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Genetics of common cerebral small vessel disease

Constance Bordes, Muralidharan Sargurupremraj, Aniket Mishra, Stephanie Debette

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Constance Bordes, Muralidharan Sargurupremraj, Aniket Mishra, Stephanie Debette. Genetics of common cerebral small vessel disease. Nature Reviews Neurology, 2022, 18 (2), pp.84-101. 10.1038/s41582-021-00592-8 . hal-03610052

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# Genetics of common cerebral small vessel disease

Constance Bordes¹, Muralidharan Sargurupremraj¹,², Aniket Mishra¹ and Stéphanie Debette¹,³+

¹Bordeaux Population Health Research Center, Inserm U1219, University of Bordeaux, Bordeaux, France

²Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, Texas, USA

³Department of Neurology, Bordeaux University Hospital, Institute for Neurodegenerative Diseases, Bordeaux, France

†email: stephanie.debette@u-bordeaux.fr

**Abstract** | Cerebral small vessel disease (cSVD) is a leading cause of ischaemic and haemorrhagic stroke and a major contributor to dementia. Covert cSVD, which is detectable with brain MRI but not manifest as clinical stroke, is highly prevalent in the general population with increasing age. Advances in technologies and collaborative work have led to substantial progress in the identification of common genetic variants that are associated with cSVD-related stroke (ischaemic and haemorrhagic) and MRI-defined covert cSVD. In this Review, we provide an overview of collaborative studies — mostly genome-wide association studies (GWAS) — that have identified >50 independent genetic loci associated with the risk of cSVD. We describe how these associations have provided novel insight into the biological mechanisms involved in cSVD, revealed patterns of shared genetic variation across cSVD traits, and shed new light on the continuum between rare, monogenic and common, multifactorial cSVD. We consider how GWAS summary statistics have been leveraged for Mendelian randomization studies to explore causal pathways in cSVD and provide genetic evidence for drug effects, and how the combination of findings from GWAS with gene expression resources and drug target databases has enabled identification of putative causal genes and provided proof-of-concept for drug repositioning potential. We also discuss opportunities for polygenic risk prediction, multiancestry approaches and integration with other omics data.
[H1] Introduction

As average lifespans have increased, stroke, cognitive impairment and dementia have become the most common age-related sources of disability and dependence and have a huge societal burden. One in three people who are healthy at 65 years of age will develop stroke, dementia or both during their remaining lifetime¹. Cerebral small vessel disease (cSVD), which encompasses a group of pathological processes that affect small penetrating vessels (arterioles, capillaries and venules) of the brain²,³, makes a major contribution to the occurrence of stroke and dementia. cSVD is a leading cause of ischaemic and haemorrhagic stroke, accounting for up to one third of strokes worldwide⁴⁻⁶. It is also the main pathological substrate of the vascular contribution to cognitive decline and dementia⁷ - the vascular component of dementia is now recognized as a major driver of the pathophysiology⁸,⁹.

Most often, vascular brain injury is covert (not associated with clinical stroke) and caused by underlying cSVD¹⁰⁻¹². Covert cSVD is highly prevalent in elderly people³ and encompasses several entities that are defined by brain MRI abnormalities¹³. The most studied of these abnormalities are white matter hyperintensities (WMHs) of presumed vascular origin (sometimes referred to as leukoaraiosis)¹⁴, lacunes of presumed vascular origin, and cerebral microbleeds¹⁵. In the past 5 to 10 years, novel markers of covert cSVD have emerged that require more elaborate imaging acquisition and/or processing; these markers included perivascular spaces¹⁶,¹⁷, microinfarcts¹⁸,¹⁹ and subtle changes of white matter microstructure detectable with diffusion tensor imaging (DTI)²⁰⁻²². Covert cSVD is a powerful determinant of stroke risk, unfavourable stroke outcome, cognitive decline and dementia¹⁴,²³,²⁴, and therefore represents a major target for the prevention of these conditions and promotion of healthier brain ageing.

The development of cSVD is influenced by a complex mix of genetic and environmental risk factors, among which age and hypertension are currently deemed the most important⁴. However, the cascade of events and molecular pathways that lead to the alterations in the structure and function of small brain vessels that characterize cSVD are largely unknown. Molecular biomarkers for clinical use have not been identified and, besides risk factor (especially blood pressure) management, no mechanism-based treatments are available to prevent cSVD progression and complications. Determining the genetic underpinnings of common, multifactorial cSVD promises to reveal molecular mechanisms
that underlie cSVD and thereby accelerate the discovery of novel biomarkers, drug targets or
drug repositioning opportunities²⁵,²⁶. Exploring the genomics of common cSVD also provides
tools to indirectly examine the effects of known or novel drugs and test causal relationships
of putative risk factors with cSVD. Furthermore, better knowledge of the genetic contribution
to cSVD could help with identification and stratification of individuals who are at high risk of
cSVD and could benefit from tailored preventive interventions (Fig. 1).

In the past 10 years, large, international collaborative work based on high-throughput
genotyping has driven major progress in the identification of common genetic risk variants
that underlie cSVD. In this Review, we provide a detailed overview of the current knowledge
on genetic variants associated with common cSVD, encompassing cSVD-related stroke
(ischaemic and haemorrhagic) and MRI-defined covert cSVD. We will not discuss single-gene
disorders that cause cSVD, which have been reviewed in detail elsewhere²⁷⁻²⁹. We first focus
on the discovery of genetic variants associated with cSVD-related stroke and MRI-defined
covert cSVD before considering the shared genetic variation between cSVD phenotypes and
vascular risk factors, and exploring the continuum between monogenic and multifactorial
forms of cSVD. Finally, we discuss the rapidly accumulating Mendelian randomization studies
of cSVD and reflect on the clinical and therapeutic implications of common cSVD genomics
research.

[H1] Discovery of genetic risk loci

In this section, we summarize the discovery of genetic risk loci for common cSVD, focusing
on robust results derived from large datasets and state-of-the-art methodology, mostly
genome-wide association studies (GWAS, **Box 1**). We also highlight pilot studies of exome
chip and next-generation sequencing approaches to explore rare genetic risk variants for
cSVD.

[H2] cSVD-related stroke

cSVD-related stroke encompasses several distinct but correlated entities: small vessel stroke (SVS; also called lacunar stroke), which results from small subcortical brain infarcts with or without associated moderate to severe confluent leukoaraiosis³⁰; lobar intracerebral haemorrhage (ICH), which originates at the cortex or cortico-subcortical junction and is
commonly associated with cerebral amyloid angiopathy; and non-lobar ICH, which originates
in deep structures of the cerebral hemispheres, the brainstem and the cerebellum, and is
typically associated with hypertensive vasculopathy³¹. Characterization of the genetic
determinants of cSVD-related stroke has been challenging owing to the clinical heterogeneity
of the condition and the small size of well-characterized datasets.

Work by large international consortia, such as the International Stroke Genetics
Consortium (ISGC), has led to substantial progress in the discovery of genes that underlie
cSVD-related stroke. To date, GWAS have identified two risk loci for the occurrence of ICH
(one for lobar ICH and one for non-lobar ICH), two risk loci associated with ICH volume (one
for lobar ICH and one for non-lobar ICH)³¹⁻³³ and seven risk loci for SVS³⁴⁻³⁸ at the genome
wide significance level (P < 5 × 10⁻⁸). The majority of these loci have been identified in
populations of European ancestry (Table 1).

The discovery of risk loci for SVS has been substantially improved not only by an
increase in sample size but also by MRI confirmation of SVS, which was shown to be more
reliable than standard phenotyping³⁴. MRI-confirmed SVS is defined as a clinical lacunar
syndrome with an anatomically compatible infarct on MRI (subcortical and ≤15 mm in
diameter)³⁹, either as a region of high intensity on diffusion-weighted imaging (acute) or as a
region of low intensity on fluid-attenuated inversion recovery (FLAIR) or T1 imaging (non-
acute)¹³, and the absence of causes of stroke other than cSVD³⁴.

Genetic discoveries were further improved by the introduction of multivariate analyses
based on the multi-trait analysis of genome-wide association summary statistics (MTAG)⁴⁰;
this approach involves joint analysis of multiple (genetically correlated) traits, which increases
statistical power to detect genetic associations for each trait (Box 1). With the use of this
method, two additional risk loci were revealed for ICH³³ in a joint analysis with SVS, and seven
additional risk loci for SVD in a joint analysis with WMH³⁴ (Table 1). Most of the identified risk
loci for cSVD-related stroke require definitive confirmation in independent follow-up
samples, even though rigorous methodology was used. Particular caution is warranted for the
additional loci derived from MTAG, as this approach relies strongly on the assumption that
the associated loci act on both traits⁴⁰, so additional sensitivity analyses are required to test
the relative contribution of the included traits.

When univariate and multivariate findings are combined, and the fact that three loci
are shared between ICH and SVS (at chr1q22, chr2q33 and chr13q34) is taken into account, a
total of 17 independent risk loci for cSVD-related stroke have been identified to date. At nearly all of these loci, the lead risk variants are common variants (minor allele frequency ≥5%), except for one low-frequency variant (minor allele frequency 3%) at chr22q13 associated with haematoma volume of lobar ICH³².

With the possible exception of risk loci in or near genes that are known to cause Mendelian forms of cSVD (for example, the *COL4A1*–*COL4A2* and *HTRA1* loci), which are discussed in detail below, the biology that underlies the genetic associations with cSVD-related stroke is still uncertain and requires further investigation. Indeed, although risk loci are often labelled with the name of the gene that is nearest to the lead risk variant, the gene through which risk variants exert their effects could be more distant and cannot be identified with certainty without extensive functional explorations. One such functional exploration is the combination of GWAS findings with those from gene expression quantitative loci eQTL studies (Box 1) in transcriptome-wide association studies (TWAS; Box 1), which can provide important information about putative causal genes. Use of this approach has suggested that the gene closest to the GWAS top hit is often not the causal gene⁴¹.

