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Background Ischemic stroke (IS) risk heritability is partly explained by genetics. Other heritable factors, such as epigenetics, could explain an unknown proportion of the IS risk. The objective of this study is to evaluate DNA methylation association with IS using epigenome-wide association studies (EWAS). Methods We performed a two-stage EWAS comprising 1,156 subjects. Differentially methylated positions (DMPs) and differentially methylated regions (DMRs) were assessed using the Infinium 450K and EPIC BeadChip in the discovery cohort (252 IS and 43 controls). Significant DMPs were replicated in an independent cohort (618 IS and 243 controls). Stroke subtype associations were also evaluated. Differentially methylated cell-type (DMCT) was analyzed in the replicated CpG sites using EpiDISH. We additionally performed pathway enrichment analysis and causality analysis with Mendelian randomization for the replicated CpG sites. Results A total of 957 CpG sites were epigenome-wide-significant (p ≤ 10 ) in the discovery cohort, being CpG sites in the top signals (logFC = 0.058, p = 2.35 × 10 ; logFC = 0.035, p = 3.22 × 10 , respectively). ZFHX3 and MAP3K1 were among the most significant DMRs. In addition, 697 CpG sites were replicated considering Bonferroni-corrected p -values (p < 5.22 × 10 ). All the replicated DMPs were associated with risk of cardioembolic, atherothrombotic, and undetermined stroke. The DMCT analysis demonstrated that the significant associations were driven by natural killer cells. The pathway enrichment analysis showed overrepresentation of genes belonging to certain pathways including oxidative stress. ZFHX3 and MAP3K1 methylation was causally associated with specific stroke-subtype risk. Conclusion Specific DNA methylation pattern is causally associated with IS risk. These results could be useful for specifically predicting stroke occurrence and could potentially be evaluated as therapeutic targets., The EPIGENESIS project (Carlos III Institute—PI17/02089, Marató TV3 and Fundació MútuaTerrassa), the MAESTRO project—PI18/01338 (Carlos III Institute), the iBioStroke project (Eranet-Neuron, European research grants), the EPINEXO project—PI20/00678 (Carlos III Institute), the SEDMAN Study (Boehringer Ingelheim), the APHAS project (Pfizer/Bristol-Myers Squibb), and the European Regional Development Fund (ERDF)/Fondo Europeo de Desarrollo Regional (FEDER) and 2017SGR-1427 (AGAUR). I.F.-C is the recipient of a research contract from the Miguel Servet Program (CP12/03298) from the Carlos III Institute. J.C.-M. is supported by an AGAUR contract (Agència de Gestió d'Ajuts Universitaris i de Recerca; FI_DGR 2019, grant number 2019_FI_B 00853) co-financed by Fons Social Europeu (FSE). M.L. is supported by a PFIS contract (Contratos Predoctorales de Formación en Investigación en Salud): FI19/00309. C.G.-F is supported by a Sara Borrell contract (CD20/00043) from the Carlos III Institute and Fondo Europeo de Desarrollo Regional (ISCIII-FEDER). E.M. is supported by a Río Hortega contract (CM18/00198) from the Carlos III Institute.

Background: Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes. Objectives: To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events. Methods: Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10−9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27). Results: Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes. Conclusions: The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.

Background and Purpose: The minor stroke concept has not been analyzed in intracerebral hemorrhage (ICH) patients. Our purpose was to determine the optimal cut point on the NIH Stroke Scale (NIHSS) for defining a minor ICH (mICH) in patients with primary ICH. Methods: An ICH was considered minor if associated with a favorable 3-month outcome (modified Rankin Scale score ≤2). For supratentorial ICH, the discovery cohort consisted of 478 patients prospectively admitted at University Hospital del Mar. Association between NIHSS at admission and 3-month outcome was evaluated with area under the curve-receiver operating characteristics (AUC-ROC) and Youden’s index to identify the optimal NIHSS cutoff point to define mICH. External validation was performed in a cohort of 242 supratentorial ICH patients from University Hospital Sant Pau. For infratentorial location, patients from both hospitals ( n = 85) were analyzed together. Results: The best ­NIHSS cutoff point defining supratentorial-mICH was 6 (AUC-ROC = 0.815 [0.774–0.857] in the discovery cohort and AUC-ROC = 0.819 [0.756–0.882] in the external validation cohort). For infratentorial ICH, the best cutoff point was 4 (AUC-ROC = 0.771 [0.664–0.877]). Using these cutoff points, 40.5% of all primary ICH cases were mICH. Of these, 70.2% were living independently at 3-month follow-up (72% for supratentorial ICH and 56.1% for infratentorial ICH) and 6.5% had died (5.3% for supratentorial ICH, and 14.6% for infratentorial ICH). For patients identified as non-mICH, good 3-month outcome was observed in 11.3% of cases; mortality was 51%. Conclusions: The definition of mICH using the NIHSS cutoff point of 6 for supratentorial ICH and 4 for infratentorial ICH is useful to identify good outcome in ICH patients.

Background and PurposeThe neurological course after stroke is highly variable and is determined by demographic, clinical, and genetic factors. However, other heritable factors such as epigenetic DNA methylation could play a role in neurological changes after stroke.MethodsWe performed a three-stage epigenome-wide association study (EWAS) to evaluate DNA methylation associated with the difference between the National Institutes of Health Stroke Scale (NIHSS) at baseline and at discharge (ΔNIHSS) in ischemic stroke patients. DNA methylation data in the Discovery (n = 643) and Replication (n = 62) Cohorts were interrogated with the 450K and EPIC BeadChip. Nominal CpG-sites from the Discovery (p-value < 10-06) were also evaluated in a meta-analysis of the Discovery and Replication cohorts, using a random-fixed effect model. Specific cell type methylation was assessed using EpiDISH. We integrated the methylation data with 1305 plasma protein expression levels measured by SOMAscan and measured RNA expression with RT-PCR in a subgroup of 13 subjects. Results The meta-analysis revealed an epigenome-wide significant association in EXOC4 (p-value = 8.4x10-08) and in MERTK (p-value = 1.56x10-07). Only the methylation in EXOC4 was also associated in the Discovery and in the Replication cohort (p-value = 1.14x10-06 and p-value = 1.3x10-02, respectively). EXOC4 methylation negatively correlated with the long-term outcome (coefficient = -4.91) and showed a tendency towards a decrease in EXOC4 expression (rho = -0.469, p-value = 0.091). Pathway enrichment from the meta-analysis revealed significant associations related with the exocytic process. Seventy-nine plasma proteins were differentially expressed in association with EXOC4 methylation. Pathway analysis of these proteins showed an enrichment in Natural Killer cell activation.ConclusionsDNA methylation of EXOC4 is associated with a worse neurological course after stroke. Pathway analysis suggests that the exocytic pathway could be a potential biological mechanism underlying this association.

