Imaging Endophenotypes of Stroke as a Target for Genetic Studies
2018; Lippincott Williams & Wilkins; Volume: 49; Issue: 6 Linguagem: Inglês
10.1161/strokeaha.117.017073
ISSN1524-4628
Autores Tópico(s)Cerebrovascular and genetic disorders
ResumoHomeStrokeVol. 49, No. 6Imaging Endophenotypes of Stroke as a Target for Genetic Studies Free AccessReview ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessReview ArticlePDF/EPUBImaging Endophenotypes of Stroke as a Target for Genetic Studies Xueqiu Jian, PhD and Myriam Fornage, PhD Xueqiu JianXueqiu Jian From the Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston. and Myriam FornageMyriam Fornage From the Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston. Originally published1 Jun 2018https://doi.org/10.1161/STROKEAHA.117.017073Stroke. 2018;49:1557–1562Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: January 1, 2018: Previous Version 1 Stroke is a heterogeneous disease leading to death of neural tissue and often resulting in the loss of motor and cognitive function. It is the fifth leading cause of death and a leading cause of severe long-term disability in the United States.1 Most strokes (≈80%–90%) are caused by an acute interruption of the brain arterial blood supply because of vascular occlusion, leading to brain tissue ischemia. Approximately 10% to 20% of strokes are caused by blood vessel rupture, leading to hemorrhage. Although a growing number of genetic loci have been identified for the major stroke risk factors, the genetic architecture of stroke and its subtypes remains largely uncharacterized. The use of imaging measures as endophenotypes in genetic studies of stroke is leading to new discoveries and may provide a better understanding of the biological mechanisms underlying stroke pathogenesis.The concept of endophenotype was first developed in the early 1970s by Gottesman and Shields2 for schizophrenia research. An endophenotype was originally defined as a quantitative characteristic of the disease, which cannot be observed by the naked eye (Endo- means internal or inside; Pheno- means showing or appearing).3 It is not a risk factor but rather an expression of the underlying disease liability. Gottesman and Gould3 identified 6 criteria that define an endophenotype (Table). Because endophenotypes are typically quantitative and lie in the causal pathway to the disease but are closer to the gene action than the clinical phenotype,3 they provide greater power than their corresponding clinical phenotypes in gene discovery, as has been shown in the genetic study of other complex diseases.4,5 This review will discuss selected imaging endophenotypes of stroke. Genetic studies referenced in this article mostly focus on genome-wide association studies (GWASs) in large population-based samples.Table. Criteria Defining an Endophenotype31. The endophenotype is associated with the disease in the population2. The endophenotype is heritable3. The endophenotype is primarily state independent (can be measured in both affected and unaffected)4. Within families, endophenotype and disease cosegregate5. The endophenotype measured in affected family members is present in nonaffected family members at a higher rate than in the general population6. The endophenotype is a trait that can be measured reliably and ideally is more strongly associated with the disease of interest than with other related conditionsBrain MRI Endophenotypes of Cerebral Small Vessel DiseaseCerebral small vessel arteriopathy manifests itself as heterogeneous lesions in the brain parenchyma detectable by magnetic resonance imaging (MRI), including white matter hyperintensities (WMH), infarcts or lacunes, dilated perivascular spaces, and cerebral microbleeds.6WMH are common neuroradiological abnormalities in the elderly and are detectable by T2-weighted and fluid attenuation inversion recovery structural MRI. WMH have been widely recognized as significant predictors of stroke, dementia, and mortality.7 Genetic factors play a