dataesr/scientific-paragraphs-categorization huggingface dataset:
We further confirmed the association of rs2289252 with venous thromboembolic disorders and that it has no association with other non-stroke-related phenotypes using the Phenoscanner database (Supplementary Table  37 ). Overall, combining evidence from genomics-driven drug discovery approaches, characterization of stroke-risk loci (missense variants, TWAS, PWAS, colocalization, pathway enrichment, MR with pQTL, MENTR and PoPS  34  ), and previous knowledge from monogenic disease models and experimental data, we found evidence for the potential functional implication of 56 genes that should be prioritized for further functional follow-up, with evidence from multiple approaches for 20 genes (Supplementary Table  38 ).
Integrative polygenic risk prediction We investigated the risk prediction potential of stroke GWASs, alone and in combination with vascular-risk-trait GWASs, first in Europeans and East Asians, using ancestry-specific PGSs. PGSs were based on ancestry-specific and cross-ancestry GWAS summary statistics. We first derived single PGS (sPGS) models from single stroke GWAS summary data (Supplementary Table  39 ). We then constructed integrative PGS (iPGS) models, which combined multiple GWAS summary data of different traits into a PGS using elastic-net logistic regression  5  (Extended Data Fig. 10 ). The iPGS analysis used two datasets for each ancestry for model training and evaluation, respectively. The participants in the training and evaluation datasets did not overlap and were not included in the input GWAS summary data.
003 (Supplementary Table  47 ). Notably, iPGS models derived from cross-ancestry stroke GWASs had a higher predictive ability compared with iPGS models derived from ancestry-specific stroke GWASs both in Europeans and East Asians (Supplementary Table  48 ). Next, we evaluated the predictive ability of the European-derived GIGASTROKE-based iPGS model in African American and indigenous African (Nigerian and Ghanaian) datasets. In 107,343 African American MVP participants, of whom 2,227 developed an AIS, the GIGASTROKE-based iPGS model showed a significant association with AIS incidence (HR per 1 s. d. = 1. 11, 95% CI = 1. 06-1. 17, P = 1. 8 × 10 -5 , ∆C-index = 0. 003; Supplementary Table  49 ), although weaker than in European MVP participants (Supplementary Table  43 ). The participants in the top 1% of the iPGS showed 1.
Article (encoded by PROC) was found to be safe for the treatment of acute ischaemic stroke after thrombolysis, mechanical thrombectomy or both in phase 1 and 2 trials (3K3A-APC, NCT02222714)  43, 44  , and is poised for an upcoming phase 3 trial. 3K3A-APC is proposed as a neuroprotectant, with evidence for the protection of white matter tracts and oligodendrocytes against ischaemic injury in mice  45. Weaker evidence was found for GP1BA, VCAM1 and LAMC2 as potential drug targets for stroke, with evidence for colocalization in only one pQTL dataset. Anfibatide, a GPIbα antagonist, reduced blood-brain barrier disruption after ischaemic stroke in mice  46  and is being tested as an antiplatelet drug in myocardial infarction (NCT01585259). Although specific VCAM1 inhibitors are not available, probucol-a lipid lowering drug with pleiotropic effects including VCAM1 inhibition-was tested for secondary prevention against atherosclerotic events in patients with CAD (PROSPECTIVE, UMIN000003307)  47.
We investigated stroke PGSs across ancestries. PGSs integrating cross-ancestry and ancestry-specific stroke GWASs with vascularrisk-factor GWASs (iPGS) analyses showed strong prediction of ischaemic stroke risk in Europeans and, importantly, in East Asians, in whom stroke incidence is highest  6. These results were confirmed in several independent datasets. The iPGS performed better than stroke PGS alone and better than the previous best iPGS models in Europeans  5. The transferability of European-specific iPGS models to East Asians was limited. While there were not enough African participants to generate an African-specific stroke PGS, the European iPGS showed a significant association with AIS in both African American and indigenous African participants, although expectedly weaker than in European participants.
For the meta-analysis combining both incident and prevalent stroke studies, a few incident stroke studies were removed because they were already part of a meta-analysis of stroke GWASs used as an input of the overall meta-analysis (WHI, Hisayama, REGARDS, JHS). We considered loci to be genome-wide significant for P < 5 x 10 -8. We applied the conditional and joint analysis approach 7 implemented in the Genome-wide Complex Trait Analysis software  52  (GCTA-COJO) to identify potentially independent signals within the same genomic region. We performed GCTA-COJO analyses on (1) European GWAS meta-analysis summary statistics using HRC imputed data of 6,489 French participants from the 3C study as in ref. 53  and (2) East Asian-ancestry-specific GWAS meta-analysis summary statistics using BBJ data as reference (Supplementary Information).