Background: Stroke onset in women occurs later in life compared with men. The underlying mechanisms of these differences have not been established. Epigenetic clocks, based on DNA methylation (DNAm) profiles, are the most accurate biological age estimate. Epigenetic age acceleration (EAA) measures indicate whether an individual is biologically younger or older than expected. Our aim was to analyze whether sexual dichotomy at age of stroke onset is conditioned by EAA. Methods: We used 2 DNAm datasets from whole blood samples of case-control genetic studies of ischemic stroke (IS), a discovery cohort of 374 IS patients (N women=163, N men=211), from GRECOS (Genotyping Recurrence Risk of Stroke) and SEDMAN (Dabigatran Study in the Early Phase of Stroke, New Neuroimaging Markers and Biomarkers) studies and a replication cohort of 981 IS patients (N women=411, N men=570) from BASICMAR register. We compared chronological age, 2 DNAm-based biomarkers of aging and intrinsic and extrinsic epigenetic age acceleration EAA (IEAA and extrinsic EAA, respectively), in IS as well as in individual IS etiologic subtypes. Horvath and Hannum epigenetic clocks were used to assess the aging rate. A proteomic study using the SOMAScan multiplex assay was performed on 26 samples analyzing 1305 proteins. Results: Women present lower Hannum-extrinsic EAA values, whereas men have higher Hannum-extrinsic EAA values (women=-0.64, men=1.24, P=1.34×10); the same tendency was observed in the second cohort (women=-0.57, men=0.79, P=0.02). These differences seemed to be specific to cardioembolic and undetermined stroke subtypes. Additionally, 42 blood protein levels were associated with Hannum-extrinsic EAA (P, This work was supported by EPIGENESIS (Epigenetic and Genetic Study Combined With Integromics and Functional Analysis to Find Genes Associated With Neurological Deterioration After Ischemic Stroke) project (Carlos III Institute (PI17/02089,); Marató TV3; MAESTRO project (Carlos III Institute, PI18/01338); SEDMAN study (Dabigatran Study in the Early Phase of Stroke, New Neuroimaging Markers and Biomarkers; Boehringer Ingelheim); BasicMar Register projects from the Carlos III Health Institute, and Ibiostroke (Eranet Neuron). Dr Fernandez-Cadenas is a recipient of a research contract from Miguel Servet Program; Dr Gallego-Fabrega is supported by a Sara Borrell contract (CD20/00043), Dr Muiño is supported by a Río Hortega Contract (CM18/00198) and M. Lledós is supported by a PFIS (Contratos Predoctorales de Formación en Investigación en Salud) Contract, by Carlos III Health Institute (CPII17/00021). J. Cárcel-Márquez is supported by AGAUR Contract (agència de gestió d’ajuts universitaris i de recerca; FI_DGR 2019, grant number 2019_FI_B 00853) co-financed with Fons Social Europeu (FSE).

Producción Científica, Stroke is one of the most common causes of death and disability. Reperfusion therapies are the only treatment available during the acute phase of stroke. Due to recent clinical trials, these therapies may increase their frequency of use by extending the time-window administration, which may lead to an increase in complications such as hemorrhagic transformation, with parenchymal hematoma (PH) being the more severe subtype, associated with higher mortality and disability rates. Our aim was to find genetic risk factors associated with PH, as that could provide molecular targets/pathways for their prevention/treatment and study its genetic correlations to find traits sharing genetic background. We performed a GWAS and meta-analysis, following standard quality controls and association analysis (fastGWAS), adjusting age, NIHSS, and principal components. FUMA was used to annotate, prioritize, visualize, and interpret the meta-analysis results. The total number of patients in the meta-analysis was 2034 (216 cases and 1818 controls). We found rs79770152 having a genome-wide significant association (beta 0.09, p-value 3.90 × 10−8) located in the RP11-362K2.2:RP11-767I20.1 gene and a suggestive variant (rs13297983: beta 0.07, p-value 6.10 × 10−8) located in PCSK5 associated with PH occurrence. The genetic correlation showed a shared genetic background of PH with Alzheimer’s disease and white matter hyperintensities. In addition, genes containing the ten most significant associations have been related to aggregated amyloid-β, tau protein, white matter microstructure, inflammation, and matrix metalloproteinases., Instituto de Salud Carlos III y Fondo Europeo de Desarrollo Regional (FEDER) - ( Projects PI 11/0176 and PI18/01338), Instituto de Salud Carlos III - (Project CM18/00198), Agencia de Gestión de Ayudas Universitarias y de Investigación (AGAUR) y Fondo Social Europeo - (Grant 2020FI_B1 00157), Instituto de Salud Carlos III y Fondo Europeo de Desarrollo Regional (FEDER) - (Project CD20/00043), Instituto de Salud Carlos III - (Projects FI19/00309, CP12/03298, CPII17/00027, CPII19/00020)

Rationale: Ischemic stroke is among the leading causes of adult disability. Part of the variability in functional outcome after stroke has been attributed to genetic factors but no locus has been consistently associated with stroke outcome. Objective: Our aim was to identify genetic loci influencing the recovery process using accurate phenotyping to produce the largest GWAS (genome-wide association study) in ischemic stroke recovery to date. Methods and Results: A 12-cohort, 2-phase (discovery-replication and joint) meta-analysis of GWAS included anterior-territory and previously independent ischemic stroke cases. Functional outcome was recorded using 3-month modified Rankin Scale. Analyses were adjusted for confounders such as discharge National Institutes of Health Stroke Scale. A gene-based burden test was performed. The discovery phase (n=1225) was followed by open (n=2482) and stringent joint-analyses (n=1791). Those cohorts with modified Rankin Scale recorded at time points other than 3-month or incomplete data on previous functional status were excluded in the stringent analyses. Novel variants in PATJ (Pals1-associated tight junction) gene were associated with worse functional outcome at 3-month after stroke. The top variant was rs76221407 (G allele, β=0.40, P =1.70×10 −9 ). Conclusions: Our results identify a set of common variants in PATJ gene associated with 3-month functional outcome at genome-wide significance level. Future studies should examine the role of PATJ in stroke recovery and consider stringent phenotyping to enrich the information captured to unveil additional stroke outcome loci.

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. Here we describe results of DNA methylation-quantitative trait loci (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTL of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We reveal that the genetic architecture of DNAm levels is highly polygenic and DNAm exhibits signatures of negative and positive natural selection. Using shared genetic control between distal DNAm sites we construct networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic factors are associated with both blood DNAm levels and complex diseases but in most cases these associations do not reflect causal relationships from DNAm to trait or vice versa indicating a more complex genotype-phenotype map than has previously been hypothesised.

DNA methylation is dynamic, varies throughout the life course, and its levels are influenced by lifestyle and environmental factors, as well as by genetic variation. The leading genetic variants at stroke risk loci identified to date explain roughly 1–2% of stroke heritability. Most of these single nucleotide polymorphisms are situated within a regulatory sequence marked by DNase I hypersensitivity sites, which would indicate involvement of an epigenetic mechanism. To detect epigenetic variants associated with stroke occurrence and stroke subtypes. A two-stage case–control epigenome-wide association study was designed. The discovery sample with 401 samples included 218 ischaemic stroke (IS) patients, assessed at Hospital del Mar (Barcelona, Spain) and 183 controls from the REGICOR cohort. In two independent samples (N = 226 and N = 166), we replicated 22 CpG sites differentially methylated in IS in 21 loci, including 2 CpGs in locus ZFHX3, which includes known genetic variants associated with stroke. The pathways associated with these loci are inflammation and angiogenesis. The meta-analysis identified 384 differentially methylated CpGs, including loci of known stroke and vascular risk genetic variants, enriched by loci involved in lipid metabolism, adipogenesis, circadian clock, and glycolysis pathways. We identified a set of 22 CpGs in 21 loci associated with IS. Our analysis suggests that DNA methylation changes may contribute to orchestrating gene expression that contributes to IS.

