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BackgroundRegional collaboration and appropriate referral management are crucial in neuro-oncological care. Lack of electronic access to medical records across health care organizations impedes interhospital consultation and may lead to incomplete and delayed referrals. To improve referral management, we have established a multidisciplinary neuro-oncological triage panel (NOTP) with digital image exchange and determined the effects on lead times, costs, and time investment.MethodsA prospective cohort study was conducted from February 2019 to March 2020. All newly diagnosed patients referred to Brain Tumor Center Amsterdam were analyzed according to referral pathway: (1) standard referral (SR), (2) NOTP. The primary outcome was lead time, defined as time-to-referral, time-to-treatment, and total time (median days [interquartile range]). Secondary outcomes were costs and time investment.ResultsIn total, 225 patients were included, of whom 153 had SR and 72 NOTP referral. Patients discussed in the NOTP were referred more frequently for first neurosurgical consultation (44.7% vs 28.8%) or combined neurological and neurosurgical consultation (12.8% vs 2.5%, P = .002). Time-to-referral was reduced for NOTP referral compared to SR (1 [0.25-4] vs 6 [1.5-10] days, P < .001). Total time decreased from 27 [14-48] days for the standard group to 15 [12-38.25] days for the NOTP group (P = .040). Costs and time investment were comparable for both groups.ConclusionImplementation of digital referral to a multidisciplinary NOTP is feasible and leads to more swift patient-tailored referrals at comparable costs and time investment as SR. This quality improvement initiative has the potential to improve collaboration and coordination of multidisciplinary care in the field of neuro-oncology.

An oculogyric crisis is a tonic conjugated deviation of the eyes, usually upward. We present two cases with a drug induced oculogyric crisis. The differential diagnoses should include epilepsy, a functional neurological movement disorder, ocular tics, ocular dyskinesia or ocular bobbing. Typically, in an oculogyric crisis the patient's awareness is intact; accompanied signs can be blepharospasm, neck flexion, jaw opening with or without tongue protrusion and autonomic symptoms. The underlying pathophysiology seems an imbalance between cholinergic and dopaminergic pathways. Most frequently an oculogyric crisis is caused by antidopaminergic medications, for example neuroleptics and metoclopramide. Treatment of medication-induced oculogyric crisis with parenteral anticholinergics typically leads to a fast remission of symptoms. Consider tocontinue anticholinergic therapy orally for a few days.

Contains fulltext : 218913.pdf (Publisher’s version ) (Open Access) The introduction of immune checkpoint inhibitors (ICI), as a novel treatment modality, has transformed the field of oncology with unprecedented successes. However, the efficacy of ICI for patients with glioblastoma or brain metastases (BMs) from any tumor type is under debate. Therefore, we systematically reviewed current literature on the use of ICI in patients with glioblastoma and BMs. Prospective and retrospective studies evaluating the efficacy and survival outcomes of ICI in patients with glioblastoma or BMs, and published between 2006 and November 2019, were considered. A total of 88 studies were identified (n = 8 in glioblastoma and n = 80 in BMs). In glioblastoma, median progression-free (PFS) and overall survival (OS) of all studies were 2.1 and 7.3 months, respectively. In patients with BMs, intracranial responses have been reported in studies with melanoma and non-small-cell lung cancer (NSCLC). The median intracranial and total PFS in these studies were 2.7 and 3.0 months, respectively. The median OS in all studies for patients with brain BMs was 8.0 months. To date, ICI demonstrate limited efficacy in patients with glioblastoma or BMs. Future research should focus on increasing the local and systemic immunological responses in these patients.