significant role in WMH susceptibility, with heritability estimates ranging from 55% to 80%.8–10 A GWAS of WMH burden conducted in middle-aged to elderly individuals who were free of dementia and stroke and were from community-based cohorts identified a locus on chromosome 17q25 near TRIM47.11 This finding has been confirmed in several independent studies12–14 and in a subsequent, expanded GWAS including participants of European, African, Hispanic, and Asian descent.15 In the latter GWAS, 4 novel loci were also identified on chr10q24 (PDCD11/NEURL/SH3PXD2A), chr2p21 (HAAO), chr1q22 (PMF1), and chr2p16 (EFEMP1).15 Remarkably, 4 of the 5 loci encompass genes that have been implicated in tumors of the glial cells, including gliomas, astrocytomas, and glioblastomas, suggesting that inflammatory and glial proliferative pathways may be involved in the development of WMH in addition to previously proposed ischemic mechanisms. Interestingly, chr1q22 (PMF1) has also been identified as a risk locus for nonlobar intracerebral hemorrhage,16 as well as all stroke and ischemic stroke17; and chr10q24 (SH3PXD2A) has been identified in a recent GWAS of all stroke,17 further demonstrating the utility of neuroimaging endophenotypes in the search for stroke genes. A genetic risk score constructed from 18 single nucleotide polymorphisms most significantly associated with WMH in the latest multiethnic GWAS15 was recently evaluated for association with lacunar stroke, cardioembolic stroke (CES), and large vessel stroke in cases obtained from hospital admissions in Europe and Australia and ancestry-matched controls.18 There was strong evidence that genetic variants associated with WMH also influence risk of lacunar stroke but not that of other stroke subtypes, supporting the notion that the 2 disorders share common pathological processes possibly affecting the small vessels of the brain.A recent GWAS of WMH in 3670 patients with stroke from the United Kingdom, United States, Australia, Belgium, and Italy did not identify genome-wide significant loci.18 However, meta-analyses of results with those derived from the large GWAS from the CHARGE consortium (Cohorts for Heart and Aging Research in Genomic Epidemiology)15 identified 4 novel loci reaching genome-wide significance: rs72934505 (NBEAL1); rs941898 (EVL); rs962888 (EFTUD2/C1QL1); and rs9515201 (COL4A2). Interestingly, COL4A2 and the adjacent COL4A1 encode α subunits of type IV collagen the major structural component of basement membranes and have been implicated in hereditary cerebral small vessel disease and intracerebral hemorrhage.19,20Because common variants detectable by GWAS have been estimated to account for at most a quarter of the WMH phenotypic variance,21 genetic studies of WMH are now focusing on rare variants and other omics.22 An analysis of 250 000 mostly rare to low frequency variants, mapping to coding regions of the genome (exome) and genotyped in 20 719 participants of European and African ancestry, showed that rare nonsynonymous variants in MRPL38, located in the previously identified chr17q25 locus, are associated with WMH independently of the known GWAS signal (manuscript submitted). MRPL38 encodes a mitochondrial ribosomal protein. Gene mutations resulting in impaired mitochondrial translation have been implicated in severe, early onset neurological disease.23 Future whole-genome sequence analysis will provide a more complete picture of the role of rare variants in WMH susceptibility.24MRI-defined brain Infarcts (BI) are common in the elderly and typically occur in the absence of clinically recognized stroke symptoms. Like WMH, they are associated with future incident cognitive decline and stroke. The majority (>90%) of BI are small subcortical brain infarcts (SSBI) 3 to 15 mm in size, which are also referred to as lacunes. The remaining 10% are larger subcortical infarcts or cortical infarcts.25 A GWAS of covert MRI infarcts in 9401 participants from 6 community-based cohorts (mean age: 69 years; 19.4% had at