Funding: This study was funded by the INVICTUS network, Generacion, Maestro EPIGENESIS, FEDER-ERDF, and BasicMar Regist projects from the Carlos III Health Institute, the AGAUR, the RecerCaixa 2013 and the European Regional Development Fund (ERDF). I. Fernandez is recipient of a research contract from Miguel Servet Program from the Carlos III Health Institute (CPII17/00021). EPIGENESIS project (CarlosIII Institute, Marató TV3 and Fundació MútuaTerrassa). Background and Purpose: Polymorphisms and serum levels of Matrix Metalloproteinases (MMP) and Tissue Inhibitor of Metalloproteinases (TIMP) have been studied with regard to atheromatous plaques and ischemic stroke, while no studies of DNA methylation (DNAm) patterns of MMP or TIMP have been performed to that end. Here, we evaluate DNAm levels of the MMP and TIMP gene families in human carotid plaques and blood samples of atherothrombotic stroke patients. Methods: We profiled the DNAm status of stable and ulcerated atherosclerotic plaques obtained as pair sets from three patients who underwent carotid endarterectomy surgery. We selected 415 CpG sites, mapping into MMPs and TIMPs genes for further study. Secondly, the statistically associated CpG sites were analyzed in blood samples from two separate atherothrombotic stroke cohorts (total sample size = 307), ischemic stroke-cohort 1 (ISC-1): 37 atherothrombotic patients and 6 controls, ischemic stroke-cohort 2 (ISC-2): 80 atherothrombotic patients and 184 controls. DNAm levels from plaque tissue and blood samples were evaluated using a high-density microarray Infinium, HumanMethylation450 BeadChip and Infinium MethylationEPIC BeadChip. Results: Three CpG sites were statistically significantly associated with unstable plaque portions; cg02969624, q-value = 0.035 (TIMP2), and cg04316754, q-value = 0.037 (MMP24) were hypermethylated, while cg24211657 q-value = 0.035 (TIMP2) was hypomethylated. Association of cg04316754 (MMP24) methylation levels with atherothrombotic risk was also observed in blood tissue: ISC-1 p-values = 0.03, ISC-2 p-value = 1.9 × 10-04. Conclusions: The results suggest different DNAm status of MMP24 between stable and unstable atherothrombotic carotid plaques, and between atherothrombotic stroke and controls in blood samples. Publisher PDF

RationaleDNA methylation is dynamic, varies throughout the life course, and its levels are influenced by lifestyle and environmental factors, as well as by genetic variation. The leading genetic variants at stroke risk loci identified to date explain roughly 1–2% of stroke heritability. Most of these single nucleotide polymorphisms are situated within a regulatory sequence marked by DNase I hypersensitivity sites, which would indicate involvement of an epigenetic mechanism.ObjectiveTo detect epigenetic variants associated to stroke occurrence and stroke subtypes.Methods and ResultsA two-stage case-control epigenome-wide association study was designed. The discovery sample with 401 samples included 218 ischemic stroke (IS) patients, assessed at Hospital del Mar (Barcelona, Spain) and 183 controls from the REGICOR cohort. In two independent samples (N=226 and N=166), we replicated 22 CpG sites differentially methylated in IS in 21 loci, including 2 CpGs in locus ZFHX3, which includes known genetic variants associated with stroke. The pathways associated to these loci are inflammation and angiogenesis. The meta-analysis identified 384 differentially methylated CpGs, including loci of known stroke and vascular risk genetic variants, enriched by loci involved in lipid metabolism, adipogenesis, circadian clock, and glycolysis pathways. Stratification analysis by stroke subtypes revealed distinct methylation patterns.ConclusionsWe identified a set of 22 CpGs in 21 loci associated with IS. Our analysis suggests that DNA methylation changes may contribute to orchestrating gene expression that contributes to IS.

BACKGROUND AND PURPOSE Alcohol overuse (AOu) is considered an important risk factor for spontaneous intracerebral hemorrhage (ICH). The clinical and outcome characteristics of these patients (AOu-ICH) are not well known. METHODS All patients with ICH admitted to a single university tertiary stroke center were prospectively studied from May 2005 to May 2015. Demographic profiles, radiologic characteristics and clinical outcomes of patients with acute ICH and previous AOu (>40 g/day or >300 g/week) were analyzed. RESULTS During the study period, 555 patients with spontaneous primary ICH met the inclusion criteria. A total of 81 patients (14.6%) reported AOu (24.3% of men vs. 3.1% of women; P

Background and purpose Patients with acute intracerebral hemorrhage (ICH) pretreated with antithrombotic drugs may have increased early hematoma growth, which would increase mortality risk. The effect of antiplatelet (AP) and vitamin K antagonist (VKA) pretreatment on ultra-early hematoma growth (uHG) and its relationship with mortality in patients with acute supratentorial ICH was analyzed. Methods This is an observational retrospective study of a prospective register of 197 ICH patients with first computed tomography (CT) scan taken

Objective:To analyze the effect of previous antiplatelet (AP) and vitamin K antagonist (VKA) treatments on outcome in patients with primary intracerebral hemorrhage (ICH).Methods:In this prospective observational study, we analyzed 529 patients according to antithrombotic pretreatment: none, AP, or VKA. Very-early (24-hour) death, 3-month mortality, and functional independence were analyzed.Results:Of 236 (44.6%) pretreated patients, 147 (27.8%) patients were taking AP and 89 (16.8%) VKA. Very-early death was observed in 13.4% and was increased in pretreated patients: 19.0% for AP and 27.0% for VKA treatment, compared to 6.5% in non-pretreated patients, p < 0.0001. Three-month mortality was 40.8% overall (49.7% for AP pretreated, 58.4% for VKA pretreated, and 31.1% for non-pretreated patients, p < 0.0001). The adjusted odds of very-early and 3-month mortality were 2.55 (p = 0.004) and 1.56 (p = 0.046) for AP-pretreated patients and 4.24 (p < 0.0001) and 2.34 (p = 0.01) for VKA-pretreated patients, respectively, compared with non-pretreated patients. The effect of antithrombotic pretreatment on mortality from 24 hours to 3 months was nonsignificant. At 3-month follow-up, 28.5% of patients remained functionally independent: 22.4% of AP-pretreated, 15.7% of VKA-pretreated, and 35.5% of non-pretreated patients (p < 0.0001).Conclusions:A high percentage of patients with ICH preventively treated with VKA or AP died during the first 24 hours after admission. Both treatments were predictors of very-early mortality. The final effect of antithrombotics on 3-month mortality remains significant through its strong effect on very-early mortality. Safety concerns about starting chronic antithrombotic treatment should be considered not only when VKA treatment is planned but also for AP treatment.

ObjectiveTo describe short-term and 5-year rates of mortality and poor outcome in patients with spontaneous aneurysmal subarachnoid hemorrhage (aSAH) who received repair treatment.MethodsIn this prospective observational study, mortality and poor outcome (modified Rankin Scale score 3–6) were analyzed in 311 patients with aSAH at 3 months, 1 year, and 5 years follow-up. Sensitivity analysis was performed according to treatment modality. In-hospital and 5-year complications were analyzed.ResultsOf 476 consecutive patients with spontaneous subarachnoid hemorrhage, 347 patients (72.9%) had aSAH. Of these, 311 (89.6%) were treated (242 endovascular, 69 neurosurgical), with a mean follow-up of 43.4 months (range, 1 to 145). Three-month, 1-year, and 5-year mortality was 18.4%, 22.9%, and 29.0%, and poor outcome was observed in 42.3%, 36.0%, and 36.0%, respectively. Adjusted poor outcome was lower in endovascular than in neurosurgical treatment at 3 months (odds ratio [OR] 0.36 [95% confidence interval [CI] 0.18-0.74]), with an absolute difference of 15.8% (number needed to treat = 6.3), and at 1 year (OR = 0.40 [95% CI 0.20-0.81]), with an absolute difference of 15.9% (number needed to treat = 6.3). Complications did not differ between the 2 procedures. However, mechanical ventilation was less frequent with the endovascular technique (OR 0.67 [95% CI 0.54–0.84]).ConclusionsPatients with aSAH treated according to current guidelines had a short-term mortality of 18.4% and 5-year mortality of 29%. The majority (64.0%) of patients remained alive without disabilities at 5-year follow-up. Patients prioritized to endovascular treatment had better outcomes than those referred to neurosurgery because endovascular coiling was not feasible.