Independent follow-up of GWAS signals First, we sought to replicate internally the 42 stroke-risk loci reaching genome-wide significance in IVW meta-analyses within individual ancestries, in at least one other ancestry group among the discovery samples, considering both nominal replication levels (P < 0. 05) and multiple-testing corrected significance at P < 1. 19 × 10 -3 (0. 05/42). Second, we gathered independent datasets totalling 89,084 AS (including 85,546 AIS; and 70. 0% European, 15. 6% African American, 10. 1% East Asian, 4. 1% Hispanic and 0. 1% South Asian) and 1,013,843 controls for external replication of associations with AS and AIS (Supplementary Tables  1  and 2 ). These comprised eight biobanks (82,263 cases, 930,988 controls) and four hospital-based cohorts (6,821 cases, 82,855 controls).
We compared the variants in credible sets of the same loci between Europeans and East Asians. To detect putative causal regulatory variants, we conducted an in silico mutagenesis analysis using MENTR (mutation effect prediction on non-coding RNA transcription; https://github. com/koido/MENTR), a machine-learning method to precisely predict transcriptional changes induced by causal variants  3, 68. The in silico mutations predicted to have strong effects are highly concordant with the observed effects of known variants in a cell-type-dependent manner. Furthermore, MENTR does not use population datasets and is therefore less susceptible to linkage-disequilibrium-dependent association signals, enabling precise prediction of the effects of causal variants on transcriptional changes. From 1,274 variants in the credible sets from the European and East Asian fine-mapping, we searched FANTOM5 promoters and enhancers, obtained by cap analysis of gene expression, within ±100 kb from each variant.
Brain single-cell expression analyses Single-nucleus RNA-sequencing data of the DLPFC region of 24 ageing individuals chosen to represent the range of pathologic and clinical diagnoses of AD dementia, from the ROS/MAP cohorts, was obtained  21. RNA profiles of cells annotated as endothelial, pericytes or smooth muscle cells and vascular leptomeningeal cells (VLMC) were used, and a pseudobulk RNA profile was generated for each cell type by averaging the expression of all genes across the cells. Average expression levels and the percentage of expressed genes were calculated for genes of interest using the DotPlot function from the Seurat package v. 4. 0. 4 in R v. 4. 1. 1. We also conducted a cell-type enrichment analysis using the STEAP pipeline (https://github. com/ComPopBio/STEAP). This is an extension of CELLECT and uses S-LDSC  78  , MAGMA  9  and H-MAGMA  79  for enrichment analysis.
Stroke GWAS summary statistics were first munged. Expression specificity profiles were then calculated using human and mouse single-cell RNA-seq databases (Supplementary Table  28 ). Cell-type enrichment was calculated using three models: MAGMA, H-MAGMA (incorporating chromatin interaction profiles from human brain tissues in MAGMA) and stratified linkage-disequilibrium score regression. P values were corrected for the number of independent cell types in each database (Bonferroni correction).
Genomics-driven drug discovery We used three methodologies for in-depth genomics-driven drug discovery as described previously 4 : (1) an overlap enrichment analysis of disease-risk genes in drug-target genes in medication categories; (2)  negative correlation tests between genetically determined case-control gene expression profiles and compound-regulated gene expression profiles; and (3) endophenotype MR. Details of the methods are described in the following sections. For the overlap enrichment analysis and the endophenotype MR-nominated drug targets, we curated drug candidates from four major drug databases: DrugBank  23  , Therapeutic Target Database (TTD)  80  , PharmGKB  81  and Open Target Platform  82. As for the endophenotype MR, we curated drugs with opposite effects against the signs of the MR effect estimates.
8 (1000 Genomes, European). In total, we used 277 lead variants for 218 drug-target proteins for MR and considered FDR < 0. 05 as the threshold to identify significant associations. We used the TwoSampleMR R package  91  for MR analysis. As post-MR quality controls, we performed (1) a directionality check of causal relationships by Steiger filtering  92  and (2) colocalization analysis for the proteins with FDR < 0. 05. To examine colocalization assuming multiple causal variants per locus, coloc  74  was applied to the decomposed signals by SuSiE  12  for the variants within 500 kb upstream and downstream of the lead variants (coloc + SuSiE)  93. If SuSiE did not converge after 10,000 iterations, coloc was used instead. coloc + SuSiE and coloc were run with their respective default parameters.
