, we trained two sets of PRS using PRS-CSx . In both sets, we paired UKBB + ICBP with the European ancestry reference panel, and BBJ with the East Asian panel, however, for PRS-CSx1, we paired MVP with the African reference panel, while for PRS-CSx2 we paired COGENT with the African reference panel. Each PRS-CSx training run resulted in a set of three ancestry-specific PRSs, which were combined with weighted and unweighted sums as described before for the GWAS-specific PRS. For PRS-CSx, as for LDPred2, we used only HapMap SNPs.We constructed sums of PRSs based on different GWAS. When using independently constructed PRS, we had four PRSs based on UKBB + ICBP  were extracted as the estimated regression coefficients of the respective PRSs. In primary analysis, PRS combination weights were trained using regression restricted to individuals from the race/ethnic background used for PRS evaluation in the TOPMed-BP dataset. In secondary analysis, we compared this to an approach that uses all available individuals from MGB Biobank to train the PRS combination weights, addressing the possibility that small sample sizes of non-White race/ethnic backgrounds in MGB Biobank will yield suboptimal combination weights compared to the larger sample size of the multi-ethnic, though majority White, sample.We obtained genomic data and health information for 36,434 unrelated individuals from the MGB Biobank, a biorepository of consented patient samples at MGB. Detailed methods are provided in the 54. Particularly, we assessed whether attempting to scale or “scale + match” the PRS distributions across datasets  will affect the PRS combination weights computed in MGB Biobank such that performance of PRS summations in TOPMed-BP analysis. This analysis is reported in the In another secondary analysis, we investigated differences in distributions of PRSs across datasets, which can be artificially caused by differences in imputation panels and imputation quality across SNPs used, in addition to real differences in allele frequencies in the source populations55 version v2.16.1 to estimate the association between the PRS and the corresponding trait, with relatedness modelled via a sparse kinship matrix. All models were further adjusted for sex, age, age2, BMI, smoking status , the first 11 genetic PCs, and combinations of study and race/ethnic background  and study site when relevant. Similarly, we accounted for differences in variances across groups defined by combinations of study and self-identified race/ethnic background using a heterogeneous residual-variances model56. For comparability across groups and datasets, effect sizes are reported per SD of the PRSs, where SDs were computed once over all individuals from the multi-ethnic TOPMed-BP dataset.To estimate PRS effect sizes, we used linear mixed models, as implemented in the GENESIS R packageWe examined performance of each PRS stratified by race/ethnic background. Performance was assessed by percent variance explained (PVE). PVE was computed using only unrelated individuals (selected via the pcairPartition function in the R GENESIS package) successively eliminating one individual from each pair with TOPMed kinship coefficient greater than 49 to compute ancestry-specific allele frequencies to variants with p-value < 0.01, clumped with R2 = 0.1 and distance = 1000 Kb, based on any of the GWAS used (SNP selection and clumping was done based on each GWAS separately). We excluded Oceania ancestry because of the small effective sample size and rescaled proportion of other ancestries so that they sum to 1 in individuals who had small fraction of Oceania ancestry. For each of the primary genetic ancestries represented in individuals in our dataset, i.e. European, African, East Asian, and American, we visualized the estimated SNP effect sizes versus effect allele ancestry-specific frequency. Combining information across SNPs, we visualized the distribution of selected PRSs across race/ethnic background group in the TOPMed-BP dataset, as well as across groups defined by continental genetic ancestry. Groups defined by genetic ancestry were made up of participants for whom at least 80% of their alleles had been assigned above to the corresponding genetic ancestry. In this visualization we included only the best-performing single-GWAS PRS and best-performing multi-PRS.As explained in the Supplementary Note To increase the number of Black participants in the dataset used for PRS evaluation, we incorporated individuals from the UK Biobank study, as described in detail in the We used the independent All of Us dataset to further study effect estimates and variance explained by the PRSs. Variance explained, our primary performance measure, depends on phenotypic variance. As we hypothesized that some groups have larger phenotypic variances due to environmental exposures, we studied potential differences in variance explained of PRS across different stratifications of study individuals. First, in TOPMed, we computed phenotypic variances, as well as residual variances after regressing the phenotype on baseline covariates, across race/ethnic backgrounds, and in sensitivity analysis, across groups based on self-reported race/ethnicity and further refined according to genetic similarity. Second, in All of Us, because BP phenotypes change with age and different participating studies and race/ethnic background groups may have different age distributions, we fit models that evaluate PRS effect sizes and performance by strata of age, sex, and BMI. Age strata were defined by In a secondary analysis, we estimated the association of the best performing SBP and DBP PRSs with multiple prevalent clinical outcomes in the All of Us dataset: hypertension, type 2 diabetes, chronic kidney disease, coronary artery disease, atrial fibrillation, and heart failure, see Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


Smallholder farming systems support the livelihoods of estimated two billion people on the planet. They are often characterized by challenging environments critically exposed to the climate crisis, with limited access to inputs including improved seed technology. To achieve the sustainable intesification of smallholder farming systems, crop breeding needs to tailor varietal development to local farmer needs. Here, we devise a method to integrate wheat genomics with participatory varietal selection that allows to capture farmers’ traditional knowledge in a fully quantitative framework and inform breeding decisions. Our work demonstrates that cultural and natural agrobiodiversity can be leveraged together to increase the effectiveness of genomics-driven breeding toward varietal development for smallholder farming systems. Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers’ appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker–trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers’ traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation.In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat ( Crop production systems worldwide are expected to be negatively affected by the climate crisis . To achiSmallholder farmers often rely on traditional varieties which evolved at the crossroads between anthropic and natural selection. If characterized and made accessible to breeding, this agrobiodiversity can contribute with adaptation traits , lowerinAgrobiodiversity that is found in smallholder farming systems is both nature and culture. When farmers select and maintain specific crop varieties and therefore specific allelic combinations, they consider a combination of traits that include performance, adaptation, and use . To suppTo effectively target local adaptation, breeding must fully embrace a data-driven approach considering both cultural and natural aspects of agrobiodiversity. Here, we use a large multiparental population of Ethiopian durum wheat lines developed from local landraces to show that farmers’ traditional knowledge may be fully integrated in genomics-driven breeding methods relying on GS and QTL mapping. We run a PVS experiment in collaboration with local men and women farmers evaluating 10,400 plots in three locations in Ethiopia, exploring their preference in relation to genotypic and phenotypic diversity of tested wheat lines. We break down