Comment inThe yin and yang of magnesium and calcium: New genetic insights for stroke? [Neurology. 2019]; International audience; Objective To determine whether serum magnesium and calcium concentrations are causally associated with ischemic stroke or any of its subtypes using the mendelian randomization approach. Methods Analyses were conducted using summary statistics data for 13 single-nucleotide polymorphisms robustly associated with serum magnesium (n = 6) or serum calcium (n = 7) concentrations. The corresponding data for ischemic stroke were obtained from the MEGASTROKE consortium (34,217 cases and 404,630 noncases). Results In standard mendelian randomization analysis, the odds ratios for each 0.1 mmol/L (about 1 SD) increase in genetically predicted serum magnesium concentrations were 0.78 (95% confidence interval [CI] 0.69-0.89; p = 1.3 × 10 −4) for all ischemic stroke, 0.63 (95% CI 0.50-0.80; p = 1.6 × 10 −4) for cardioembolic stroke, and 0.60 (95% CI 0.44-0.82; p = 0.001) for large artery stroke; there was no association with small vessel stroke (odds ratio 0.90, 95% CI 0.67-1.20; p = 0.46). Only the association with cardioembolic stroke was robust in sensitivity analyses. There was no association of genetically predicted serum calcium concentrations with all ischemic stroke (per 0.5 mg/dL [about 1 SD] increase in serum calcium: odds ratio 1.03, 95% CI 0.88-1.21) or with any subtype. Conclusions This study found that genetically higher serum magnesium concentrations are associated with a reduced risk of cardioembolic stroke but found no significant association of genetically higher serum calcium concentrations with any ischemic stroke subtype.

Background and purpose To analyze long-term stroke recurrence (SR) characteristics after transient ischemic attack (TIA) according to initial etiological classification. Methods A prospective cohort of 706 TIA patients was followed up in a single tertiary stroke center. Endpoint was SR. Etiologic subgroup was determined according to the evidence-based causative classification system. Location of TIA and SR was recorded as right, left, or posterior territory. Disability stroke recurrence (DSR) was defined as modified Rankin Scale (mRS) score >1 or a onepoint increase in those with previous mRS >1 at 3-month follow-up. Results During a follow-up of 3,493 patient-years (mean follow-up of 58.9±35.9 months), total SR was 125 (17.7%), corresponding to 3.6 recurrences per 100 patient-years. The etiology subgroups with a higher risk of SR were the unclassified (more than one cause) and large-artery atherosclerosis (LAA) categories. Of the SR cases, 88 (70.4%) had the same etiology as the index TIA; again, LAA etiology was the most frequent (83.9%). Notably, cardioaortic embolism was the most frequent cause (62.5%) of SR in the subgroup of 24 patients with undetermined TIA. Overall, SR occurred in the same territory in 74 of 125 patients (59.2%), with significant differences between etiological TIA subgroups (P=0.015). Eighty-two of 125 (65.6%) with SR had DSR, without differences between etiologies (P=0.453). Conclusions SR occurred mainly with the same etiology and location as initial TIA, although undetermined TIA was associated with a high proportion of cardioaortic embolism SR. More than half of the recurrences caused some disability, regardless of etiology.

ObjectiveTo validate the Genot-PA score, a clinical-genetic logistic regression score that stratifies the thrombolytic therapy safety, in a new cohort of patients with stroke.MethodsWe enrolled 1,482 recombinant tissue plasminogen activator (rtPA)-treated patients with stroke in Spain and Finland from 2003 to 2016. Cohorts were analyzed on the basis of ethnicity and therapy: Spanish patients treated with IV rtPA within 4.5 hours of onset (cohort A and B) or rtPA in combination with mechanical thrombectomy within 6 hours of onset (cohort C) and Finnish participants treated with IV rtPA within 4.5 hours of onset (cohort D). The Genot-PA score was calculated, and hemorrhagic transformation (HT) and parenchymal hematoma (PH) risks were determined for each score stratum.ResultsGenot-PA score was tested in 1,324 (cohort A, n = 726; B, n = 334; C, n = 54; and D, n = 210) patients who had enough information to complete the score. Of these, 213 (16.1%) participants developed HT and 85 (6.4%) developed PH. In cohorts A, B, and D, HT occurrence was predicted by the score (p = 2.02 × 10−6, p = 0.023, p = 0.033); PH prediction was associated in cohorts A through C (p = 0.012, p = 0.034, p = 5.32 × 10−4). Increased frequency of PH events from the lowest to the highest risk group was found (cohort A 4%–15.7%, cohort B 1.5%–18.2%, cohort C 0%–100%). The best odds ratio for PH prediction in the highest-risk group was obtained in cohort A (odds ratio 5.16, 95% confidence interval 1.46–18.08, p = 0.009).ConclusionThe Genot-PA score predicts HT in patients with stroke treated with IV rtPA. Moreover, in an exploratory study, the score was associated with PH risk in mechanical thrombectomy-treated patients.

OBJECTIVE: To assess the relationship between short-term exposure to outdoor ambient air pollutants (fine particulate matter [PM2.5] and black carbon [BC]), ischemic stroke (IS) and its different subtypes, and the potential modifying effect of neighborhood greenspace and noise. METHODS: This time-stratified case-crossover study was based on IS and transient ischemic attacks (TIA) recorded in a hospital-based prospective stroke register (BASICMAR 2005-2014) in Barcelona (Catalonia, Spain). Daily and hourly pollutant concentrations and meteorological data were obtained from monitoring stations in the city. Time-lags (from previous 72h to acute stroke onset) were analyzed. Greenness and noise were determined from the Normalized Difference Vegetation Index (NDVI) and daily average noise level at the street nearest to residential address, respectively. RESULTS: The 2742 cases with known onset date and time, living in the study area, were analyzed. After adjusting for temperature, no statistically significant association between pollutants exposure and overall stroke risk was found. In subtype analysis, an association was detected between BC exposure at 24-47h (odds ratio, 1.251; 95% confidence interval [CI], 1.001-1.552; P = 0.042) and 48-72h (1.211; 95% CI, 0.988-1.484; P = 0.065) time-lag prior to stroke onset and large-artery atherosclerosis subtype. No clear modifying effect of greenness or noise was observed. CONCLUSIONS: Overall, no association was found between PM2.5 and BC exposure and acute IS risk. By stroke subtype, large-artery atherosclerotic stroke could be triggered by daily increases in BC, a diesel fuel-related pollutant in the study area.

Age and stroke severity are the main mortality predictors after ischemic stroke. However, chronological age and biological age are not exactly concordant. Age-related changes in DNA methylation in multiple CpG sites across the genome can be used to estimate biological age, which is influenced by lifestyle, environmental factors, and genetic variation. We analyzed the impact of biological age on 3-month mortality in ischemic stroke. We assessed 594 patients with acute ischemic stroke in a cohort from Hospital del Mar (Barcelona) and validated the results in an independent cohort. Demographic and clinical data, including chronological age, vascular risk factors, initial stroke severity (NIHSS score), recanalization treatment, and previous modified Rankin scale were registered. Biological age was estimated with an algorithm based on DNA methylation in 71 CpGs. Biological age was predictive of 3-month mortality (p = 0.041; OR = 1.05, 95% CI 1.00–1.10), independently of NIHSS score, chronological age, TOAST, vascular risk factors, and blood cell composition. Stratified by TOAST classification, biological age was associated with mortality only in large-artery atherosclerosis etiology (p = 0.004; OR = 1.14, 95% CI 1.04–1.25). As estimated by DNA methylation, biological age is an independent predictor of 3-month mortality in ischemic stroke regardless of chronological age, NIHSS, previous modified Rankin scale, and vascular risk factors.