least 1 MRI infarct) identified novel associations in the MACROD2/FLRT3 region of chromosome 20p12.26 A more recent transethnic meta-analysis of GWAS in 20 949 participants from 5 ethnicities has been completed. Both MRI-defined BI (n=3726) and SSBI (n=2021) were analyzed. Two loci reached genome-wide significance for association with BI: FBN2 and LINC00539/ZDHHC20. However, associations were not replicated in a smaller independent sample of 3143 participants, including 1134 with BI and 543 with SSBI. The inconsistent findings among studies and the failure of replication efforts may not only be attributable to insufficient power, genetic heterogeneity across ethnic groups, and the use of different definition of BI (eg, diameter threshold may vary across studies) but may also in part explain these observations. These loci did not seem to associate with ischemic stroke and pathologically defined BI, warranting additional studies to validate these findings and further examine the shared pathogenesis between covert and overt brain vascular disease.22Recent application of high resolution structural MRI and ongoing development of semiautomated detection techniques have allowed assessment of cortical cerebral microinfarcts (diameter <1 mm).27 The cause of these brain abnormalities is likely heterogeneous. Cerebral microinfarcts have been associated with dementia, cognitive decline, and motor function impairment.28 The genetic basis of cerebral microinfarcts is unknown.Perivascular spaces (also known as Virchow–Robin spaces) are fluid-filled spaces that follow the typical course of penetrating vessels through the brain parenchyma. They seem either linear if imaged parallel to the course of the vessel or round or ovoid (diameter 60%).34 Cerebral microbleeds located in deep subcortical or infratentorial regions are typically associated with hypertensive vascular disease while those located in lobar regions are usually associated with cerebral amyloid angiopathy.35 A systemic review and meta-analysis of candidate gene associations showed that the APOE ε4 allele is associated with a higher risk of microbleeds at any location, but the association was stronger with strictly lobar cerebral microbleeds.36 In a GWAS of intracerebral hemorrhage, APOE ε4 was associated with both lobar and deep intracerebral hemorrhage.37 To date, no GWAS of microbleeds has been reported.White matter microstructure and cerebral small vessel disease: White matter microstructure can be assessed by diffusion tensor imaging. Fractional anisotropy and mean diffusivity are commonly used diffusion tensor imaging metrics to quantify changes in white matter integrity that are not typically detected on conventional MRI.38 These changes have been shown to represent early predictors of the course of age-related white matter degeneration with time and to precede irreversible white matter lesions (WMH).39,40 In the Rotterdam study, a large population-based cohort of middle-aged and older adults, measures of white matter microstructural integrity were associated with an increased risk of stroke, independent of cardiovascular risk factors and MRI-defined white matter lesions and lacunar infarcts.41 Heritability of whole-brain fractional anisotropy and regional fractional anisotropy was recently estimated to range between 0.66 and 0.90.42 A GWAS of global fractional anisotropy in a sample of 776 Mexican-American members of extended pedigrees identified 5 mostly intergenic loci, but no replication was attempted as part of this study.43 Additional genetic studies in larger samples are needed to uncover the genetic basis of white matter integrity and its role in stroke susceptibility.Carotid Ultrasound Imaging Endophenotypes of Large Artery StrokeCarotid atherosclerosis, as a source of microemboli or a cause of ischemia because of flow limiting stenosis, is responsible for 15% to 20% of strokes. Carotid intima–media thickness and carotid plaque burden can be assessed using noninvasive high resolution ultrasound