farmers’ overall appreciation (OA) into correlated agronomic traits to understand the effect of gender and location on farmers’ choice. We compare the predictive ability of grain yield (GY) with that of OA given by local farmers, finding that farmers’ OA can predict yield in untested environments with higher accuracy than agronomic measures. We then use forward genetics to map the genetic basis of OA, identifying QTL for phenology, yield, and farmer preference. Our results support the value of incorporating PVS in genomics-driven breeding to enhance genetic gain for local agriculture.SI Appendix, Fig. S1). PVS was conducted on DP lines involving men and women smallholder farmers with experience of wheat cultivation in the tested areas, collecting farmers’ OA on a Likert scale from one (poor) to five (very good). PVS data analysis on the DP was previously reported . EtNAM RILs can be considered prebreeding materials in an early phase of varietal development. Both the DP and the EtNAM were phenotyped for yield, phenology, and yield components. PVS was conducted on the EtNAM on the same fields used for phenotyping, following the same procedure employed on the DP. Different farmer groups evaluated the genetic materials in each location. In all locations, the farmers involved in PVS were chosen to be representative of those residing in the area and to be expert wheat growers. Men and women were kept separated during PVS to untangle gender-specific differences in evaluating genetic materials. The DP and the EtNAM were both genotyped with dense molecular markers  of 400 varieties, mainly local landraces, was previously phenotyped for two consecutive seasons in two locations representative of Ethiopian agriculture  . Throughout the season, this site experienced the highest variability both in terms of weekly temperature range and in mean temperature . The experimental locations were chosen at sites commonly used by local breeding programs to test prebreeding materials and, regardless of local specificities, are all representative of the average climate of the wheat cropping area in the country .To better frame the wheat genotypes’ performance in different locations, we conducted a climatological characterization of the experimental sites. Geregera  and Kulumsa  are in tepid submoist mid highlands. Hagreselam  is in the warm submoist lowlands, while Adet  is in tepid moist mid highlands. The three EtNAM locations had different planting and harvesting dates and experienced different temperature and rainfall regimes throughout the cropping season. Kulumsa experienced the highest temperatures and the most consistent rainfall between flowering and full maturity of the EtNAM lines . The distribution of farmer evaluations was consistent across genders, though men provided higher OA than women in Adet and Kulumsa, and lower in Geregera (P < 0.001) (2H) of farmer scores combined across genders (2H = 0.45) was comparable to that of yield (2H = 0.49) and of yield component traits , meaning that farmer preference for genetic materials is a repeatable, genetically determined trait that can be targeted by breeding programs and contribute to genetic gain. While evaluating OA in the EtNAM lines, men farmers provided higher heritability (2H = 0.54) than women . The genetic makeup of lines selected by both men and women farmers can be reconducted to the crossing that was used to develop the EtNAM  B. Althou< 0.001) C, farmerhe EtNAM . The EtNSI Appendix, Fig. S4). PVS studies conducted in Ethiopia on teff . We found that OA was always positively correlated with yield and yield components, and mostly negatively correlated with phenology, suggesting that farmers preferred high yielding, tall and thick plants with early maturity, in agreement with previous assessments . Yield and farmer appreciation were higher for modern varieties and EtNAM RILs than for landraces in all locations, though the advantage over landraces was less evident in Geregera, the most limiting environment. Some EtNAM RILs greatly outperformed both landraces and modern varieties for both yield and OA, supporting the breeding relevance of these genetic materials . Farmers’ OA was positively correlated with biomass and days to maturity in Kulumsa, but not in Adet and Geregera. The highest correlation was observed in modern varieties, followed by EtNAM RILs . Landraces grown in Geregera achieved yield and OA comparable to those of modern varieties, a hallmark of local adaptation. Modern wheat varieties that have a longer time to maturity, when grown in locations that allow a longer growing season like Kulumsa , express higher yield and are preferred by farmers. The relation between farmers’ preference and phenology is opposite in Geregera, a growing environment exposed to terminal drought. The selection of farmers thus depends on local adaptation and on the combination of traits in the materials of choice.Farmers involved in the PVS were local wheat growers representative of the socioeconomic context in the surroundings of the experimental sites. Although they cannot be considered representative of the diversity of Ethiopian farming systems, they are representative of smallholder farmers living in the wheat-growing areas in the country . OA values given by farmers in Geregera, the most limiting environment, were significantly anticorrelated with yield variation across environments (2R = −0.29) and significantly correlated with measures of increased stability across locations. The same does not hold true for OA given by farmers in high potential areas  , suggesting that OA expressed by wheat growers in challenging environments may capture not only yield (2R = 0.45 for men and 2R = 0.47 for women), but also its stability across environments.Farmers’ appreciation derives from a combination of traits depending on local uses and cropping conditions; thus, GY alone cannot summarize OA , 27. To We used a Plackett–Luce model –30 to brP < 0.001) . In the other two locations, farmers’ choices were also influenced by gender. In Adet, women and men favored genotypes based on plant height (PH) and spike length (shorter spikes), while men also preferred genotypes with early maturity (flowering) but late booting. In Kulumsa, both men and women directed their choices toward genotypes with higher GY and biomass, and shorter days to flowering (DF) and heading (P < 0.001). Men in this location also preferred genotypes with shorter spikes, a trait valued in durum wheat breeding and associated to larger grains. In all cases, individual EtNAM RIL families could outperform the reference modern variety, Asassa . Although the magnitude and direction of prediction accuracy depended on the experimental sites, OA consistently outperformed GY in predicting EtNAM GY . Kulumsa, a CIMMYT test site representing high potential wheat-growing areas, was negatively predicted by the DP. This may be due to an opposite effect of allelic combinations that were suitable for highland cultivation, captured by the DP sites  and depending on local conditions. Despite the substantial inadequacy of DP data in predicting the EtNAM performance at Kulumsa, models trained on OA still performed better relative than GY when predicting yield and yield components. We found that OA scores could predict a combination of traits, with highest accuracies for biomass (0.15), SPS (0.15), PH (0.26), and thousand seed weight (0.27) . The prediction accuracy of both GY and OA improved when GS was restricted to the EtNAM germplasm, and the model was trained and tested across locations . These results suggest that OA may be especially relevant as a predictor when the training set and the test set of the GS are furthest apart, in line with farmers’ ability to capture wheat potential.Having assessed that farmer scores were repeatable, heritable, and aligned with local performance of wheat genotypes, we tested whether PVS scores provided by men and women farmers could improve GS accuracy in the considered environments. To do this, we used GY and OA measured in the DP, that included genotypes that were used to produce the EtNAM , to predEtNAM GY A. LikewiEtNAM GY B. In allSI Appendix, Table S6). Conversely, GS models trained on GY measured in the EtNAM could predict GY in the DP only with an accuracy of 0.02. It