Background The loss of noradrenergic neurones of the locus coeruleus is a major feature of Alzheimer's disease (AD). Dopamine β-hydroxylase (DBH) catalyses the conversion of dopamine to noradrenaline. Interactions have been reported between the low-activity -1021T allele (rs1611115) of DBH and polymorphisms of the pro-inflammatory cytokine genes, IL1A and IL6, contributing to the risk of AD. We therefore examined the associations with AD of the DBH -1021T allele and of the above interactions in the Epistasis Project, with 1757 cases of AD and 6294 elderly controls. Methods We genotyped eight single nucleotide polymorphisms (SNPs) in the three genes, DBH, IL1A and IL6. We used logistic regression models and synergy factor analysis to examine potential interactions and associations with AD. Results We found that the presence of the -1021T allele was associated with AD: odds ratio = 1.2 (95% confidence interval: 1.06-1.4, p = 0.005). This association was nearly restricted to men < 75 years old: odds ratio = 2.2 (1.4-3.3, 0.0004). We also found an interaction between the presence of DBH -1021T and the -889TT genotype (rs1800587) of IL1A: synergy factor = 1.9 (1.2-3.1, 0.005). All these results were consistent between North Europe and North Spain. Conclusions Extensive, previous evidence (reviewed here) indicates an important role for noradrenaline in the control of inflammation in the brain. Thus, the -1021T allele with presumed low activity may be associated with misregulation of inflammation, which could contribute to the onset of AD. We suggest that such misregulation is the predominant mechanism of the association we report here.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access. To identify common variants contributing to normal variation in two specific domains of cognitive functioning, we conducted a genome-wide association study (GWAS) of executive functioning and information processing speed in non-demented older adults from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium. Neuropsychological testing was available for 5429-32 070 subjects of European ancestry aged 45 years or older, free of dementia and clinical stroke at the time of cognitive testing from 20 cohorts in the discovery phase. We analyzed performance on the Trail Making Test parts A and B, the Letter Digit Substitution Test (LDST), the Digit Symbol Substitution Task (DSST), semantic and phonemic fluency tests, and the Stroop Color and Word Test. Replication was sought in 1311-21860 subjects from 20 independent cohorts. A significant association was observed in the discovery cohorts for the single-nucleotide polymorphism (SNP) rs17518584 (discovery P-value=3.12 × 10(-8)) and in the joint discovery and replication meta-analysis (P-value=3.28 × 10(-9) after adjustment for age, gender and education) in an intron of the gene cell adhesion molecule 2 (CADM2) for performance on the LDST/DSST. Rs17518584 is located about 170 kb upstream of the transcription start site of the major transcript for the CADM2 gene, but is within an intron of a variant transcript that includes an alternative first exon. The variant is associated with expression of CADM2 in the cingulate cortex (P-value=4 × 10(-4)). The protein encoded by CADM2 is involved in glutamate signaling (P-value=7.22 × 10(-15)), gamma-aminobutyric acid (GABA) transport (P-value=1.36 × 10(-11)) and neuron cell-cell adhesion (P-value=1.48 × 10(-13)). Our findings suggest that genetic variation in the CADM2 gene is associated with individual differences in information processing speed. National Foundation for Alzheimer's disease and related disorders Institut Pasteur de Lille Centre National de Genotypage Inserm FRC (fondation pour la recherche sur le cerveau) Rotary LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease) MEDIALZ Project - ERDF (European Regional Development Fund) 11001003 Conseil Regional Nord Pas de Calais Fondation pour la Recherche Medicale Caisse Nationale Maladie des Travailleurs Salaries Direction Generale de la Sante MGEN Institut de la Longevite Agence Francaise de Securite Sanitaire des Produits de Sante Aquitaine and Bourgogne Regional Councils Fondation de France joint French Ministry of Research/INSERM 'Cohortes et collections de donnees biologiques' program Eisai Wellcome Trust British Heart Foundation Chief Scientist Office of the Scottish Executive NIA N01-AG-12100 National Heart, Lung and Blood Institute Contracts HHSN268201100005C HHSN268201100006C HHSN268201100007C HHSN268201100008C HHSN268201100009C HHSN268201100010C HHSN268201100011C HHSN268201100012C R01HL70825 R01HL087641 R01HL59367 R01HL086694 National Human Genome Research Institute U01HG004402 NIH Roadmap for Medical Research Austrian Science Fond (FWF) P20545-P05 P13180 Intramural Research Program of the NIH, National Institute on Aging NHLBI HHSN268201200036C HHSN268200800007C N01HC55222 N01HC85079 N01HC85080 N01HC85081 N01HC85082 N01HC85083 N01HC850863 U01HL080295 R01HL087652 R01HL105756 R01HL103612 R01HL120393 National Institute of Neurological Disorders and Stroke (NINDS) National Institute on Aging (NIA) R01AG023629 R01AG15928 R01AG20098 R01AG027058 National Center for Advancing Translational Sciences CTSI UL1TR000124 National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) DK063491 Medical Research Council (UK) European Commission Framework 6 project EUROSPAN LSHG-CT-2006-018947 Republic of 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Chief Scientist Office of the Scottish Government Health Directorates CZD/16/6 Scottish Funding Council HR03006 Academy of Finland Finnish Diabetes Research Society Folkhalsan Research Foundation Novo Nordisk Foundation Finska Lakaresallskapet Signe and Ane Gyllenberg Foundation University of Helsinki Ministry of Education Ahokas Foundation Emil Aaltonen Foundation Juho Vainio Foundation Wellcome Trust WT089062 University of Newcastle's Strategic Initiative Fund Vincent Fairfax Family Foundation Hunter Medical Research Institute Italian Ministry of Health ICS 110.1RS97.71 US National Institute on Aging N01[AG]916413 N01[AG]821336 263 MD 9164 13 263 MD 821336 Intramural Research Program, National Institute on Aging, National Institutes of Health BBSRC Royal Society Chief Scientist Office of the Scottish Government Research Into Ageing UK Biotechnology and Biological Sciences Research Council (BBSRC) Engineering and Physical Sciences Research Council (EPSRC) Economic and Social Research Council (ESRC) MRC NHMRC 401184 Australian National Health & Medical Research Council 350833 568969 Capacity Building Grant 568940 NHMRC Project 525453 National Institutes of Health CA87969 CA49449 HL34594 U01HG004399 DK058845 CA65725 CA67262 CA50385 5UO1CA098233 EY09611 EY015473 HG004728 HL35464 CA55075 CA134958 DK070756 MRC Human Genetics Unit Arthritis Research UK European Union LSHG-CT-2006-018947 Bristol-Myers Squibb Netherlands Heart Foundation 2001 D 032 European commission 223004 Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging) 050-060-810 Netherlands Organisation of Scientific Research NWO Investments 175.010.2005.011 911-03-012 Research Institute for Diseases in the Elderly 014-93-015 Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) project 050-060-810 Erasmus Medical Center Erasmus University, Rotterdam Netherlands Organization for the Health Research and Development (ZonMw) Research Institute for Diseases in the Elderly (RIDE) Ministry of Education, Culture and Science Ministry for Health, Welfare and Sports European Commission 49 (DG XII) Municipality of Rotterdam ZonMW Veni Grant 916.13.054 NIA Grants P30AG10161 R01AG15819 R01AG17917 R01AG30146 K08AG34290 K25AG41906 Federal Ministry of Education and Research 01ZZ9603 01ZZ0103 01ZZ0403 Ministry of Cultural Affairs Social Ministry of the Federal State of Mecklenburg-West Pomerania National Cancer Institute CA047988 Donald W Reynolds Foundation Fondation Leducq Amgen info:eu-repo/grantAgreement/EC/FP7/223004

