Update on the Genetics of Stroke and Cerebrovascular Disease 2007
2008; Lippincott Williams & Wilkins; Volume: 39; Issue: 2 Linguagem: Inglês
10.1161/strokeaha.107.510503
ISSN1524-4628
AutoresRobert A. Hegele, Martin Dichgans,
Tópico(s)RNA regulation and disease
ResumoHomeStrokeVol. 39, No. 2Update on the Genetics of Stroke and Cerebrovascular Disease 2007 Free AccessReview ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessReview ArticlePDF/EPUBUpdate on the Genetics of Stroke and Cerebrovascular Disease 2007 Robert A. Hegele, MD, FRCPC, FACP and Martin Dichgans, MD Robert A. HegeleRobert A. Hegele From the Robarts Research Institute and Schulich School of Medicine and Dentistry (R.A.H.), University of Western Ontario, London, Canada; and Neurologische Klinik (M.D.), Klinikum Groβhadern, Ludwig-Maximilians-Universität, München. and Martin DichgansMartin Dichgans From the Robarts Research Institute and Schulich School of Medicine and Dentistry (R.A.H.), University of Western Ontario, London, Canada; and Neurologische Klinik (M.D.), Klinikum Groβhadern, Ludwig-Maximilians-Universität, München. Originally published10 Jan 2008https://doi.org/10.1161/STROKEAHA.107.510503Stroke. 2008;39:252–254Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: January 10, 2008: Previous Version 1 The genetic story-of-the-year for 2007 is undoubtedly the dramatic and surprising success of large-scale genome-wide association (GWA) studies in finding consistent and replicable genetic markers of several complex diseases of adulthood. While GWA studies of stroke are on the verge of being reported, much can be learned already from reports of other complex diseases with robust genetic associations identified since the spring of 2007. These have included coronary artery disease (CAD),1–6 type 2 diabetes,3,7–9 rheumatoid arthritis,3,10 bipolar disorder,3 Crohn disease,3 multiple sclerosis11 and amyotrophic lateral sclerosis.12 Cumulatively, these findings have begun to transform our understanding of the genetics of complex diseases. Also, these studies might help discover new biological mechanisms, diagnostic methods and treatments for complex adult diseases.13–15Genetic association studies, in their most common incarnation, compare cases and matched controls at the genomic level. If resources and computing power permitted, the most thorough comparison would involve direct sequence analysis of the individual 3.2 billion nucleotide bases per human genome and detection of patterns that differ statistically between the genomes of cases and controls. Although we are still a few years away from such studies, in September 2007 the first complete genomic sequence of a single human being was reported.16 This tour-de-force provided proof-of-concept that complete genomic sequencing is possible, albeit at a cost of tens of millions of dollars and hundreds of human-years of effort. However, the cost and time are expected to fall markedly and quickly.In the absence of complete directly determined individual sequence data, current comparative cases and control studies must use indirect screening sets of DNA markers. The most frequently used markers are derived from commonly occurring single letter changes in the DNA sequence, called single nucleotide polymorphisms (SNPs). SNPs can be chosen from within candidate genes that have some a priori evidence for a pathogenic role in the disease of interest. Alternatively, SNPs can be chosen based on their chromosomal position, with no bias related to the identity or function of neighborhood genes. This latter approach, when expanded to the extreme, evaluates a genome-wide set of SNPs that blankets all chromosomes. SNP genotype frequency profiles in cases and controls are compared. Strong statistical association between a SNP and a trait identifies the general locale of the offending alteration, even if the marker SNP is not