is worth stressing that farmer groups evaluating the EtNAM and the DP were different, as different were farmer groups conducting the PVS experiment in each of the locations. Differences in GS prediction performance by gender reflected differences in the heritability of OA traits . When PVS traits collected on the DP were used to predict phenology and yield components on the EtNAM, we found that spike morphology appreciation provided prediction accuracies comparable to those of OA for yield component traits, including biomass, number of spikelets per spike, PH, and thousand seed weight . The appreciation of earliness, which was strongly anticorrelated with DF and maturity, provided negative prediction accuracies for yield components and phenology.PVS traits for farmer appreciation of earliness, spike morphology, and tillering capacity on the DP were collected with a method similar to that used for OA . PVS traSI Appendix, Fig. S10). High prediction accuracies for OA derived from farmers’ ranking of wheat genotypes were also observed in a data-driven decentralized breeding approach (3D-breeding) focusing on local adaptation via on-farm testing . A genome-wide association study (GWAS) identified altogether 81 unique marker–trait associations (MTAs), 10 of which were for farmers’ OA, while the rest were for agronomic traits . Men and women OA scores identified significant associations on chromosomes 3B, 4B, 5B, and 6A. Gender differences in the evaluations were also reflected by the different set of loci targeted by men and women. The MTA for OA on chromosome 4B comapped with an MTA for GY , yet PVS MTAs were not exclusive to GY loci. OA comapped with thousand grain weight, a measure of seed size, in several loci including at approximately 650 Mb on chromosome 1B, 740 Mb on chromosome 3B , and 689 Mb on chromosome 5B. OA matched phenology MTAs on the short arm of chromosomes 2A and 2B. Interestingly, an MTA for OA at approximately 175 Mb on chromosome 6B did not comap with any of the agronomic traits measured in this study. Farmers’ evaluation is based on a combination of different traits and may provide genetic targets beyond those for yield and yield components. Farmer scores could be thus used to complement molecular breeding to prioritize loci for breeding and support local adaptation and varietal acceptance.To further explore the potential contribution of PVS to molecular breeding, we used marker data developed on the EtNAM to conduct forward genetics approaches aimed at describing QTL for farmers’ preference and agronomic performance. The single-nucleotide polymorphisms (SNPs) used to genotype the EtNAM were assigned to their estimated physical positions mapping the array SNP probes to the A for GY A. PrevioA for GY , that coSI Appendix, Table S9). We developed linkage groups in numbers ranging from 32 (EtNAM N16) to 46 (EtNAM N1), with a total length from 162 cM to 311 cM. Family N51, developed by the intercross of Asassa with the modern variety Bidi, is the only one lacking an Ethiopian landrace parent and showed the shortest genetic map span, with 1,187 markers included . We used individual genetic maps to support QTL mapping specific to subfamilies, identifying 382 partially overlapping QTL . These individual genetic maps can be used to identify relevant haplotypes at QTL and locate significant associations on genetic backgrounds of interest to wheat breeding . These included chromosome 1A and 1B in EtNAM families N5, N19, N45, as well as several signals on chromosome 2A in families N16 and N5, which correspond to meta-QTL already reported in literature . These positions may not be relevant to the trait in other genetic backgrounds  and may support the prioritization of EtNAM subfamilies for breeding. In both GWAS and QTL mapping on the EtNAM, accuracy is currently insufficient to support the identification of candidate genes. However, thanks to the rapid development of genomic tools on durum wheat  but some were bread wheat (Triticum aestivum L.)  originated from a selection of landraces from the DP. The DP was sourced from the ex situ wheat collection at the Ethiopian Biodiversity Institute (EBI) and is composed of 372 wheat landraces and 28 improved varieties derived from breeding. Landraces were for the most part durum wheat (ivum L.) . Landracivum L.)  at Traitivum L.) .Bidi) with the improved durum wheat variety Asassa with international background, all belonging to the DP. Selection of the EtNAM founder lines was aimed at maximizing i) genetic diversity, ii) segregation of traits of agronomic relevance, and iii) farmer preference of genetic materials. Details on the development of the population are given in the study by Kidane et al. . The remaining families  were derived from Asassa and landrace parentals. EtNAM RILs in the selection were genotyped with a subset of 13,000 most informative markers from the Infinium 90k wheat chip  and Hagreselam (13°38′N/39°10′E) . GY was measured as grams of grains produced per plot and then converted into t·ha−1. Full details are given in the study by Mengistu et al. . On the EtNAM, field technicians measured days to booting (DB), heading (DH), flowering (DF), and maturity (DM) when 50% of each plot reached such phenological stage. After harvesting, five plants per plot were selected at random and used to measure PH (cm), number of total tillers per plant (NTPP), spike length , number of seeds per spike (SPS), and number of spikelets per spike (NSPKPS). Biomass , , and thousands grain weight  were measured on full plots’ harvest. Methods for the climatic characterization of experimental locations are given in the SI Appendix.The DP and the EtNAM were evaluated in multiple locations using similar experimental designs. Details on the agronomic management of field trials are given in the u et al. . The EtNSI Appendix.Participatory variety selection (PVS) was conducted when half of the plots reached flowering stage so as to maximize the discernment capacity between plots. Farmers taking part in the PVS were wheat growers living in the surroundings of each of the phenotyping locations. Before PVS, farmers were divided by gender and sorted in groups with five people each. Farmer groups were conducted across the field from random entry points and asked to evaluate their overall appreciation of each individual plot, defined as OA, on a Likert scale from 1 (poor) to 5 (very good). OA may be expressed as an answer to the following question: “how much do you like this plot?”. Therefore, OA scores are not bound to predefined traits, but rather reflect a measure of how much a farmer likes a specific variety. Farmer scores were individually recorded and numerically analyzed as phenotypes with details in the section below. Details about farmer selection and scoring procedure are given in the SI Appendix. Pearson’s correlations were performed among BLUPs for PVS and agronomic traits on the DP and the EtNAM if not stated otherwise. To avoid incurring in the Simpson’s paradox—that is, misleading correlation estimates due to stratification in the data—we independently performed correlation according to groupings in the data . Stability metrics of EtNAM genotypes performance across different locations were computed following the multitrait stability index method  and heterozygosity (<50%) in both DP and EtNAM data. R/rrBLUP  was usedP-value of 0.05. A less stringent FDR-corrected p-value threshold (The same SNP set used in GS was employed in a GWAS focusing on BLUPs for agronomic traits and farmer appreciation traits. Sequences of SNP marker probes were obtained by TraitGenetics GmbH (Germany) and mapped on the Svevo reference genome  availablhreshold  is repor2 value of >23.9. For each family, linkage groups (LGs) were determined using the group function including markers with a recombination frequency <0.35 and a minimum LOD = 6.0. LGs were selected to possibly include makers belonging to the same chromosome or markers from different chromosomes that were not separated at LOD 16. Details for marker cleaning and LG consolidation are given in SI Appendix.SNPs were loaded in JoinMap® 5  to constMapping of QTL was performed in R/qtl2 . LinkageAppendix 01 (PDF)Click here for additional data file.Dataset S01 (XLSX)Click here for additional data file.