A combination of transcriptome-wide association studies⁴² and colocalization analyses (Box 1)⁴³ has been used to gain insight into specific genes associated with SVS. These studies rely on existing gene expression databases in brain and vascular tissues that were not specifically designed for the study of cSVD⁴⁴. Nevertheless, they provide important information that enables loci identified in GWAS (some of which encompass a large number of genes) to be refined to a limited set of putative causal genes that can be prioritized for functional follow-up. This approach has indicated that the expression level of several genes is associated with SVS: *SLC25A44* at chr1q22, *ULK4* at chr3p22 and *ICA1L*, *FAM117B*, *CARF* and *NBEAL1* at chr2q33³⁴.

*SLC25A44* encodes a mitochondrial solute transporter of branched-chain amino acids³⁴. Interestingly, another locus associated with SVS also contains the solute carrier gene *SLC39A13* — this gene encodes a zinc transport transmembrane protein, mutation of which causes spondylocheirodysplastic Ehlers-Danlos syndrome, an inherited connective tissue disorder with rare vascular complications⁴⁵. Solute carriers are known to play a major role in cerebrospinal fluid secretion and transport of various substances at the blood–cerebrospinal fluid barrier⁴⁶ and the blood–brain barrier⁴⁷. Their expression is enriched in human blood–
brain barrier tissue and they are thought to have a role in xenobiotic transport<sup>48,49</sup>. Solute carriers could, therefore, be relevant in cSVD because dysfunction of the blood-brain barrier is thought to be an important feature of the condition and to play a deleterious role by promoting inflammation, inhibiting myelin formation and repair processes, and contributing to stiffening vessel walls<sup>12</sup>.

ULK4 encodes a serine-threonine protein kinase<sup>34</sup> that has a role in neurogenesis in mice<sup>50</sup> and deficiency of which results in hypomyelination<sup>51</sup>. In UKL4 mutant mice, major transcription factors involved in oligodendrogenesis are downregulated and myelination is reduced by 50%, demonstrating that ULK4 is an important regulator of myelination<sup>51</sup>. Neuropathological and experimental studies suggest that myelin injury is an important and early feature of cSVD<sup>52,53</sup>. At the chr2q33 locus, TWAS and colocalization analyses suggested that expression levels of both ICA1L and NBEAL1 are associated with cSVD, in brain tissue for ICA1L and vascular tissue for NBEAL1<sup>44</sup>. ICA1L encodes islet cell autoantigen 1 like and mutations in this gene cause juvenile amyotrophic lateral sclerosis<sup>54</sup>. NBEAL1 encodes neurobeachin-like 1 protein, which regulates cholesterol metabolism by modulating expression of low-density lipoprotein-receptor expression<sup>55</sup>. Further studies are needed to explore the potential functional links of these genes with cSVD.

We have deliberately chosen not to discuss candidate gene association studies (Box 1) owing to methodological flaws in the majority of these studies. Nevertheless, two risk loci for cSVD-related stroke that were identified by GWAS had already been described in large candidate gene association studies. These were the association of the APOE ε2 and ε4 alleles (at chr19q13) with an increased risk of ICH<sup>56</sup> and the association of the COL4A1-COL4A2 locus (at chr13q34) with ICH<sup>57,58</sup> and SVS<sup>58</sup>.

## [H2] MRI markers of covert cSVD

Large collaborative projects conducted by the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the ISGC in combination with the large UK Biobank brain imaging dataset have led to substantial progress in the discovery of genes associated with MRI markers of covert cSVD. Genetic associations with WMHs are the most extensively studied. Some studies have also been done to explore the genetic underpinnings
of MRI-defined brain infarcts, lacunes, cerebral microbleeds and DTI markers of white matter integrity, such as fractional anisotropy and mean diffusivity. These DTI markers are less specific than the aforementioned, more traditional MRI markers of cSVD but can reveal alterations at a young age, and some evidence suggests that they precede the occurrence of WMHs in cSVD⁵⁹. Overall, GWAS of quantitative MRI markers of cSVD traits (WMHs and DTI metrics) that largely rely on automated measurements have been better powered than similarly sized GWAS of binary traits (brain infarcts, lacunes and cerebral microbleeds) that are assessed with semi-quantitative visual rating.

To date, GWAS have identified a total of 44 independent genetic risk loci for MRI markers of covert cSVD at the genome wide significance level, in 35 regions (multiple independent signals have been identified at some loci). Twenty-nine genomic regions are associated with larger WMH volume²¹,²²,⁴⁴,⁶⁰⁻⁶³; two independent signals were identified in four of these regions and three independent signals were identified in one region, leading to 34 independent associations. Two loci, at *NOS3* (chr7q36) and *TSPAN14* (chr10q23) were specifically associated with periventricular WMH volume (not with overall or deep WMH volume) at genome-wide significance (**Table 2**). Seven genomic regions are associated with reduced fractional anisotropy, and two independent signals were identified in one of these regions, leading to eight independent loci²¹; six loci are associated with higher mean diffusivity²¹, one with the risk of cerebral microbleeds⁶⁴ and two with the risk of brain infarcts⁶⁵ (**Table 2**). Again, associations were observed predominantly in populations of European ancestry and all lead single-nucleotide polymorphisms (SNPs) were common variants. According to gold standards, replication of these associations in independent samples of adequate size is required for definitive confirmation, but maximizing the size of discovery samples has so far been prioritized.

The biology underlying genetic associations with MRI markers of covert cSVD remains largely elusive. However, TWAS combined with colocalization analyses have provided preliminary evidence that expression of many genes is associated with WMH volume: *CALCR* at chr2q32; *ICA1L*, *CARF*, *NBEAL1* and *KRT8P15* at chr2q33; *DEGS2* and *CCDC88C* at chr14q32; *DCAKD*, *NMT1*, *NT5C3B* and *STAT5B* at chr17q21; and *TRIM47*, *TRIM65*, *WBP2* and *RP11-552F3.9* at chr17q25²¹,⁴⁴.
CALCRL encodes a component of the calcitonin gene-related peptide receptor (CGRP), and low levels of the CALCRL transcript in arterial and nerve tissue and high levels in blood were associated with larger WMH volume. At the chr2q33 locus, three of the identified genes<sup>44</sup> are the same as genes identified in the SVS (ICA1L, CARF and NBEAL1)<sup>34</sup>. DEGS2 encodes a bifunctional enzyme involved in the biosynthesis and metabolism of sphingolipids, which have been associated with white matter integrity<sup>66</sup> and stroke outcome<sup>67</sup>, and a common missense variant in DEGS2 is associated with cognition in schizophrenia<sup>68</sup>. CCDC88C encodes a coiled-coil domain-containing protein that acts as a negative regulator of the Wnt signalling pathway, mutations of which are known to cause autosomal recessive hydrocephalus<sup>69</sup>. The canonical Wnt-β-catenin pathway plays an important role in regulating blood-brain barrier integrity and function<sup>70</sup>, is involved in response to vascular lesions or ischaemic events in vascular cells<sup>71</sup>, and seems to be involved in oligodendrogenesis and myelination<sup>72</sup>, all of which are factors in cSVD. More work is needed to fully understand the functional roles of these genes in cSVD.

The chr17q21 region is intriguing, as it lies within a region of high linkage
disequilibrium that spans ~1.8 megabases and contains a 900 kb inversion polymorphism
that encompasses MAPT (which encodes microtubule associated protein tau) and other
genes, including CRHR1 and KANSL1<sup>73</sup>. This inversion polymorphism has been associated
with brain disorders<sup>74-79</sup>, cognitive<sup>80</sup> and behavioural<sup>81-83</sup> phenotypes and brain MRI
markers<sup>84-86</sup>. The lead WMH risk variants at chr17q21 are located upstream of the inversion
polymorphism, in an intron of NMT1 and near DCAKD, NT5C3B and STAT5B, whereas the
lead mean diffusivity risk variant in this region lies in an intron of MAPT. Interestingly TWAS
and colocalization analyses revealed transcriptome-wide associations of WMH volume not
only with NMT1, DCAKD, NT5C3B and STAT5B but also with MAPT, CRHR1 and KANSL1<sup>21,44</sup>,
highlighting the distant cis-effect conferred by an SNP-eQTL pair. DCAKD encodes
dephospho-CoA kinase domain containing protein and has a putative role in
neurodevelopment<sup>87</sup>. NMT1 encodes N-myristoyltransferase 1, which is thought to affect
astrocyte function<sup>61</sup> and is involved in early embryogenesis<sup>87</sup>. eQTL analyses have indicated
that DCAKD and NMT1 are also associated with fractional anisotropy and mean diffusivity in
multiple white matter tracts<sup>87</sup>. Chromosomal rearrangements are common in this dynamic
region<sup>88,89</sup>, so the study of structural variants could provide insight into the risk architecture
of this pleotropic and complex genetic locus.
The chr17q25 locus is the most significant risk locus for MRI markers of covert cSVD, specifically for WMH volume, and its association has been replicated in multiple independent European and Japanese samples⁹⁰⁻⁹². In this gene-rich region (40 genes within 500 kb of the lead variant), *TRIM47*, *TRIM65*, *WBP2* and *RP11-552F3.9* are the only significantly associated genes in TWAS and colocalization studies²¹,⁴⁴, which makes them prime candidates for causal genes (**Box 1**). *TRIM47* and *TRIM65* encode E3 protein ligases, which are crucial regulatory proteins in multiple signalling pathways. *WBP2* is involved in the aforementioned Wnt-β-catenin signalling pathway. Extensive functional studies are warranted to identify the causal gene or genes at this locus. Use of exome chip data⁹³ has also revealed an association of two low-frequency missense variants in *MRPL38*, which is located at chr17q25.1, with WMH burden⁹³, but this association requires confirmation.

Finally, data from a study published in 2020 provide important insight into the lifetime effects of genetic risk factors for cSVD⁴⁴. Risk variants associated with extensive WMH volume in old age (in aggregate and individually, most prominently a variant in *VCAN*) were already associated with subtle changes in white matter microstructure detectable with diffusion-weighted imaging in adults in their twenties. This finding suggests that biological pathways contributing to WMHs in old age already have an impact on brain microstructure at a very young age⁴⁴. The changes could reflect a very early stage of cSVD or could indicate pleiotropy between genes associated with cSVD and those that influence white matter maturation, which peaks in early adulthood.