ObjectiveTo determine long-term cardiovascular risk after TIA and to identify the factors associated with increased risk.MethodsThis was a prospective observational registry of TIA patients admitted to the emergency room of our tertiary stroke center from June 2006 to January 2016. New vascular events (NVEs) were recorded from 3 months after TIA onset until June 2017, including both stroke and nonstroke events (coronary and peripheral disease). We registered TIA etiology, age, sex, vascular risk factors, radiologic data, and clinical TIA features and analyzed these variables in relation to NVE long-term risk.ResultsIn total, 676 patients 71.7 ± 13.7 years of age were included, with a mean follow-up of 48.8 ± 32.7 months. An NVE was detected in 173 patients (25.6%) without significant differences between event types (p = 0.84). Univariate analysis associated NVEs with etiologic subgroup, male sex, diabetes mellitus, hypertension, previous vascular disease, duration and clinical features of TIA, and signs of acute infarction. Multivariable analysis showed an independent association of NVEs with etiologic TIA subgroup, signs of acute infarction, and duration of TIA symptoms. Large artery atherosclerosis and cardioaortic embolism had the highest NVE risk, with a slightly higher percentage of nonstroke events. The small artery disease subgroup had the lowest NVE risk, with a higher percentage of stroke events.ConclusionsEtiology subgroup was the main factor determining high long-term risk of vascular events in patients with TIA. Large artery atherosclerosis carried the highest vascular risk, both nonstroke and stroke, followed by cardioaortic embolism.

Background: Stroke has a great impact in functional status of patients, although there are substantial interindividual differences in recovery capacity. Apart from stroke severity, age is considered an important predictor of outcome after stroke, but aging is not only due to chronological age. There are age-related DNA-methylation changes in multiple CpG sites across the genome that can be used to estimate the biological age (b-Age), and we seek to analyze the impact of this b-Age in recovery after an ischemic stroke. Methods: We include 600 individuals with acute ischemic stroke assessed in Hospital del Mar (Barcelona). Demographic and clinical data such as chronological age (c-Age), vascular risk factors, NIHSS at admission, recanalization treatment (rtPA or endovascular treatment), previous modified Rankin scale (p-mRS) and 3 months post stroke functional status (3-mRS) were registered. Biological age (b-Age) was estimated with Hannumm algorithm, based on DNA methylation in 71 CpGs. Results: The bivariate analyses for association with 3-mRS showed a significant results of NIHSS, c-Age, b-Age, p-mRS, and current smoking (all with p Conclusions: Biological Age, estimated by DNA methylation, is an independent predictor of stroke prognosis, irrespective to chronological age. "Healthy aging” affects the capacity of recovering after an ischemic stroke.

Cerebrovascular diseases, and specifically stroke, are heterogeneous multifactorial diseases and are among the leading causes of mortality and long-term disability in the world. Epidemiological data provides evidence for a genetic component to the diseases; however, the epigenetic contribution to stroke is still largely unknown. A lack of heritability has been described in stroke that might be explained by the effect of epigenetic mechanisms that are sequence-independent and might be environmentally induced. In order to better understand the pathogenicity of this complex disease, the influence of environmental factors, and polygenic susceptibility must be determined. This chapter focuses on stroke-related epigenetic mechanisms described to date, especially DNA methylation, and cardiovascular risk factors that modulate the risk of stroke

Objective:To analyze the effect of age-related DNA methylation changes in multiple cytosine-phosphate-guanine (CpG) sites (biological age [b-age]) on patient outcomes at 3 months after an ischemic stroke.Methods:We included 511 patients with first-ever acute ischemic stroke assessed at Hospital del Mar (Barcelona, Spain) as the discovery cohort. Demographic and clinical data, including chronological age (c-age), vascular risk factors, initial stroke severity, recanalization treatment, and previous and 3-month modified Rankin Scale (p-mRS and 3-mRS, respectively) were registered. B-age was estimated with an algorithm, based on DNA methylation in 71 CpGs. Bivariate analysis determined variables associated with 3-mRS for inclusion in ordinal multivariate analysis.Results:After ordinal regressions for 3-month ischemic stroke outcome (3-mRS), b-age was associated with outcome (odds ratio 1.04 [95% confidence interval 1.01–1.07]), nullifying c-age. Stepwise regression kept b-age, basal NIH Stroke Scale, sex, p-mRS, and recanalization treatment as better explanatory variables, instead of c-age. These results were successfully replicated in an independent cohort.Conclusions:B-age, estimated by DNA methylation, is an independent predictor of ischemic stroke outcome regardless of chronological years.

Introduction: The aims of this study are to describe the incidence of paroxysmal atrial fibrillation (pAF) in patients with ischemic stroke (IS) or transient ischemic attack (TIA), and to create a risk prediction model, using immediately available clinical data associated with new pAF diagnosis. Methods: We analyzed data from the BASICMAR stroke register, with 5 inclusion criteria: (1) diagnosis of IS/TIA; (2) no history of AF or structural cardiopathy; (3) stroke unit (SU) monitoring after normal electrocardiogram in the emergency room; (4) complete etiologic study; and (5) 3-month follow-up. We investigated clinical predictors of pAF detection; we analyzed newly diagnosed pAF according to 4 cardiac monitoring screening methods and created a pAF-risk prediction model. Results: The final cohort included 1,240 patients. pAF was diagnosed in 139 patients (11.2%), the majority at the SU (54.7%). Multivariate predictors of new-pAF diagnosis during 3-month follow-up after ischemic event were age 75 years, female gender, history of congestive heart failure, and initial National Institute of Health Stroke Scale 15, with a predicted AF risk of 64%. Conclusions: This risk prediction model can be helpful to estimate the risk of an underlying pAF within 3 months after suffering an IS/TIA, contributing to increased AF detection efforts, thereby starting the correct secondary prevention treatment.

Background: Intravenous (i.v.) thrombolysis within 4.5 h of symptom onset has proven efficacy in acute ischemic stroke treatment, although half of all outcomes are unfavorable. The recently published DRAGON score aims to predict the 3-month outcome in stroke patients who have received i.v. alteplase. The purpose of this study was an external validation of the results of the DRAGON score in a Spanish cohort. Methods: Patients with acute stroke treated with alteplase were prospectively registered in our BasicMar database. We collected demographic characteristics, vascular risk factors, the time from stroke onset to treatment, baseline serum glucose levels and stroke severity for this population. We then reviewed hyperdense cerebral artery signs and signs of early infarct on the admission CT scan. We calculated the DRAGON score and used the developers' 3-month prognosis categories: good [modified Rankin Scale score (mRS) 0-2], poor (mRS 3-6) and miserable (mRS 5-6) outcome. Discrimination was tested using the area under the receiver operator curve (AUC-ROC). Calibration was assessed by the Hosmer-Lemeshow test. Results: Our final cohort of 297 patients was older (median age 74 years, IQR 65-80) and had more risk factors and severe strokes [median National Institutes of Health Stroke Scale (NIHSS) points 13, IQR 7-19] than the original study population. Poor prognosis was observed in 143 (48.1%) patients. Higher DRAGON scores were associated with a higher risk of poor prognosis. None of our treated stroke patients with a DRAGON score ≥8 at admission experienced a favorable outcome after 3 months. All DRAGON variables were significantly associated with a worse outcome in the multivariate analysis except for onset-to-treatment time (p = 0.334). Discrimination to predict poor prognosis was very good (AUC-ROC 0.84) and the score had good Hosmer-Lemeshow calibration (p = 0.84). Conclusions: The DRAGON score is easy to perform and offers a rapid, reliable prediction of poor prognosis in acute-stroke patients treated with alteplase. This study replicates the original results in a different population.

