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Background and ObjectivesTo examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in lesion pattern–specific ways.MethodsMR neuroimaging and NIH Stroke Scale data at index stroke and the modified Rankin Scale (mRS) score at 3–6 months after stroke were obtained from the MRI–Genetics Interface Exploration study of patients with acute ischemic stroke (AIS). Individual WMH volume was automatically derived from fluid-attenuated inversion recovery images. Stroke lesions were automatically segmented from diffusion-weighted imaging (DWI) images, parcellated into atlas-defined brain regions and further condensed to 10 lesion patterns via machine learning–based dimensionality reduction. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS score >2) were modeled within purpose-built Bayesian linear and logistic regression frameworks. Interaction effects between stroke lesions and a high vs low WMH burden were integrated via hierarchical model structures. Models were adjusted for age, age2, sex, total DWI lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts.ResultsA total of 928 patients with AIS contributed to acute stroke severity analyses (age: 64.8 [14.5] years, 40% women) and 698 patients to long-term functional outcome analyses (age: 65.9 [14.7] years, 41% women). Stroke severity was mainly explained by lesions focused on bilateral subcortical and left hemispherically pronounced cortical regions across patients with both a high and low WMH burden. Lesions centered on left-hemispheric insular, opercular, and inferior frontal regions and lesions affecting right-hemispheric temporoparietal regions had more pronounced effects on stroke severity in case of high compared with low WMH burden. Unfavorable outcomes were predominantly explained by lesions in bilateral subcortical regions. In difference to the lesion location–specific WMH effects on stroke severity, higher WMH burden increased the odds of unfavorable outcomes independent of lesion location.DiscussionHigher WMH burden may be associated with an increased stroke severity in case of stroke lesions involving left-hemispheric insular, opercular, and inferior frontal regions (potentially linked to language functions) and right-hemispheric temporoparietal regions (potentially linked to attention). Our findings suggest that patients with specific constellations of WMH burden and lesion locations may have greater benefits from acute recanalization treatments. Future clinical studies are warranted to systematically assess this assumption and guide more tailored treatment decisions.

Background: Atrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke. Methods: To determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in 5 prospective studies comprising 18 919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a separate study of 509 ischemic stroke cases (202 cardioembolic [40%]) and 3028 referents. Scores were based on 11 to 719 common variants (≥5%) associated with AF at P values ranging from −3 to −8 in a prior independent genetic association study. Results: Incident AF occurred in 1032 individuals (5.5%). AF genetic risk scores were associated with new-onset AF after adjustment for clinical risk factors. The pooled hazard ratio for incident AF for the highest versus lowest quartile of genetic risk scores ranged from 1.28 (719 variants; 95% confidence interval, 1.13–1.46; P =1.5×10 −4 ) to 1.67 (25 variants; 95% confidence interval, 1.47–1.90; P =9.3×10 −15 ). Discrimination of combined clinical and genetic risk scores varied across studies and scores (maximum C statistic, 0.629–0.811; maximum ΔC statistic from clinical score alone, 0.009–0.017). AF genetic risk was associated with stroke in age- and sex-adjusted models. For example, individuals in the highest versus lowest quartile of a 127-variant score had a 2.49-fold increased odds of cardioembolic stroke (95% confidence interval, 1.39–4.58; P =2.7×10 −3 ). The effect persisted after the exclusion of individuals (n=70) with known AF (odds ratio, 2.25; 95% confidence interval, 1.20–4.40; P =0.01). Conclusions: Comprehensive AF genetic risk scores were associated with incident AF beyond associations for clinical AF risk factors but offered small improvements in discrimination. AF genetic risk was also associated with cardioembolic stroke in age- and sex-adjusted analyses. Efforts are warranted to determine whether AF genetic risk may improve identification of subclinical AF or help distinguish between stroke mechanisms.

ObjectiveTo assess potential mechanisms of cortical superficial siderosis (cSS), a central MRI biomarker in cerebral amyloid angiopathy (CAA), we performed a collaborative meta-analysis of APOE associations with cSS presence and severity.MethodsWe pooled data from published studies reporting APOE genotype and MRI assessment of cSS in 3 distinct settings: (1) stroke clinic patients with symptomatic CAA (i.e., lobar intracerebral hemorrhage, transient focal neurologic episodes) according to the Boston criteria; (2) memory clinic patients; and (3) population-based studies. We compared cSS presence and severity (focal or disseminated vs no cSS) in participants with ε2+ or ε4+ genotype vs the ε3/ε3 genotype, by calculating study-specific and random effects pooled, unadjusted odds ratios (ORs).ResultsThirteen studies fulfilled inclusion criteria: 7 memory clinic cohorts (n = 2,587), 5 symptomatic CAA cohorts (n = 402), and 1 population-based study (n = 1,379). There was no significant overall association between APOE ε4+ and cSS presence or severity. When stratified by clinical setting, APOE ε4+ was associated with cSS in memory clinic (OR 2.10; 95% confidence interval [CI] 1.11–3.99) but not symptomatic CAA patients. The pooled OR showed significantly increased odds of having cSS for APOE ε2+ genotypes (OR 2.42, 95% CI 1.48–3.95) in both patient populations. This association was stronger for disseminated cSS in symptomatic CAA cohorts. In detailed subgroup analyses, APOE ε2/ε2 and APOE ε2/ε4 genotypes were most consistently and strongly associated with cSS presence and severity.ConclusionCAA-related vasculopathic changes and fragility associated with APOE ε2+ allele might have a biologically meaningful role in the pathophysiology and severity of cSS.