## [H2] Biological insights

More work is required to precisely decipher the molecular pathways that underlie the observed genetic associations with cSVD, but the existing data provide some preliminary indications. However, several biological processes are implicated by genetic associations, including the structure and function of the extracellular matrix (ECM), myelination and membrane transport (**Table 3**). In addition, some genes associated with cSVD are also associated with other brain disorders, such as Alzheimer disease and psychiatric disorders, suggesting either mechanistic overlap or genetic pleiotropy.
[H3] Molecular pathways

A considerable number of genetic associations point to an important role for genes that are involved in the structure and function of the extracellular matrix (ECM; **Table 3**), including: genes that can harbour common variants that predispose to cSVD or rare mutations that cause monogenic cSVD (*COL4A2* and *HTRA1*); genes that TWAS and colocalization analyses suggest are causally implicated in cSVD (*EFEMP1*, *ADAMTSL4*, *VCAN*); and genes that are in the immediate vicinity of loci associated with cSVD and covert cSVD (*GPR126*, *LOX*, *SH3PXD2A*, *NID2*, *FBN2* and *HBEGF*). The roles of these genes in the ECM indicate overlap with mechanisms of monogenic SVD, even for genes that are not known to harbour mutations that cause monogenic cSVD. For example, in cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL; a form of monogenic cSVD caused by *HTRA1* mutations), accumulation of versican core protein, encoded by *VCAN*, has been observed in the thickened arterial wall⁹⁴. Similarly, application of heparin-binding EGF-like growth factor, which is encoded by *HBEGF*, in mouse a model of CADASIL (the most common monogenic cSVD caused by *NOTCH3* mutations) rescued reduced myogenic responses of pial and parenchymal arteries through functional upregulation of *Kv1* channels at the plasma membrane of arterial smooth muscle cells⁹⁵. Thus, in line with converging evidence from monogenic SVD, perturbations of the ECM also seem to play a central role in common cSVD pathophysiology⁹⁶.

Specifically, perturbations of the cerebrovascular matrisome have been proposed as a convergent pathological pathway in monogenic cSVD and preliminary indirect evidence suggests that this also applies to common cSVD⁹⁶. The term matrisome refers to the ensemble of proteins that make up the ECM together with proteins that are associated with the ECM⁹⁶,⁹⁷, which seem to be expressed in an organ-specific pattern. Preliminary characterization of the matrisome of pial arteries and brain microvessels indicates the presence of several proteins encoded by genes associated with cSVD (*COL4A2*, *HTRA1*, *ADAMTSL4*, *NID2*, *FBN2* at pial arteries and *COL4A2*, *VCAN*, *NID2* at microvessels), and others by genes (*EFEMP1* and *LOX*) that are involved in the cerebrovascular matrisome⁹⁶.

Risk loci for common cSVD also indicate involvement of genes that are known to be involved in myelination: *ULK4*, *VCAN* and *ADGRG6* (**Table 3**). As described above, *ULK4* is an important regulator of myelination⁵¹. Mutations of *adgrg6* in zebrafish and mice lead to
Schwann cell myelination dysfunction in the PNS⁹⁸,⁹⁹ and *VCAN* encodes versican core protein, which can form complexes that inhibit oligodendrocyte maturation and affect myelination¹⁰⁰. Combined TWAS and colocalization analyses have also identified significant associations of *ULK4* and *VCAN* expression with cSVD in relevant tissues⁵¹,¹⁰⁰, indicating causal involvement of these genes. Interestingly, in zebrafish, mutations of *adgrg6* lead to a failure to down-regulate *versican*¹⁰¹,¹⁰², indicating that *ADGRG6* might be involved via downstream effects on *VCAN* expression.

Several cSVD lead risk variants also involve genes that encode membrane transport proteins, including ion channel proteins (*KCNK2*, encoding a potassium channel expressed most prominently in fetal brain) and the solute carrier proteins (*SLC25A44*, *SLC39A13*) discussed above³⁴,⁴⁴ (**Table 3**). Interestingly, in a genetic model of cSVD (CADASIL), potassium channelopathy-like defects were shown to underlie early-stage cerebrovascular dysfunction, although these did not directly involve *KCNK2*⁹⁵. Further research is needed to explore the possible involvement of these genes in cSVD.

Analysis of brain-specific, single-cell expression data in mice has revealed that genes within loci associated with WMHs are enriched in endothelial cells⁴⁴. Interestingly, endothelial cell dysfunction has been described in the spontaneously hypertensive stroke-prone rat model of cSVD, — vascular tight junctions were compromised, and endothelial dysfunction impaired oligodendrocyte maturation and, consequently, myelination, and increased microglial activation¹⁰³. Nominally significant enrichment of genes within WMH risk loci has also been observed in vascular leptomeningeal cells, oligodendrocytes, oligodendrocyte precursors and ependymal astrocytes⁴⁴, suggesting that these cell types are also involved in cSVD pathophysiology.

## [H3] Associations with other brain disorders

Several risk loci associated with covert cSVD are near genes that have a role in the pathogenesis of Alzheimer disease (AD) or psychiatric disorders, which can be associated with features of cSVD. For AD, these loci include *APOE*, *ECHDC3*, *HAAO*, *MAPT* and *SH3PXD2A*. The *APOE ε4* allele is the most important genetic risk factor for AD¹⁰⁴. *ECHDC3* has been associated with AD in multi-ancestry GWAS and its expression is altered in the brains of
people with AD compared with controls without AD<sup>105</sup>. HAAO is involved in the kynurenine pathway, which has been implicated in the pathogenesis of neurodegenerative disorders, including AD<sup>106</sup>. MAPT encodes microtubule associated protein tau, which is a key protein in AD pathogenesis<sup>107</sup>. SH3PXD2A is involved in neurotoxicity of amyloid-β, which is widely accepted as the pathological protein that drives AD<sup>108</sup>. However, whether these associations reflect pleiotropy, whereby some genetic risk loci independently influence cSVD and AD, or whether they point to shared mechanisms or mediating effects between both diseases cannot be inferred from these data<sup>23,44</sup>.

Some loci identified in TWAS analyses as being associated with cSVD have also been associated with psychiatric disorders<sup>21</sup>. Most of them (PLEKHM1, CRHR1-IT1, DND1P1 and KANSL1-AS1) are located in the chr17q21 region described above and have been associated with depressive symptoms and neuroticism<sup>109</sup>. Others (BTN3A2<sup>110</sup>, MICA<sup>111</sup> and ZNF165<sup>111</sup>) have been associated with schizophrenia. These genes were mainly associated with fractional anisotropy and mean diffusivity<sup>21</sup>, suggesting that DTI markers could capture alterations in brain networks that are involved in the genesis of psychiatric disorders. How these genes may be involved in cSVD requires further investigation.

## [H1] Shared genetic variation

Use of linkage disequilibrium score regression (Box 1) has revealed significant genetic correlation (reflecting shared genetic determinants at the genome-wide level) between WMH volume and all stroke, ischaemic stroke, SVS, and lower general cognitive function<sup>44</sup>. Strong evidence also indicates significant shared genetic risk factors between WMH, fractional anisotropy and mean diffusivity<sup>21</sup>.

Considerable overlap also exists between the genome-wide significant risk loci associated with different cSVD-related phenotypes (Fig. 2A). From a total of 52 independent genome-wide significant risk loci for cSVD phenotypes, 34% were associated with at least two distinct cSVD traits at the genome-wide significance level. Some genomic regions are particularly pleiotropic: the chr16q24 locus at ZCCHC14 is associated with WMH<sup>21,44</sup>, fractional anisotropy<sup>21</sup> and SVS<sup>36,37,112</sup>; the chr19q13 locus at APOE is associated with WMHs<sup>21,44</sup> ICH<sup>31</sup> and cerebral microbleeds<sup>64</sup>; the chr2q33 locus at ICA1L, CARF and NBEAL1 is
a risk locus for fractional anisotropy, WMH<sup>21,44,61</sup> and ICH<sup>33</sup>; and the chr5q14 locus at VCAN is
associated with WMHs, fractional anisotropy and mean diffusivity<sup>21</sup>.

We systematically explored the overlap between published genome-wide significant risk loci for cSVD (brain infarcts, cerebral microbleeds, fractional anisotropy, mean diffusivity, ICH and SVS) and variants associated with established vascular risk factors (systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure<sup>113</sup>, type 2 diabetes mellitus<sup>114</sup>, low-density lipoprotein [LDL] cholesterol, high-density lipoprotein [HDL] cholesterol, triglycerides<sup>115</sup>, BMI<sup>114</sup> and lifetime smoking index)<sup>116</sup>. Briefly, variants located in close proximity (±250 kb) to the lead cSVD risk variants and variants in moderate to high linkage disequilibrium (r<sup>2</sup> >0.5) with these lead SNPs were explored for significant associations with the vascular risk factors, while accounting for the number of independent cSVD risk variants and vascular risk factors tested **(Fig. 2B)**. Of the 29 WMH risk loci, 18 (62%) were significantly associated with at least one other vascular risk factor<sup>2,44</sup>, especially blood pressure traits (59%), most often at genome-wide significance. A much smaller proportion (25–29%) of overlap was observed for fractional anisotropy and mean diffusivity (**Fig. 2B**), a finding that can at least partly be attributed to the smaller sample size available for these traits. The cSVD risk locus with the most prominent ‘vascular pleiotropy’ was at chr2q33 (ICA1L, CARF and NBEAL1) and was significantly associated with smoking, BMI, type 2 diabetes mellitus, lipid levels and blood pressure (**Fig. 2B**).

Importantly, the relationship of pleiotropic loci with cSVD and vascular risk factors is
not necessarily driven by the same causal variant and does not imply mediation of the
genetic association with cSVD by the vascular risk factors. Moreover, 25 of 52 cSVD risk loci
are not shared with any vascular risk factor, suggesting novel mechanistic pathways. These
loci are involved in organization of the ECM (HTRA1, NID2, VCAN, LOX, FBN2, GPR126),
myelination (VCAN, GPR126), membrane transport (KCNK2), and loci that are known to be
target genes for microRNA-mediated silencing (DENND1B, KCNK2, ZDHHC20, ZCCHC14,
FOXQ1, NID2, VCAN)<sup>117</sup>.

