|Year : 2015 | Volume
| Issue : 1 | Page : 79-87
A molecular/genetic approach to cerebral small-vessel disease: Beyond aging and hypertension
Sharyl R Martini1, Stephen R Williams2, Paolo Moretti3, Daniel Woo4, Bradford B Worrall2
1 Neurology Care Line; Center for Translational Research in Inflammatory Diseases (CTRID), Michael E. DeBakey Veterans Affairs Medical Center; Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
2 Department of Neurology; Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
3 Neurology Care Line, Michael E. DeBakey Veterans Affairs Medical Center; Department of Neurology; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
4 Department of Neurology and Rehabilitation Medicine, University of Cincinnati, Cincinnati, Ohio, USA
|Date of Submission||14-Apr-2015|
|Date of Acceptance||23-Jun-2015|
|Date of Web Publication||30-Sep-2015|
Sharyl R Martini
Department of Veterans Affairs, Michael E. DeBakey VA Medical Center, Neurology Care Line, 2002 Holcombe Boulevard, MS 127 Houston, Texas - 77030-4211
Source of Support: None, Conflict of Interest: None
Lacunar infarction, white matter hyperintensities (WMH), deep cerebral microbleeds (dCMB), and deep intracerebral hemorrhage (ICH) are increasingly recognized as manifestations of a common underlying vasculopathy, encompassed by the term "cerebral small-vessel disease" (CSVD). Epidemiologic studies have found robust associations of the individual aspects of CSVD with aging and hypertension; however, heritability estimates and the disproportionate burden of CSVD in underrepresented minorities suggest that genetic factors contribute substantially to CSVD risk. Here we present the rationale for studying these phenotypes as part of a spectrum of CSVD, review aspects of genetic study design, summarize current knowledge of genetic contribution to CSVD, and discuss the next steps required to translate these genetic discoveries into therapies for this devastating disease. Genetic studies were identified using PubMed. Regions achieving genome-wide significance in association studies, meta-analyses of candidate gene studies, and studies of genes associated with Mendelian conditions exhibiting CSVD phenotypes have been summarized.
Keywords: Cerebral microbleeds, cerebral small-vessel disease (CSVD), genetics, intracerebral hemorrhage (ICH), lacunar stroke, white matter hyperintensities (WMH)
|How to cite this article:|
Martini SR, Williams SR, Moretti P, Woo D, Worrall BB. A molecular/genetic approach to cerebral small-vessel disease: Beyond aging and hypertension. Brain Circ 2015;1:79-87
|How to cite this URL:|
Martini SR, Williams SR, Moretti P, Woo D, Worrall BB. A molecular/genetic approach to cerebral small-vessel disease: Beyond aging and hypertension. Brain Circ [serial online] 2015 [cited 2023 Jun 3];1:79-87. Available from: http://www.braincirculation.org/text.asp?2015/1/1/79/166376
Sharyl R Martini∗, Stephen R Williams∗ and Bradford B Worrall∗
∗These authors contributed equally to this work.
| Introduction|| |
The term cerebral small-vessel disease (CSVD) encompasses a spectrum of pathologies affecting brain vessels 50-500 μm in diameter. Age- and hypertension-related pathologies causing intrinsic disease of the small penetrating arterioles is the most common type of CSVD. CSVDs such as cerebral amyloid angiopathy involve other small brain arteries [Figure 1].  Age- and hypertension-related CSVD pathologies manifest as small subcortical "lacunar" ischemic strokes, deep intracerebral hemorrhage (ICH), magnetic resonance imaging (MRI) white matter hyperintensities (WMH), or deep cerebral microbleeds (dCMB). These manifestations frequently coexist, often silently, and are associated with progressive cognitive impairment and vascular dementia. , Our understanding of the molecular underpinnings of CSVD has lagged significantly behind our pathological and epidemiologic understanding, impeding development of targeted therapies for this common disease. Recognizing the interrelatedness of these clinical and radiographic manifestations has allowed us to start uncovering their shared and distinct pathophysiologic mechanisms. The purpose of this review is to frame CSVD as a spectrum of interrelated conditions with shared etiologies, review underlying genetic mechanisms (many of which are shared), and discuss the steps following successful identification of genes contributing to CSVD.