itself the direct functional cause of disease. The next step is fine resolution DNA sequence analysis in the region of the statistical signal to pinpoint the actual causative variant.Over the past 20 years, genetic association studies—mainly of the candidate gene variety carried out in small single samples using a few SNPs—have been disappointing, yielding largely inconsistent and nonreplicable findings.17,18 However, the breakthroughs of 2007 were accomplished because of 2 fundamental changes in the execution of association studies. First, extremely large case-control samples—involving tens of thousands of patients—were assembled, frequently through multicenter collaborations that combined several study samples. Second, new high-density microarrays allowed for rapid and cost-effective screening of genome-wide sets ranging from 100 000 to >1 000 000 SNPs. Together, this unprecedented volume of genotyping has yielded consistent, albeit modest, genetic associations in several complex diseases. The large samples allowed associations with relative risk ratios ≈1.5 to be detected with high statistical confidence. These results are even more impressive considering that several of the conditions evaluated do not have a strong genetic component and develop in the presence of interacting environmental factors, in contrast to single gene traits, in which the genetic signal is strong and the contribution from environmental factors is modest.Among the GWA results reported in 2007, most relevant to stroke is the highly replicated association of CAD with markers on a stretch of the short arm of chromosome 9, specifically band 9p21, in several white samples.1–6 The potential importance of these replicated observations stems from the fact that more than half the white population carries the "high-risk" genotype, which has a relative risk compared with the "low-risk" genotype of between 1.2 and 1.6. The most strongly associated and consistently replicated SNP markers lie between the CKDN2A and CKDN2B genes that are related mechanistically to various cancers19,20 but not previously to atherosclerosis. The linked markers with the highest levels of significance do not affect known coding or regulatory DNA sequences.1–6 In fact, markers within the coding sequences of the CKDN2A and CKDN2B genes are much less significantly associated than the SNPs within the "no-man's land" between the 2 genes.1 Although the genetic signals are strong and unequivocal, understanding the biological function of this risk-associated genomic region will be challenging, perhaps requiring study of microRNA or discovery of some novel regulatory mechanism. Furthermore, this locus cannot fully explain the clustering of CAD in families, suggesting that other genetic risk variants remain to be discovered.Because ischemic stroke and CAD share several aspects of etiology and pathogenesis, the chromosome 9p21 region became a logical candidate for stroke association. Smaller focused studies have already reported associations of stroke phenotypes with the chromosome 9p21 locus.21,22 However, preliminary analysis of the first GWA study did not reveal any single locus conferring a large effect on risk for ischemic stroke, including 9p21.23 In contrast, the Framingham Study showed an association of the same chromosome 9p21 region with a composite phenotype that included both stroke and CAD.5 There was no comparable association in Framingham with quantitative phenotypes determined using brain MRI and cognitive testing.24 Interestingly, another genome-wide study found modest linkage of Alzheimer disease with different chromosome 9p21 markers.25The coming months and years will see a flurry of GWA studies of stroke and related phenotypes performed in multiple population samples representing the range of human geographic ancestries with even more extensive phenotypic characterization using an approach