Distinguishing true from false positive findings is a major challenge in human genetic epidemiology. Several strategies have been devised to facilitate this, including the positive predictive value (PPV) and a set of epidemiological criteria, known as the "Venice" criteria. The PPV measures the probability of a true association, given a statistically significant finding, while the Venice criteria grade the credibility based on the amount of evidence, consistency of replication and protection from bias. A vast majority of journals use significance thresholds to identify the true positive findings. We studied the effect of p value thresholds on the PPV and used the PPV and Venice criteria to define usable thresholds of statistical significance. Theoretical and empirical analyses of data published on AlzGene show that at a nominal p value threshold of 0.05 most "positive" findings will turn out to be false if the prior probability of association is below 0.10 even if the statistical power of the study is higher than 0.80. However, in underpowered studies (0.25) with a low prior probability of 1 × 10(-3), a p value of 1 × 10(-5) yields a high PPV (>96 %). Here we have shown that the p value threshold of 1 × 10(-5) gives a very strong evidence of association in almost all studies. However, in the case of a very high prior probability of association (0.50) a p value threshold of 0.05 may be sufficient, while for studies with very low prior probability of association (1 × 10(-4); genome-wide association studies for instance) 1 × 10(-7) may serve as a useful threshold to declare significance.

Extracellular deposition of amyloid beta peptide (A beta) has been implicated as a critical step in the pathogenesis of Alzheimer's disease (AD). In Down syndrome (DS), Alzheimer's disease is assumed to be caused by the triplication and overexpression of the gene for amyloid precursor protein (APP), located on chromosome 21. Plasma concentrations of A beta 1-40 and A beta 1-42 were determined in a population based study of 506 persons with DS, who were screened annually for dementia. We used Cox proportional hazards models to determine the risk of dementia. Demented persons with DS have a significantly higher plasma A beta 1-40 concentration than the nondemented (p = 0.05). Those with the highest concentrations of A beta 1-40 and A beta 1-42 have a higher risk to develop dementia. The risk to develop dementia during follow-up (mean 4.7 years) increased to 2.56 (95% confidence interval, 1.39-4.71) for A beta 1-42 and 2.16 (95% confidence interval, 1.14-4.10) for A beta 1-40. High plasma concentration of plasma A beta 1-40 and A beta 1-42 are determinants of the risk of dementia in persons with DS. (C) 2012 Elsevier Inc. All rights reserved.