techniques.44 Carotid intima–media thickness reflects the thickening of the inner 2 layers of the carotid artery wall—the intima and media, and plaque represents established atherosclerotic disease in the lumen. Asymptomatic carotid stenosis is common in the general population, with prevalence estimated at up to 7.5% for moderate asymptomatic carotid stenosis and up to 3.5% for severe asymptomatic carotid stenosis.45 Presence of carotid artery plaque may be even higher, ranging between 15% and 35% of adults.46,47 Both measures have been associated with an increased risk of future stroke48,49 and are influenced by genetic factors.50 A GWAS of subclinical atherosclerosis in >40 000 individuals of European ancestry identified 3 genome-wide significant loci associated with carotid intima–media thickness (8q24: ZHX2; 19q13: APOC1; 8q23.1: PINX1) and 2 with carotid plaque (7q22: PIK3CG; 4q31: EDNRA).50 In the largest GWAS of stroke to date, the EDNRA locus was significantly associated with large artery stroke, exclusive of other stroke subtypes.17 These findings underscore the utility of endophenotypes for uncovering possible biological mechanisms of stroke pathophysiology.Endophenotypes of Cardioembolic and Cryptogenic StrokeCES accounts for 15% to 30% of all ischemic strokes while undetermined (cryptogenic) stroke accounts for 30% to 40%.51,52 Atrial fibrillation (AF), the most common cardiac arrhythmia, is a major cause of CES.53 To date, at least 30 genetic loci have been associated with AF.54 Two of these are among the first and most consistently identified ischemic stroke loci (4q25: PITX2 and 16q22: ZFHX3), with ZFHX3 exclusively associated with CES.55 Thus, information about the genetic architecture of AF may be relevant to stroke. This is also supported by the reported association of a comprehensive AF polygenic risk score with stroke, more specifically CES.56Thrombus formation in the left atrial appendage (LAA) in patients with AF is the most common cause of cardioembolic events.57 Measures of LAA structure and function can be assessed by echocardiography (transesophageal echocardiography or transthoracic echocardiographic) and may represent valuable endophenotypes of cardioembolic or cryptogenic stroke.58 Three anatomic features of the LAA have been associated with increased risk of ischemic stroke in patient with AF: shape, orifice size, and fibrosis.59 In addition to LAA structure, LAA function, commonly assessed by echocardiographic measurement of LAA blood flow velocities, has also been correlated with stroke risk, with lower velocities associated with greater ischemic stroke risk and thrombus formation.59 Studies are needed to investigate the genetic basis of LAA structure and function and the possible role of the underlying genes in determining ischemic stroke risk, particularly cardioembolic and cryptogenic strokes.Recent data indicate that left atrial thromboembolism can occur even in the absence of AF. Hence, a broader concept considering atrial dysfunction, or atrial cardiopathy, as a stroke risk factor has been recently proposed.60 Biomarkers of atrial cardiopathy may, thus, represent valuable endophenotypes of stroke, especially embolic stroke.Left atrial size measured by echocardiography has been associated with age-adjusted ischemic stroke risk in the Framingham Heart Study61 and in the ethnically diverse NOMASS (Northern Manhattan Stroke Study).62 Linkage analysis in 100 Caribbean Hispanic families followed by association analysis in 825 participants from NOMASS identified a region on chr.17p10 harboring multiple susceptibility genes implicated in heart structure (NTN1, MYH10, COX10, and MYOCD).63 In a recent meta-analysis of GWAS of left atrial size from 21 studies with up to 30 201 individuals, no association reached genome-wide significance for left atrial size.64 The strongest association was with rs2292864 (P=5.15×10−7), located in an intron of ITGB3, encoding the β chain β 3 of integrin, a cell-surface receptor involved in cell adhesion