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Background In the United States, rare disease (RD) is defined as a condition that affects fewer than 200,000 individuals. Collectively, RD affects an estimated 30 million Americans. A significant portion of RD has an underlying genetic cause; however, this may go undiagnosed. To better serve these patients, the Mayo Clinic Program for Rare and Undiagnosed Diseases (PRaUD) was created under the auspices of the Center for Individualized Medicine (CIM) aiming to integrate genomics into subspecialty practice including targeted genetic testing, research, and education. Methods Patients were identified by subspecialty healthcare providers from 11 clinical divisions/departments. Targeted multi-gene panels or custom exome/genome-based panels were utilized. To support the goals of PRaUD, a new clinical service model, the Genetic Testing and Counseling (GTAC) unit, was established to improve access and increase efficiency for genetic test facilitation. The GTAC unit includes genetic counselors, genetic counseling assistants, genetic nurses, and a medical geneticist. Patients receive abbreviated point-of-care genetic counseling and testing through a partnership with subspecialty providers. Results Implementation of PRaUD began in 2018 and GTAC unit launched in 2020 to support program expansion. Currently, 29 RD clinical indications are included in 11 specialty divisions/departments with over 142 referring providers. To date, 1,152 patients have been evaluated with an overall solve rate of 14.1% and as high as 66.7% depending on the phenotype. Noteworthy, 42.7% of the solved or likely solved patients underwent changes in medical management and outcome based on genetic test results. Conclusion Implementation of PRaUD and GTAC have enabled subspecialty practices advance expertise in RD where genetic counselors have not historically been embedded in practice. Democratizing access to genetic testing and counseling can broaden the reach of patients with RD and increase the diagnostic yield of such indications leading to better medical management as well as expanding research opportunities.

Introduction We assessed patients’ perceptions of physician empathy during telemedicine consultations as compared to in-person consultations during clinical encounters for acute stroke. Methods This prospective cohort study was undertaken at a comprehensive stroke centre hub in collaboration with a distant community hospital spoke site. Eligible participants presented to hub or spoke emergency departments with suspected acute stroke within three hours of symptom onset. Participants were evaluated at the hub site in person or at the remote site via telemedicine by the same group of neurologists. Following acute care decisions, single-visit data including participant-reported assessments of physician empathy were collected within 24 h. The primary outcome was the Consultation and Relational Empathy score. The secondary outcome for the telemedicine cohort was the Telemedicine Patient Satisfaction Measure score. Results Between 31 May 2013–13 March 2019, 70 patients completed the study. Fifty patients were seen by telemedicine and 20 patients were seen in person. Median Consultation and Relational Empathy scores (with a possible score of 10–50) were 49 (range 27–50) for telemedicine and 45 (range 26–50) for in-person consultations (Wilcoxon rank sum p = 0.18). Each item of the Consultation and Relational Empathy questionnaire was rated very good or excellent by at least 87% of participants in the telemedicine group. The median Telemedicine Patient Satisfaction Measure score was 54 (range 12–60), with each item rated agree or strongly agree by at least 84% of participants. Discussion We found no difference between telemedicine and in-person visits in patient perception of physician empathy in acute stroke care. Therefore, we conclude that empathy can be conveyed by facial expression, voice and attentiveness in a telemedicine encounter and, in the setting of acute stroke care, does not require physical touch or proximity.