European traditional tomato varieties have been selected by farmers given their consistent performance and adaptation to local growing conditions. Here we developed a multipurpose core collection, comprising 226 accessions representative of the genotypic, phenotypic, and geographical diversity present in European traditional tomatoes, to investigate the basis of their phenotypic variation, gene×environment interactions, and stability for 33 agro-morphological traits. Comparison of the traditional varieties with a modern reference panel revealed that some traditional varieties displayed excellent agronomic performance and high trait stability, as good as or better than that of their modern counterparts. We conducted genome-wide association and genome-wide environment interaction studies and detected 141 quantitative trait loci (QTLs). Out of those, 47 QTLs were associated with the phenotype mean (meanQTLs), 41 with stability (stbQTLs), and 53 QTL-by-environment interactions (QTIs). Most QTLs displayed additive gene actions, with the exception of stbQTLs, which were mostly recessive and overdominant QTLs. Both common and specific loci controlled the phenotype mean and stability variation in traditional tomato; however, a larger proportion of specific QTLs was observed, indicating that the stability gene regulatory model is the predominant one. Developmental genes tended to map close to meanQTLs, while genes involved in stress response, hormone metabolism, and signalling were found within regions affecting stability. A total of 137 marker–trait associations for phenotypic means and stability were novel, and therefore our study enhances the understanding of the genetic basis of valuable agronomic traits and opens up a new avenue for an exploitation of the allelic diversity available within European traditional tomato germplasm. A multipurpose core collection of European traditional tomatoes has been established and analysed, revealing QTLs associated with agro-morphological trait stability and their mode of inheritance to exploit this genetic resource. Investigating the genetic basis of complex traits is key for crop breeding. In the last 30 years, extensive quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) and their meta-analyses have resulted in a better understanding of the genetic basis of yield and associated traits, plant architecture, fruit appearance, and quality traits of tomato  and phenUnderstanding the extent of how the environment can modify the phenotype of a trait is of great interest in plant breeding. From an applied point of view, the appropriate degree of plasticity depends on the breeding objective . A high Solanum lycopersicum L.) to the environment have been conducted on experimental populations exposed to only two conditions (i.e. control versus stress)  to gain insights into the architecture of phenotypic variation  have beeSolanum lycopersicum accessions, as well as semi-wild (S. lycopersicum var. cerasiforme) and wild relatives such as S. pimpinellifolium, S. arcanum, S. habrochaites, S. pennellii, S. galapagense, S. cheesmaniae, S. chmielewskii, S. neorickii, S. peruvianum, S. corneliomulleri, S. chilense, and S. habrochaites. The initial SNP dataset containing 138 839 common markers was quality control checked and filtered. Plink, version 1.90 (https://www.cog-genomics.org/plink2) was used for initial filtering and pre-processing of the data. As 139 000 SNP markers is an overkill excessive for the expected variation, a random sample of 20% of the markers was taken. Genotypes with >30% missing data points were removed, as were markers with >10% of missing data points. The remaining missing genotype scores were imputed by assigning the score of the major occurring allele to that data point. The Adegenet R package  genotyping data that were previously published from 1850 tomato accessions. These comprised 1342 TRADITOM traditional accessions , a free online platform for data analysis and visualization. Violin plots and Wilcox test were used to assess differences in quantitative trait distribution. Mosaic plots, χ2 independence test, and standardized Pearson’s residuals (dij) were used to evaluate qualitative variable distribution and class coverage. ggplot2  which were recorded in each plant.The traditional core collection and the modern tomato collection were phenotyped in the experimental fields of COMAV-UPV (Spain) and UNITUS  during spring–summer 2016, and in INRA (France) and HUJI-ARO (Israel) experimental fields during spring–summer 2017, by using their current cultivation practices , location, or environment (E), and their interaction (GEI). The ANOVA was performed in metan , fittingijk is the response variable of the kth replicate of the ith genotype in the jth location ; μ is the grand mean; Gi is the main effect of the ith genotype; Ej is the main effect of the jth location; (G×E)ij is the interaction effect of the ith genotype with the jth location; and εijk is the random error distributed. ANOVA and variance components estimations were performed using a linear mixed model using residual maximum likelihood, taking the expected genotype means μ as a fixed effect and the rest of factors as random, assuming Gi~NID ; Ej ~NID ; (G×E)ij~NID ; εijk~NID , where NID means normally, identically, and independently distributed. The statistical significance of variance components was estimated by a likelihood ratio test, and probability was obtained by a two-tailed χ2 test with one degree of freedom. The best linear unbiased predictors (BLUPs) per variety across environments (BLUPg) and per variety for the interaction G×E (BLUPge), and broad sense heritability (H2) were obtained employing a linear mixed model using both mean and environment effect as fixed, and genotype and interaction as random variables. Broad sense heritability was estimated across environments as:Where YIn the case of the traits collected on a per accession mean basis , the effects of environment and genotype were estimated in R  using tyStability indexes were calculated in metan  for accei is the weighted average of absolute scores of the ith genotype, IPCAik is the score of the ith genotype in the kth IPCA, and EPk is the amount of the variance explained by the kth IPCA. The genotype with the lowest WAASB value was considered the most stable, that is, the one that deviates least from the average performance across locations.where WAASBij) can be written as:In the case of traits collected on a per accession mean basis, the stability index was estimated as the projection of the genotype i onto the average environment coordination (AEC) ordinate , on the j is the location j environmental main effect; εij=residual effect~N ; λk=the singular decomposition value  for the component (PC); ξik=the eigen-vector of genotype i for PCk; ηjk=the eigen-vector of environment j for PCk; K is the number of PC axes retained in the model [K≤min  and K=2 for a two-dimensional biplot]; and εij=the residual associated with genotype i in location j. For stability estimation, the mean of PC1 and PC2 scores for all locations calculated using genotype‐focused singular value partitioning (SVP) was used to define an average environment. The line passing through this average location and the biplot origin serves as the abscissa of the AEC and represents the mean genotype. The ordinate of the AEC is the line that passes through the origin and is perpendicular to the AEC abscissa and approximate to the GEI associated with each genotype. The length of the projection of a genotype onto the AEC ordinate (PAECi), regardless of the direction, is an estimation of stability. The more stable the genotype, the shorter the projection onto the AEC ordinate.Where µ is the grand mean; EViolin plots depicting variability in trait stability were constructed in gg2plot  using unThe 111 110 SNPs generated by genome by sequencing (GBS) in To model stability–SNP