Of note, brain MRI features of cSVD with or without clinical symptoms are not entirely
specific for underlying cSVD. For example, in rare instances, lacunes or SVS could be caused
by emboli that originate from undetected cardiac sources or parent artery atheroma. GWAS
have identified no overlap of genome-wide significant risk loci for SVS with cardioembolic
stroke, large artery stroke or atrial fibrillation³⁶, but significant genetic correlation was
observed between large artery stroke and SVS, suggesting that some genetic contribution to
these phenotypes is shared¹¹⁸. Further research is needed to differentiate causally distinct
subtypes of lacunes and SVS, and use of more descriptive terminology that does not imply
aetiology has been recommended; for example, 'recent small subcortical infarcts' to refer to
neuroimaging evidence of recent infarction in the territory of one perforating arteriole¹³.
Composite brain imaging phenotypes that combine lacunes and recent small subcortical
infarcts with extensive WMH have also been proposed as a way to increase specificity for
cSVD³⁰,¹¹⁹.

[H1] Continuum with monogenic cSVD

Mounting evidence suggests a continuum between the genetic causes of monogenic cSVD
and of common cSVD²⁷. In particular, genetic variants that affect the *HTRA1* and *COL4A1*-
*COL4A2* loci have been associated with monogenic and common forms of cSVD¹²⁰,¹²¹.

Mutations in *HTRA1* cause CARASIL, a rare autosomal recessive form of cSVD that is
caused by loss of function of *HTRA1* and has been reported mainly in the Japanese
population⁹⁴, and *HTRA1*-autosomal dominant cSVD, caused by dominant negative
mutations that lead to decreased serine protease HTRA1 activity¹²². Mutations in the *COL4A1*
and *COL4A2* genes lead to impaired deposition of collagen in vessel walls and can cause
collagen 4A1/A2 microangiopathy, an autosomal dominant cSVD that was initially thought to
primarily cause ICH. However, the spectrum of phenotypes caused by *COL4A1* and *COL4A2*
mutations seems to be very broad, including specific entities such as hereditary angiopathy,
nephropathy, aneurysms and cramps (HANAC), pontine autosomal dominant
microangiopathy and leukoencephalopathy (PADMAL), and multi-infarct dementia of the
Swedish type²⁷,¹²³,¹²⁴.

Common variants in or near *HTRA1* and the *COL4A1-COL4A2* locus have been
associated at the genome-wide significance level with WMH burden (*COL4A1*-
*COL4A2*)²¹,⁴⁴,⁶⁰,⁶¹, with ICH in multi-trait analyses with SVS (*COL4A1-COL4A2*)³³ and with SVS
in multivariate analyses accounting for WMH (*COL4A1-COL4A2* and *HTRA1*) (**Tables 1 and
2*)³⁴. In addition, well-powered candidate gene studies had previously identified associations
of common variants at the *COL4A1-COL4A2* locus with ICH⁵⁷,⁵⁸, SVS⁵⁸ and WMH burden¹²⁵,
and of a common variant near *HTRA1* (also an eQTL of *HTRA1*) with SVS and a composite MRI-defined extreme cSVD phenotype with extensive WMH burden and/or presence of lacunes<sup>119</sup>. In the largest published GWAS for stroke, which involved >67,000 patients, common variants at the *HTRA1* and *COL4A1-COL4A2* loci were also associated with any stroke<sup>36,126</sup>, probably reflecting an underlying contribution of cSVD.

Another locus that causes monogenic cSVD and is associated with common cSVD is the *SH3PXD2A-STN1* locus at chr10q24. Mutations in *STN1* cause cerebroretinal microangiopathy known as Coats-plus syndrome<sup>127</sup>. Common variants at the *SH3PXD2A-STN1* locus have been associated at the genome-wide significance level with WMH burden, mean diffusivity, and SVS in a multitrait GWAS with WMH burden (Tables 1 and 2).

Published GWAS of cSVD phenotypes have not revealed any genome-wide significant associations with common variants in or near *NOTCH3*, mutation of which causes cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), the most prevalent monogenic form of cSVD<sup>27</sup>. However, a population-based candidate gene studyidentified a nominally significant association of common variants in or near the *NOTCH3* locus with WMH volume and WMH progression in participants with hypertension<sup>128</sup>, and a candidate whole-exome sequencing study demonstrated a significant association between the burden of protein-modifying rare or low-frequency variants in *NOTCH3* and extreme cSVD in people aged ≥65 years<sup>119</sup>. In addition, cysteine-altering rare variants in *NOTCH3* that are typical in CADASIL are much more common in community exome biobanks (1 in 300) and in elderly people in the community (1 in 300-500)<sup>119,129-131</sup> than expected on the basis of the reported prevalence of CADASIL (~4 in 100,000<sup>120,121</sup>), especially variants in EGFr domains 7-34 of *NOTCH3*, which are associated with milder CADASIL phenotypes than mutations in EGFr domains 1-6, which cause severe CADASIL phenotypes<sup>130</sup>. Interestingly, in over 200,000 participants of the population-based UK Biobank, these 'asymptomatic' cysteine-altering *NOTCH3* variants were associated with an increased risk of stroke and vascular dementia, increased WMH volume and an increased prevalence of lacunes and cerebral microbleeds<sup>131</sup>, accounting for an increased burden of apparently 'sporadic' stroke and dementia<sup>131</sup>.

Another important locus that has been associated with both monogenic and multifactoral cSVD is *FOXF2-FOXC1*. Common variants near *FOXF2* have been associated with
all stroke, with a strong indication that the signal was driven by underlying cSVD<sup>132</sup>, and with
SVS at genome-wide significance in a multitrait analysis with WMH<sup>34</sup>. *FOXF2* is located near
*FOXC1*, mutations or copy number variations in which cause Axenfeld–Rieger syndrome, a rare
monogenic ophthalmological condition co-occurring with cerebral lesions seen in cSVD. The
WMH burden is nearly tenfold greater among patients with segmental deletions that
encompass both *FOXC1* and *FOXF2* than in patients with deletions of *FOXC1* only<sup>132</sup>,
suggesting involvement of *FOXF2* in cSVD pathogenesis. Further supporting this conclusion,
deletion of *Foxf2* in adult mice leads to cerebral infarction, reactive gliosis and
microhaemorrhage, and cerebral vessels of mutant *foxf2b*<sup>-/-</sup> zebrafish have decreased smooth
muscle cell and pericyte coverage, suggesting that the mutation leads to defects in
differentiation of cerebral vascular mural cells<sup>132</sup>. Interestingly, although *FOXF2* does not
directly encode an ECM protein, its protein product, forkhead box protein F2 (FOXF2) is also
involved in vascular stability<sup>132-135</sup> and blood–brain barrier formation<sup>136</sup>. In the context of
cancer, FOXF2 modulates the degradation of the ECM by activating metalloproteinase
inhibitor 3 (TIMP3)<sup>137</sup>, which has an important role in CADASIL pathophysiology<sup>96</sup>.

[H1] Mendelian randomization studies

Mendelian randomization is an instrumental variable analysis that can be used to investigate
the likelihood that an exposure contributes to the causal pathway of a specified outcome
(Box 2)¹³⁸. Numerous Mendelian randomization studies have been done to explore the causal
relationships of putative risk factors and biomarkers with cSVD, and the list is continuously
growing (Fig. 3). We acknowledge that the following summary of Mendelian randomization
studies might soon be outdated, given the rapid growth of this body of literature. Moreover,
we have not systematically reported how potential biases in estimating the causal inference
have been addressed, as methods used differ across studies and a comprehensive description
of these is beyond the scope of this Review.

Mendelian randomization has been used in a number of studies to explore the causal
relationship between vascular and lifestyle risk factors and cSVD phenotypes. Although these
factors are established risk factors for other common types of ischaemic stroke, especially
large artery stroke, their relationship to cSVD-related stroke and MRI-defined covert cSVD
has been controversial. The most undisputed association, which has been confirmed by
Mendelian randomization approaches<sup>44,139</sup>, is the strong relationship between high blood pressure and most cSVD phenotypes. Importantly, genetically predicted systolic and diastolic blood pressure were associated with WMH volume even in participants without clinically defined hypertension (systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg or antihypertensive drug intake), highlighting that higher blood pressure is likely to be causally associated with covert cSVD even at blood pressures below the thresholds that typically define hypertension<sup>44</sup>.

cSVD phenotypes also seem to be associated with genetically determined lipid levels, but these associations are more complex. Mendelian randomization studies have shown that genetically predicted higher levels of HDL-cholesterol are associated with a lower risk of SVS<sup>36,140,141</sup> and, in some studies, lower WMH burden<sup>141</sup>. However, genetically predicted higher levels of triglycerides were associated with a higher risk of SVS and cerebral microbleeds<sup>64,140,141</sup>. Genetically determined high levels of LDL-cholesterol have been associated with an increased risk of SVS and a lower risk of ICH in some studies<sup>140,142</sup> but not all<sup>34,141,143</sup>. Single studies have identified significant associations of genetically predicted higher levels of lipoprotein (a)<sup>144</sup> and apolipoprotein A-I<sup>140</sup> with a lower risk of SVS and higher levels of apolipoprotein B with an increased risk of SVS<sup>140</sup>.

Mendelian randomization studies have also been done to explore the relationship between type 2 diabetes mellitus and cSVD. Several studies of partly overlapping cohorts of patients with SVS have shown associations between genetically predicted type 2 diabetes mellitus and SVS<sup>34,36,145-147</sup> and no significant association of diabetes with ICH<sup>145,147</sup>. The largest of three studies<sup>44,145,147</sup> showed a significant association of genetically predicted type 2 diabetes mellitus with larger WMH volume, although residual pleiotropy was observed after removal of outlier variants. In the UK Biobank, genetically predicted type 2 diabetes mellitus has been associated with lower mean fractional anisotropy<sup>145,147</sup>.