ObjectiveAPOE ε2 and ε4 alleles have been associated with lobar intracerebral hemorrhage (ICH) in predominately white populations; we sought to evaluate whether this held true among black and Hispanic populations.MethodsThe Ethnic/Racial Variations of Intracerebral Hemorrhage study is a prospective, multicenter case-control study of ICH among white, black, and Hispanic participants. Controls were recruited to match cases based on age, ethnicity/race, sex, and geographic location. APOE genotyping and ICH location was determined blinded to clinical data.ResultsThere were 907 cases of lobar ICH and 2,660 controls with APOE results. Both APOE ε2 (odds ratio [OR] 1.5, 95% confidence interval [CI] 1.1–2.0, p = 0.01) and APOE ε4 (OR 2.0, 95% CI 1.5–2.6, p < 1 × 10−4) were associated with lobar ICH among white participants. Among black participants, neither APOE ε2 (OR 1.0, 95% CI 0.7–1.5, p = 0.97) nor APOE ε4 (OR 1.0, 95% CI 0.7–1.4, p = 0.90) were independent risk factors for lobar ICH. Similarly, among Hispanic participants, neither APOE ε2 (OR 1.0, 95% CI 0.6–1.8, p = 0.89) nor APOE ε4 (OR 1.2, 95% CI 0.8–1.7, p = 0.36) were associated with lobar ICH. Hypertension was a significant risk factor for lobar ICH in all 3 racial/ethnic groups.ConclusionIn contrast to Caucasian patients, in which amyloid risk factors predominate in lobar ICH, we found that hypertension was the predominant risk factor for lobar ICH. While APOE alleles are a risk factor for lobar ICH in white patients, they appear to have a much lower effect in lobar ICH in African American and Hispanic American populations. This suggests APOE ε2 and APOE ε4 do not affect lobar ICH risk homogeneously across ethnic populations. In addition, hypertension has a prominent role in lobar ICH risk, particularly among minorities.

Objective:To validate various noncontrast CT (NCCT) predictors of hematoma expansion in a large international cohort of ICH patients and investigate whether intensive blood pressure (BP) treatment reduces ICH growth and improves outcome in patients with these markers.Methods:We analyzed patients enrolled in the Antihypertensive Treatment of Acute Cerebral Hemorrhage II (ATACH-II) randomized controlled trial. Participants were assigned to intensive (systolic BP 33% and unfavorable outcome was defined as modified Rankin Scale score >3 at 90 days. Logistic regression was used to identify predictors of ICH expansion and explore the association between NCCT signs and clinical benefit from intensive BP treatment.Results:A total of 989 patients were included (mean age 62 years, 61.9% male), of whom 186/869 experienced hematoma expansion (21.4%) and 361/952 (37.9%) had unfavorable outcome. NCCT markers independently predicted ICH expansion (all p < 0.01) with overall accuracy ranging from 61% to 78% and good interrater reliability (k > 0.6 for all markers). There was no evidence of an interaction between NCCT markers and benefit from intensive BP reduction (all p for interaction >0.10).Conclusions:NCCT signs reliably identify ICH patients at high risk of hematoma growth. However, we found no evidence that patients with these markers specifically benefit from intensive BP reduction.Clinicaltrials.gov identifier:NCT01176565.