In DNA methylation, methyl groups are covalently bound to CpG dinucleotides. However, the assumption that methyl groups are not lost during routine DNA extraction has not been empirically tested. To avoid nonbiological associations in DNA methylation studies, it is essential to account for potential batch effect bias in the assessment of this epigenetic mechanism. Our purpose was to determine if the DNA isolation method is an independent source of variability in methylation status. We quantified Global DNA Methylation (GDM) by luminometric methylation assay (LUMA), comparing the results from 3 different DNA isolation methods. In the controlled analysis (n = 9), GDM differed slightly for the same individual depending on extraction method. In the population analysis (n = 580) there were significant differences in GDM between the 3 DNA isolation methods (medians, 78.1%, 76.5% and 75.1%; p

Background and Aims Diet appears to have some role in stroke development. The objective of our study was to describe the dietary habits in patients admitted with acute ischemic stroke and compare selected dietary components with healthy controls. Adherence to healthy diet behaviors was also assessed. Methods A case-control study of consecutive patients with acute ischemic stroke admitted to the Neurology Department of Hospital del Mar from 2007 to 2010. Patients were matched by age and sex with control subjects. A previously validated nutritional survey was administered to patients and controls. Demographic data, vascular risk factors, caloric intake and dietary nutrients were evaluated. Intention to follow a healthy diet was also assessed in both groups. Results A total of 300 acute ischemic stroke patients and 300 controls with evaluation of dietary habits. No differences were observed in vascular risk factors, except smoking habit, diabetes and ischemic heart disease. Stroke patients reported a higher caloric intake: 2444.8(1736.8–3244.5) vs 2208.7(1753.1–2860.7) Kcal, p = 0.001. After adjusting for energy intake, patients had higher intake of proteins (p

Lipid traits (total, low-densityand high-density lipoproteincholesterol, and triglycerides) are risk factors for cardiovascular disease. DNA methylation is an inherited but also modifiable epigenetic mark that has been related tocardiovascular risk factors. Our aim was to identify loci showing differential DNA methylation related to serum lipid levels. Blood DNA methylation was assessed using the Illumina HumanMethylation450 BeadChip. Atwo-stage epigenome-wide association study was performed, with a discovery sample intheREGICOR study (n=645)and validation in the Framingham Offspring Study (n=2,542).FourteenCpG sites located in 9 genes (SREBF1, SREBF2, PHOSPHO1, SYNGAP1, ABCG1, CPT1A, MYLIP, TXNIP andSLC7A11) and 2 intergenic regions showeddifferential methylation in association with lipid traits. Six of these genes and 1 intergenic region were new discoveries showing differential methylation relatedto total cholesterol (SREBF2), HDL-cholesterol (PHOSPHO1, SYNGAP1 and an intergenic region in chromosome 2) and triglycerides (MYLIP, TXNIP andSLC7A11).These CpGs explained0.7%, 9.5% and18.9% of the variability of total cholesterol, HDL cholesterol and triglycerides in the Framingham Offspring Study, respectively. The expression of the genesSREBF2and SREBF1was inversely associated with methylation of their corresponding CpGs(p-value=0.0042 and 0.0045, respectively) in participants of the GOLDN study(n=98). In turn, SREBF1expression wasdirectly associated with HDL cholesterol(p-value=0.0429). Genetic variants in SREBF1, PHOSPHO1, ABCG1and CPT1Awerealso associated with lipid profile. Further research is warranted to functionally validatethesenew loci and assess the causality ofnew and established associationsbetween these differentially methylated lociand lipid metabolism. This work was supported by the following sources: Agència de Gestio Ajuts Universitaris de Recerca [2014 SGR 240]; the Spanish Ministry of Economy through the Carlos III Health Institute [ISCIII-FIS-FEDER-ERDF PI12-00232, PI12-01238, PI11-01801, PI08-1327, PI05-1251, PI05-1297, PI02-0471, FIS99/0013-01, FIS96/0026-01, FIS93/0568, FIS92/0009-05], and the Red de Investigacion Cardiovascular [RD12/0042/0013, RD12/0042/0020, RD12/0042/0055, RD12/0042/0061]. S.S-B. was funded by a contract from Instituto de Salud Carlos III FEDER [IFI14/00007] and Daniel Bravo Andreu Private Foundation. GOLDN: The GOLDN study (AND, DA, JO, SA, DKA) was funded by the US National Institute of Health (NIH)/National Heart, Lung and Blood Institutes (http://www.nhlbi.nih.gov) grants R01HL104135 and U01HL72524.