called "phenomics".26 Newly discovered loci for type 2 diabetes,7–9 hypertension and other risk-associated conditions also deserve attention in future GWA studies of ischemic stroke, as do loci for atrial fibrillation for thromboembolic stroke.27 Finally, the recent mapping of prevalent and ubiquitous larger genomic variants, such as deletions, duplications and inversions ranging from 300 to 500 000 bases (or more) in size—collectively called copy number variations (CNVs)28—has opened a new field for evaluation of genomic variation with complex traits, including stroke. Already, common CNVs have been mapped onto known cardiovascular disease loci,29 and it will be of interest to determine whether any of these are associated with predisposition to various stroke phenotypes. The promise of obtaining detailed genomic results is that newly identified regions, genes and mechanisms should ultimately provide novel insights into disease risk and pathogenesis. There is also the hope that both SNP and CNV markers themselves can be used clinically for risk prediction.How realistic are the hopes and promises arising from GWA studies? A cautious approach in both research and clinical practice should balance the unbridled optimism and hubris that have accompanied the success stories of 2007.13,14 The chromosome 9p21-CAD association highlights some of the challenges, caveats and core limitations of findings from GWA studies of common, complex disease phenotypes.15One issue is the potential clinical and biological importance of a risk allele with a modest odds ratio. The chances that this type of marker will be meaningful clinically are slim. First, such markers indicate susceptibility rather than causation and denote a level of risk that is much lower than that associated with conventional risk factors such as smoking, diabetes, dyslipidemia or even family history. A glimpse into the potential use of adding a weakly associated marker to a risk engine was recently shown for a range of novel serological biomarkers that had modest associations (odds ratios 1.5 to 1.8) with CAD.30 The results were underwhelming with minimal increment in diagnostic performance when novel biomarkers such as C-reactive protein were added to a risk algorithm containing conventional CAD risk factors.30 Although increasing the number of markers tested may increase the cumulative risk, individuals who carry several markers will also represent a small fraction of the overall population, again potentially limiting the effectiveness of testing a panel of such markers. Also, the mechanistic basis for a collection of weak associations between SNPs and a disease phenotype may be difficult to define. In the future, new genetic markers for both CAD and stroke will require similar evaluation to determine whether they provide any meaningful increment over conventional risk factors.Other concerns for GWA studies also include false-positives due to multiple comparisons and selection bias of cases. In addition, GWA studies currently use only a fraction of the perhaps 20 million SNPs in the genome. Much of the genome remains inaccessible or unassayed. Also, CNVs are not completely accounted for by current GWA SNP technology platforms. Finally, GWA studies with SNPs may only be a way-station en route to GWA studies in 5 to 10 years that use next-generation whole genome sequencing technologies, which will provide the most complete profile of genomic differences between cases and controls.Sources of FundingR.A.H. is supported by the Jacob J. Wolfe Distinguished Medical Research Chair, the Edith Schulich Vinet Canada Research Chair (Tier I) in Human Genetics, a Career Investigator award from the Heart and Stroke Foundation of Ontario, operating grants from the Canadian Institutes for Health Research (MOP-13430, MT-8014), the Heart and Stroke Foundation of Ontario (NA-5320, T-5603, PRG-5967) and