Objective:To evaluate the associations among diffusion-weighted imaging (DWI) lesions, blood pressure (BP) dysregulation, MRI markers of small vessel disease, and poor outcome in a large, prospective study of primary intracerebral hemorrhage (ICH).Methods:The Ethnic/Racial Variations of Intracerebral Hemorrhage (ERICH) study is a multicenter, observational study of ICH among white, black, and Hispanic patients.Results:Of 600 patients, mean (±SD) age was 60.8 ± 13.6 years, median (interquartile range) ICH volume was 9.1 mL (3.5–20.8), and 79.6% had hypertension. Overall, 26.5% of cases had DWI lesions, and this frequency differed by race/ethnicity (black 33.8%, Hispanic 24.9%, white 20.2%, overallp= 0.006). A logistic regression model of variables associated with DWI lesions included lower age (odds ratio [OR] 0.721,p= 0.002), higher first recorded systolic BP (10-unit OR 1.12,p= 0.002), greater change in mean arterial pressure (MAP) prior to the MRI (10-unit OR 1.10,p= 0.037), microbleeds (OR 1.99,p= 0.008), and higher white matter hyperintensity (WMH) score (1-unit OR 1.16,p= 0.002) after controlling for race/ethnicity, leukocyte count, and acute in-hospital antihypertensive treatment. A second model of variables associated with poor 90-day functional outcome (modified Rankin Scale scores 4–6) included DWI lesion count (OR 1.085,p= 0.034) as well as age, ICH volume, intraventricular hemorrhage, Glasgow Coma Scale score, WMH score, race/ethnicity, acute in-hospital antihypertensive treatment, and ICH location.Conclusions:These results support the hypotheses that acute BP dysregulation is associated with the development of DWI lesions in primary ICH and that DWI lesions are, in turn, associated with poor outcomes.

Objective: Genetic variants e2/e4 within the APOE gene are established risk factors for lobar intracerebral hemorrhage (ICH). Published preliminary data suggest a potential role for APOE e4 in risk of nonlobar ICH. We therefore investigated the role of APOE in recurrent nonlobar ICH, and sought to clarify whether effects of APOE on circulating lipids mediate this association. Methods: Three hundred sixty-three survivors of nonlobar ICH were followed prospectively for ICH recurrence, with APOE genotype determined at enrollment. All participants had clinical, demographic, and laboratory data captured at time of index ICH and during follow-up. Using a multivariate model, we performed association and interaction analyses of the relationships among APOE genotype, lipid levels, and recurrent nonlobar ICH. Results: We observed 29 nonlobar ICH recurrences among 363 survivors. APOE e4 was associated with recurrent nonlobar ICH (hazard ratio = 1.31; 95% confidence interval = 1.02–2.69; p = 0.038) after adjustment for age/sex/ethnicity and cardiovascular risk factors. Increasing low-density lipoprotein (LDL) levels were associated with decreased risk of recurrent nonlobar ICH ( p = 0.027), as were decreasing HDL levels ( p = 0.046). LDL levels modified the association of APOE e4 with recurrent nonlobar ICH (mediation p APOE e2 and recurrent nonlobar ICH. Conclusion: APOE e4 is associated with recurrent ICH in nonlobar brain regions, providing further evidence for its causal role in ICH unrelated to cerebral amyloid angiopathy. LDL levels modulated this effect, suggesting that circulating lipid levels may mediate a portion of the role of APOE e4 in nonlobar ICH.

Objective: To evaluate the local effect of small asymptomatic infarctions detected by diffusion-weighted imaging (DWI) on white matter microstructure using longitudinal structural and diffusion tensor imaging (DTI). Methods: Nine acute to subacute DWI lesions were identified in 6 subjects with probable cerebral amyloid angiopathy who had undergone high-resolution MRI both before and after DWI lesion detection. Regions of interest (ROIs) corresponding to the site of the DWI lesion (lesion ROI) and corresponding site in the nonlesioned contralateral hemisphere (control ROI) were coregistered to the pre- and postlesional scans. DTI tractography was additionally performed to reconstruct the white matter tracts containing the ROIs. DTI parameters (fractional anisotropy [FA], mean diffusivity [MD]) were quantified within each ROI, the 6-mm lesion-containing tract segments, and the entire lesion-containing tract bundle. Lesion/control FA and MD ratios were compared across time points. Results: The postlesional scans (performed a mean 7.1 ± 4.7 months after DWI lesion detection) demonstrated a decrease in median FA lesion/control ROI ratio (1.08 to 0.93, p = 0.038) and increase in median MD lesion/control ROI ratio (0.97 to 1.17, p = 0.015) relative to the prelesional scans. There were no visible changes on postlesional high-resolution T1-weighted and fluid-attenuated inversion recovery images in 4 of 9 lesion ROIs and small (2–5 mm) T1 hypointensities in the remaining 5. No postlesional changes in FA or MD ratios were detected in the 6-mm lesion-containing tract segments or full tract bundles. Conclusions: Asymptomatic DWI lesions produce chronic local microstructural injury. The cumulative effects of these widely distributed lesions may directly contribute to small-vessel–related vascular cognitive impairment.