Publisher's version (útgefin grein), Objective: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. Methods We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n=20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p[BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p[BI]= 4.4 × 10-10; p [SSBI] = 1.2 × 10 -4), diabetes (p[BI] = 1.7 × 10 -8; p [SSBI] = 2.8 × 10 -3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10 -24), and MRI-defined white matter hyperintensity burden (p [BI]=1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. Conclusion: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI., CHAP: R01-AG-11101, R01-AG-030146, NIRP-14-302587. SMART: This study was supported by a grant from the Netherlands Organization for Scientific Research–Medical Sciences (project no. 904-65–095). LBC: The authors thank the LBC1936 participants and the members of the LBC1936 research team who collected and collated the phenotypic and genotypic data. The LBC1936 is supported by Age UK (Disconnected Mind Programme grant). The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1). The brain imaging was performed in the Brain Research Imaging Centre (https://www.ed.ac.uk/clinical-sciences/edinburgh-imaging), a center in the SINAPSE Collaboration (sinapse.ac.uk) supported by the Scottish Funding Council and Chief Scientist Office. Funding from the UK Biotechnology and Biological Sciences Research Council (BBSRC) and the UK Medical Research Council is acknowledged. Genotyping was supported by a grant from the BBSRC (ref. BB/F019394/1). PROSPER: The PROSPER study was supported by an investigator-initiated grant obtained from Bristol-Myers Squibb. Prof. Dr. J.W. Jukema is an Established Clinical Investigator of the Netherlands Heart Foundation (grant 2001 D 032). Support for genotyping was provided by the seventh framework program of the European commission (grant 223004) and by the Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging grant 050-060-810). SCES and SiMES: National Medical Research Council Singapore Centre Grant NMRC/CG/013/2013. C.-Y.C. is supported by the National Medical Research Council, Singapore (CSA/033/2012), Singapore Translational Research Award (STaR) 2013. Dr. Kamran Ikram received additional funding from the Singapore Ministry of Health's National Medical Research Council (NMRC/CSA/038/2013). SHIP: SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs, as well as the Social Ministry of the Federal State of Mecklenburg–West Pomerania, and the network “Greifswald Approach to Individualized Medicine (GANI_MED)” funded by the Federal Ministry of Education and Research (grant 03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers, Erlangen, Germany, and the Federal State of Mecklenburg–West Pomerania. Whole-body MRI was supported by a joint grant from Siemens Healthineers, Erlangen, Germany, and the Federal State of Mecklenburg–West Pomerania. The University of Greifswald is a member of the Caché Campus program of the InterSystems GmbH. OATS (Older Australian Twins Study): OATS was supported by an Australian National Health and Medical Research Council (NHRMC)/Australian Research Council (ARC) Strategic Award (ID401162) and by a NHMRC grant (ID1045325). OATS was facilitated via access to the Australian Twin Registry, which is supported by the NHMRC Enabling Grant 310667. The OATS genotyping was partly supported by a Commonwealth Scientific and Industrial Research Organisation Flagship Collaboration Fund Grant. NOMAS: The Northern Manhattan Study is funded by the NIH grant “Stroke Incidence and Risk Factors in a Tri-Ethnic Region” (NINDS R01NS 29993). TASCOG: NHMRC and Heart Foundation. AGES: The study was funded by the National Institute on Aging (NIA) (N01-AG-12100), Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament), with contributions from the Intramural Research Programs at the NIA, the National Heart, Lung, and Blood Institute (NHLBI), and the National Institute of Neurological Disorders and Stroke (NINDS) (Z01 HL004607-08 CE). ERF: The ERF study as a part of European Special Populations Research Network (EUROSPAN) was supported by European Commission FP6 STRP grant no. 018947 (LSHG-CT-2006-01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007–2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the programme “Quality of Life and Management of the Living Resources” of 5th Framework Programme (no. QLG2-CT-2002-01254). High-throughput analysis of the ERF data was supported by a joint grant from Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). Exome sequencing analysis in ERF was supported by the ZonMw grant (project 91111025). Najaf Amin is supported by the Netherlands Brain Foundation (project no. F2013[1]-28). ARIC: The Atherosclerosis Risk in Communities study was performed as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HSN268201100006C, HSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL70825, R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and NIH contract HHSN268200625226C. Infrastructure was partly supported by grant no. UL1RR025005, a component of the NIH and NIH Roadmap for Medical Research. This project was also supported by NIH R01 grant NS087541 to M.F. FHS: This work was supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (contracts no. N01-HC-25195 and no. HHSN268201500001I), and its contract with Affymetrix, Inc. for genotyping services (contract no. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. This study was also supported by grants from the NIA (R01s AG033040, AG033193, AG054076, AG049607, AG008122, and U01-AG049505) and the NINDS (R01-NS017950, UH2 NS100605). Dr. DeCarli is supported by the Alzheimer's Disease Center (P30 AG 010129). ASPS: The research reported in this article was funded by the Austrian Science Fund (FWF) grant nos. P20545-P05, P13180, and P20545-B05, by the Austrian National Bank Anniversary Fund, P15435, and the Austrian Ministry of Science under the aegis of the EU Joint Programme–Neurodegenerative Disease Research (JPND) (jpnd.eu). LLS: The Leiden Longevity Study has received funding from the European Union's Seventh Framework Programme (FP7/2007–2011) under grant agreement no. 259679. This study was supported by a grant from the Innovation-Oriented Research Program on Genomics (SenterNovem IGE05007), the Centre for Medical Systems Biology, and the Netherlands Consortium for Healthy Ageing (grant 050-060-810), all in the framework of the Netherlands Genomics Initiative, Netherlands Organization for Scientific Research (NWO), UnileverColworth, and by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007). CHS: This CHS research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC15103, and HHSN268200960009C and grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, R01HL085251, and R01HL130114 from the NHLBI with additional contribution from NINDS. Additional support was provided through R01AG023629 from the NIA. A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Rotterdam Study: The generation and management of GWAS genotype data for the Rotterdam Study is supported by the Netherlands Organisation of Scientific Research (NWO) Investments (no. 175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/NWO project no. 050-060-810. The Rotterdam Study is funded by Erasmus MC Medical Center and Erasmus MC University, Rotterdam, Netherlands Organization for Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. M.A.I. is supported by an NWO Veni grant (916.13.054). The 3-City Study: The 3-City Study is conducted under a partnership agreement among the Institut National de la Santé et de la Recherche Médicale (INSERM), the University of Bordeaux, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, Mutuelle Générale de l’Education Nationale (MGEN), Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Programme “Cohortes et collections de données biologiques.” C.T. and S.D. have received investigator-initiated research funding from the French National Research Agency (ANR) and from the Fondation Leducq. S.D. is supported by a starting grant from the European Research Council (SEGWAY), a grant from the Joint Programme of Neurodegenerative Disease research (BRIDGET), from the European Union's Horizon 2020 research and innovation programme under grant agreements No 643417 & No 640643, and by the Initiative of Excellence of Bordeaux University. Part of the computations were performed at the Bordeaux Bioinformatics Center (CBiB), University of Bordeaux. This work was supported by the National Foundation for Alzheimer's Disease and Related Disorders, the Institut Pasteur de Lille, the Labex DISTALZ, and the Centre National de Génotypage. ADGC: The Alzheimer Disease Genetics Consortium is supported by NIH. NIH-NIA supported this work through the following grants: ADGC, U01 AG032984, RC2 AG036528; NACC, U01 AG016976; NCRAD, U24 AG021886; NIA LOAD, U24 AG026395, U24 AG026390; Banner Sun Health Research Institute, P30 AG019610; Boston University, P30 AG013846, U01 AG10483, R01 CA129769, R01 MH080295, R01 AG017173, R01 AG025259, R01AG33193; Columbia University, P50 AG008702, R37 AG015473; Duke University, P30 AG028377, AG05128; Emory University, AG025688; Group Health Research Institute, UO1 AG06781, UO1 HG004610; Indiana University, P30 AG10133; Johns Hopkins University, P50 AG005146, R01 AG020688; Massachusetts General Hospital, P50 AG005134; Mayo Clinic, P50 AG016574; Mount Sinai School of Medicine, P50 AG005138, P01 AG002219; New York University, P30 AG08051, MO1RR00096, UL1 RR029893, 5R01AG012101, 5R01AG022374, 5R01AG013616, 1RC2AG036502, 1R01AG035137; Northwestern University, P30 AG013854; Oregon Health & Science University, P30 AG008017, R01 AG026916; Rush University, P30 AG010161, R01 AG019085, R01 AG15819, R01 AG17917, R01 AG30146; TGen, R01 NS059873; University of Alabama at Birmingham, P50 AG016582, UL1RR02777; University of Arizona, R01 AG031581; University of California, Davis, P30 AG010129; University of California, Irvine, P50 AG016573, P50, P50 AG016575, P50 AG016576, P50 AG016577; University of California, Los Angeles, P50 AG016570; University of California, San Diego, P50 AG005131; University of California, San Francisco, P50 AG023501, P01 AG019724; University of Kentucky, P30 AG028383, AG05144; University of Michigan, P50 AG008671; University of Pennsylvania, P30 AG010124; University of Pittsburgh, P50 AG005133, AG030653; University of Southern California, P50 