|Figure 1: CSVD. (a) Regions affected by CSVD (b-d) Axial FLAIR image demonstrating lacunar infarcts (B, arrows), deep ICH (C, asterisk), and diffuse WMH (e) Axial gradient echo image demonstrating dCMB (arrow) and deep ICH (asterisk). |
CSVD = Cerebral small-vessel disease, FLAIR = fluid-attenuated inversion recovery, ICH = Intracerebral hemorrhage, WMH = White matter hyperintensities, dCMB = Deep cerebral microbleeds
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To accomplish this purpose, we took the following approach: First we discuss reconceptualization of the CSVD spectrum, allowing for more accurate phenotyping. We next describe CSVD clinical presentations and provide examples of "phenocopies" that reduce the power of genetic studies. Finally, we describe the advantages and disadvantages of genetic approaches including family-based studies, candidate gene studies, and genome-wide association studies (GWAS), detailing methods for molecular characterization of the identified variants. For this review, genetic studies were identified using PubMed. Regions achieving genome-wide significance and meta-analyses of candidate gene studies were identified using search terms for each phenotype ("lacunar" or "small vessel" and stroke, "white matter hyperintensities," "cerebral microbleeds" or "intracerebral hemorrhage") plus ("gwas," "gene," or "genetic") to identify cohort-based association studies. For polymorphisms with multiple conflicting independent studies, only meta-analyses were summarized. Genes associated with Mendelian conditions exhibiting CSVD phenotypes were searched individually by combining the CSVD phenotype search terms mentioned above with the gene name.
| Reconceptualizing the CSVD Phenotype|| |
Reliance on phenomenology to classify cerebrovascular diseases has led to the collapsing of distinct diseases into broad categories or the failure to recognize related processes due to distinct clinical, pathological, or radiographic features. Advances in neuroimaging, vascular imaging, and various "-omics" strategies have created the opportunity to reclassify brain and vascular disorders based on shared associations and advances in our understanding of underlying pathophysiology. This strategy allowed reconceptualization of CVSD through a shared pathogenesis encompassing lacunar stroke, hypertensive hemorrhage, leukoaraiosis, and cerebral microbleeds, [Figure 2] ,, and recognition of the overlap with extracerebral small-vessel vasculopathies. , Precise definitions of the clinical phenotypes are crucial for the design of successful genetic and mechanistic studies.
|Figure 2: Reconceptualizing manifestations of CSVD.|
CSVD = Cerebral small-vessel disease, dCMB = Deep cerebral microbleeds, ICH = Intracerebral hemorrhage
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The important roles that aging and hypertension play as risk factors for CSVD ,,,,,,,,,,, led to theories that such disease is inevitable "wear and tear" on the brain. However, heritability studies have found that 34% of ICH risk is conferred by genetic makeup,  and studies of WMH estimate heritability at 55-80%. ,, This evidence for heritability suggests a substantial molecular component, with potential to yield new targets for prevention or intervention.
CSVD has a range of phenotypes that can be studied individually, although each has advantages and disadvantages [Table 1]. Lacunar infarcts represent 20% of ischemic strokes and occur with high frequency in the population,  providing ample power. However, phenocopies (infarcts of similar appearance due to alternate etiologies) reduce the homogeneity of the clinical subtype, making it more difficult to identify true associations. ,,,, Examples of such phenocopies include occlusion of a small penetrating artery from parent vessel atherosclerosis or cardioembolism. This may be one reason that the heritability estimate for lacunar stroke was only 16.1%, much lower than for other manifestations of CSVD.  Another disadvantage of lacunar infarctions is that up to 89% are clinically silent, ,, requiring cohort study design to avoid selection bias. Other issues include variable progression pattern and radiographic permanence.  Deep ICH refers to the rupture of small cerebral vessels. Deep ICH is readily detected by noncontrast head computed tomography (CT), >90% after age 45 are due to intrinsic CSVD,  and most come to clinical attention, allowing for case-based ascertainment.  A significant disadvantage in studying deep ICH is that the incidence rate is approximately 20 per 100,000 per year,  limiting recruitment and thereby power. The high observed early mortality associated with ICH may result in selection bias,  although "hot-pursuit" methodology , or validation using cohort studies that recruit cases prior to stroke occurrence can attenuate this bias.
Studying intermediate phenotypes in progression toward clinical disease may allow for more robust association with causative genetic variants.  WMH and dCMB are two such intermediate CSVD phenotypes detected by MRI. WMH increase with age and hypertension, and are typically located around the lateral ventricles or in the subcortical or brainstem white matter. The advantages of studying WMH include their high prevalence in the population ,, as well their quantifiable nature, allowing volumetric analysis. , Disadvantages include the need for MRI to sensitively define lesion burden, variable appearance depending on acquisition parameters, and the possibility of regression over time. ,, Like lacunes, WMHs have significant phenocopies: Demyelinating disease, remote injuries, migraine headaches, and metabolic disorders/genetic leukodystrophies.  Like WMH, dCMBs are highly associated with other manifestations of CSVD, quantifiable, and require MRI for detection. Both dCMBs and WMHs tend to be clinically silent. ,,, Although there is a lower incidence of dCMB compared to WMH, no significant dCMB phenocopies exist. , Given the clinically silent nature of most WMH and dCMB, , cohort design studies are required to minimize selection bias.