Item does not contain fulltext Iron overload may contribute to the risk of Alzheimer's disease (AD). In the Epistasis Project, with 1757 cases of AD and 6295 controls, we studied 4 variants in 2 genes of iron metabolism: hemochromatosis (HFE) C282Y and H63D, and transferrin (TF) C2 and -2G/A. We replicated the reported interaction between HFE 282Y and TF C2 in the risk of AD: synergy factor, 1.75 (95% confidence interval, 1.1-2.8, p = 0.02) in Northern Europeans. The synergy factor was 3.1 (1.4-6.9; 0.007) in subjects with the APOEepsilon4 allele. We found another interaction, between HFE 63HH and TF -2AA, markedly modified by age. Both interactions were found mainly or only in Northern Europeans. The interaction between HFE 282Y and TF C2 has now been replicated twice, in altogether 2313 cases of AD and 7065 controls, and has also been associated with increased iron load. We therefore suggest that iron overload may be a causative factor in the development of AD. Treatment for iron overload might thus be protective in some cases. 01 januari 2012

Cathepsin D (CTSD) is a gene involved in amyloid precursor protein processing and is considered a candidate for Alzheimer's disease (AD). The aim of the current study was to examine if variation in CTSD increases the risk of AD. We performed a candidate-gene analysis in a population-based cohort study (N = 7983), and estimated the effect of CTSD on the risk of AD. Additionally, a large meta-analysis was performed incorporating our data and previously published data. The T-allele of CTSD rs17571 was associated with an increased risk of AD (p-value 0.007) in the Rotterdam Study. This association was predominantly found in APOE epsilon 4 noncarriers. A meta-analysis of previously published data showed a significantly increased risk of AD in carriers of the T-allele of rs17571 (OR 1.22, 95% CI 1.03-1.44), irrespective of APOE epsilon 4 carrier status. This study adds to the evidence that CTSD increases the risk of AD, although the effect size is moderate. (C) 2009 Elsevier Inc. All rights reserved.

The heat shock protein (HSP) 70 family has been implicated in the pathology of Alzheimer's disease (AD). In this study, we examined common genetic variations in the 80 genes encoding HSP70 and its co-chaperones. We conducted a study in a series of 462 patients and 5238 unaffected participants derived from the Rotterdam Study, a population-based study including 7983 persons aged 55 years and older. We genotyped a total of 12,053 Single Nucleotide Polymorphisms (SNPs) using the HumanHap550K Genotyping BeadChip from Illumina. Replication was performed in two independent cohort studies, the Framingham Heart study (FHS; n = 806) and Cardiovascular Health Study (CHS; n = 2150). When adjusting for multiple testing, we found a small but consistent, though not significant effect of rs12118313 located 32 kb from PFDN2, with an OR of 1.19 (p-value from meta-analysis = 0.003). However this SNP was in the intron of another gene, suggesting it is unlikely this SNP reflects the effect of PFDN2. In a formal pathway analysis we found nominally significant evidence for an association of BAG, DNAJA and prefoldin with AD. These findings corroborate with those of a study of 2032 AD patients and 5328 controls, in which several members of the prefoldin family showed evidence for association to AD. Our study did not reveal evidence for a genetic variant if the HSP70 family with a major effect on AD. However, our findings of the single SNP analysis and pathway analysis suggest that multiple genetic variants in prefoldin are associated with AD.

Background— The burden of cerebral white matter hyperintensities (WMH) is associated with an increased risk of stroke, dementia, and death. WMH are highly heritable, but their genetic underpinnings are incompletely characterized. To identify novel genetic variants influencing WMH burden, we conducted a meta-analysis of multiethnic genome-wide association studies. Methods and Results— We included 21 079 middle-aged to elderly individuals from 29 population-based cohorts, who were free of dementia and stroke and were of European (n=17 936), African (n=1943), Hispanic (n=795), and Asian (n=405) descent. WMH burden was quantified on MRI either by a validated automated segmentation method or a validated visual grading scale. Genotype data in each study were imputed to the 1000 Genomes reference. Within each ethnic group, we investigated the relationship between each single-nucleotide polymorphism and WMH burden using a linear regression model adjusted for age, sex, intracranial volume, and principal components of ancestry. A meta-analysis was conducted for each ethnicity separately and for the combined sample. In the European descent samples, we confirmed a previously known locus on chr17q25 ( P =2.7×10 −19 ) and identified novel loci on chr10q24 ( P =1.6×10 −9 ) and chr2p21 ( P =4.4×10 −8 ). In the multiethnic meta-analysis, we identified 2 additional loci, on chr1q22 ( P =2.0×10 −8 ) and chr2p16 ( P =1.5×10 −8 ). The novel loci contained genes that have been implicated in Alzheimer disease (chr2p21 and chr10q24), intracerebral hemorrhage (chr1q22), neuroinflammatory diseases (chr2p21), and glioma (chr10q24 and chr2p16). Conclusions— We identified 4 novel genetic loci that implicate inflammatory and glial proliferative pathways in the development of WMH in addition to previously proposed ischemic mechanisms.