and cell-surface–mediated signaling. Whether genetic variants in genes implicated in left atrial size to date are associated with stroke has not been examined.Increased P-wave terminal force in lead V1 is the most commonly used electrocardiographic marker of left atrial abnormality, including left atrial fibrosis, elevated filling pressures, and dilatation.65 In a recent meta-analysis, increased P-wave terminal force in lead V1, defined either as a continuous or categorical variable, was associated with an 18% to 22% increased risk of incident ischemic stroke.66 In the NOMASS, there was no association of P-wave terminal force in lead V1 with non-CES subtypes.67 Similarly, in the Atherosclerosis Risk in Communities studies, this association was limited to nonlacunar stroke.68 To our knowledge, no genetic studies of P-wave terminal force in lead V1 have been performed to date. Of note, a newly-identified genetic locus for CES, NKX2-5, has also been previously reported associated with ECG-defined PR interval, a marker of atrial and atrioventricular nodal conduction,69 but not consistently with AF.17 This result further highlights the relevance of identifying genes underlying atrial cardiopathy to understand stroke pathogenesis.Integrating Multiple Endophenotypes to Study the Genetics of StrokeInvestigating stroke endophenotypes individually may not provide the most efficient or powerful approach to dissecting the genetic architecture of stroke. Because many endophenotypes have a vascular origin, they tend to be correlated with each other, some are even highly correlated. For example, microbleeds are associated with SSBI independently of WMH70; most incident lacunes are located at the edge of WMH.71 Thus, a Bonferroni correction applied to multiple GWASs of correlated traits may be too conservative and may reduce statistical power. In addition, although endophenotypes of cerebral small vessel disease (SVD) are generally correlated, only 1 (or few) endophenotype may reflect a specific pathogenic component of the disease rather than capturing the global effect of disease on the brain. The recent discovery of the FOXF2 locus via GWAS of incident all stroke, regardless of subtype, suggests that there may be an advantage in applying strategies of gene discovery that target global mechanisms of brain dysfunction.72 To increase the power to identify genetic variants associated with stroke, efforts should be made in (1) developing statistical methods accounting for correlation among traits, such as multivariate methods or the combination of univariate analyses results correcting for phenotypic correlations73; or (2) integrating multiple specific endophenotypes into a common one, either binary or ordinal. For example, Klarenbeek et al74 developed a simple ordinal total SVD score ranging from 0 to 4 by counting the presence of 4 SVD endophenotypes in an individual: lacunes, WMH, perivascular spaces, and microbleeds. Various studies showed that a higher score predict stroke severity75,76 and cognitive decline77–79 and is associated with SVD risk factors, such as hypertension, age, and smoking.74,80,81 These data indicate that total SVD score may be a better measure of the total SVD burden than individual measures and, thus, could be used in large-scale genetic studies not only to increase statistical power but also to ease the burden of multiple testing. However, the ordinal score only considers 4 of the SVD endophenotypes and assigns equal weight to each one, thus leaving sufficient room for improvement, for example, by including additional components, such as SSBI and brain atrophy, or weighting each trait differentially based on its prevalence in the population, the location in the brain, or the number and size of the damage.Conclusions and PerspectivesGenome-wide genetic studies of stroke conducted to date have required large sample sizes and have yielded a limited (albeit growing) number of loci in comparison to other complex diseases. Genetic and phenotypic