To examine telemedicine as it applies to acute ischemic stroke care at a spoke hospital and the effect on patient outcomes, including the timeliness of response, quality of care, safety, morbidity, and mortality when compared to standard hub hospital stroke center care.Retrospective review of prospectively entered quality/performance stroke/telestroke patient catalog data were completed for 1000 adult patients who presented with an acute ischemic stroke to the Mayo Clinic Hospitals (500 patients) or to one of thirteen Mayo Clinic affiliated telestroke spoke hospitals in the regions (500 patients). The primary outcome of interest was the percentage of accurate decision making for eligibility of IV alteplase administration assessed by blinded adjudication and the secondary outcomes pertained to complications, discharge parameters, and standard quality metrics.There was no difference in the spoke hospital versus hub hospital groups in identifying and making the correct decision regarding which patients were eligible for IV alteplase administration (96% [95% confidence interval (CI): 94%-97%] versus 97% [95% CI: 95%-98%]; P = 0.32). There was no difference among the groups in proportion receiving IV alteplase, sustaining symptomatic intracranial hemorrhage, and mortality. Patients in the spoke group were less likely to have a favorable outcome at discharge, as defined by National Institutes of Health Stroke Scale (NIHSS): 0-1 or mRS: 0-1 or Glasgow Outcome Scale (GOS): 0-1 (21% versus, 35%; P0.001), were less likely to have venous thromboembolism prophylaxis (46% versus 63%; P0.01), were less likely to have received antithrombotic therapy (85% versus 90%; P = .02), were less likely to be discharged on anticoagulation when indicated (56% versus 64%; P = .01), and were less likely to be prescribed cholesterol reducing treatment (68% versus 72%; P.001). The initial acute care hospital length of stay was longer for the spoke hospital group by one day (median: 4 versus 3; P.001).The key findings were that evidence-based stroke thrombolysis eligibility decision making, thrombolysis administration, and thrombolysis emergency stroke metrics were uniformly excellent for the spoke hospital group when compared to the standard hub hospital group. However, evidence-based stroke hospitalization and discharge metrics were inferior for the spoke hospital group when compared to the standard hub hospital.

Stroke subtype classification, applying standard criteria to clinical data, can reduce the heterogeneity of ischemic stroke for genetics studies. To date, most of the replicated genetic loci for ischemic stroke appear subtype-specific. Increasing homogeneity of phenotype comes with a trade-off in sample size, which influences the potential for successful identification of new loci. We estimated genetic associations using the union and intersection of two widely used stroke subtyping systems to assess the influence of sample size and homogeneity on test statistics. Methods: The Stroke Genetics Network (SiGN) study used both the Causative Classification of Stroke (CCS) and the Trial of ORG10172 Acute Stroke Trial (TOAST) classification systems. The CCS generates both a causal and a phenotypic subtype. Using all available data from case-control strata from the a previously performed GWAS, we focused on three main stroke subtypes: cardioembolic (CE), large artery atherosclerosis (LAA), and small artery occlusion (SAO). We estimated genetic associations with the union of the two CCS outputs and TOAST ( eg called CE by at least one method) and the intersection ( eg called CE by all three methods). Our modelling approach included a fixed-effects meta-analysis, i.e. pooling stratum specific estimates from logistic regression models using 10 principle components to account for genetic ancestry responsible for population stratification. Results: The majority of confirmatory findings from SiGN ( PITX2 and ZFHX3 for CE and HDAC9 for LAA) were evident in both models and in both the union and intersection of the subtyping systems. Some findings ( TSPAN2 in LAA) showed up best in the intersection with stronger corresponding p-values and odds ratios whereas other findings (12q24 for SAO) appear stronger in all of the union analyses, but were also evident in intersection analyses. We identify 16 potential novel loci, 6 of which appear in numerous analyses. Conclusion: Both expanding and refining stroke subtypes may help in the identification of additional stroke genetic risk variants and should be considered as complementary to a single classification system. We are pursuing replication of the novel findings.