Mendelian randomization studies have also identified at least one significant association of several other factors with cSVD, including other vascular risk factors<sup>36,44,64,65,125,145-149</sup>, lifestyle factors such as diet<sup>36,44,150-154</sup>, comorbidities<sup>36,44,155,156</sup> and some circulating biomarkers<sup>157-161</sup> (Fig. 3). In most of these Mendelian randomization studies, two-sample Mendelian randomization has been used (Box 2), and where several studies have been published on the same exposure–outcome relationship, they were often based on the same or overlapping datasets. Discrepancies in results between studies are mostly related to
variation in the power of the summary statistics used for the outcome (for example, use of
results from the earlier METASTROKE GWAS or the more recent MEGASTROKE SVS GWAS), in
the definition of genetic instruments, or in the extent to which residual pleiotropy or reverse
causation was addressed.

[H1] Clinical and therapeutic implications

Clinical interpretation of the aforementioned genetic risk loci for cSVD requires caution, as
most of them need independent replication and/or functional validation in future studies.
Transitioning from discovery of genetic risk loci to robust identification of affected genes and
pathways that underlie cSVD pathophysiology is one of the major challenges ahead. This
process is likely to require various complementary approaches, such as next-generation
sequencing, multiancestry fine-mapping, systems genetics, multiomics, single-cell genomics,
bioinformatics approaches and, importantly, experimental follow-up. Such experimental
follow-up could range from high-throughput assays that provide functional readouts for
putative regulatory elements and variants, such as Massively Parallel Reporter Assays
(MPRA)¹⁶², to elaborate, customized experiments in various model organisms, such as
zebrafish or mice¹³². Below, we consider the potential of common cSVD genomics for
eventual clinical and therapeutic application.

Although still in its infancy, bioinformatics and experimental follow-up of loci
identified in GWAS can inform drug development, with the potential to accelerate drug
discovery through the identification of novel drug targets, indirect measurement of drug
effects using genetic instruments, and by providing evidence for drug repositioning (Fig.
1)²⁶,¹⁶³. Strong bioinformatics and preliminary experimental evidence support the involvement
of several genes in the pathophysiology of cSVD that are currently being followed up
experimentally to confirm their involvement and, when relevant, explore their potential as
drug targets for cSVD. Moreover, increasingly rich molecular and bioinformatics resources are
being used to generate additional evidence on the cSVD risk loci identified to discover
further candidates for experimental follow-up.

With the increasing popularity of Mendelian randomization, there is also growing
interest in the use of genetic instruments to mimic the effect of drugs of interest²⁵. In this
approach, genetic instruments from genetic regions involved in the production of a drug
target are prioritized when constructing instruments for a given exposure (for example ACE, which encodes a target for ACE inhibitors which lower blood pressure, would be prioritized when constructing instruments for high blood pressure)¹⁶⁴. It has been estimated that genetic support of drug effects through such Mendelian randomization approaches could double the success rate of clinical trials²⁵. Mendelian randomization can also be used as an indication for preferential effects of certain drug classes on a given phenotype, keeping in mind methodological limitations and caveats and the need for validation in a trial setting. For instance, genetic proxies for calcium channel blockers — that is, in genetic instruments that mimic the effects of calcium channel blockers — but not β-blockers were associated with a lower risk of SVS and WMH¹³⁹, though some evidence indicated an association with diverticulosis¹⁶⁴. Similarly, genetic proxies for cholestylester transfer protein (CETP) inhibitors (which raise levels of HDL) were shown to be associated with a lower risk of SVS and a lower WMH burden, whereas variants at the CETP locus were associated with a higher risk of ICH (both deep and lobar)¹⁴¹. Confirmation of these findings in a clinical trial setting is required.

Mendelian randomization can also be used to explore pathways of therapeutic relevance. For example, Mendelian randomization studies have shown that a predisposition to β-cell dysfunction was associated with SVS and ICH, which might have implications for use of anti-diabetic treatments to target these mechanisms¹⁴⁵. A Mendelian randomization approach based on data from the MEGASTROKE GWAS identified a significant association between a weighted genetic risk score for venous thromboembolism and all ischaemic stroke subtypes except SVS, despite similar or larger power across subtypes, suggesting that thrombotic processes play a less important role in SVS. This insight has potential implications for therapeutic research on antithrombotic treatment in cSVD³⁶,¹⁶⁵.

Drug target enrichment analyses can be used to seek evidence for drug repositioning opportunities. For instance, this approach has been applied to the largest WMH GWAS to quantify the enrichment of the 39 associated genes that emerged from the TWAS-colocalization analysis. Use of curated drug-target lists classified on the basis of the International Classification of Diseases 10 (ICD10)⁴⁴ showed that of the 39 genes, four (*MAPT*, *CRHR1*, *CALCRL* and *NOTCH4*) were registered as targets of approved drugs in the DrugBank database and the Therapeutic Target Database. *MAPT* is a drug target under investigation for neurodegenerative disorders and TWAS suggested that larger WMH volume is associated
with upregulated MAPT expression. CGRP, the product of CALCRL, is targeted by monoclonal
antibodies in the treatment of migraine⁴⁴.

Findings from GWAS also have the potential to enable risk stratification and risk
prediction of cSVD and its major complications (stroke and dementia)¹⁶⁶. In epidemiological
studies, MRI markers of covert cSVD have repeatedly been associated with an increased risk
of developing stroke (ischaemic and haemorrhagic) and dementia, including AD²³. Extensive
WMH burden has also been shown to predict amyloid-β positivity after the age of 65 years,
although the causal relationship between cSVD and AD was uncertain¹⁶⁷. Mendelian
randomization has provided evidence for an association of larger genetically determined
WMH volume with an increased risk of ischaemic stroke, intracerebral haemorrhage and
Alzheimer type dementia, supporting causality⁴⁴. Covert cSVD, therefore, represents a major
opportunity to detect individuals who are at risk of developing major neurological events and
could benefit from targeted preventive interventions. However, risk stratification in people
with cSVD is lacking, as is accurate, quantitative estimates of the ability of the full covert cSVD
spectrum to predict the risk of stroke and dementia. This estimate needs to account for
several methodological challenges, such as time-varying exposure, competing risk of death,
and modifying factors such as coexisting neurodegenerative disease and vascular
comorbidity. Integration of cSVD genetic risk data into risk stratification and risk prediction
algorithms is becoming more and more relevant, as increasingly powerful genetic
instruments are available that at least partly address the aforementioned challenges.

Recent work has highlighted the potential of polygenic risk scores (PRS) for prediction
of common diseases¹⁶⁸,¹⁶⁹. PRS aggregate small effects of independent genetic variants
associated with a disease (at varying significance thresholds) into a single score, which
reflects the cumulative genetic risk of a disease¹⁷⁰. For some common diseases, use of PRS
has identified individuals with a risk equivalent to that for people with a monogenic mutation
that causes the diseases; for example, PRS showed that 8% of the population with the largest
number of risk alleles for coronary artery disease had a threefold higher risk of developing
this condition than the remainder of the population¹⁷¹. A novel approach called metaGRS
combines several PRS into the same model and enables greater risk discrimination, including
for stroke¹⁷². UK biobank participants (from the general population) who had metaGRS scores
in the top 0.25% for stroke and vascular risk factors also had a threefold higher risk of
developing ischaemic stroke during follow-up than the rest of the population¹⁷². Similar approaches could be efficient ways to model cSVD risk and could guide prevention approaches by identifying individuals who are at the highest risk of developing specific cSVD complications and could benefit from lifestyle modifications in a precision medicine setting¹⁷³ or could be enrolled in clinical trials to assess preventive interventions. The added value of genetic risk variants in the prediction of stroke or dementia above and beyond covert cSVD MRI markers still needs to be demonstrated, but genetic variants have the advantage of being measurable at increasingly low cost and throughout a lifetime, including before visible covert cSVD develops, so could enable very early detection of high-risk populations.

**[H1] Conclusions and perspectives**

In summary, through large international collaborative efforts, major progress has been made in deciphering the genetic underpinnings of cSVD. Fifty-two independent genetic risk loci for various cSVD-related phenotypes have been identified by combining large population-based and hospital-based cohorts with cutting-edge analytical approaches. Moreover, association statistics from published GWAS have been leveraged for a wide array of secondary analyses, such as assessment of shared genetic variation across cSVD phenotypes and exploration of causal pathways using Mendelian randomization. Although more research is required to derive clinical and therapeutic applications, these findings already provide extensive novel insight into the biological pathways that underlie cSVD, the roles of putative risk factors, the clinical significance of cSVD across the adult lifespan, and the potential implications for therapy. They also highlight the complexity of the cSVD spectrum by revealing that partly distinct genetic profiles underlie the different phenotypic expressions of the disease, and that a continuum exists between common variants associated with common cSVD and genes that harbour causal mutations for rare monogenic cSVD or other genes involved in monogenic cSVD pathophysiology.

Additional progress is to be expected in the near future as a result of multiple developments. First, to enhance genomic discovery, more cSVD genomic studies are needed that encompass exploration of emerging MRI markers of covert cSVD, such as dilated perivascular spaces and novel diffusion-based metrics. These markers also provide an opportunity to capture early mechanisms, as they can be detected and quantified much
earlier in life than traditional MRI phenotypes of cSVD. Preliminary results discussed above support such a lifespan approach. Second, the availability of next-generation sequencing (whole-exome and whole-genome sequencing) of increasingly large samples provides opportunities to discover relevant genetic variation other than single nucleotide variants, such as structural variants or short tandem repeats, and to maximize the fine-mapping potential of GWAS loci to identify causal variants. Third, cSVD genomics have been explored predominantly in populations of European ancestry to date, so expanding analyses to other ethnic groups, some of which have a considerably higher prevalence of cSVD than Europeans (for example, populations of African and East-Asian ancestry)¹⁷⁴⁻¹⁷⁶, will be crucial to establish the generalizability of associations and maximize the potential for fine-mapping and risk prediction¹⁷⁷. Finally, combining cSVD genomics with other molecular approaches (such as epigenomics, transcriptomics, proteomics and metabolomics) will be essential to capture the complex interplay of factors beyond DNA sequence that lead to disease¹⁷⁸. These combined approaches are currently being explored in the CHARGE and ISGC consortia¹⁷⁹, as well as in several funded initiatives, including COSTREAM, MarkVCID, Discovery or SHIVA.