heterogeneity pose a major challenge to the elucidation of the genetic basis of stroke. Endophenotypes such as those described above represent a prospect for understanding stroke pathophysiology, improving stroke diagnosis, and developing therapeutic targets for stroke treatment. The ability to measure them quantitatively, reliably, and noninvasively in large population samples provides an opportunity to improve statistical power to detect small genetic effects over that of the binary disease trait. To date, use of stroke endophenotypes has been successful in the discovery of genes associated with WMH, whereas more work remains to be done for others. Different yet related measures can be used to study the mechanisms of different stroke subtypes or uncover global mechanisms common to all stroke subtypes. Stroke gene discovery may also be benefit from integrating multiple endophenotypes into a composite measure or using longitudinal design to assess genetic underpinnings of disease progression.Sources of FundingThe authors are supported, in part, by grant R01-NS087541 from the National Institutes of Health.DisclosuresNone.FootnotesCorrespondence to Myriam Fornage, PhD, UTHealth Institute of Molecular Medicine, 1825 Pressler St, No. 530.F, Houston, TX 77030. E-mail [email protected]References1. Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2017 update: a report from the American Heart Association.Circulation. 2017; 135:e146–e603. doi: 10.1161/CIR.0000000000000485.LinkGoogle Scholar2. Gottesman II, Shields J. Genetic theorizing and schizophrenia.Br J Psychiatry. 1973; 122:15–30.CrossrefMedlineGoogle Scholar3. Gottesman II, Gould TD. The endophenotype concept in psychiatry: etymology and strategic intentions.Am J Psychiatry. 2003; 160:636–645. doi: 10.1176/appi.ajp.160.4.636.CrossrefMedlineGoogle Scholar4. Gur RE, Calkins ME, Gur RC, Horan WP, Nuechterlein KH, Seidman LJ, et al. The Consortium on the Genetics of Schizophrenia: neurocognitive endophenotypes.Schizophr Bull. 2007; 33:49–68. doi: 10.1093/schbul/sbl055.CrossrefMedlineGoogle Scholar5. Reitz C, Mayeux R. Endophenotypes in normal brain morphology and Alzheimer's disease: a review.Neuroscience. 2009; 164:174–190. doi: 10.1016/j.neuroscience.2009.04.006.CrossrefMedlineGoogle Scholar6. Gouw AA, Seewann A, van der Flier WM, Barkhof F, Rozemuller AM, Scheltens P, et al. Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations.J Neurol Neurosurg Psychiatry. 2011; 82:126–135. doi: 10.1136/jnnp.2009.204685.CrossrefMedlineGoogle Scholar7. Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis.BMJ. 2010; 341:c3666.CrossrefMedlineGoogle Scholar8. Atwood LD, Wolf PA, Heard-Costa NL, Massaro JM, Beiser A, D'Agostino RB, et al. Genetic variation in white matter hyperintensity volume in the Framingham Study.Stroke. 2004; 35:1609–1613. doi: 10.1161/01.STR.0000129643.77045.10.LinkGoogle Scholar9. Carmelli D, DeCarli C, Swan GE, Jack LM, Reed T, Wolf PA, et al. Evidence for genetic variance in white matter hyperintensity volume in normal elderly male twins.Stroke. 1998; 29:1177–1181.LinkGoogle Scholar10. Turner ST, Jack CR, Fornage M, Mosley TH, Boerwinkle E, de Andrade M. Heritability of leukoaraiosis in hypertensive sibships.Hypertension. 2004; 43:483–487. doi: 10.1161/01.HYP.0000112303.26158.92.LinkGoogle Scholar11. Fornage M, Debette S, Bis JC, Schmidt H, Ikram MA, Dufouil C, et al. Genome-wide association studies of cerebral white matter lesion burden: the CHARGE consortium.Ann Neurol. 2011; 69:928–939. doi: 10.1002/ana.22403.CrossrefMedlineGoogle Scholar12. Tabara Y, Igase M, Okada Y, Nagai T, Uetani E, Kido T, et al. Association of Chr17q25 with cerebral white matter hyperintensities and cognitive impairment: the J-SHIPP study.Eur J Neurol. 2013; 20:860–862. doi: 10.1111/j.1468-1331.2012.03879.x.CrossrefMedlineGoogle Scholar13. Verhaaren BF, de Boer R, Vernooij MW, Rivadeneira F, Uitterlinden AG, Hofman A, et al. Replication study of chr17q25 with cerebral white matter lesion volume.Stroke. 