associations, a GWAS was performed using a linear mixed linear model including the co-ancestry kinship matrix among genotypes as a random effect:n×1 vector of the estimated stability index vector, X is the molecular marker matrix with n×p dimensions, β is the unknown n×1 vector of allelic effects to be estimated, g is an n×n co-ancestry kinship matrix, and e is the random error n×1 vector. The GWASs were performed using the R package EMMA (Where Y is an To model trait means and variance (GEI term), we performed a two-stage approximation. First, we estimated the mean across locations and the GEI effects using a linear fixed effect model:ij is the adjusted phenotypic value of the ith genotype in the jth location ; μ is the estimated grand mean; Gi is the main effect of the ith genotype; Ej is the main effect of the jth location; (GE)ij is the interaction effect of the ith genotype with the jth location; and εij is the random error distributed. Then, we performed the GWAS on the estimated trait means and GEI effects vectors following a linear mixed model including the co-ancestry kinship matrix among genotypes as a random effect as described above.Where YFor both models, the significant threshold for GWAS results was adjusted for multiple testing using the effective number of independent tests .r2) higher than the interchromosomal linkage disequilibrium (LD) at the 95th percentile  and within a distance <2 Mb. Loci were defined by merging lead SNP candidate regions that physically overlapped and were found in an associated LD block as follows: for each lead SNP, regional LD was calculated within the candidate gene region using the imputed SNP matrix  were combined to conform associated loci using beddtools (The confidence region for each significant SNP from GWAS and genome-wide by environment interaction studies (GWEIS) was defined on a window size defined by the SNPs with an intervariant allele count squared correlations  on the expression of a trait was modelled using the following linear models using the lm4 R package  across environments obtained from the ANOVA model, with intercept β0; β1 the marker effect; SNPi a vector of the marker allelic states; and εij the random error distributed.Where BLUPgIn the case of the stability index associations:i is the stability index (WAASB or PAECi) of the trait in the ith variety  across environments; with intercept β0; β1 the marker effect; SNPi a vector of the marker allelic states; and εij the random error distributed.Where StabThen, marginal means for the different marker allelic states were extracted from the linear models using the R package emmeans (If B has a positive effect (BB>AA)If B has a negative effect (AA>BB)When all models are rejected and the AB is higher than both AA and BB, then it is considered overdominance.Where the additive genotypic value is:and the dominance genotypic value is:To study the mode of inheritance of significant QTI of quantitative traits, the allelic state effect of a given SNP on the expression of a trait at individual environmental combinations was modelled using the following linear models utilizing the lm4 R package  in the jth location obtained from the model in 0 is the global intercept; β1 is the marker effect; SNPi is a vector of the marker allelic state; (SNP×E)ij is the interaction effect of the ith marker genotype with the jth location; and εij is the random error distributed.Where BLUPgeT2 statistics using the mvcontrast function. Additivity, recessivity, and dominance were tested using the contrast described above.Then, marginal means for the different marker allelic states were extracted from the linear models using the R package emmeans  value for all pairwise combinations of SNPs spaced <1 Mb within the region of interest. The LD and haplotype analysis were performed using Haploview default parameters . Haplotype blocks partitioning ; μ is the grand mean; Hi is the main effect of the ith haplotype; Cj is the main effect of the jth collection; (H×C)ij is the interaction effect of the ith haplotype with the jth collection; and εijk is the random error distributed.Where YP<0.05). The mean and the SD for each haplotype genotype in each collection were extracted from the ANOVA model using the R package emmeans  test  for pairwise nominal and ordinal comparisons of the proportions in each collection. Enrichment of phenotypic classes within genotypic classes was analysed using standardized Pearson’s residuals (dij). The results were presented as a mosaic plot with the ‘vcd’ R package  with respect to TRADITOM, particularly on chr04, chr05, chr11, and chr12 , with the exception of FIRM  differences for most trait means. In brief, traditional tomatoes showed higher fruit weight    and yield FW1.4) , but lowd FW1.4) . Furtherd FW1.4) . In addid FW1.4) . Incidend FW1.4) . Compared FW1.4) . It is rd FW1.4) . No signd FW1.4) . The comd FW1.4) .G), environment (VE), and their interaction (VGE) variances . We found highly significant VG and VE  for almost all the traits  and yield components Yield1.4c, YIELD1.4T, and EstYIELD. Similarly, VGE was highly significant  for all 16 quantitative traits related to agronomical performance and fruit quality (GE contributing from 7.01% (fec.L*) to 37.31% (FN1.4nc) of the total variance. The broad sense heritability across environments (H2) ranged from ~7% (FN1.4nc) to ~76% (fec.H)  . In the  (fec.H)  and mean (fec.H)  suggesteThe significant GEI indicated that at least some of the accessions may have a low stability. The particular contribution of each accession to the GEI term was evaluated using stability indexes  for almost all the traits  for the stability of most of the traits. The only exceptions were fec.L*, FSH, FAS, ITS, CCRACK, BER, FLOW, and RUN , consisting of 141 QTLs and involving 86 SNPs across 62 independent loci, were identified for the phenotypic means and the stability  the SNP effects on the phenotypic mean of the trait across environments (meanQTL); (ii) stability assessed as the SNP effects modulated by the environment (QTI) (i.e. departure from stability); and (iii) the SNP effects on the stability index (stbQTL). We used the panel of 197 TCC accessions and 2946 markers with MAF >5% . Modern tability . Out of At the chromosomal level, the significant MTAs were distributed on all chromosomes , except The comparison of the meanQTLs, stbQTLs, and QTIs for each trait at each locus revealed that the majority of QTLs were specific to either mean or stability . SpecifiP<0.05) or recessive  mode of inheritance , while no enrichment was found for the other QTL types. The traits showing non-additive inheritance modes mainly corresponded to those related to reproductive traits such as fruit number and yield, and also fruit colour CIELAB coordinate  for each SNP included within QTLs, and the direction of additive effects for the minor allele relative to the major allele, and the dominance deviation . We wereeritance . When theritance , we founordinate . Regardiordinate . A furthordinate  indicateTo further validate the efficiency of the TCC as a GWAS panel, we first compared the TCC associations with the meta-analysis of the entire TRADITOM collection . Of thesIn total, 23 meanQTLs with 38 MTAs in 14 loci  co-localized with known QTLs for the phenotypic mean. Furthermore, eight MTAs in five stbQTLs in four loci , and 21 MTAs in 14 QTIs in 13 loci  mapped in regions where GEI effects had been reported in previous studies .fas/CLV3 (fw11.3/CSR (cd2) for cracking (The highest frequency of previously published QTLs mapped within the TCC_L62 locus on chr11 (~19.12%) . In the 11.3/CSR , two of 11.3/CSR , with ~111.3/CSR  also inccracking , frn1.1 sit (LeETR4 (u) mutation affecting chloroplast biogenesis in fruit and the intensity of the green shoulder (We explored genomic regions that flanked (<2 Mb) meanQTLs, stbQTLs, and QTIs to scan for candidate genes that may be physically