Objective: To determine the clinical features and presence in CSF of antineuronal antibodies in patients with pathologically proven autoimmune encephalitis derived from a cohort of patients with suspected Creutzfeldt-Jakob disease (CJD). Methods: The Dutch Surveillance Centre for Prion Diseases performed 384 autopsies on patients with suspected CJD over a 14-year period (1998-2011). Clinical information was collected from treating physicians. Antineuronal antibodies were tested in CSF obtained postmortem by immunohistochemistry on fresh frozen rat brain sections, by Luminex assay for the presence of wellcharacterized onconeural antibodies, and by cell-based assays for antibodies against NMDAR, GABABR1/2, GABAAR GLUR1/2, LGI1, Caspr2, and DPPX. Results: In 203 patients, a diagnosis of definite CJD was made, while in 181 a variety of other conditions were diagnosed, mainly neurodegenerative. In 22 of these 181, the neuropathologist diagnosed autoimmune encephalitis. One patient was excluded because of lack of clinical information. Inflammatory infiltrates were predominantly perivascular and consisted mainly of T cells. The predominant locations were basal ganglia and thalamus (90%) and temporal lobes and hippocampus (81%). In 6 patients (29%), antineuronal antibodies were detected in postmortem CSF, directed against Hu, NMDAR, GABABR1/2, Caspr2, and an unidentified synaptic antigen in 2. The most frequent symptoms were dementia (90%), gait disturbance (86%), cerebellar signs (67%), and neuropsychiatric symptoms (67%). Immunopathologic and clinical findings did not differ between autoantibody-negative patients and patients with antineuronal antibodies. Conclusions: It is important to consider immune-mediated disorders in the differential diagnosis of rapidly progressive neurologic deficits.

Precedent of causative multiplication of key gene loci exists in familial forms of both Alzheimer's and Parkinson's diseases. Genetic Creutzfeldt-Jakob disease (CJD) is often clinically indistinguishable from sporadic disease and inexplicably, a negative family history of a similar disorder occurs in around 50-90% of patients harboring the most common, disease-associated, prion protein gene (PRNP) mutations. We undertook semi-quantitative analysis of the PRNP copy number in 112 CJD patients using quantitative polymerase chain reaction. All included cases satisfied classification criteria for probable or definite sporadic CJD, ascertained as part of longstanding, prospective, national surveillance activities. No examples of additional copies of the PRNP locus as an explanation for their disease was found in any of the 112 sporadic CJD patients. Hence, contrasting with more common, age-related neurodegenerative diseases, the genetic aetiology in human prion disease continues to appear entirely restricted to small scale mutations within a single gene, with no evidence of multiplication of this validated candidate gene locus as a cause. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

The NEO-Five-Factor Inventory divides human personality traits into five dimensions: neuroticism, extraversion, openness, conscientiousness and agreeableness. In this study, we sought to identify regions harboring genes with large effects on the five NEO personality traits by performing genome-wide linkage analysis of individuals scoring in the extremes of these traits (>90th percentile). Affected-only linkage analysis was performed using an Illumina 6K linkage array in a family-based study, the Erasmus Rucphen Family study. We subsequently determined whether distinct, segregating haplotypes found with linkage analysis were associated with the trait of interest in the population. Finally, a dense single-nucleotide polymorphism genotyping array (Illumina 318K) was used to search for copy number variations (CNVs) in the associated regions. In the families with extreme phenotype scores, we found significant evidence of linkage for conscientiousness to 20p13 (rs1434789, log of odds (LOD) = 5.86) and suggestive evidence of linkage (LOD >2.8) for neuroticism to 19q, 21q and 22q, extraversion to 1p, 1q, 9p and 12q, openness to 12q and 19q, and agreeableness to 2p, 6q, 17q and 21q. Further analysis determined haplotypes in 21q22 for neuroticism (P-values = 0.009, 0.007), in 17q24 for agreeableness (marginal P-value = 0.018) and in 20p13 for conscientiousness (marginal P-values = 0.058, 0.038) segregating in families with large contributions to the LOD scores. No evidence for CNVs in any of the associated regions was found. Our findings imply that there may be genes with relatively large effects involved in personality traits, which may be identified with next-generation sequencing techniques. Molecular Psychiatry (2012) 17, 1031-1041; doi:10.1038/mp.2011.97; published online 9 August 2011