Introduction: Outcomes from endovascular procedures are highly dependent on the experience and skill of the operating physician. The multi-disciplinary CREST-2 Interventional Management Committee (IMC) was charged with credentialing a cohort of skilled interventionists with adequate contemporary case volumes. Methods: Applicants were required to submit 25 consecutive cases completed within 5 years as primary operator out of a required total experience of ≥ 50 cases (≥ 20 for operators completing training). Interventionists not approved on initial review were asked to submit additional cases (with procedural angiograms), the number depending on quality and recent-quantity of the cases. Results: The IMC has had 102 meetings, and 283 interventionists have been evaluated: 104 (37%) interventionists were cardiologists, 64 (23%) vascular surgeons, 42 (15%) neurosurgeons, 32 (11%) neuroradiologists, 26 (9%) neurologists, 9 (3%) interventional radiologists, and 6 (2%) other. The mean total experience among the 226 interventionists with available information was 220±263 carotid stent cases (median 135; range 10-2500). A total of 7037 cases have been reviewed by the IMC, dating from August 2001 to April 2016, with 3366 symptomatic, 3541 asymptomatic and 130 undetermined. The range of cases reviewed per interventionist was 5 to 50. Of the 251 interventionists with sufficient periprocedural follow-up data, no stroke events were reported by 152 (60.5%), and at least one or more stroke events were reported by 99 (39.5%). The IMC has approved 115 interventionists, 29 at the first review and 86 subsequently, based upon submission and review of 631 additional contemporary cases (mean=7 cases per interventionist); 122 have approval pending submission of additional cases; 33 have been denied; 8 have been deferred; 4 have been approved for the CREST-2 Companion Registry only; and 1 is pending decision. Discussion: Rigorous evaluation and credentialing of carotid stenters in CREST-2 has been demanding, for the candidates and for the evaluators. Yet the cohort of interventionists so selected should be able to provide the high-quality stenting outcomes necessary for acceptance of the trial results.

Background and Purpose— Recently, a novel locus at 17q25 was associated with white matter hyperintensities (WMH) on MRI in stroke-free individuals. We aimed to replicate the association with WMH volume (WMHV) in patients with ischemic stroke. If the association acts by promoting a small vessel arteriopathy, it might be expected to also associate with lacunar stroke. Methods— We quantified WMH on MRI in the stroke-free hemisphere of 2588 ischemic stroke cases. Association between WMHV and 6 single-nucleotide polymorphisms at chromosome 17q25 was assessed by linear regression. These single-nucleotide polymorphisms were also investigated for association with lacunar stroke in 1854 cases and 51 939 stroke-free controls from METASTROKE. Meta-analyses with previous reports and a genetic risk score approach were applied to identify other novel WMHV risk variants and uncover shared genetic contributions to WMHV in community participants without stroke and ischemic stroke. Results— Single-nucleotide polymorphisms at 17q25 were associated with WMHV in ischemic stroke, the most significant being rs9894383 ( P =0.0006). In contrast, there was no association between any single-nucleotide polymorphism and lacunar stroke. A genetic risk score analysis revealed further genetic components to WMHV shared between community participants without stroke and ischemic stroke. Conclusions— This study provides support for an association between the 17q25 locus and WMH. In contrast, it is not associated with lacunar stroke, suggesting that the association does not act by promoting small-vessel arteriopathy or the same arteriopathy responsible for lacunar infarction.

Background The Joint Commission (JC) certifies primary stroke centers in the United States. Whether certification promotes enrollment of study subjects into stroke research studies is not known. We examined whether enrollment performance of centers was related to JC certification status. Methods The 51 US Siblings with Ischemic Stroke Study (SWISS) centers were characterized by JC certification status, year of certification, year initiated into SWISS, center location, and whether the center had a vascular/stroke neurology fellowship program accredited by the Accreditation Council for Graduate Medical Education. Performance measures included days elapsed from initiation to first enrollment, total enrollments within 12 months after initiation, and annual rate of enrollment thereafter. Results In all, 36 of 51 SWISS sites (71%) were JC certified. A total of 32 (63%) were initiated into the study from 2000 through 2002, and 19 (37%) were initiated from 2005 through May 2008. Comparison of certified and noncertified sites showed no significant difference in the time to first enrollment (median, 77.5 v 115 days; P = .90), total enrollees in the first year (median, 3 v 2 probands; P = .69), or annual enrollment rate (median, 1.9 v 1.8 probands; P = .72). The rate of enrollment or time to first enrollment was not different between 2000-to-2002 and 2005-to-2008 sites. Early-initiated centers tended to have better year-1 enrollment than later-initiated centers (3 v 2 probands; P = .056). Conclusions JC certification did not have a significant effect on SWISS center enrollment. The JC should encourage the research mission among certified stroke centers.