proximal to associated SNPs. Within 141 QTL regions, we identified a total of 11 094 annotated genes, ranging from 28 to 550 genes per QTL, with a mean of 259 genes per QTL. To select candidate genes within the QTL region, in the case of meanQTLs, we prioritized genes with reported roles controlling the trait, while genes with reported roles in the integration of both external (biotic and abiotic stress) and internal factors (e.g. phytohormones) were prioritized for stbQTLs and QTIs . We searcu-3/SlBRI1), polyamine biosynthesis and transport , cytokinin biosynthesis (SlIPT4 and SlCKX1), and jasmonate biosynthesis and signalling (SlLOX8 and SlJAZ9). Some of these genes were also reported in hormone–stress crosstalk. Among these genes in stbQTLs and QTIs, some of them had been previously associated with stability roles in tomato or other plants , a gene affecting adaptability of rosette size and fecundity in response to heat stress , while 12 and five were at a frequency <10% in each case (minor haplotypes). The remaining haplotypes, with frequencies <1%, were considered rare haplotypes . SimilarP<0.05) FW  FW , while H0.05) FW . With a 0.05) FW . Regardi0.05) FW . The sig0.05) FW  was due 0.05) FW .The objective of the present work is to estimate and identify the genetic architecture of agro-morphological traits and its stability in European traditional tomatoes, taking advantage of the availability of the TRADITOM collection  that fulMET analysis of TCC accessions .Fig. S17. Location-specific allelic effects of MTAs in identified QTIs.Fig. S18. Linkage disequilibrium (LD) and haplotype analysis of TCC_L62 and TTC_L53 loci in the TCC and TRADITOM collection.Figu. S19. Association between absence and presence of TCC_L62 HA haplotypes on FW in the TCC and TRADITOM collection.Fig. S20. Association between absence and presence of TCC_L62 HB haplotypes on FW in the TCC and TRADITOM collection.Fig. S21. Association between absence and presence of TCC_L62 HC haplotypes on FW in the TCC and TRADITOM collection.Fig. S22. Association between absence and presence of TCC_L53 locus hap6 haplotype on PUF in the TCC and TRADITOM collection.Table S1. Field cultivation conditions for each location.Table S2. Traits studied and respective abbreviations.Table S3. Passport data for accessions included in this work.Table S4. Modern counterparts and their characteristics.Table S5. Phenotypic mean values per accession.Table S6. Analysis of phenotypic variance and heritability.Table S7. Estimated stability indexes.Table S8. Genotypic data.Table S9. Summary of GWAS and GWEIS results.Table S10. Mode of inheritance of QTLs.Table S11. Haplotypes and haplotype frequency in target loci in the TCC and TRADITOM collection.Table S12. ANOVA of haplotype and collection effects on trait value.erad306_suppl_Supplementary_Protocols_S1-S2_figures_S1-S22Click here for additional data file.erad306_suppl_Supplementary_tables_S1-S12Click here for additional data file.


Our study aims to investigate an intrinsic link underlying sex hormone-binding globulin (SHBG) and rheumatoid arthritis (RA), which remains inconclusive in observational settings.adjBMI; Noverall = 368,929; Nmen = 180,094; Nwomen = 188,908), crude SHBG , and RA . A genome-wide cross-trait design was performed to quantify global and local genetic correlation, identify pleiotropic loci, and infer a causal relationship.Summary statistics were collected from the largest GWAS(s) on SHBG adjusted for BMI (SHBGadjBMI and RA (P = 1.0 × 10−4) which was further supported by local signal (1q25.2). A total of 18 independent pleiotropic SNPs were identified, of which three were highly likely causal variants and four were found to have effects on both traits through gene expression mediation. A putative causal association of SHBGadjBMI on RA was demonstrated  without evidence of reverse causality . Sex-specific analyses revealed distinct shared genetic regions  and diverse pleiotropic SNPs  underlying SHBGadjBMI and RA, demonstrating biological disparities between sexes. Replacing SHBGadjBMI with crude SHBG, a largely similar yet less significant pattern of results was observed.Among the overall population, a significant global genetic correlation was observed for SHBGOur cross-trait analysis suggests an intrinsic, as well as a sex-specific, link underlying SHBG and RA, providing novel insights into disease etiology.The online version contains supplementary material available at 10.1186/s40246-023-00528-x. Human autoimmune disorders often present sex-specific characteristics. In rheumatoid arthritis (RA), women account for more than 70% of all cases  whom alsNIV = 13) and a small number of RA cases (NRA = 4017), substantially restricting the robustness of the results.Sex hormone-binding globulin (SHBG) acts as a carrier of sex steroids and regulates the extent to which these hormones are delivered to body tissues , mediatiNoverall = 370,125), data on sex-specific SHBG  and BMI-adjusted SHBG  were further incorporated to detect sex disparity and to control for the confounding effect of BMI. We first quantified the global and local genetic correlation underlying SHBG and RA, and then identified potential pleiotropic loci affecting both traits. We conducted functional annotation of these loci and performed fine-mapping analysis and transcriptome-wide association study (TWAS) to provide biological insight. We finally evaluated a putative causal relationship. A flowchart of the overall study design is shown in Fig. The increasing availability of genetic data produced by large-scale genome-wide association studies (GWAS) enables the utilization of a compiled analytical strategy named genome-wide cross-trait analysis. This analysis features several analytical aspects and permits the quantification of shared and distinct etiology underlying complex traits. Our study, therefore, aims to extend previous findings by implementing a comprehensive genome-wide cross-trait approach, leveraging summary statistics of the hitherto largest GWAS(s) conducted for SHBG and RA. In addition to genetic data of overall circulating SHBG (SHBG GWAS The latest and largest GWAS of circulating SHBG was conducted using UK Biobank (UKBB), which involved 370,125 participants of European ancestry. Genetic variants were imputed using the Haplotype Reference Consortium and the 1000 Genomes Project reference panel. In addition to the sample quality control metrics performed centrally by UKBB, white European ancestry was determined by K-means clustering combined with self-report. Genetic variants with minor allele frequency (MAF) > 1% as well as those that passed quality control in batches were involved. Association test was performed using a linear mixed model, adjusted for genotyping chip, age at baseline, and ten genetically derived principal components (PCs). Genome-wide significant index variants (P < 5 × 10–8) were identified using 1 Mb distance-based clumping with linkage disequilibrium (LD) < 0.05 across all variants [variants .adjBMI) along with the aforementioned variables. Sex-specific analyses were also performed  [To account for the confounding effect of body mass index (BMI), additional analysis was performed among 368,929 UKBB individuals, which consisted of individuals enrolled in the SHBG GWAS with BMI records. BMI was adjusted  .adjBMI, 477 SNPs were identified, with corresponding figures of 251 for men and 271 for women. The characteristics of SHBG-associated  index SNPs used for MR analysis are presented in  were used as instrumental variables (IVs). For crude SHBG, 335 SNPs were identified, with corresponding figures of 188 for men and 176 for women. For SHBGRA GWAS The hitherto largest GWAS of RA was conducted by meta-analyzing 25 cohorts involving 22,350 cases and 74,823 controls of European ancestry. Genotype quality control, imputation, and case–control association analyses were conducted separately for each