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## Acknowledgements

We thank Dr Sabrina Schilling for her editorial assistance. S.D. is supported by a grant overseen by the French National Research Agency (ANR) as part of the "Investment for the Future Programme" ANR-18-RHUS-0002, by the EU Joint Programme – Neurodegenerative Disease Research (JPND), and by funding from the European Research Council (ERC) and the European Union Horizon 2020 research and innovation programme under grant agreements No 643417, 640643, 667375, and 754517.

## Author contributions

C.B. researched data for the article. All authors made substantial contributions to discussion of the content, wrote the article and edited and/or reviewed the manuscript before submission.

## Competing interests
The authors declare no competing interests.

**Peer review information**

*Nature Reviews Neurology* thanks A. Joutel, R. Kalaria and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

**Review criteria**

For our literature review, we searched PubMed for papers published between January 2007 and March 2021. We focused on this period because we chose to focus on genome-wide association studies (GWAS), which were not available at earlier timepoints in the field of cerebral small vessel disease (cSVD). We considered only genetic associations with common cSVD phenotypes that reached genome-wide significance ($P < 5 \times 10^{-8}$). When several genome-wide significant associations on the same locus were reported in several GWAS, we present the lead single-nucleotide polymorphism (SNP) and association statistics of the GWAS performed on the largest sample size (largest number of cases for binary traits) but still report references of the other publications. Where several genome-wide significant associations with different SNPs have been identified in the same locus, we report only those that correspond to independent signals (linkage disequilibrium $r^2 < 0.1$); for correlated SNPs ($r^2 \ge 0.1$), we selected the SNP with the lowest *P* value (if reported in the same study) or that from the GWAS performed on the largest sample size (if reported in two different studies). Results of candidate gene association studies were included if the studies were large (*n* > 500), robust methodology was used and the findings complement or support GWAS findings. Only peer-reviewed papers in English were considered. For Mendelian randomization studies, we searched the literature for publications that relate to cSVD phenotypes and include Mendelian randomization in the title or abstract. We also systematically screened all published GWAS of cSVD-related phenotypes for Mendelian randomization analyses.

**Related links**

COSTREAM http://www costream eu/

MarkVCID https://markvcidpartners org/

Discovery https://discoverystudy org/

SHIVA https://rhu-shiva com/

**Key points**
* Fifty-two independent genetic loci have been associated with cerebral small vessel disease (cSVD) at the genome-wide significance level, including loci associated with cSVD-related stroke and loci associated with covert, MRI-defined cSVD.

* In silico functional explorations of the observed genetic associations point to a major role of blood pressure-related pathways and mechanisms independent of vascular risk factors, such as extracellular matrix structure and function.

* Transcriptome-wide association studies have provided evidence for associations between one or several genes at a cSVD risk locus and the corresponding cSVD traits, enabling prioritization of putative causal genes for functional follow-up.

* Mendelian randomization studies have been conducted to investigate the causal link between various factors and cSVD-related phenotypes; a dedicated systematic review and meta-analysis is required to confirm some causal relationships.

* Preliminary results provide proof-of-concept that cSVD genomics can inform therapeutic strategies by providing genetic evidence for drug effects, indicating pathways with therapeutic relevance and revealing potential for drug repositioning.

* Use of high-throughput molecular approaches, such as epigenomics, transcriptomics, proteomics and metabolomics, will enable integration of genetic associations with functional data to decipher the biological roles of genetic risk loci in cSVD.
<table><caption>**Table 1 | Risk loci associated with cerebral small vessel disease-related stroke at the genome-wide significance level**</caption><thead><tr><th>Lead variant</th><th>Chromosome position</th><th>Risk allele</th><th>RAF</th><th>Nearest gene(s)</th><th>Population</th><th>Phenotype</th><th>Main ref<sup>a</sup></th><th>Other ref<sup>a</sup></th></tr></thead><tbody><tr><td colspan="9"><strong>ICH</strong></td></tr><tr><td>rs2984613</td><td>1q22</td><td>C</td><td>0.68</td><td>SLC25A44<br>PMF1<br>BGLAP</td><td>EUR</td><td>Non-lobar ICH</td><td>31</td><td>33</td></tr><tr><td>rs11655160</td><td>17p12<sup>b</sup></td><td>G</td><td>0.76</td><td>PIRT (intergenic)</td><td>EUR</td><td>Haematoma volume of<br>non-lobar ICH</td><td>32</td><td>NA</td></tr><tr><td>rs5117</td><td>19q13</td><td>C</td><td>NA</td><td>APOE</td><td>EUR</td><td>Lobar ICH</td><td>33</td><td>NA</td></tr><tr><td>rs9614326</td><td>22q13<sup>b</sup></td><td>A</td><td>0.97</td><td>PARVB</td><td>EUR</td><td>Haematoma volume of<br>lobar ICH</td><td>32</td><td>NA</td></tr><tr><td colspan="9"><strong>ICH accounting for SVS, multivariate analysis</strong></td></tr><tr><td>rs72932727<sup>c</sup></td><td>2q33</td><td>G</td><td>0.87</td><td>ICA1L<br>WDR12<br>CARF<br>NBEAL1</td><td>EUR</td><td>Non-lobar ICH<br>accounting for SVS<sup>d</sup></td><td>33</td><td>NA</td></tr><tr><td>rs9515201</td><td>13q34</td><td>A</td><td>0.33</td><td>COL4A1<br>COL4A2</td><td>EUR</td><td>Non-lobar ICH<br>accounting for SVS<sup>d</sup></td><td>33</td><td>NA</td></tr><tr><td colspan="9"><strong>SVS</strong></td></tr><tr><td>rs72934535<sup>c</sup></td><td>2q33</td><td>T</td><td>0.89</td><td>ICA1L<br>FAM117B<br>CARF<br>NBEAL1</td><td>EUR</td><td>SVS<sup>e,f</sup></td><td>34</td><td>NA</td></tr><tr><td>rs4621303</td><td>3p22</td><td>T</td><td>0.83</td><td>ULK4</td><td>TRANS</td><td>SVS<sup>e,f</sup></td><td>34</td><td>NA</td></tr><tr><td>rs10029218</td><td>4q26<sup>b</sup></td><td>A</td><td>0.12</td><td>CAMK2D</td><td>EUR</td><td>SVS<sup>e,g</sup></td><td>35</td><td>NA</td></tr><tr><td>rs2293576</td><td>11p11</td><td>G</td><td>0.67</td><td>SPI1<br>SLC39A13<br>PSMC3<br>RAPSN</td><td>EUR, TRANS</td><td>SVS<sup>e,f</sup></td><td>34</td><td>NA</td></tr><tr><td>rs10744777</td><td>12q24</td><td>T</td><td>0.66</td><td>SH2B3</td><td>EUR</td><td>SVS<sup>g</sup></td><td>38</td><td>35</td></tr><tr><td>rs12445022</td><td>16q24<sup>b</sup></td><td>A</td><td>0.37</td><td>ZCCHC14</td><td>EUR</td><td>SVS<sup>e,f</sup></td><td>34</td><td>36,37</td></tr><tr><td>rs9958650</td><td>18p11<sup>b</sup></td><td>G</td><td>0.10</td><td>ZBTB14<br>EPB41L3</td><td>TRANS</td><td>SVS<sup>e,f</sup></td><td>34</td><td>NA</td></tr><tr><td colspan="9"><strong>SVS accounting for WMH burden, multivariate analysis</strong></td></tr><tr><td>rs2984613</td><td>1q22</td><td>C</td><td>0.64</td><td>SLC25A44<br>PMF1<br>BGLAP</td><td>EUR</td><td>SVS<sup>e,f</sup></td><td>34</td><td>NA</td></tr><tr><td>rs2303655</td><td>5q23<sup>b</sup></td><td>T</td><td>0.81</td><td>LOX<br>ZNF474<br>LOC100505841</td><td>EUR</td><td>SVS<sup>e,f</sup></td><td>34</td><td>NA</td></tr><tr><td>rs7766042</td><td>6p25<sup>b</sup></td><td>C</td><td>0.11</td><td>FOXF2<br>FOXQ1</td><td>EUR</td><td>SVS<sup>e,f</sup></td><td>34</td><td>NA</td></tr><tr><td>rs225744</td><td>6q24<sup>b</sup></td><td>C</td><td>0.77</td><td>VTA1-GPR126</td><td>EUR</td><td>SVS<sup>e,f</sup></td><td>34</td><td>NA</td></tr><tr><td>rs61000833</td><td>10q24</td><td>T</td><td>0.60</td><td>SH3PXD2A<br>STN1</td><td>EUR</td><td>SVS<sup>e,f</sup></td><td>34</td><td>NA</td></tr><tr><td>rs79043147</td><td>10q26<sup>b</sup></td><td>T</td><td>0.07</td><td>HTRA1</td><td>EUR</td><td>SVS<sup>e,f</sup></td><td>34</td><td>NA</td></tr><tr><td>rs11838776</td><td>13q34</td><td>A</td><td>0.29</td><td>COL4A1<br>COL4A2</td><td>EUR</td><td>SVS<sup>e,f</sup></td><td>34</td><td>NA</td></tr></tbody></table>

<footnote><sup>a</sup>When several GWAS reported a genome-wide significant association with the same locus, we present the results of the GWAS performed on the largest sample size (largest number of cases), referred to as "main ref"; other studies reporting a genome-wide significant association with the same locus are listed under "other ref". <sup>b</sup>Not associated with vascular risk</footnote>
factors. rs72932727 (ICH) and rs72934535 (SVS) are in linkage disequilibrium ($r^2$ = 0.8317).