Item does not contain fulltext Plasma concentrations of Abeta40 and Abeta42 rise with age and are increased in people with mutations that cause early-onset Alzheimer's disease (AD). Amyloid beta (Abeta) plasma levels were successfully used as an (endo)phenotype for gene discovery using a linkage approach in families with dominant forms of disease. Here, we searched for loci involved in Abeta plasma levels in a series of non-demented patients with hypertension in the Erasmus Rucphen Family study. Abeta40 and Abeta42 levels were determined in 125 subjects with severe hypertension. All patients were genotyped with a 6,000 single nucleotide polymorphisms (SNPs) illumina array designed for linkage analysis. We conducted linkage analysis of plasma Abeta levels. None of the linkage analyses yielded genome-wide significant logarithm of odds (LOD) score over 3.3, but there was suggestive evidence for linkage (LOD > 1.9) for two regions: 1q41 (LOD = 2.07) and 11q14.3 (LOD = 2.97), both for Abeta40. These regions were followed up with association analysis in the study subjects and in 320 subjects from a population-based cohort. For the Abeta40 region on chromosome 1, association of several SNPs was observed at the presenilin 2 gene (PSEN2) (p = 2.58 x 10(-4) for rs6703170). On chromosome 11q14-21, we found some association (p = 3.1 x 10(-3) for rs2514299). This linkage study of plasma concentrations of Abeta40 and Abeta42 yielded two suggestive regions, of which one points toward a known locus for familial AD.

BACKGROUND: Asymptomatic cerebral lesions on MRI such as white matter lesions (WML), lacunes and microbleeds are commonly seen in older people. We examined the role of a series of candidate genes involved in blood pressure regulation and amyloid metabolism.MATERIALS AND METHODS: The study was embedded in a family-based cohort sampled from a Dutch genetically isolated population. We selected individuals between 55 and 75 years of age with hypertension (N=129). Volumes of WML and presence of lacunes and microbleeds were assessed with MRI. We studied three genes involved in blood pressure regulation (angiotensin, angiotensin II type 1 receptor, α-adducin) and two genes involved in the amyloid pathway (apolipoprotein E (APOE) and sortilin-related receptor gene (SORL1)).RESULTS: All participants had WML (median volume, 3.1 ml; interquartile range, 1.5-6.5 ml); lacunar infarcts were present in 15.5% and microbleeds in 23.3%. Homozygosity for the APOE ε4 allele was associated with lacunes (OR, 4.8; 95% CI, 1.2 to 19.3). Individuals carrying two copies of the variant allele of four single nucleotide polymorphism (SNPs) located at the 3'-end of SORL1 (rs1699102, rs3824968, rs2282649, rs1010159) had significantly more often microbleeds (highest OR, 6.87; 95% CI, 1.78 to 26.44).CONCLUSION: The association of SORL1 with microbleeds suggests that the amyloid cascade is involved in the aetiology of microbleeds in populations with hypertension.

Accumulation of cathepsin D immunoreactive lysosomes correlates with tissue pathology in sporadic Creutzfeldt-Jakob disease (CJD) brains. The C-to-T transition within exon 2 of the cathepsin D (CTSD) gene is associated with altered enzymatic activity. Possession of the TT genotype is a risk factor for variant CJD. To verify the association between the CTSD position 224T allele and the risk for and survival in sporadic and genetic CJD, we genotyped 540 sporadic, 101 genetic CJD, and 723 control individuals. Genotype data and duration of illness were compared using multiple logistic regression and Kruskal-Wallis test. Multivariate survival analysis was performed using Cox's regression model. The distribution of CTSD position 224 alleles was approximately the same in all groups. We observed a trend for shorter survival in sporadic CJD patients harboring the T allele at position 224 of the CTSD gene in particular in sporadic CJD patients with the prion protein gene position 129 MM genotype. We conclude that the CTSD position 224 polymorphism alone is not a significant risk or disease-modifying factor in sporadic or genetic CJD.

To describe our experience with the diagnostics of Creutzfeldt-Jakob disease (CJD) and other prion diseases in the Netherlands over a period of 11 years (1997-2007).Retrospective.In the period 1997-2007 autopsies were carried out on 280 patients with probable or possible CJD at the Dutch Surveillance Center for Prion Diseases in Utrecht. We registered clinical details, results of additional investigations such as EEG, MRI and cerebrospinal fluid tests, and outcomes of neurological investigations. The contribution of the different disorders within this group was estimated retrospectively.A prion disease was diagnosed in 146 patients (52%) with probable or possible CJD. 133 (91%) of these had the sporadic form. 2 patients were diagnosed with the 'variant CJD' (caused by bovine spongiform encephalopathy). 5 patients were diagnosed as having an iatrogenic form of CJD and 6 patients had a genetic form of the disease. A different disease was diagnosed in 134 patients (48%), such as Alzheimer disease (40%), multi-infarct dementia (13%), neoplasm (10%) and Lewy body dementia (8%). In this group, periodic sharp wave complexes were observed on EEG in 17 patients (13%), most frequently in those with Alzheimer disease. The 14-3-3 protein test on cerebrospinal fluid was positive in 28 of these patients (21%), most frequently in patients with vascular dementia and Alzheimer disease.In all cases of an unclear clinical picture suggestive of neurodegenerative disease, prion disease must be considered. Periodic sharp wave complexes on EEG and a positive 14-3-3 protein test on cerebrospinal fluid alone are not diagnostic of CJD.