2011; 42:3297–3299. doi: 10.1161/STROKEAHA.111.623090.LinkGoogle Scholar14. Adib-Samii P, Rost N, Traylor M, Devan W, Biffi A, Lanfranconi S, et al; Australian Stroke Genetics Collaborative; Wellcome Trust Case-Control Consortium-2 (WTCCC2); METASTROKE; International Stroke Genetics Consortium. 17q25 Locus is associated with white matter hyperintensity volume in ischemic stroke, but not with lacunar stroke status.Stroke. 2013; 44:1609–1615. doi: 10.1161/STROKEAHA.113.679936.LinkGoogle Scholar15. Verhaaren BF, Debette S, Bis JC, Smith JA, Ikram MK, Adams HH, et al. Multiethnic genome-wide association study of cerebral white matter hyperintensities on MRI.Circ Cardiovasc Genet. 2015; 8:398–409. doi: 10.1161/CIRCGENETICS.114.000858.LinkGoogle Scholar16. Woo D, Falcone GJ, Devan WJ, Brown WM, Biffi A, Howard TD, et al; International Stroke Genetics Consortium. Meta-analysis of genome-wide association studies identifies 1q22 as a susceptibility locus for intracerebral hemorrhage.Am J Hum Genet. 2014; 94:511–521. doi: 10.1016/j.ajhg.2014.02.012.CrossrefMedlineGoogle Scholar17. Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, et al; AFGen Consortium; Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium; International Genomics of Blood Pressure (iGEN-BP) Consortium; INVENT Consortium; STARNET; BioBank Japan Cooperative Hospital Group; COMPASS Consortium; EPIC-CVD Consortium; EPIC-InterAct Consortium; International Stroke Genetics Consortium (ISGC); METASTROKE Consortium; Neurology Working Group of the CHARGE Consortium; NINDS Stroke Genetics Network (SiGN); UK Young Lacunar DNA Study; MEGASTROKE Consortium; MEGASTROKE Consortium:. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.Nat Genet. 2018; 50:524–537. doi: 10.1038/s41588-018-0058-3.CrossrefMedlineGoogle Scholar18. Traylor M, Zhang CR, Adib-Samii P, Devan WJ, Parsons OE, Lanfranconi S, et al; International Stroke Genetics Consortium. Genome-wide meta-analysis of cerebral white matter hyperintensities in patients with stroke.Neurology. 2016; 86:146–153. doi: 10.1212/WNL.0000000000002263.CrossrefMedlineGoogle Scholar19. Gunda B, Mine M, Kovács T, Hornyák C, Bereczki D, Várallyay G, et al. COL4A2 mutation causing adult onset recurrent intracerebral hemorrhage and leukoencephalopathy.J Neurol. 2014; 261:500–503. doi: 10.1007/s00415-013-7224-4.CrossrefMedlineGoogle Scholar20. Lanfranconi S, Markus HS. COL4A1 mutations as a monogenic cause of cerebral small vessel disease: a systematic review.Stroke. 2010; 41:e513–e518. doi: 10.1161/STROKEAHA.110.581918.LinkGoogle Scholar21. Adib-Samii P, Devan W, Traylor M, Lanfranconi S, Zhang CR, Cloonan L, et al. Genetic architecture of white matter hyperintensities differs in hypertensive and nonhypertensive ischemic stroke.Stroke. 2015; 46:348–353. doi: 10.1161/STROKEAHA.114.006849.LinkGoogle Scholar22. Debette S, Saba Y, Vojinovic D, Jian X, Adams H, Chauhan G, et al. 19th Workshop of the International Stroke Genetics Consortium, April 28–29, 2016, Boston, Massachusetts, USA: 2016.001 MRI-defined cerebrovascular genomics-The CHARGE consortium.Neurol Genet. 2017; 3:S2–S11.CrossrefMedlineGoogle Scholar23. Boczonadi V, Horvath R. Mitochondria: impaired mitochondrial translation in human disease.Int J Biochem Cell Biol. 2014; 48:77–84. doi: 10.1016/j.biocel.2013.12.011.CrossrefMedlineGoogle Scholar24. Sarnowski C, Satizabal CL, DeCarli C, Pitsillides AN, Cupples LA, Vasan RS, et al; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Neurocognitive Working Group. Whole genome sequence analyses of brain imaging measures in the Framingham Study.Neurology. 2018; 90:e188–e196. doi: 10.1212/WNL.0000000000004820.CrossrefMedlineGoogle Scholar25. Vermeer SE, Koudstaal PJ, Oudkerk M, Hofman A, Breteler MM. Prevalence and risk factors of silent brain infarcts in the population-based Rotterdam Scan Study.Stroke. 2002; 33:21–25.LinkGoogle Scholar26. Debette S, Bis JC, Fornage M, Schmidt H, Ikram MA, Sigurdsson S, et al. Genome-wide association studies of MRI-defined brain infarcts: meta-an
Referência(s)