cohort. Quality control excluded samples with low call rate, closely related individuals, or outliers based on ancestries identified through PC analysis. Additionally, variants with low call rate, low MAF, or low Hardy–Weinberg P value were excluded. Genotyping imputation was performed using 1000 Genomes Phase 3 panel with minimum imputation accuracy (r2) of 0.3. SNPs associated with RA were detected assuming additive effects using logistic regression models, correcting for sex and PCs. Finally, effect sizes of the identical SNPs across all participating studies were combined using a fixed-effect inverse variance weighted meta-analysis [P < 5 × 10–8) using 1 Mb distance-based clumping with linkage disequilibrium (LD) < 0.05 across all variants after removing the Major Histocompatibility Complex (MHC) region. A total of 182 RA-associated index SNPs were identified, of which 175 were available and used as IVs in our bidirectional MR analysis. The full set of GWAS summary statistics was downloaded and used for other analyses. To the best of our knowledge, none of these 25 studies overlapped with participants in UKBB, our exposure GWAS(s) [analysis . Index S GWAS(s) . For allz-scores from two studies presents a comparable relationship with χ2 statistics for a single study. The algorithm is described in the formula as follows:Genome-wide genetic correlation (n (LDSC) , 14. ThiP value of 0.05/3 was used to define statistical significance.Among them, ßP value of 0.05/(1703 × 3) was used to define statistical significance, considering analyses across 1703 LD-independent regions for the overall and sex-specific populations.Genomic correlation collapses the effect of all SNPs across the whole genome. Even with a negligible global genetic correlation, there might be specific regions in the genome affecting both traits. We next quantified local genetic correlation using heritability estimation from summary statistics (ρ-HESS). The genome was partitioned into 1,703 LD-independent regions with an average size of 1.6 Mb. Firstly, genetic covariance was used to evaluate trait similarity within each LD-independent region under a fixed-effect model. Genetic covariance quantifies the covariation of traits on their original scales. To enable comparisons across different traits and genomic regions, genetic correlation was further employed to standardize covariation using the jackknife approach . The MHCSHet (rather than SHom) which improves statistical power in the presence of trait heterogeneous effects was therefore used to combine association evidence. We obtained independent top-associated loci applying PLINK LD-based clumping function (parameters: –clump-p1 5e−8 –clump-p2 1e−5 –clump-r2 0.2 –clump-kb 500). Significant pleiotropic SNPs were defined as variants with Psingle-trait < 1 × 10–5 in both traits and PCPASSOC < 5 × 10–8 in paired traits. These SNPs were further divided into four categories. First, a “known” pleiotropic SNP was one that reached genome-wide significance in both single traits . These SNPs were naturally pleiotropic even without performing CPASSOC. Second, a “single-trait-driven” pleiotropic SNP was one that reached genome-wide significance in one of two single traits (PSHBG < 5 × 10–8 or PRA < 5 × 10–8 and PCPASSOC < 5 × 10–8). Third, an “LD-tagged” pleiotropic SNP was one that, despite not reaching genome-wide significance in any single trait (5 × 10–8 < PSHBG / RA < 1 × 10–5 and PCPASSOC < 5 × 10–8), was in LD (r2 threshold = 0.2) with index SNPs (or any SNP located within ± 250 kb around the index SNPs) identified by single-trait GWAS(s). Finally, a “novel” pleiotropic SNP was of great interest to us, which was defined as those that neither reached genome-wide significance in any single trait nor in LD with previously identified SHBG- or RA-associated SNPs. Ensembl Variant Effect Predictor (VEP) was used to map pleiotropic SNPs to the nearest genes based on location [Genetic correlation suggests shared genetic components—either due to genetic variants having an independent effect on both traits  or genetic variants influencing one trait via its effect on the other . To detect potential pleiotropic loci, we applied a cross-trait meta-analysis using cross-phenotype association analysis (CPASSOC) . This allocation .Index SNPs are not necessarily causal variants. We further identified a 99% credible set of causal variants through FM-summary method, a simplified Bayesian fine-mapping method using summary statistics. Briefly, each pleiotropic SNP and the variants within 500 kb around, extracted from the pooled results of cross-trait meta-analysis, were used as input for FM-summary. FM-summary then set a flat prior and generated a posterior inclusion probability (PIP) of a true trait/disease association for each variant using the steepest descent approximation , 20. A 9Cross-trait meta-analysis identifies pleiotropic loci affecting both traits without considering gene expression, while many pleiotropic loci influence complex traits by modulating gene expression levels. To identify relevant genes whose expression patterns vary across tissues, we performed a TWAS analysis. Imputable genes were provided by pre-trained joint-tissue imputation prediction models (GTEx v8) . Gene-phA two-sample bidirectional MR analysis was performed to identify a putative causal relationship. Inverse variance weighted (IVW) approach was used as our primary approach, assuming all IVs were valid, or the overall pleiotropy was balanced to zero . A serieF-statistic.Statistical power was calculated using the non-centrality parameter of the test statistic as suggested by Brion et al. . The proP value threshold of 0.05/3 was used to define statistical significance in MR. Additionally, a P value of < 0.05 was employed for suggestive significance.All analyses were conducted with packages “TwoSampleMR”, “MRInstruments”, “MendelianRandomization”, “MR-PRESSO” and “mr.raps” in R v3.6.3. Given analyses were repeated three times , a Bonferroni-corrected This was a secondary analysis of existing, publicly available summary-level GWAS data. The statement of ethics for each GWAS can be found elsewhere, approved by relevant ethics committees , 12.P < 0.05/3), as shown in Table P = 3.9 × 10–2). The effects remained null in both men (P = 3.2 × 10–2) and women (P = 0.10), possibly due to the confounding effect of BMI as studies have found a decreased level of SHBG [adjBMI and RA (P = 1.0 × 10–4), and this effect remained consistent in sex-specific analysis . Therefore, in our subsequent analysis, SHBGadjBMI was used as primary exposure, complemented by crude SHBG.After correcting for multiple testing ( of SHBG  but an i of SHBG  among inadjBMI with RA at one region (1q25.2)  was detected as a novel SNP and located in JAZF1, a gene implicated in transcriptional repression and inflammation suppression [adjBMI-driven” SNPs, three “RA-driven” SNPs, and three “LD-tagged” SNPs. The sex-specific analyses revealed distinct pleiotropic SNPs underlying SHBGadjBMI and RA for men and women. For men, 16 independent pleiotropic SNPs were detected, among which rs7512646 (PCPASSOC = 6.61 × 10–10) was novel, located in IL6R, a gene encoding a subunit of the interleukin 6 receptor complex [PCPASSOC = 7.70 × 10–10), located in ARHGAP26, a gene encoding GTPase activating protein that regulates tumor immunity [PCPASSOC = 3.59 × 10–10), an intergenic variant.Motivated by the significant global and local genetic correlation, we further conducted a cross-trait meta-analysis to identify pleiotropic SNPs affecting both traits Fig.  