For the two pQTL studies without public summary statistics  86, 90  , we compared the r 2 between the lead variants of the pQTL study and the stroke GWAS. We considered that colocalization occurred when the maximum posterior probability (that is, PP. H4) was greater than 0. 75 or r 2 was greater than 0. 8. To provide further support for our findings, we conducted MR analyses with two additional recent independent pQTL datasets, using the same methodology and significance thresholds (FDR < 0. 05 for MR and PP. H4 > 0. 75 for colocalization) as above: one study comprised both plasma (n = 529) and cerebrospinal fluid (n = 835) pQTL datasets  94  , the second is one of the largest plasma pQTL studies conducted in 35,559 Icelandic individuals  95.
PheWAS PheWAS analysis was performed using R (v. 4. 0. 3). We used the PheWAS R package  96  (https://github. com/PheWAS/PheWAS) function create-Phenotypes to translate ICD10 diagnosis codes into phecodes for the PheWAS analysis. We tested the associations between phecodes and genetic variants using logistic regression and adjusting for sex, birth year and ten genotype PCs. We applied Bonferroni correction to select statistically significant associations (number of tested phecodes: 1,809; number of tested SNPs: 8; corrected P-value threshold: 0. 05/(1,809 × 8) = 3. 45 × 10 -6 ). The results were visualized using the PheWAS library. To further characterize the associations of the genetic variants with other phenotypes, we searched for all eight SNPs in the PhenoScanner database  97, 98.
Polygenic risk prediction We constructed iPGS models for stroke in European and East Asian individuals (Extended Data Fig. 10 ). For each ancestry, independent datasets were used for model training and evaluation. We used as input summary statistics data of multiple GWAS analyses for stroke outcomes and vascular-risk traits to derive iPGS models. We denote the number of input GWASs as N. For each of the N GWAS summary data, 37 candidate single-trait polygenic score (sPGS) models were generated using the P+T  99, 100  , LDpred  101  and PRScs 102 algorithms with an ancestry-specific linkage-disequilibrium reference panel from the 1000 Genomes Project  103  (Supplementary Methods). The plink (v. 1. 90b6. 8)  104 , LDpred (v. 1. 0. 11)  101  and PRScs. py (5 June 2021)  102  programs were used to compute the P+T, LDpred and PRScs models, respectively.
Subsequently, among the 37 candidate models, the best sPGS model, which was defined as the model that showed a maximal improvement in AUC over a base model (age, sex and top five PCs were included in the base model), was selected using the model training dataset  5, 100. Then, N best sPGS models were selected from the N input GWASs. Among the N best sPGS models, we retained models that were significantly associated with AIS in the model-training dataset (Bonferroni-corrected P < 0. 05). Then, each retained best sPGS was z-transformed (zero mean and unit s. d. ) over the model-training dataset, followed by elastic-net logistic regression  105  to model the associations between the N sPGS variables and AIS with the adjustments for age, sex and top five genetic PCs. Two regularization parameters (α and λ) were optimized using tenfold cross-validation.
Coefficients (weights) for the retained sPGS models were then determined by elastic-net logistic regression with the optimal regularization parameters, followed by integration of the sPGS models into a single iPGS model according to a formula presented previously  5. Elastic-net regression was performed using the glmnet R package  106. The predictive ability of the iPGS model was estimated using the model-evaluation dataset, whereby we evaluated the improvement in C-index for a prospective cohort dataset or AUC for a case-control dataset over a base model that includes age, sex and top five genetic PCs. We used EstBB data for the model training and evaluation of iPGS model in Europeans. The model-training dataset was composed of 1,003 cases of prevalent AIS at the baseline and 8,997 control individuals.
The control individuals were randomly selected among EstBB participants who had no history of AS at the baseline and who did not develop AS during the follow-up. The remaining 102,099 EstBB participants were used for the model evaluation (mean ± s. d. age at the baseline, 44. 0 ± 15. 7 years; 37. 8% men). Among the participants in the model-evaluation dataset, 1,128 cases of incident AIS were observed during 4. 6 ± 4. 8 years. To derive the European iPGS model, we incorporated 5 ancestry-specific and 5 cross-ancestry stroke GWAS analyses (AS, AIS, LAS, SVS and CES) from the GIGASTROKE project, and 12 GWAS analyses of vascular-risk traits from other groups (Extended Data Fig. 10 ). To avoid the overlap of participants across datasets, the GWAS summary statistics for stroke outcomes were recalculated for the iPGS analysis by excluding the EstBB from the meta-analysis of GIGASTROKE studies.