dMultitrait analysis of non-lobar ICH and SVS. eSVS was defined according to Trial of Org 10172 Acute Stroke Treatment (TOAST)¹⁸⁰, which is based on a clinical lacunar syndrome (lacunar infarct ≤15 mm in diameter) and the absence of other causes of stroke, or non-lacunar infarction on CT. fSVS was defined as a clinical lacunar syndrome with an anatomically compatible lesion on MRI (subcortical infarct ≤15 mm in diameter). gSVS was defined according to Causative Classification of Stroke¹⁸¹, which is based on imaging evidence of a clinically relevant acute infarction (≤20 mm in diameter) and the absence of other causes of stroke. Von Berg et al.³⁵ used an intersect phenotype (an individual must be assigned the same subtype across all subtyping systems). Traylor et al.³⁴ re-analyzed cases of lacunar stroke, identified with the TOAST system, with MRI confirmation. EUR, European; RAF, relative allele frequency; TRANS, transethnic; ICH, intracerebral haemorrhage; SVS, small vessel stroke; WMH, white matter hyperintensity.
<table><caption>Table 2 | Loci associated with MRI markers of covert cerebral small vessel disease at the genome-wide significance level</caption><thead><tr><th>Lead variant<sup>a</sup></th><th>Chromosome position</th><th>Risk allele</th><th>RAF</th><th>Nearest gene(s)</th><th>Population</th><th>Mean ref<sup>b</sup></th><th>Other ref<sup>b</sup></th></tr></thead><tbody><tr><td colspan="8"><strong>Brain infarcts</strong></td></tr><tr><td>rs39938<sup>c</sup></td><td>5q23<sup>d</sup></td><td>T</td><td>0.21</td><td>FBN2</td><td>EUR</td><td>65</td><td>NA</td></tr><tr><td>rs12583648</td><td>13q12<sup>d</sup></td><td>C</td><td>0.33</td><td>ZDHHC20</td><td>EUR</td><td>65</td><td>NA</td></tr><tr><td colspan="8"><strong>Cerebral microbleeds</strong></td></tr><tr><td>rs769449</td><td>19q13</td><td>A</td><td>0.13</td><td>APOE</td><td>EUR</td><td>64</td><td>NA</td></tr><tr><td colspan="8"><strong>Fractional anisotropy</strong></td></tr><tr><td>rs76122535</td><td>2q33</td><td>G</td><td>0.13</td><td>ICA1L, CARF, NBEAL1</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs34380167</td><td>2q35<sup>d</sup></td><td>ID</td><td>0.27</td><td>SMARCAL1</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs35544841</td><td>5q14<sup>d</sup></td><td>ID</td><td>0.20</td><td>VCAN</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs4150221</td><td>5q31</td><td>C</td><td>0.26</td><td>HBEGF</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs3129171<sup>e</sup></td><td>6p22<sup>d</sup></td><td>A</td><td>0.24</td><td>ZFP57, OR2J2, OR2H4P</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs374598428<sup>e</sup></td><td>6p22<sup>d</sup></td><td>ID</td><td>0.14</td><td>VN1R12P</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs112730611</td><td>16q24<sup>d</sup></td><td>T</td><td>0.17</td><td>SPIRE2, TCF25</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs6062264</td><td>20q13</td><td>T</td><td>0.28</td><td>MIR1, 1HG</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td colspan="8"><strong>Mean diffusivity</strong></td></tr><tr><td>rs35544841</td><td>5q14<sup>d</sup></td><td>ID</td><td>0.20</td><td>VCAN</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs4150221</td><td>5q31</td><td>C</td><td>0.26</td><td>HBEGF</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs7772614</td><td>6p21<sup>d</sup></td><td>A</td><td>0.38</td><td>HLA-B, HLA-S</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs1233587</td><td>6p22<sup>d</sup></td><td>T</td><td>0.30</td><td>ZFP57</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs11813268</td><td>10q24</td><td>T</td><td>0.15</td><td>SH3PXD2A, STN1, PDCD11</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs55939347</td><td>17q21</td><td>ID</td><td>0.22</td><td>MAPT, DCAKD, NMT1, NT5C3B, STAT5B</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td colspan="8"><strong>White matter hyperintensities</strong></td></tr><tr><td>rs786921</td><td>1p22</td><td>A</td><td>0.60</td><td>PKN2</td><td>EUR</td><td>44</td><td>NA</td></tr><tr><td>rs2984613</td><td>1q22</td><td>C</td><td>0.65</td><td>SLC25A44, PMF1, BGLAP</td><td>TRANS</td><td>63</td><td>NA</td></tr><tr><td>rs12120143</td><td>1q31<sup>d</sup></td><td>T</td><td>0.03</td><td>DENND1B</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs6540873</td><td>1q41<sup>d</sup></td><td>A</td><td>0.61</td><td>KCNK2</td><td>TRANS</td><td>44</td><td>NA</td></tr><tr><td>rs7596872</td><td>2p16</td><td>A</td><td>0.10</td><td>EFEMP1</td><td>EUR</td><td>44</td><td>22,63,44,60,21,61</td></tr><tr><td>rs73923006</td><td>2p21</td><td>G</td><td>0.81</td><td>HAAO</td><td>EUR/TRANS</td><td>44</td><td>21,61</td></tr><tr><td>rs62172472</td><td>2q32</td><td>G</td><td>0.79</td><td>CALCR1<sup>f</sup></td><td>EUR/TRANS</td><td>44</td><td>21</td></tr><tr><td>rs7603972</td><td>2q33</td><td>A</td><td>0.87</td><td>ICA11<sup>f</sup>, CARF<sup>f</sup>, NBEAL1<sup>f</sup>, KRT8P15<sup>f</sup></td><td>EUR/TRANS</td><td>44</td><td>21,61,60</td></tr><tr><td>rs6797002</td><td>3q27</td><td>C</td><td>0.73</td><td>KLHL24<sup>f</sup></td><td>EUR/TRANS</td><td>44</td><td>21</td></tr><tr><td>rs17205972</td><td>5q14<sup>d</sup></td><td>T</td><td>0.20</td><td>VCAN</td><td>EUR/TRANS</td><td>44</td><td>NA</td></tr><tr><td>rs2303655<sup>c</sup></td><td>5q23<sup>d</sup></td><td>T</td><td>0.78</td><td>FBN2</td><td>EUR/TRANS</td><td>44</td><td>21</td></tr><tr><td>rs6940540</td><td>6q25</td><td>G</td><td>0.41</td><td>PLEKHG1</td><td>EUR/TRANS</td><td>44</td><td>61</td></tr><tr><td>rs3215395</td><td>7q22<sup>d</sup></td><td>ID</td><td>0.29</td><td>ZBN</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs1799983</td><td>7q36</td><td>T</td><td>0.32</td><td>NOS3</td><td>EUR</td><td>61</td><td>NA</td></tr><tr><td>rs73184312<sup>e</sup></td><td>8p23</td><td>G</td><td>0.72</td><td>TNKS, XKR6, SGK223</td><td>EUR/TRANS</td><td>44</td><td>NA</td></tr><tr><td>rs11249945<sup>e</sup></td><td>8p23</td><td>A</td><td>0.35</td><td>TNKS, XKR6, SGK223</td><td>EUR/TRANS</td><td>44</td><td>NA</td></tr><tr><td>rs11257311</td><td>10p14<sup>d</sup></td><td>G</td><td>0.70</td><td>ECHDC3</td><td>AFR/TRANS</td><td>44</td><td>NA</td></tr><tr><td>rs1993484</td><td>10q23</td><td>T</td><td>0.23</td><td>TSPAN14</td><td>EUR</td><td>61</td><td>NA</td></tr><tr><td>rs10786772<sup>h</sup></td><td>10q24</td><td>G</td><td>0.64</td><td>SH3PXD2A, STN1, PDCD11</td><td>EUR/TRANS</td><td>44</td><td>63,60</td></tr><tr><td>rs71471298<sup>h</sup></td><td>10q24</td><td>T</td><td>0.11</td><td>SH3PXD2A, STN1, PDCD11</td><td>EUR/TRANS</td><td>44</td><td>NA</td></tr><tr><td>rs4630220<sup>h</sup></td><td>10q24</td><td>G</td><td>0.71</td><td>SH3PXD2A, STN1, PDCD11</td><td>EUR/TRANS</td><td>44</td><td>NA</td></tr><tr><td>rs55940034</td><td>13q34</td><td>G</td><td>0.29</td><td>COL4A1, COL4A2</td><td>EUR/TRANS</td><td>44</td><td>21,61,60</td></tr><tr><td>rs72680374</td><td>14q22<sup>d</sup></td><td>T</td><td>0.63</td><td>NID2</td><td>EUR</td><td>44</td><td>NA</td></tr><tr><td>rs1285847<sup>i</sup></td><td>14q32</td><td>T</td><td>0.55</td><td>EVL, DEGS2<sup>f</sup>, CCDC88C<sup>f</sup></td><td>EUR/TRANS</td><td>44</td><td>21,60</td></tr><tr><td>rs7157599<sup>i</sup></td><td>14q32</td><td>C</td><td>0.29</td><td>EVL, DEGS2<sup>f</sup>, CCDC88C<sup>f</sup></td><td>EUR/TRANS</td><td>44</td><td>NA</td></tr><tr><td>rs12443113</td><td>15q22</td><td>G</td><td>0.55</td><td>MTFMT, SLC51B, RASL12</td><td>EUR</td><td>44</td><td>21</td></tr><tr><td>rs1948948</td><td>16q12<sup>d</sup></td><td>C</td><td>0.56</td><td>SALL1</td><td>EUR/TRANS</td><td>44</td><td>21</td></tr><tr><td>rs12921170</td><td>16q24<sup>d</sup></td><td>A</td><td>0.58</td><td>SPIRE2, TCF25</td><td>EUR/TRANS</td><td>44</td><td>21,61</td></tr><tr><td>rs6587216</td><td>17p11</td><td>G</td><td>0.19</td><td>EPN2</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs6503417<sup>i</sup></td><td>17q21</td><td>C</td><td>0.63</td><td>MAPT, DCAKD<sup>f</sup>, NMT1<sup>f</sup>, NT5C3B<sup>f</sup>, STAT5B<sup>f</sup></td><td>EUR/TRANS</td><td>44</td><td>21,61</td></tr><tr><td>rs962888<sup>i</sup></td><td>17q21</td><td>G</td><td>0.71</td><td>MAPT, DCAKD<sup>f</sup>, NMT1<sup>f</sup>, NT5C3B<sup>f</sup>, STAT5B<sup>f</sup></td><td>EUR</td><td>60</td><td>NA</td></tr><tr><td>rs34974290</td><td>17q25</td><td>A</td><td>0.19</td><td>TRIM47<sup>f</sup>, TRIM65<sup>f</sup>, NEURL, WBP2<sup>f</sup>, RP11-552F3.9<sup>f</sup></td><td>EUR/TRANS</td><td>44</td><td>62,61,21,63,60,22</td></tr></tbody></table>
<table><tr><td>rs429358</td><td>19q13</td><td>C</td><td>0.15</td><td>APOE</td><td>EUR</td><td>21</td><td>NA</td></tr><tr><td>rs5762197</td><td>22q12<sup>c</sup></td><td>C</td><td>0.71</td><td>MN1</td><td>EUR</td><td>44</td><td>NA</td></tr></table>