by Genome Canada through the Ontario Genomics Institute. M.D. is supported by the German Research Foundation (DI-722/8-1 and 8-2, SFB 596 TP-A4).DisclosuresNone.FootnotesCorrespondence to Robert A. Hegele, MD, FRCPC, FACP, Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, 406-100 Perth Drive, London, Ontario, Canada N6A 5K8. E-mail [email protected]References1 McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR, Hinds DA, Pennacchio LA, Tybjaerg-Hansen A, Folsom AR, Boerwinkle E, Hobbs HH, Cohen JC. A common allele on chromosome 9 associated with coronary heart disease. Science. 2007; 316: 1488–1491.CrossrefMedlineGoogle Scholar2 Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, Jonasdottir A, Sigurdsson A, Baker A, Palsson A, Masson G, Gudbjartsson DF, Magnusson KP, Andersen K, Levey AI, Backman VM, Matthiasdottir S, Jonsdottir T, Palsson S, Einarsdottir H, Gunnarsdottir S, Gylfason A, Vaccarino V, Hooper WC, Reilly MP, Granger CB, Austin H, Rader DJ, Shah SH, Quyyumi AA, Gulcher JR, Thorgeirsson G, Thorsteinsdottir U, Kong A, Stefansson K. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science. 2007; 316: 1491–1493.CrossrefMedlineGoogle Scholar3 Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007; 447: 661–678.CrossrefMedlineGoogle Scholar4 Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B, Dixon RJ, Meitinger T, Braund P, Wichmann HE, Barrett JH, Konig IR, Stevens SE, Szymczak S, Tregouet DA, Iles MM, Pahlke F, Pollard H, Lieb W, Cambien F, Fischer M, Ouwehand W, Blankenberg S, Balmforth AJ, Baessler A, Ball SG, Strom TM, Braenne I, Gieger C, Deloukas P, Tobin MD, Ziegler A, Thompson JR, Schunkert H; WTCCC and the Cardiogenics Consortium. Genomewide association analysis of coronary artery disease. N Engl J Med. 2007; 357: 443–453.CrossrefMedlineGoogle Scholar5 Larson MG, Atwood LD, Benjamin EJ, Cupples LA, D'Agostino RB Sr, Fox CS, Govindaraju DR, Guo CY, Heard-Costa NL, Hwang SJ, Murabito JM, Newton-Cheh C, O'Donnell CJ, Seshadri S, Vasan RS, Wang TJ, Wolf PA, Levy D. Framingham Heart Study 100K project: genome-wide associations for cardiovascular disease outcomes. BMC Med Genet. 2007; 8 (Suppl 1): S5.CrossrefMedlineGoogle Scholar6 O'Donnell CJ, Cupples LA, D'Agostino RB, Fox CS, Hoffmann U, Hwang SJ, Ingellson E, Liu C, Murabito JM, Polak JF, Wolf PA, Demissie S Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI's Framingham Heart Study. BMC Med Genet. 2007; 8 (Suppl 1): S4.CrossrefMedlineGoogle Scholar7 Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson TJ, Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding DJ, Meyre D, Polychronakos C, Froguel P. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007; 445: 881–885.CrossrefMedlineGoogle Scholar8 Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, Timpson NJ, Perry JR, Rayner NW, Freathy RM, Barrett JC, Shields B, Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR, Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney AS; Wellcome Trust Case Control Consortium (WTCCC), McCarthy MI, Hattersley AT. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007; 316: 1336–1341.CrossrefMedlineGoogle Scholar9 Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research, Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix JJ, Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L, Altshuler D, Almgren P, Florez JC, Meyer J, Ardlie K, Bengtsson Bostrom K, Isomaa B, Lettre G, Lindblad U, Lyon HN, Melander O, Newton-Cheh C, Nilsson P, Orho-Melander M, Rastam L, Speliotes EK, Taskinen MR, Tuomi T, Guiducci C, Berglund A, Carlson J, Gianniny L, Hackett R, Hall L, Holmkvist J, Laurila E, Sjogren M, Sterner M, Surti A, Svensson M, Tewhey R, Blumenstiel B, Parkin M, Defelice M, Barry R, Brodeur W, Camarata J, Chia N, Fava M, Gibbons J, Handsaker B, Healy C, Nguyen K, Gates C, Sougnez C, Gage D, Nizzari M, Gabriel SB, Chirn GW, Ma Q, Parikh H, Richardson D, Ricke D, Purcell S. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007; 316: 1331–1336.CrossrefMedlineGoogle Scholar10 Plenge RM, Seielstad M, Padyukov L, Lee AT, Remmers EF, Ding B, Liew A, Khalili H, Chandrasekaran A, Davies LR, Li W, Tan AK, Bonnard C, Ong RT, Thalamuthu A, Pettersson S, Liu C, Tian C, Chen WV, Carulli JP, Beckman EM, Altshuler D, Alfredsson L, Criswell LA, Amos CI, Seldin MF, Kastner DL, Klareskog L, Gregersen PK. TRAF1–C5 as a risk locus for rheumatoid arthritis–a genomewide study. N Engl J Med. 2007; 357: 1199–1209.CrossrefMedlineGoogle Scholar11 International Multiple Sclerosis Genetics Consortium, Hafler DA, Compston A, Sawcer S, Lander ES, Daly MJ, De Jager PL, de Bakker PI, Gabriel SB, Mirel DB, Ivinson AJ, Pericak-Vance MA, Gregory SG, Rioux JD, McCauley JL, Haines JL, Barcellos LF, Cree B, Oksenberg JR, Hauser SL. Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med. 2007; 357: 851–862.CrossrefMedlineGoogle Scholar12 van Es MA, Van Vught PW, Blauw HM, Franke L, Saris CG, Andersen PM, Van Den Bosch L, de Jong SW, van 't Slot R, Birve A, Lemmens R, de Jong V, Baas F, Schelhaas HJ, Sleegers K, Van Broeckhoven C, Wokke JH, Wijmenga C, Robberecht W, Veldink JH, Ophoff RA, van den Berg LH. ITPR2 as a susceptibility gene in sporadic amyotrophic lateral sclerosis: a genome-wide association study. Lancet Neurol. 2007; 6: 869–877.CrossrefMedlineGoogle Scholar13 Bowcock AM. Genomics: guilt by association. Nature. 447, 645–646.CrossrefMedlineGoogle Scholar14 Rosenzweig A. Scanning the genome for coronary risk. N Engl J Med. 2007; 357: 497–499.CrossrefMedlineGoogle Scholar15 Loscalzo J. Biomarker and genetic data: association studies in an era of too much information. Circulation. 2007; 116: 1866–1870.LinkGoogle Scholar16 Levy S, Sutton G, Ng PC, Feuk L, Halpern AL, Walenz BP, Axelrod N, Huang J, Kirkness EF, Denisov G, Lin Y, Macdonald JR, Pang AW, Shago M, Stockwell TB, Tsiamouri A, Bafna V, Kravitz SA, Busam DA, Beeson KY, McIntosh TC, Remington KA, Abril JF, Gill J, Borman J, Rogers YH, Frazier ME, Scherer SW, Strausberg RL, Venter JC. The diploid genome sequence of an individual human. PLoS Biol. 2007; 5: e254.CrossrefMedlineGoogle Scholar17 Hegele RA. SNP judgments and freedom of association. Arterioscler Thromb Vasc Biol. 2002; 22: 1058–1061.LinkGoogle Scholar18 Dichgans M, Markus HS. Genetic association studies in stroke: methodological issues and proposed standard criteria. Stroke. 2005; 36: 2027–2031.LinkGoogle Scholar19 Krimpenfort P, Ijpenberg A, Song JY, van der Valk M, Nawijn M, Zevenhoven J, Berns A. p15Ink4b is a critical tumour suppressor in the absence of p16Ink4a. Nature. 2007; 448: 943–946.CrossrefMedlineGoogle Scholar20 Suzuki N, Onda T, Yamamoto N, Katakura A, Mizoe JE, Shibahara T. Mutation of the p16/CDKN2 gene and loss of heterozygosity in malignant mucosal melanoma and adenoid cystic carcinoma of the head and neck. Int J Oncol. 2007; 31: 1061–1067.MedlineGoogle Scholar21 Zee RY, Ridker PM. Two common gene variants on chromosome 9 and risk of atherothrombosis. Stroke. 2007; 38: e111.LinkGoogle Scholar22 Matarin M, Brown M, Singleton A, Hardy JA, Meschia JF. Whole genome analysis suggests ischemic stroke and heart disease share an association with polymorphisms on chromosome 9p21. Stroke. 2008; in press.Google Scholar23 Matarin M, Brown WM, Scholz S, Simon-Sanchez J, Fung HC, Hernandez D, Gibbs JR, De Vrieze FW, Crews C, Britton A, Langefeld CD, Brott TG, Brown RD Jr., Worrall BB, Frankel M, Silliman S, Case LD, Singleton A, Hardy JA, Rich SS, Meschia JF. A genome-wide genotyping study in patients with ischaemic stroke: initial analysis and data release. Lancet Neurol. 2007; 6: 414–420.CrossrefMedlineGoogle Scholar24 Seshadri S, DeStefano AL, Au R, Massaro JM, Beiser AS, Kelly-Hayes M, Kase CS, D'Agostino RB Sr, Decarli C, Atwood LD, Wolf PA. Genetic correlates of brain aging on MRI and cognitive test measures: a genome-wide association and linkage analysis in the Framingham Study. BMC Med Genet. 