To enable comparison with a previous European iPGS model based on the MEGASTROKE GWAS  5  , we incorporated 12 GWAS analyses of vascular-risk traits (atrial fibrillation, CAD, T2D, SBP, DBP, TC, LDL-C, HDL-C, TG, BMI, height and smoking)  54, 56, [59] [60] [61] 107, 108  into the GIGASTROKE-based iPGS model. The iPG  model for Europeans was further evaluated in two external cohorts of European ancestry (MVP and pooled data of clinical trials) as well as in two studies of participants with African ancestry (MVP and SIREN). For the East Asian iPGS model, we used BBJ data for the model training and evaluation. The model-training dataset was composed of 577 cases of AIS and 9,232 control individuals, whereas there were 1,470 cases of AIS and 40,459 control individuals in the model-evaluation dataset.
The mean ± s. d. of age at recruitment was 69. 2 ± 10. 8 years for cases and 66. 5 ± 12. 5 years for controls in the model evaluation dataset. The percentage of male participants was 70. 0% for cases and 53. 1% for controls. The two case-control datasets were not included in the meta-analysis of GIGASTROKE studies and, therefore, the overlap of participants across datasets was avoided. To derive the East Asian iPGS model, we incorporated 5 ancestry-specific and 5 cross-ancestry stroke GWAS analyses (AS, AIS, LAS, SVS and CES) from the GIGASTROKE project, and 12 GWAS analyses of vascular-risk traits (Extended Data Fig. 10 ). The iPGS model for East Asian individuals was further evaluated in an external study of East Asian ancestry (TWB).
We also looked separately at associations with incident stroke in participants with and without previous stroke. significantly different from zero at a P < 0. 05 shown as a full-sized square. Genetic correlations or causal estimates that are significant after multiple testing correction (P < 4. 17 × 10 -3 ) are marked with an asterisk. Two-sided P-values were calculated using LD score regression (LDSC) for genetic correlations and inverse variance weighted analysis for MR. (B) The significant PGS models were used as the variables for elastic-net logistic regression and the weights for the variables were trained using the model training dataset. The European iPGS model consisting of 1,213,574 variants and an East-Asian iPGS model consisting of 6,010,730 variants were constructed by combining the 11 and 7 significant PGS models using the elastic-net derived weights respectively.
The European and East Asian iPGS models were evaluated in the European (a European prospective cohort data with 102,099 participants including 1,128 incident IS cases) and East-Asian (AIS case-control data, Ncases/ Ncontrol = 1,470/40,459) model evaluation dataset (Methods); AS indicates any stroke; AIS, any ischaemic stroke; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; AF, atrial fibrillation; CAD, coronary artery disease; T2D, type 2 diabetes; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; BMI, body mass index; AUC indicates area under the curve; EUR, European; EAS, East Asian; GWAS, genome-wide association study; LD, linkage disequilibrium; PGS, polygenic score.
F11 and F11a inhibitors (such as abelacimab, BAY 2433334 and BMS-986177) are currently being examined in phase 2 trials for primary or secondary stroke prevention (NCT04755283, NCT04304508, NCT03766581). pQTL-based MR suggested PROC as a potential drug target for stroke. A recombinant variant of human activated protein C 45-55%, reference group 55-100%, HR = 1.
Heatmap of the transcriptome-wide association studies (TWAS) of stroke (any stroke and stroke subtypes) showing transcriptome-wide significant associations with supporting evidence from colocalization; Coloured squares are TWAS significant associations based on two-sided p-values after multiple testing (p < 2. 0 × 10 -6 ); * Conditionally significant (p < 0. 05) and COLOC PP4 ≥ 0. 75; Genes are presented on the x-axis, those underlined in blue are in a stroke GWAS locus, those underlined in purple are not within a genome-wide significant stroke risk locus (Methods); Tissue types are on the y-axis (blue: cross-tissue weights; pink: arterial; orange: heart; green: brain). VLMC (A) Single-cell gene expression data of TWAS-COLOC genes in dorsolateral prefrontal cortex (ROS-MAP study) (B) Proteome-wide association study (PWAS) of stroke in brain tissue (C) Cell-type enrichment in human and mouse single cell RNA-seq databases using STEAP Single-nucleus gene expression/enrichment analysis and proteome-wide association study (PWAS) of stroke in brain tissue.