Objectives: To determine whether small diffusion-weighted imaging (DWI) lesions occur beyond the acute posthemorrhage time window in patients with intracerebral hemorrhage (ICH) and to characterize their spatial distribution in patients with lobar and deep cerebral hemorrhages. Methods: In this cross-sectional study, we retrospectively analyzed 458 MRI scans obtained in the acute (≤7 days after ICH) or nonacute (>14 days after ICH) phases from 392 subjects with strictly lobar (n = 276) and deep (n = 116) ICH (48.7% women; mean age 72.8 ± 11.7 years). DWI, apparent diffusion coefficient maps, fluid-attenuated inversion recovery, and T2* MRIs were reviewed for the presence and location of DWI lesions. Results: We identified 103 DWI hyperintense lesions on scans from 62 subjects, located mostly in lobar brain regions (90 of 103, 87.4%). The lesions were not uniformly distributed throughout the brain lobes; patients with strictly lobar ICH had relative overrepresentation of lesions in frontal lobe, and patients with deep ICH in parietal lobe ( p = 0.002). Although the frequency of DWI lesions tended to be greater on scans performed within 7 days after ICH (39 of 214, 18.2%), they continued at high frequency in the nonacute period as well (23 of 178, 12.9%, odds ratio 1.5, 95% confidence interval 0.86–2.6 for acute vs nonacute). There was also no difference in frequency of lesions on acute and nonacute scans among 66 subjects with MRIs in both time periods (8 of 66 acute, 10 of 66 nonacute, odds ratio 0.77, 95% confidence interval 0.25–2.4). Conclusions: The high frequency of DWI lesions beyond the acute post-ICH period and their characteristic distributions suggest that they are products of the small vessel diseases that underlie ICH.

Objective: Accumulated evidence suggests that a variant within the CR1 gene (single nucleotide polymorphism rs6656401), known to increase risk for Alzheimer disease (AD), influences β-amyloid (Aβ) deposition in brain tissue. Given the biologic overlap between AD and cerebral amyloid angiopathy (CAA), a leading cause of intracerebral hemorrhage (ICH) in elderly individuals, we investigated whether rs6656401 increases the risk of CAA-related ICH and influences vascular Aβ deposition. Methods: We performed a case-control genetic association study of 89 individuals with CAA-related ICH and 280 individuals with ICH unrelated to CAA and compared them with 324 ICH-free control subjects. We also investigated the effect of rs6656401 on risk of recurrent CAA-ICH in a prospective longitudinal cohort of ICH survivors. Finally, association with severity of histopathologic CAA was investigated in 544 autopsy specimens from 2 longitudinal studies of aging. Results: rs6656401 was associated with CAA-ICH (odds ratio [OR] = 1.61, 95% confidence interval [CI] 1.19–2.17, p = 8.0 × 10 −4 ) as well as with risk of recurrent CAA-ICH (hazard ratio = 1.35, 95% CI 1.04–1.76, p = 0.024). Genotype at rs6656401 was also associated with severity of CAA pathology at autopsy (OR = 1.34, 95% CI 1.05–1.71, p = 0.009). Adjustment for parenchymal amyloid burden did not cancel this effect, suggesting that, despite the correlation between parenchymal and vascular amyloid pathology, CR1 acts independently on both processes, thus increasing risk of both AD and CAA. Conclusion: The CR1 variant rs6656401 influences risk and recurrence of CAA-ICH, as well as the severity of vascular amyloid deposition.

Oral anticoagulation therapy (OAT) with warfarin increases mortality and disability after intracerebral hemorrhage (ICH), the result of increased ICH volume and risk of hematoma expansion. We investigated whether OAT also influences risk of development of intraventricular hemorrhage (IVH), the volume of IVH and IVH expansion, and whether IVH is a substantive mediator of the overall effect of OAT on ICH outcome.We performed a retrospective analysis of a prospectively collected single-center cohort of 1,879 consecutive ICH cases (796 lobar, 865 deep, 153 cerebellar, 15 multiple location, 50 primary IVH) from 1999 to 2009. ICH and IVH volumes at presentation, as well as hematoma expansion (33% or6 mL increase) and IVH expansion (2 mL increase), were determined using established semiautomated methods. Outcome was assessed at 90 days using either the modified Rankin Scale or Glasgow Outcome Scale.Warfarin use was associated with IVH risk, IVH volume at presentation, and IVH expansion in both lobar and deep ICH (all p0.05) in a dose-response relationship with international normalized ratio. Warfarin was associated with poor outcome in both lobar and deep ICH (p0.01), and95% of this effect was accounted for by baseline ICH and IVH volumes, as well as ICH and IVH expansion.Warfarin increases IVH volume and risk of IVH expansion in lobar and deep ICH. These findings (along with effects on ICH volume and expansion) likely represent the mechanisms by which anticoagulation worsens ICH functional outcome.

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.