Regardless of the specific manifestation of CSVD studied, its increasing occurrence with age is an important consideration to prevent presymptomatic individuals from being misclassified as controls. Age matching for case-control studies and selecting older-aged individuals for cohort studies reduces this concern.
Once the phenotype is precisely defined, the presumed mode of inheritance should be considered when designing a genetic study [Figure 3]. Families with multiple affected individuals, particularly with a Mendelian inheritance pattern, can help identify genetic variants with large effect sizes. Although such monogenic disorders are usually responsible for a small fraction of disease, identification of the genes responsible may reveal the association of less severe variants with sporadic disease and shed light on the underlying molecular pathogenesis. Several monogenic disorders cause CSVD, resulting in lacunar infarcts, deep ICH, or WMH [Table 2]. These disorders include cerebral autosomal dominant arteriopathy with silent infarcts and leukoencephalopathy (CADASIL), cerebral autosomal recessive arteriopathy with silent infarcts and leukoencephalopathy (CARASIL), Fabry disease, COL41A-related disorders, and retinal vasculopathy with cerebral leukodystrophy (RVCL).  The recently described autosomal recessive syndrome "deficiency of ADA2" (DADA2) includes small-vessel, lacunar-type stroke, deep hemorrhage, microbleeds, and systemic inflammation in a largely pediatric population.  The putative genetic variants were loss-of-function mutations in CECR1 (cat eye syndrome chromosome region, candidate 1) encoding the adenosine deaminase 2 (ADA2) protein: the same gene implicated in Sneddon's syndrome.
|Figure 3: Strategies for identifying genetic variants. Allele frequency and magnitude of effect determine the most suitable genetic study design. Increasing allele frequency (x axis) is indicated by increasing yellow intensity; increasing genetic variant effect size (y axis) is indicated by increasing blue intensity|
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Variants in genes for these Mendelian disorders have been associated with sporadic disease. Variants in the CADASIL gene Notch3 have been associated with sporadic WMH.  Variants in the Fabry disease gene alpha galactosidase A (or reductions in enzymatic activity) have been found in 1-4% of Portuguese  and Belgian  young stroke populations, and are associated with multiple lacunar infarctions in the Japanese elderly.  Recently, variants in the COL4A1/COL4A2 genomic region were found to be associated with sporadic deep ICH, with nonsignificant trends toward association with lacunar stroke and WMHs.  This result confirms a prior report of COL4A1 mutations in spontaneous late-onset ICH.  Point mutations in TREX1, the gene responsible for RVCL, were found in two apparently sporadic young stroke patients with severe white matter disease and recurrent strokes.  Two brothers heterozygous for a disease-associated mutation of CECR1 presented with typical, albeit aggressively recurrent, lacunar disease in their seventh and eighth decades.  The identification of variants for single-gene disease in sporadic cases (presumably lacking the classic clinical picture) underscores the utility of studying Mendelian disorders to understand sporadic disease.
Candidate gene studies
Candidate gene approaches select genes for study by their potential pathophysiologic mechanism, association with related phenotypes or pathway analyses. Unfortunately, these studies tend to limit candidate selection to pathways that are already presumed important for the phenotype, and have frequently yielded results not confirmed by subsequent studies. Meta-analyses have found the angiotensin-converting enzyme [ACE; Online Mendelian Inheritance in Man (OMIM) 106180] insertion/deletion polymorphism to be associated with ischemic stroke (particularly small-vessel), ICH and WMH with the largest effect in Asian populations. ,, Similarly, the CC polymorphism of methylenetetrahydrofolate reductase (MTHFR; OMIM 607093) has been associated in meta-analyses with large-vessel ischemic stroke and ICH, but not significantly with WMH. ,, Polymorphism of protein kinase C-eta (PRKCH; OMIM 605437) has been associated with lacunar stroke and ICH in a Chinese population.  Recent data associate apolipoprotein E alleles APOE2 and APOE4 (OMIM: 107741) with deep ICH, albeit with smaller effect sizes than lobar ICH due to cerebral amyloid angiopathy, for which these variants are best known. 