(A) Single-nucleus gene expression data of TWAS-COLOC genes in dorsolateral prefrontal cortex (ROS-MAP study) 21  ; Dot plot of the mean expression level in expressing cells (colour) and percent of expressing cells (circle size) of selected genes across different cell types; (B) Proteome-wide association study (PWAS) of stroke in brain tissue; Box plot showing effect estimates (odds ratio) for associations of pQTL of ICA1L in the ROS-MAP (N = 376 independent samples) and Banner (N = 152 independent samples) studies with any stroke (AS) and any ischaemic stroke (AIS), identified in PWAS after multiple testing correction. Odds ratios ± 95% CIs are shown. Dashed line indicates an odds ratio of 1. Two-sided p-values were computed using the TWAS-COLOC approach. (C) Cell-type enrichment in human and mouse single cell RNA-seq databases using STEAP; the UpSet plot displays the number of significant enrichment results, by stroke subtype (horizontally; 2 for CES, 5 for AIS, 6 for AS, and 12 for LAS) and by cell subtype (vertically; 2 cell-types show significant enrichment in LAS, AIS, and AS, 2 cell-types in AIS and AS, and 1 cell-type in LAS and AS, while 9 cell-types show significant enrichment in LAS only, 2 in CES only and 1 in AS and AIS respectively); details are displayed in Supplementary Table.
10| Derivation and evaluation of integrative polygenic score models for Europeans and East Asians. (A) With summary statistics of 22 GWAS (10 GIGASTROKE and 12 on vascular risk factors) and linkage disequilibrium reference data of 1000 Genomes Europeans (n = 503) and East Asians (n = 504), we computed 37 candidate PGS models using P+T, LDpred, and PRScs algorithms. For each GWAS, the best PGS model was selected based on the maximal area under the curve (AUC) values in the training dataset of Europeans (any ischaemic stroke [AIS] case-control data, Ncases/Ncontrols = 1,003/8,997) and East Asians (AIS case-control data, Ncases/Ncontrols = 577/9,232). Out of 22 selected PGS models derived from the 22 GWAS, 11 and 7 were significantly associated with AIS in the European and East Asian training dataset respectively (Bonferroni-corrected P < 0.
Acknowledgements Detailed acknowledgements are provided in the Supplementary Information. We thank the participants and staff of contributing studies. We also thank  Michèle M. Sale , who passed away in 2018, for her important contributions to some of the studies included in this manuscript, as principal investigator of one of the grants that funded "The  Sea Islands Genetics Network (SIGNET) " ( R01 DK084350 ). She contributed specifically to SIGNET-REGARDS from the  REasons for Geographic And Racial Differences in Stroke (REGARDS ,  U01 NS041588 ) and served as a key contributor to SiGN, COMPASS and  METASTROKE. Research, University Hospital Würzburg. A. Mishra , R. M. , T. J. , S. N. , D. C. P. ,  M. Koido ,  Q. L. G. , M. Shi , Y. H. , M. K. G. , I. C. , K. K. ,  Yi-Ching Liaw , F. C. V.
Code availability The code for computation of the integrated polygenic risk score of stroke are available at GitHub (https://github. com/hacchy1983/ iPGS-construction). The drug discovery analysis was conducted using the following publicly available tools: GREP (https://github. com/saorisakaue/GREP), Trans-Phar (https://github. com/konumat/Trans-Phar), and the TwoSampleMR (https://mrcieu. github. io/TwoSampleMR/), coloc (https://chr1swallace. github. io/coloc/) and susieR (https://stephenslab. github. io/susieR/index. html) R packages.
The data shown for the different IAPP:lipid ratios and DOPC:DOPS ratios are representative of at least two independent datasets. Global Fitting. The AmyloFit webserver was used for global fitting of the kinetic data. 27  The raw ThT fluorescence data were transformed into the required format as described in the protocols provided with AmyloFit. The equation used to fit the data to a secondary nucleation dominated mechanism is  26, 27  = + + • + + + + + + + + i k j j j j j y { z z z z z i k j j j j j y { z z z z z M M M M B C B C B C B C 1 1 e e e t t t 0 / 2 (1) using = = + = ± ± = + + + + = = ± + ± + + + + + + + + + ± m k m k k k m C k P k M m k k P k k m n k k M m n k k m n n C C B 2 2 2 2 (2 ) 4 4 4 ( 1) 2 2 n n nc n n c n n 0 0 2 0 0 0 0 2 2 0 2 0 2 0 0 2 2 2 0 1 2 2 2 2 c 2 2 where m 0 is the initial monomer concentration, M is the fibril mass concentration, P is the number concentration of fibrils, n c and n 2 are the reaction orders of primary and secondary nucleation, and k n , k 2 , and k + are the rate constants for primary nucleation, secondary nucleation, and elongation, respectively.