Cases of Creutzfeldt Jakob disease (CJD) have been centrally registered in the Netherlands since 1997. Each year about 35 cases are reported. All patients with a possible, probable or definite diagnosis are registered in a European database. Recently, patients with a transfusion-transmitted form of CJD have been diagnosed in the United Kingdom. Such cases have not yet occurred in the Netherlands. It is important to continue to detect and centrally register patients with CJD.

Background Chronic inflammation is a characteristic of Alzheimer's disease (AD). An interaction associated with the risk of AD has been reported between polymorphisms in the regulatory regions of the genes for the pro-inflammatory cytokine, interleukin-6 (IL-6, gene: IL6), and the anti-inflammatory cytokine, interleukin-10 (IL-10, gene: IL10). Methods We examined this interaction in the Epistasis Project, a collaboration of 7 AD research groups, contributing DNA samples from 1,757 cases of AD and 6,295 controls. Results We replicated the interaction. For IL6 rs2069837 AA × IL10 rs1800871 CC, the synergy factor (SF) was 1.63 (95% confidence interval: 1.10–2.41, p = 0.01), controlling for centre, age, gender and apolipoprotein E ε4 (APOEε4) genotype. Our results are consistent between North Europe (SF = 1.7, p = 0.03) and North Spain (SF = 2.0, p = 0.09). Further replication may require a meta-analysis. However, association due to linkage disequilibrium with other polymorphisms in the regulatory regions of these genes cannot be excluded. Conclusion We suggest that dysregulation of both IL-6 and IL-10 in some elderly people, due in part to genetic variations in the two genes, contributes to the development of AD. Thus, inflammation facilitates the onset of sporadic AD.

Several molecular subtypes of sporadic Creutzfeldt-Jakob disease have been identified and electroencephalogram and cerebrospinal fluid biomarkers have been reported to support clinical diagnosis but with variable utility according to subtype. In recent years, a series of publications have demonstrated a potentially important role for magnetic resonance imaging in the pre-mortem diagnosis of sporadic Creutzfeldt-Jakob disease. Magnetic resonance imaging signal alterations correlate with distinct sporadic Creutzfeldt-Jakob disease molecular subtypes and thus might contribute to the earlier identification of the whole spectrum of sporadic Creutzfeldt-Jakob disease cases. This multi-centre international study aimed to provide a rationale for the amendment of the clinical diagnostic criteria for sporadic Creutzfeldt-Jakob disease. Patients with sporadic Creutzfeldt-Jakob disease and fluid attenuated inversion recovery or diffusion-weight imaging were recruited from 12 countries. Patients referred as 'suspected sporadic Creutzfeldt-Jakob disease' but with an alternative diagnosis after thorough follow up, were analysed as controls. All magnetic resonance imaging scans were assessed for signal changes according to a standard protocol encompassing seven cortical regions, basal ganglia, thalamus and cerebellum. Magnetic resonance imaging scans were evaluated in 436 sporadic Creutzfeldt-Jakob disease patients and 141 controls. The pattern of high signal intensity with the best sensitivity and specificity in the differential diagnosis of sporadic Creutzfeldt-Jakob disease was identified. The optimum diagnostic accuracy in the differential diagnosis of rapid progressive dementia was obtained when either at least two cortical regions (temporal, parietal or occipital) or both caudate nucleus and putamen displayed a high signal in fluid attenuated inversion recovery or diffusion-weight imaging magnetic resonance imaging. Based on our analyses, magnetic resonance imaging was positive in 83 of cases. In all definite cases, the amended criteria would cover the vast majority of suspected cases, being positive in 98. Cerebral cortical signal increase and high signal in caudate nucleus and putamen on fluid attenuated inversion recovery or diffusion-weight imaging magnetic resonance imaging are useful in the diagnosis of sporadic Creutzfeldt-Jakob disease. We propose an amendment to the clinical diagnostic criteria for sporadic Creutzfeldt-Jakob disease to include findings from magnetic resonance imaging scans.