Genome-wide association studies
Unlike Mendelian diseases in which a single gene with high penetrance is responsible for disease, in a polygenic model multiple genetic variants, each with a small effect size, contribute to disease risk. This is the likely model for most complex traits and chronic diseases such as CSVD. Genetic and environmental variables such as aging interact with each genetic variant to modify its contribution to disease pathogenesis [Figure 4].
|Figure 4: Genetic and environmental factors interact to influence development and progression of CSVD. CSVD = Cerebral smallvessel disease|
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Complete sequencing of the human genome allows unbiased approaches to evaluate the polygenic model of disease. Such studies simultaneously examine hundreds of thousands to millions of variants with high degrees of genome coverage. Although a given variant may not be causative, a nearby causative variant may be captured, as it is inherited along with the marker: a phenomenon known as linkage disequilibrium.
The genome-wide association study (GWAS) is predicated on the polygenic model. Most GWASs examine polymorphisms present in >5% of the population, since these are the single-nucleotide polymorphisms (SNPs) on most commercially available platforms. The simultaneous testing of hundreds of thousands of associations requires rigorous correction for multiple testing. A P value threshold for significance of <5 × 10 -8 is commonly applied (correcting for 1 million comparisons, as opposed to <0.05 for a single hypothesis or association). 
The consistent association of a SNP with disease (typically through replication) indicates that either that change itself or a tightly linked variant in a nearby position of the genome predisposes to disease. This approach allows identification of genes or molecular pathways not previously suspected to play a role in the pathophysiology of disease. Current GWAS technologies do not evaluate all regions of the genome with equal depth. For example, current commercial platforms cover the APOE region poorly as a biallelic variant. One limitation of GWAS is that the specific functional variants responsible for disease susceptibility are rarely identified, requiring additional effort to confirm the role of that genomic region using fine mapping and functional studies. , Another limitation is that common SNP platforms represent genetic variation well in whites of European descent, but may not capture genetic variation as effectively in other racial/ethnic groups.
GWAS studies have identified a number of loci associated with CSVD phenotypes [Table 3]. Variants at 1q22 have achieved genome-wide significance for deep ICH as well as WMH. ,, Additional loci achieving genome-wide significance for WMH include 17q25, 10q24, 2p21, and 2p16. ,,, Focused GWA analysis at 6p25 identified SNPs associated with WMH strongly influencing expression of the forkhead box C1 transcription factor (FOXC1).  Interestingly, this study also found that human subjects with alterations in FOXC1 and paired-like homeodomain transcription factor 2 (PITX2) (OMIM: 601542) had CSVD, although PITX2 SNPs have only achieved genome-wide significance for cardioembolic stroke.  Despite the high prevalence of lacunar stroke and large-scale meta-analyses of stroke by subtype no SNPs have yet achieved genome-wide significance for lacunar stroke. The greater number of SNPs associated with WMH relative to ICH underscores the power of evaluating intermediate phenotypes while searching for causative variants of complex diseases.
| Sequencing and Rare Variant Analysis|| |
Unbiased approaches can also investigate the rare variant hypothesis that common diseases are due to a multitude of rare variants or private mutations, each with a large effect size. In this model, rare variants from different individuals will cluster preferentially in genes playing a role in the pathophysiology of the disease and cumulatively account for the population burden of disease.
Detection of rare variants is currently feasible using whole-genome or -exome sequencing [Table 4]. Burden and other analysis methods then identify those genes with a preponderance of rare variants.  For example, in Parkinson's disease, rare variants would be expected to cluster in alpha-synuclein (SNCA; OMIM 163890), Parkinson's disease 1 (PARK1; OMIM 168601) and leucine-rich repeat kinase (LRRK2; OMIM 609007) in cases but not controls, implicating these genes in disease pathogenesis. This approach has been piloted in ischemic stroke, including small-vessel (lacunar) disease, with promising results. , Small sample sizes for rare diseases may lack sufficient power to detect association with rare variants. As deep sequencing of the entire genome becomes more common, variants will not be limited to coding regions, requiring a more comprehensive investigation (similar to that currently required for GWAS-identified SNPs) in order to understand their biological effects.  The analytic approaches for whole-exome sequencing have proved computationally intensive, requiring innovation and creativity. Whole-genome sequencing will undoubtedly require further analytic ingenuity. 