AG005142; University of Texas Southwestern, P30 AG012300; University of Miami, R01 AG027944, AG010491, AG027944, AG021547, AG019757; University of Washington, P50 AG005136; Vanderbilt University, R01 AG019085; and Washington University, P50 AG005681, P01 AG03991. The Kathleen Price Bryan Brain Bank at Duke University Medical Center is funded by NINDS grant NS39764, NIMH MH60451, and by GlaxoSmithKline. Genotyping of the TGEN2 cohort was supported by Kronos Science. The TGen series was also funded by NIA grant AG041232, the Banner Alzheimer's Foundation, The Johnnie B. Byrd Sr. Alzheimer's Institute, the Medical Research Council, and the state of Arizona and also includes samples from the following sites: Newcastle Brain Tissue Resource (funding via the Medical Research Council [MRC], local NHS trusts, and Newcastle University), MRC London Brain Bank for Neurodegenerative Diseases (funding via the Medical Research Council), South West Dementia Brain Bank (funding via numerous sources including the Higher Education Funding Council for England [HEFCE], Alzheimer's Research Trust [ART], BRACE, as well as North Bristol NHS Trust Research and Innovation Department and DeNDRoN), The Netherlands Brain Bank (funding via numerous sources including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, and Universitat de Barcelona). ADNI: Funding for ADNI is through the Northern California Institute for Research and Education by grants from Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson & Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., Alzheimer's Association, Alzheimer's Drug Discovery Foundation, the Dana Foundation, and the National Institute of Biomedical Imaging and Bioengineering and NIA grants U01 AG024904, RC2 AG036535, and K01 AG030514. Support was also provided by the Alzheimer's Association (LAF, IIRG-08-89720; MAP-V, IIRG-05-14147) and the US Department of Veterans Affairs Administration, Office of Research and Development, Biomedical Laboratory Research Program. SiGN: Stroke Genetic Network (SiGN) was supported in part by award nos. U01NS069208 and R01NS100178 from NINDS. Genetics of Early-Onset Stroke (GEOS) Study was supported by the NIH Genes, Environment and Health Initiative (GEI) grant U01 HG004436, as part of the GENEVA consortium under GEI, with additional support provided by the Mid-Atlantic Nutrition and Obesity Research Center (P30 DK072488); and the Office of Research and Development, Medical Research Service, and the Baltimore Geriatrics Research, Education, and Clinical Center of the Department of Veterans Affairs. Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to Johns Hopkins University (contract no. HHSN268200782096C). Assistance with data cleaning was provided by the GENEVA Coordinating Center (U01 HG 004446; PI Bruce S. Weir). Study recruitment and assembly of datasets were supported by a Cooperative Agreement with the Division of Adult and Community Health, Centers for Disease Control and Prevention, and by grants from NINDS and the NIH Office of Research on Women's Health (R01 NS45012, U01 NS069208-01). METASTROKE: ASGC: Australian population control data were derived from the Hunter Community Study. This research was funded by grants from the Australian National and Medical Health Research Council (NHMRC Project Grant ID: 569257), the Australian National Heart Foundation (NHF Project Grant ID: G 04S 1623), the University of Newcastle, the Gladys M Brawn Fellowship scheme, and the Vincent Fairfax Family Foundation in Australia. E.G.H. was supported by a Fellowship from the NHF and National Stroke Foundation of Australia (ID: 100071). J.M. was supported by an Australian Postgraduate Award. BRAINS: Bio-Repository of DNA in Stroke (BRAINS) is partly funded by a Senior Fellowship from the Department of Health (UK) to P.S., the Henry Smith Charity, and the UK-India Education Research Institutive (UKIERI) from the British Council. GEOS: Genetics of Early Onset Stroke (GEOS) Study, Baltimore, was supported by GEI Grant U01 HG004436, as part of the GENEVA consortium under GEI, with additional support provided by the Mid-Atlantic Nutrition and Obesity Research Center (P30 DK072488), and the Office of Research and Development, Medical Research Service, and the Baltimore Geriatrics Research, Education, and Clinical Center of the Department of Veterans Affairs. Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to the Johns Hopkins University (contract no. HHSN268200782096C). Assistance with data cleaning was provided by the GENEVA Coordinating Center (U01 HG 004446; PI Bruce S. Weir). Study recruitment and assembly of datasets were supported by a Cooperative Agreement with the Division of Adult and Community Health, Centers for Disease Control and Prevention, and by grants from NINDS and the NIH Office of Research on Women's Health (R01 NS45012, U01 NS069208-01). HPS: Heart Protection Study (HPS) (ISRCTN48489393) was supported by the UK MRC, British Heart Foundation, Merck and Co. (manufacturers of simvastatin), and Roche Vitamins Ltd. (manufacturers of vitamins). Genotyping was supported by a grant to Oxford University and CNG from Merck and Co. J.C.H. acknowledges support from the British Heart Foundation (FS/14/55/30806). ISGS: Ischemic Stroke Genetics Study (ISGS)/Siblings With Ischemic Stroke Study (SWISS) was supported in part by the Intramural Research Program of the NIA, NIH project Z01 AG-000954-06. ISGS/SWISS used samples and clinical data from the NIH-NINDS Human Genetics Resource Center DNA and Cell Line Repository (ccr.coriell.org/ninds), human subjects protocol nos. 2003-081 and 2004-147. ISGS/SWISS used stroke-free participants from the Baltimore Longitudinal Study of Aging (BLSA) as controls. The inclusion of BLSA samples was supported in part by the Intramural Research Program of the NIA, NIH project Z01 AG-000015-50, human subjects protocol no. 2003-078. The ISGS study was funded by NIH-NINDS Grant R01 NS-42733 (J.F.M.). The SWISS study was funded by NIH-NINDS Grant R01 NS-39987 (J.F.M.). This study used the high-performance computational capabilities of the Biowulf Linux cluster at the NIH (biowulf.nih.gov). MGH-GASROS: MGH Genes Affecting Stroke Risk and Outcome Study (MGH-GASROS) was supported by NINDS (U01 NS069208), the American Heart Association/Bugher Foundation Centers for Stroke Prevention Research 0775010N, the NIH and NHLBI's STAMPEED genomics research program (R01 HL087676), and a grant from the National Center for Research Resources. The Broad Institute Center for Genotyping and Analysis is supported by grant U54 RR020278 from the National Center for Research resources. Milan: Milano–Besta Stroke Register Collection and genotyping of the Milan cases within CEDIR were supported by the Italian Ministry of Health (grant nos.: RC 2007/LR6, RC 2008/LR6; RC 2009/LR8; RC 2010/LR8; GR-2011-02347041), FP6 LSHM-CT-2007-037273 for the PROCARDIS control samples. WTCCC2: Wellcome Trust Case-Control Consortium 2 (WTCCC2) was principally funded by the Wellcome Trust, as part of the Wellcome Trust Case Control Consortium 2 project (085475/B/08/Z and 085475/Z/08/Z and WT084724MA). The Stroke Association provided additional support for collection of some of the St George's, London cases. The Oxford cases were collected as part of the Oxford Vascular Study, which is funded by the MRC, Stroke Association, Dunhill Medical Trust, National Institute of Health Research (NIHR), and the NIHR Biomedical Research Centre, Oxford. The Edinburgh Stroke Study was supported by the Wellcome Trust (clinician scientist award to C.L.M.S.) and the Binks Trust. Sample processing occurred in the Genetics Core Laboratory of the Wellcome Trust Clinical Research Facility, Western General Hospital, Edinburgh. Much of the neuroimaging occurred in the Scottish Funding Council Brain Imaging Research Centre (https://www.ed.ac.uk/clinical-sciences/edinburgh-imaging), Division of Clinical Neurosciences, University of Edinburgh, a core area of the Wellcome Trust Clinical Research Facility, and part of the SINAPSE (Scottish Imaging Network: A Platform for Scientific Excellence) collaboration (sinapse.ac.uk), funded by the Scottish Funding Council and the Chief Scientist Office. Collection of the Munich cases and data analysis was supported by the Vascular Dementia Research Foundation. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements no. 666881, SVDs@target (to M.D.) and no. 667375, CoSTREAM (to M.D.); the DFG as part of the Munich Cluster for Systems Neurology (EXC 1010 SyNergy) and the CRC 1123 (B3) (to M.D.); the Corona Foundation (to M.D.); the Fondation Leducq (Transatlantic Network of Excellence on the Pathogenesis of Small Vessel Disease of the Brain) (to M.D.); the e:Med program (e:AtheroSysMed) (to M.D.) and the FP7/2007-2103 European Union project CVgenes@target (grant agreement no. Health-F2-2013-601456) (to M.D.). M.F. and A.H. acknowledge support from the BHF Centre of Research Excellence in Oxford and the Wellcome Trust core award (090532/Z/09/Z). VISP: The GWAS component of the Vitamin Intervention for Stroke Prevention (VISP) study was supported by the US National Human Genome Research Institute (NHGRI), grant U01 HG005160 (PI Michèle Sale and Bradford Worrall), as part of the Genomics and Randomized Trials Network (GARNET). Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to Johns Hopkins University. Assistance with data cleaning was provided by the GARNET Coordinating Center (U01 HG005157; PI Bruce S. Weir). Study recruitment and collection of datasets for the VISP clinical trial were supported by an investigator-initiated research grant (R01 NS34447; PI James Toole) from the US Public Health Service, NINDS, Bethesda, MD. Control data obtained through the database of genotypes and phenotypes (dbGAP) maintained and supported by the United States National Center for Biotechnology Information, US National Library of Medicine. WHI: Funding support for WHI-GARNET was provided through the NHGRI GARNET (grant no. U01 HG005152). Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GARNET Coordinating Center (U01 HG005157). Funding support for genotyping, which was performed at the Broad Institute of MIT and Harvard, was provided by the GEI (U01 HG004424). R.L. is a senior clinical investigator of FWO Flanders. F.W.A. is supported by a Dekker scholarship-Junior Staff Member 2014T001–Netherlands Heart Foundation and UCL Hospitals NIHR Biomedical Research Centre.