Background: Recruitment and phenotyping constitute major costs in stroke genetics research. Clinical trial datasets that include biological samples provide an opportunity to leverage richly phenotyped cases for genomic research. However, the heterogeneity of stroke and methodological differences across studies present challenges. We developed and validated a systematic method of mapping data abstracted from a multicenter randomized clinical trial to the inputs of the CCS stroke subtyping system for the SiGN study. Methods: The SiGN Phenotype Committee and the Secondary Prevention of Small Subcortical Strokes (SPS3) research team systematically reviewed SPS3 case report forms (CRF) to identify key elements required to generate CCS subtyping, and drafted and refined mapping rules using a derivation set of 30 charts. The resultant algorithm was compared to manual entry of clinical information into the CCS in a test set of 30 randomly selected charts. This identified problems due to multiple versions of CRFs (up to 7 versions) and prompted revision of the mapping rules. We assessed the revised algorithm in an independent test set. Results: None of the subtype classifications agreed in the initial testing prompting revision to allow capture information from different versions of the CRFs. The revised mapping algorithm applied to the same test set performed well; 29/30 agreed - 20 small artery occlusion (SAO) evident, 4 SAO possible, 2 SAO probable and 3 supra-aortic large artery atherosclerosis evident). The one chart that disagreed was classified as SAO evident by manual entry but as undetermined by the data mapping rules. In the 2nd independent test set, 30/30 agreed: 22 SAO evident, 4 SAO possible, 3 supra-aortic large artery atherosclerosis evident and 1 undetermined unknown - incomplete evaluation. Conclusions: We created mapping rules using CRFs from a clinical trial to allow reliable subtype classification using the CCS. Issues related to multiple iterations of CRFs presented challenges. Highly phenotyped stroke cases from clinical trials represent a cost-effective opportunity for genomic research.

Summary Background The discovery of disease-associated loci through genome-wide association studies (GWAS) is the leading genetic approach to the identification of novel biological pathways underlying diseases in humans. Until recently, GWAS in ischaemic stroke have been limited by small sample sizes and have yielded few loci associated with ischaemic stroke. We did a large-scale GWAS to identify additional susceptibility genes for stroke and its subtypes. Methods To identify genetic loci associated with ischaemic stroke, we did a two-stage GWAS. In the first stage, we included 16 851 cases with state-of-the-art phenotyping data and 32 473 stroke-free controls. Cases were aged 16 to 104 years, recruited between 1989 and 2012, and subtypes of ischaemic stroke were recorded by centrally trained and certified investigators who used the web-based protocol, Causative Classification of Stroke (CCS). We constructed case-control strata by identifying samples that were genotyped on nearly identical arrays and were of similar genetic ancestral background. We cleaned and imputed data by use of dense imputation reference panels generated from whole-genome sequence data. We did genome-wide testing to identify stroke-associated loci within each stratum for each available phenotype, and we combined summary-level results using inverse variance-weighted fixed-effects meta-analysis. In the second stage, we did in-silico lookups of 1372 single nucleotide polymorphisms identified from the first stage GWAS in 20 941 cases and 364 736 unique stroke-free controls. The ischaemic stroke subtypes of these cases had previously been established with the Trial of Org 10 172 in Acute Stroke Treatment (TOAST) classification system, in accordance with local standards. Results from the two stages were then jointly analysed in a final meta-analysis. Findings We identified a novel locus (G allele at rs12122341) at 1p13.2 near TSPAN2 that was associated with large artery atherosclerosis-related stroke (first stage odds ratio [OR] 1·21, 95% CI 1·13–1·30, p=4·50 × 10 −8 ; joint OR 1·19, 1·12–1·26, p=1·30 × 10 −9 ). Our results also supported robust associations with ischaemic stroke for four other loci that have been reported in previous studies, including PITX2 (first stage OR 1·39, 1·29–1·49, p=3·26 × 10 −19 ; joint OR 1·37, 1·30–1·45, p=2·79 × 10 −32 ) and ZFHX3 (first stage OR 1·19, 1·11–1·27, p=2·93 × 10 −7 ; joint OR 1·17, 1·11–1·23, p=2·29 × 10 −10 ) for cardioembolic stroke, and HDAC9 (first stage OR 1·29, 1·18–1·42, p=3·50 × 10 −8 ; joint OR 1·24, 1·15–1·33, p=4·52 × 10 −9 ) for large artery atherosclerosis stroke. The 12q24 locus near ALDH2 , which has previously been associated with all ischaemic stroke but not with any specific subtype, exceeded genome-wide significance in the meta-analysis of small artery stroke (first stage OR 1·20, 1·12–1·28, p=6·82 × 10 −8 ; joint OR 1·17, 1·11–1·23, p=2·92 × 10 −9 ). Other loci associated with stroke in previous studies, including NINJ2 , were not confirmed. Interpretation Our results suggest that all ischaemic stroke-related loci previously implicated by GWAS are subtype specific. We identified a novel gene associated with large artery atherosclerosis stroke susceptibility. Follow-up studies will be necessary to establish whether the locus near TSPAN2 can be a target for a novel therapeutic approach to stroke prevention. In view of the subtype-specificity of the associations detected, the rich phenotyping data available in the Stroke Genetics Network (SiGN) are likely to be crucial for further genetic discoveries related to ischaemic stroke. Funding US National Institute of Neurological Disorders and Stroke, National Institutes of Health.