Several studies have investigated the role of the neuronal sortilin-related receptor (SORL1) gene in Alzheimers disease (AD), but findings have been inconsistent. We conducted a study of 7 single nucleotide polymorphisms (SNPs), rs668387, rs689021, rs641120, rs1699102, rs3824968, rs2282649, and rs1010159, in the SORL1 gene that were associated to AD in previous studies. We tested for association with AD and cognitive function in 6741 participants of the Rotterdam Study and in 2883 individuals from the Erasmus Rucphen Family study. We performed meta-analyses on AD using our data together with those of previous studies published prior to September 2008 in Caucasians. Further, we studied up to 76 SNPs in a 400 kb region within and flanking the gene to evaluate the evidence that other genetic variants are associated with AD or cognitive function. There was no significant evidence for association between SORL1 SNPs and incident AD patients in the Rotterdam Study. In a meta-analysis of our data with those of others, six out of seven SNPs attained borderline significance. However, removal of the first study reporting association from the meta-analysis resulted in non-significant odds ratios for all SNPs. SNPs rs668387, rs689021, and rs641120 were associated with cognitive function in non-demented individuals at borderline statistical significance in two independent Dutch cohorts, but in the opposite direction. Testing for association using dense SNPs in the SORL1 gene did not reveal significant association with AD, or with cognitive function when adjusting for multiple testing. In conclusion, our data do not support the hypothesis that genetic variants in SORL1 are related to the risk of AD.

The E4 allele of the apolipoprotein E gene (APOE) is a well-established determinant of Alzheimer's disease but its relation to cognitive function is much less understood. We studied the age-specific effects of the APOE*E4 allele on cognitive function and cardiovascular risk factors in 2208 related individuals. APOE*E4 allele was significantly associated with reduced test scores for Adult Verbal Learning Test, particularly on the memory and learning sub domains, in persons older than 50 years of age. The effect of APOE*E4 was independent of the effect of APOE*E4 on vascular risk factors and most pronounced on learning ability. Our findings suggest that APOE*E4 has an effect on cognitive function predominantly in the elderly, independent of vascular risk factors. Crown Copyright (C) 2008 Published by Elsevier Inc. All rights reserved.

Contains fulltext : 108169.pdf (Publisher’s version ) (Open Access) Prion diseases are rare and fatal neurodegenerative disorders that can be sporadic, inherited or acquired by infection. Based on a national surveillance program in the Netherlands we describe here the clinical, neuropathological, genetic and molecular characteristics of 162 patients with neuropathologically confirmed prion disease over a 12-year period (1998-2009). Since 1998, there has been a relatively stable mortality of Creutzfeldt-Jakob disease (CJD) in the Netherlands, ranging from 0.63 to 1.53 per million inhabitants per annum. Genetic analysis of the codon 129 methionine/valine (M/V) polymorphism in all patients with sporadic CJD (sCJD) showed a trend for under-representation of VV cases (7.0%), compared with sCJD cohorts in other Western countries, whereas the MV genotype was relatively over-represented (22,4%). Combined PrP(Sc) and histopathological typing identified all sCJD subtypes known to date, except for the VV1 subtype. In particular, a "pure" phenotype was demonstrated in 60.1% of patients, whereas a mixed phenotype was detected in 39.9% of all sCJD cases. The relative excess of MV cases was largely accounted for by a relatively high incidence of the MV 2K subtype. Genetic analysis of the prion protein gene (PRNP) was performed in 161 patients and showed a mutation in 9 of them (5.6%), including one FFI and four GSS cases. Iatrogenic CJD was a rare phenomenon (3.1%), mainly associated with dura mater grafts. Three patients were diagnosed with new variant CJD (1.9%) and one with variably protease-sensitive prionopathy (VPSPr). Post-mortem examination revealed an alternative diagnosis in 156 patients, most commonly Alzheimer's disease (21.2%) or vascular causes of dementia (19.9%). The mortality rates of sCJD in the Netherlands are similar to those in other European countries, whereas iatrogenic and genetic cases are relatively rare. The unusual incidence of the VV2 sCJD subtype compared to that reported to date in other Western countries deserves further investigation.

While type 2 diabetes is well-known to be associated with poorer cognitive performance, few studies have reported on the association of metabolic syndrome (MetS) and contributing factors, such as insulin-resistance (HOMA-IR), low adiponectin-, and high C-reactive protein (CRP)- levels. We studied whether these factors are related to cognitive function and which of the MetS components are independently associated. The study was embedded in an ongoing family-based cohort study in a Dutch population. All participants underwent physical examinations, biomedical measurements, and neuropsychological testing. Linear regression models were used to determine the association between MetS, HOMA-IR, adiponectin levels, CRP, and cognitive test scores. Cross-sectional analyses were performed in 1,898 subjects (mean age 48 years, 43% men). People with MetS had significantly higher HOMA-IR scores, lower adiponectin levels, and higher CRP levels. MetS and high HOMA-IR were associated with poorer executive function in women (P = 0.03 and P = 0.009). MetS and HOMA-IR are associated with poorer executive function in women.

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.