Table S7pression . Additio complex  and part complex . For womimmunity  and inflimmunity , as welladjBMI with crude SHBG, a similar pattern of results was observed. In overall population, 70.0% of pleiotropic SNPs were either overlapped or in LD with SNPs identified using SHBGadjBMI. The proportion was 66.7% in men and 76.9% in women when analyzed by sex. Functional annotations of these pleiotropic SNPs are shown in  showed a posterior probability of 1.00. In sex-specific analysis, 327 candidate causal SNPs were identified for men, and 385 for women.For each of the pleiotropic SNPs, we further determined a 99% credible set of causal SNPs, providing targets for future downstream experimental analysis  shared by SHBGadjBMI and RA, and the corresponding figure was four for men and 11 for women . The estimates remained directionally consistent in weighted median approach although with large uncertainty. In sex-specific analyses, the magnitude of effect size indicated potential disparities between men and women, with an OR of 1.13 (95% CI = 0.93–1.34) in women, and an OR of 1.07 (95% CI = 0.90–1.26) in men, although none of these estimates reached statistical significance.We performed a bidirectional two-sample MR analysis to evaluate a causal relationship. As shown in Fig. adjBMI with RA were remained in overall population after correcting outliers , removing confounding SNPs  or palindromic SNPs   , but this trend was not observed either in men  or women .Replacing SHBGadjBMI-associated IVs to be 0.178, our study had 80% power to detect a 6% change for the risk of RA with SHBGadjBMI. The corresponding effect sizes were 6% for men and 7% for women. For crude SHBG, with the phenotypic variance explained by index SNPs to be 0.132, our study had 80% power to detect a 7% change, with the corresponding effect sizes of 6% for men and 8% for women  and assuming an α of 0.05/3, given the phenotypic variance explained by SHBGadjBMI and RA overall. Such a genetic correlation could be further decomposed into horizontal pleiotropy and vertical pleiotropy, reflected by the multiple independent pleiotropic loci identified in cross-trait meta-analysis as well as the putative causal relationship confirmed by MR. Looking into men and women separately, most findings were replicated in both sexes, while sex-specific results were also observed, reflecting a distinct role of SHBG in affecting RA susceptibility across sex. Replacing SHBGadjBMI with crude SHBG, a largely similar yet less significant pattern of results was observed, corroborating main findings.To the best of our knowledge, this is the first large-scale genome-wide cross-trait design that comprehensively investigates the shared genetic architecture underlying SHBG and RA. We found a significant shared genetic basis, both globally and locally, between SHBGP = 6.01 × 10–10) or between RA and its well-established risk factor obesity (P > 0.05) or between RA and its possible consequence bone mineral density (P = 0.005) [The SHBG-RA relationship remains largely inconclusive in clinical and epidemiological investigations –8. A stu= 0.005) , 38. Our= 0.005) , 40. PHF= 0.005) –43. Howe= 0.005) . The lev= 0.005)  and endo= 0.005) , has bee= 0.005)  includinIn addition to an overall landscape, distinct genetic architecture and biological mechanisms were identified for men and women separately through our sex-specific analysis. In addition to sex-specific shared genetic regions, nearly half of the pleiotropic loci we identified were sex-specific. We also observed potential differences in the pattern of pleiotropic genes between sexes, which may indicate the distinct pathways. Future studies are needed to validate the sex-specific role of these genes. TRIM38 and ZSCAN23 are men-specific genes. The former plays a role in modulating the severity of autoimmune disease  and negaadjBMI on RA (1.19 vs. 1.21) in MR analysis, which indicates a less likely biased result. Fifth, considering the uncertainty of the effect in a few sensitivity analyses and in sex-specific MR analysis, further validation may be needed to establish a robust causal relationship between SHBGadjBMI and RA.Our analyses must be interpreted with caution. First, due to limited data availability, we could not examine RA subtypes characterized by anti-citrullinated antibody status or classify subgroups according to the severity of RA . To our In conclusion, leveraging the hitherto largest genome-wide genetic data and advanced statistical approaches, the current study expands understanding of the observational association of SHBG with RA by providing evidence of genetic correlation, pleiotropic loci, and causal relationships. Our findings demonstrate an intrinsic, as well as a potential sex-specific link underlying SHBG and RA, and further shed novel light on biological mechanisms. Future studies are warranted to validate the function of the identified variants and genes, and to extend our findings on the diagnostic and therapeutic values of SHBG in RA.Additional file 1: Table S1. The characteristic of sex hormone-binding globulin adjusted for BMI associated index SNPs, their effect sizes with exposure and outcome, as well as their associations with potential confounders. Table S2. The characteristic of sex hormone-binding globulin adjusted for BMI associated index SNPs in men, their effect sizes with exposure and outcome, as well as their associations with potential confounders. Table S3. The characteristic of sex hormone-binding globulin adjusted for BMI associated index SNPs in women, their effect sizes with exposure and outcome, as well as their associations with potential confounders. Table S4. The characteristic of crude sex hormone-binding globulin associated index SNPs, their effect sizes with exposure and outcome, as well as their associations with potential confounders. Table S5. The characteristic of crude sex hormone binding globulin associated index SNPs in men, their effect sizes with exposure and outcome, as well as their associations with potential confounders. Table S6. The characteristic of crude sex hormone-binding globulin associated index SNPs in women, their effect sizes with exposure and outcome, as well as their associations with potential confounders. Table S7. Cross-trait meta-analysis between sex hormone-binding globulin adjusted for BMI and rheumatoid arthritis, excluding the MHC region . Table S8. Cross-trait meta-analysis between crude sex hormone-binding globulin and rheumatoid arthritis, excluding the MHC region . Table S9. Functional annotation of SNPs shared between sex hormone-binding globulin adjusted for BMI and rheumatoid arthritis identified from the cross-trait meta-analysis, excluding the MHC region. Table S10. Functional annotation of SNPs shared between crude sex hormone-binding globulin and rheumatoid arthritis identified from the cross-trait meta-analysis, excluding the MHC region. Table S11. List of 99% credible set SNPs in each sex hormone-binding globulin adjusted for BMI and rheumatoid arthritis shared locus identified from fine-mapping analysis (r2 threshold = 0.6). Table S12. Shared significant genes between sex hormone binding globulin adjusted for BMI and rheumatoid arthritis from transcriptome-wide association studies using gene expressions across 49 GTEx tissues. Table S13. Bidirectional causal associations between genetically predicted sex hormone-binding globulin levels and risk of rheumatoid arthritis. Table S14. Sensitivity analysis of causal association between genetically predicted sex hormone-binding globulin levels and risk of rheumatoid arthritis. Table S15. Causal association between sex hormone-binding globulin levels and risk of rheumatoid arthritis based on the robust adjusted profile score (RAPS). Table S16. Power calculation in assumed and actual OR in our study.Additional file 2: Fig. S1. Local genetic correlation between crude sex hormone-binding globulin and rheumatoid arthritis. Colored bars represent loci with significant local genetic correlation, covariance, and SNP-heritability after multiple testing adjustment. SHBG: sex hormone-binding globulin; RA: rheumatoid arthritis. Fig. S2. Leaving one SNP out at a time for the association between sex hormone-binding globulin and rheumatoid arthritis.