2007; 8 (Suppl 1): S15.CrossrefMedlineGoogle Scholar25 Hamshere ML, Holmans PA, Avramopoulos D, Bassett SS, Blacker D, Bertram L, Wiener H, Rochberg N, Tanzi RE, Myers A, Wavrant-De Vrieze F, Go R, Fallin D, Lovestone S, Hardy J, Goate A, O'donovan M, Williams J, Owen MJ Genome-wide linkage analysis of 723 affected relative pairs with late-onset Alzheimer's Disease. Hum Mol Genet. 2007; 16: 2703–2712.CrossrefMedlineGoogle Scholar26 Hegele RA, Oshima J. Phenomics and lamins: from disease to therapy. Exp Cell Res. 2007; 313: 2134–2143.CrossrefMedlineGoogle Scholar27 Gudbjartsson DF, Arnar DO, Helgadottir A, Gretarsdottir S, Holm H, Sigurdsson A, Jonasdottir A, Baker A, Thorleifsson G, Kristjansson K, Palsson A, Blondal T, Sulem P, Backman VM, Hardarson GA, Palsdottir E, Helgason A, Sigurjonsdottir R, Sverrisson JT, Kostulas K, Ng MC, Baum L, So WY, Wong KS, Chan JC, Furie KL, Greenberg SM, Sale M, Kelly P, MacRae CA, Smith EE, Rosand J, Hillert J, Ma RC, Ellinor PT, Thorgeirsson G, Gulcher JR, Kong A, Thorsteinsdottir U, Stefansson K. Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature. 2007; 448: 353–357.CrossrefMedlineGoogle Scholar28 Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, Fiegler H, Shapero MH, Carson AR, Chen W, Cho EK, Dallaire S, Freeman JL, Gonzalez JR, Gratacos M, Huang J, Kalaitzopoulos D, Komura D, MacDonald JR, Marshall CR, Mei R, Montgomery L, Nishimura K, Okamura K, Shen F, Somerville MJ, Tchinda J, Valsesia A, Woodwark C, Yang F, Zhang J, Zerjal T, Zhang J, Armengol L, Conrad DF, Estivill X, Tyler-Smith C, Carter NP, Aburatani H, Lee C, Jones KW, Scherer SW, Hurles ME. Global variation in copy number in the human genome. Nature. 2006; 444: 444–454.CrossrefMedlineGoogle Scholar29 Pollex RL, Hegele RA. Copy number variation in the human genome and its implications for cardiovascular disease. Circulation. 2007; 115: 3130–3138.LinkGoogle Scholar30 Wang TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Cheh C, Jacques PF, Rifai N, Selhub J, Robins SJ, Benjamin EJ, D'Agostino RB, Vasan RS. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006; 355: 2631–2639.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Kofke W and Kositratna G (2016) Future of Critical Care Medicine The Intensivist's Challenge, 10.1007/978-3-319-30454-0_15, (125-146), . Marousi S, Ellul J, Antonacopoulou A, Gogos C, Papathanasopoulos P and Karakantza M (2010) Functional polymorphisms of interleukin 4 and interleukin 10 may predict evolution and functional outcome of an ischaemic stroke, European Journal of Neurology, 10.1111/j.1468-1331.2010.03228.x, 18:4, (637-643), Online publication date: 1-Apr-2011. Bang O (2011) Biomarkers of Stroke, Korean Journal of Stroke, 10.5853/kjs.2011.13.2.57, 13:2, (57), . Stengård J, Dyson G, Frikke-Schmidt R, Tybj�rg-Hansen A, Nordestgaard B and Sing C (2009) Context-Dependent Associations Between Variation in Risk of Ischemic Heart Disease and Variation in the 5′ Promoter Region of the Apolipoprotein E Gene in Danish Women, Circulation: Cardiovascular Genetics, 3:1, (22-30), Online publication date: 1-Feb-2010. Andrew Kofke W (2010) FUTURE ADVANCES IN NEUROANESTHESIA Cottrell and Young's Neuroanesthesia, 10.1016/B978-0-323-05908-4.10030-2, (439-453), . Hazrati L, Bergeron C and Butany J (2009) Neuropathology of cerebrovascular diseases, Seminars in Diagnostic Pathology, 10.1053/j.semdp.2009.08.002, 26:2, (103-115), Online publication date: 1-May-2009. February 2008Vol 39, Issue 2 Advertisement Article InformationMetrics https://doi.org/10.1161/STROKEAHA.107.510503PMID: 18187682 Manuscript receivedNovember 20, 2007Manuscript acceptedNovember 21, 2007Originally publishedJanuary 10, 2008 Keywordsstrokecerebrovascular diseasegeneticsPDF download Advertisement
Referência(s)