| Population-Specific Considerations|| |
Initial commercially available platforms ("SNP chips") were developed based on sequencing information largely derived from individuals of European descent. Genetic variation, and thus genetic risk for disease, may differ among populations. There are several examples of population-specific differences in the genetic architecture of CSVD. An ancestral deletion involving the gene TM4SF20 (OMIM 615404) found exclusively in individuals of Southeast Asian descent predisposes to WMH.  In another example, we found no association of 1q22 SNPs with ICH in Hispanic individuals, despite their robust association in other race/ethnic groups.  Hypertension is a significant risk factor for CSVD, and the frequency of related polymorphisms varies among populations. For example, specific polymorphisms in the alpha-1- and alpha-2-adrenergic receptors are much more common in African Americans. , The relationship between specific genetic polymorphisms and hypertension is complex, but polymorphisms in alpha adrenergic receptors are associated with lacunar infarcts,  and hypertension risk allele burden has been shown to increase ICH risk.  These examples underscore the importance of evaluating genetic differences within specific populations.
| Following Up Regions of Interest|| |
After studies identify regions of interest, the next task is to determine precisely which variant(s) within that particular stretch of DNA predispose to disease. On occasion, the identified SNP is itself the causal variant. In most cases however, the disease-associated SNP is merely "a signpost around which one must do a finer search."  Initial steps in that finer search might include haplotype analysis and deep sequencing of the region. Identifying causal variants can be challenging due to the extensive sequence variation in human DNA and the difficulty in interpreting functional effects of that variation.
Interpretation of DNA variation within the coding sequence of a gene is more straightforward than interpretation of noncoding variants. The altered sequence may cause absent or reduced levels of the protein impeding its normal function, or may cause the protein to gain an additional, abnormal function (often called a dominant negative). Other times, the protein or its RNA message cannot be degraded and becomes toxic. Noncoding variants may affect splicing or stability of the RNA message, or may affect gene expression through alteration in promoter and enhancer regions, DNA methylation, or chromatin structure. Analysis of expression quantitative trait loci (eQTL) determines the effect of a SNP on expression of nearby genes (cis), or genes on different chromosomes (trans). Effects on gene expression are often cell-type-specific  and brain or cerebral vessels are not generally accessible, making gene expression studies in these tissues difficult. Large consortia such as the Genotype-Tissue Expression project (GTEx) are beginning to address this limitation in a systematic fashion. 
Once the genetic variant is identified, defining the context within which it predisposes to a complex disease may still prove challenging. The variant itself is likely not sufficient to cause disease, and will predispose to disease through its interaction with other genetic or environmental factors. Mathematical models are one solution for deciphering this complexity. Another is to identify a molecular or biochemical phenotype associated with the genetic variant, which can then be linked with the disease.  Such intermediate molecular phenotypes may exhibit stronger association with disease, as they are more proximal to the disease state than the genetic variant. 
Finally, experimentation in model systems is required to elucidate the functional role of these genetic and biochemical effectors in development of CSVD. Such experimentation may be carried out in cell culture. The use of induced pluripotent stem cells that can be reprogrammed into disease-specific cell types allows investigation into functional effects of genetic variation in a model that is more biologically relevant than cell lines used previously. In fact, such cells may be induced to form "organoids" reminiscent of particular tissues. , Culture models of the neurovascular unit have yet to be developed, making animal studies particularly useful. Although no animal model perfectly recapitulates human CSVD, the spontaneously hypertensive stroke-prone rat model has a number of pathophysiologic similarities to human CSVD.  With the advent of the clustered regularly interspaced short pallindromic repeats (CRISPR)/Cas9 system, we have the ability to introduce specific genetic variants into cell culture and model systems with great accuracy and speed.  Both cell culture and animal work will be required to elucidate the molecular basis for CSVD and assess the effects of manipulating identified pathways in order to slow or reverse CSVD.
| Future Directions|| |
Genetic studies are a powerful tool for understanding the molecular underpinnings of CSVD. As classification of CSVD phenotypic variation becomes more accurate, additional genes that directly affect clinical risk factors and outcomes will be identified. Maximizing information gained from genetic studies requires collaborative study utilizing thousands of human samples as well as cell culture and animal models to clarify specific disease mechanisms. Additionally, understanding how age and environmental exposures interact with genetic background will greatly enhance our ability to predict CSVD risk. Studying underrepresented minority populations is critical, as CSVD occurs earlier and often with greater severity than in individuals of European descent. Indeed, genetic differences in CSVD susceptibility likely contribute to racial/ethnic disparities in stroke. Once disease mechanisms underlying CSVD are established, this iterative bedside-to-bench-to-bedside approach will be imperative for translating that knowledge into targeted therapies. Perhaps, as we gain a deeper understanding of these complex mechanisms of disease, individual genetic risk profiles may suggest particularly effective medical or dietary therapies to halt CSVD's common and devastating manifestations.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4]
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