Background and Purpose— Ischemic stroke (IS) is a multifactorial disorder with strong evidence from twin, family, and animal model studies suggesting a genetic influence on risk and prognosis. Several candidate genes for IS have been proposed, but few have been replicated. We investigated the contribution of 67 candidate genes (369 single nucleotide polymorphisms [SNPs]) on the risk of IS in a North American population of European descent. Methods— Two independent studies were performed. In the first, 342 SNPs from 52 candidate genes were genotyped in 307 IS cases and 324 control subjects. The SNPs significantly associated with IS were tested for replication in another cohort of 583 IS cases and 270 control subjects. In the second study, 212 SNPs from 62 candidate genes were analyzed in 710 IS cases with subtyping available and 3751 control subjects. Results— None of the candidate genes (SNPs) were significantly associated with IS risk independent of known stroke risk factors after correction for multiple hypotheses testing. Conclusion— These results are consistent with previous meta-analyses that demonstrate an absence of genetic association of variants in plausible candidate genes with IS risk. Our study suggests that the effect of the investigated SNPs may be weak or restricted to specific populations or IS subtypes.

The range of behavioral changes occurring after stroke has not yet been fully characterized. To evaluate behavioral symptoms after stroke and clinical characteristics that may influence the number and frequency of such symptoms, we compared 53 survivors of mild ischemic stroke with 30 stroke-free controls. Stroke survivor and control participants completed self-ratings of behavioral symptoms and were administered measures of cognitive status (ie, Beck Depression Inventory II, Mini-Mental State Examination, and Controlled Oral Word Association Test). Informants of stroke survivors and controls completed ratings of behavioral symptoms and functional status (ie, Neuropsychiatric Inventory Questionnaire, Informant Questionnaire on Cognitive Decline in the Elderly, and Functional Activities Questionnaire). More behavioral symptoms were observed in stroke survivors than in controls (mean [standard deviation] total number of symptoms on the Neuropsychiatric Inventory Questionnaire, 2.1 [2.0] vs 1.1 [1.5]; P = .02). Informants of stroke survivors were more likely to recognize behavioral symptoms than were stroke survivors themselves. Higher initial stroke severity was associated with more behavioral symptoms. With more behavioral symptoms, there was more functional impairment. Our findings suggest that behavioral symptoms can have unique and troublesome effects on stroke patients. Future research is needed to understand how the identification of behavioral symptoms after stroke can improve care in stroke survivors.

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.