ªIf several independent single nucleotide polymorphisms have been associated with cerebral small vessel disease at the genome-wide significance level in the same region (linkage disequilibrium, r² > 0.1), all of them are reported in the table. ᵇWhen several GWAS reported a genome-wide significant association with the same locus, we present the results of the GWAS performed on the largest sample size (largest number of cases), referred to as "main ref"; other studies reporting a genome-wide significant association with the same locus are listed under "other ref". ᶜr² = 0.002. ᵈnot associated with vascular risk factors. ᵉrs3129171 and rs374598428 have been reported as independent²¹. ᶞExpression of these genes was associated with MRI-defined covert cerebral small vessel disease in TWAS and colocalization analyses⁴⁴. ᵚr² = 0.073. ᵛall r² < 0.01. ᶜr² = 0.002. ᵚr² = 0.013. ID, insertion–deletion; EUR, European; RAF, relative allele frequency; TRANS, transethnic.
<table><caption>Table 3 | Involvement of genes associated with cerebral small vessel disease in biological processes.</caption><thead><tr><th>Biological process<sup>a</sup></th><th>Gene</th><th>Protein</th><th>Involvement of protein in biological process</th><th>Ref</th></tr></thead><tbody><tr><td rowspan="7">Extracellular matrix structure and function</td><td>EFEMP1</td><td>Fibulin 3</td><td>ECM glycoprotein localized in the basement membrane. Proteolytic target of serine protease HTRA1.</td><td>182</td></tr><tr><td>LOX</td><td>Lysyl oxidase</td><td>Copper-dependent enzyme that cross-links collagens and elastin.</td><td>96</td></tr><tr><td>SH3PXD2A</td><td>SH3 and PX domain-containing protein 2A</td><td>Adapter protein involved in ECM degradation via invadopodia and podosome formation</td><td>183</td></tr><tr><td>NID2</td><td>Nidogen 2</td><td>ECM glycoprotein and a major component of basement membranes.</td><td>96</td></tr><tr><td>ADAMTSL4</td><td>ADAMTS-like protein 4</td><td>Involved in cellular adhesion, angiogenesis and patterning of the developing nervous system.</td><td>96</td></tr><tr><td>FBN2</td><td>Fibrillin 2</td><td>Component of connective tissue microfibrils that might be involved in elastic fibre assembly.</td><td>65</td></tr><tr><td>VCAN</td><td>Versican core protein</td><td>Proteoglycan involved in cell adhesion and ECM assembly.</td><td>184</td></tr><tr><td rowspan="2">Myelination</td><td>AGRG6</td><td>Adhesion G-protein coupled receptor 6</td><td>ECM protein produced by astrocytes and involved in pericyte differentiation and blood-brain barrier development and function; activated by type IV collagen and binds to laminin211.</td><td>102,185</td></tr><tr><td>HBEGF</td><td>Heparin-binding EGF-like growth factor</td><td>EGF-like is one of the characteristic domains of ECM proteins.</td><td>96</td></tr><tr><td rowspan="3">Membrane transport</td><td>ULK4<sup>b</sup></td><td>Serine-threonine protein kinase ULK4</td><td>Regulates a core set of factors that are essential in the development of oligodendrocytes.</td><td>51</td></tr><tr><td>VCAN</td><td>Versican core protein</td><td>Forms complexes that inhibit oligodendrocyte maturation</td><td>100</td></tr><tr><td>AGRG6</td><td>Adhesion G-protein coupled receptor 6</td><td>Involved in Schwann cell myelination in the PNS</td><td>98,99</td></tr><tr><td rowspan="3">Membrane transport</td><td>KCNK2</td><td>Potassium channel subfamily K member 2</td><td>Enables potassium transport across the cytoplasmic membrane; defects underlie early-stage cerebrovascular dysfunction in CADASIL.</td><td>95</td></tr><tr><td>SLC39A13</td><td>Solute carrier family 39 member 13</td><td>Transmembrane zinc transporter; mutations cause a form of Ehlers-Danlos syndrome, which can cause stroke</td><td>186</td></tr><tr><td>SLC25A44<sup>b</sup></td><td>Solute carrier family 25 member 44</td><td>Mitochondrial carrier protein with a role in catabolism of branched-chain amino acids in brown adipose tissue</td><td>187</td></tr></tbody></table>

<footnote><sup>a</sup>Selected biological processes associated with genes that have been associated with common small vessel disease. <sup>b</sup>Genes have been associated with common small vessel disease in transcriptome-wide analysis studies and colocalization analyses, providing stronger evidence for their causal involvement.</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 1 | Potential clinical applications of cerebral small vessel disease genomics.</figcaption></figure>

Knowledge of the genetic determinants of small vessel disease has several implications for
drug development and clinical management.
<figure><img src="image_4.png" /><figcaption>Fig. 2 | Shared risk loci for cSVD phenotypes and associations with vascular risk factors.</figcaption></figure>
Loci reaching genome-wide significance in multivariate analyses based on two cSVD phenotypes are not included in this figure a | Venn diagram that shows the extent to which different cSVD phenotypes share genome-wide significant risk loci. The cSVD phenotypes shown include overt phenotypes (small vessel stroke (SVS) and intracerebral haemorrhage (ICH)) and covert, MRI-defined markers of ccSVD (white matter hyperintensities (WMH), brain infarcts (BI), cerebral microbleeds (CMB), fractional anisotropy (FA) and mean diffusivity (MD)). Where risk loci are shared between phenotypes, the chromosome coordinates and the nearest genes to these co-ordinates are specified in the surrounding boxes. b| Chord diagram that summarizes the association of genome-wide significant risk variants for cSVD traits (top half) with vascular risk factors (bottom half). The width of each of the inner stems reflects the number of traits associated with a given locus (top half) or the number of loci associated with a given vascular risk factor (bottom half). Black arrows indicate genome-wide significant associations. Outer coloured segments in the top half match colours in part a, and dark green represents lacunar stroke loci. <sup>a</sup>Locus overlaps with other cSVD traits — see part a for details.
<figure><img src="image_5.png" /><figcaption>Fig. 3 | Associations between genetically predicted risk factors and the risk of cSVD.</figcaption></figure>

Blue boxes indicate that at least one Mendelian randomization study has shown a significant association between genetically predicted higher exposure to the risk factor and an increased risk or burden of the cerebral small vessel disease (cSVD). Pink boxes indicate that at least one Mendelian randomization study has shown a significant association between genetically predicted higher exposure to the risk factor and a reduced risk or burden of the cSVD. Numbers indicate the fraction of Mendelian randomization studies conducted that have shown a significant association. Dotted blue boxes indicate consistently non-significant associations. Empty boxes indicate no published studies. **For lacunes, the association of genetically predicted risk factors with lacunes was tested but pleiotropy was not formally ruled out. *For FA, association of genetically predicted risk factors with lower FA is being tested. †Only nominally significant after accounting for the number of risk factors tested. ‡Not significant when the weighted median or the Mendelian randomization Egger method were used. ‡Only
nominally significant after removing outlier single-nucleotide polymorphisms. <sup>§</sup>Weak instruments (pleiotropy not tested). <sup>¶</sup>Borderline significant.
**Box 1 | Methods to explore the genomics of common (multifactorial) diseases or traits**

**Candidate gene association studies**

Candidate gene association studies test associations between genetic variants within a gene or region of interest and a relevant phenotype (disease or trait). The genes of interest are usually selected on the basis of a *priori* hypotheses on their relevance to the phenotype under investigation.

**Genome-wide association studies**

Genome-wide association studies (GWAS) offer an 'agnostic' approach by testing associations of millions of common genetic variants (minor allele frequency ≥5%) or low-frequency variants (1%≤ minor allele frequency <5%) with a phenotype of interest at the genome-wide scale. To avoid false positive findings when multiple association tests are performed for millions of variants, a *P* value threshold of <5×10⁻⁸ is usually used to define genome-wide significance. Most genetic variants studied in GWAS are single-nucleotide polymorphisms (SNPs).

**Expression quantitative loci studies**

Expression quantitative loci (eQTL) refer to genetic variants associated with mRNA expression levels of a given gene within a given tissue. eQTL studies can be useful for in silico functional exploration of genetic variants identified in GWAS, by linking SNPs to tissue-specific changes in gene expression.

**Transcriptome-wide association studies**

Transcriptome-wide association studies enable integration of data from GWAS and eQTL studies to derive associations between tissue-specific gene expression levels and a given phenotype.

**Colocalization analyses**

Colocalization analyses test whether the same causal variant is responsible for the association of a genetic variant with a molecular trait, such as expression levels of a gene in a given tissue, and with a disease of interest.
**Linkage disequilibrium score regression**

Linkage disequilibrium score regression (LDSR) examines the relationships of linkage disequilibrium score and SNP association test statistics with a phenotype. LDSR is used to estimate SNP-based heritability of a given phenotype considering genome-wide significant SNPs and enables testing the genetic correlation between two phenotypes.

**Multi-trait analysis of genome-wide association summary statistics**

Multi-trait analysis of genome-wide association summary statistics (MTAG) enables a joint association analysis of multiple traits on the basis of individual trait GWAS summary statistics while accounting for sample overlap. This approach increases statistical power for detection of associations with genetically correlated traits.

**Mendelian randomization**

Mendelian randomization studies enable use of genetic variants to identify causal associations between specific factors or exposures and a specific trait or disease (see **Box 2**)
**Box 2 | Mendelian randomization**

Mendelian randomization is an analytical approach in which genetic variants are used as instrumental variables that approximate the effect of a given exposure or factor (X) to seek evidence for a causal association between this exposure and a disease or trait (Y). The method is based on the fact that genetic alleles are assorted randomly during meiosis, meaning that the analysis is less prone to confounding and reverse causation than are observational epidemiological studies. As a result, Mendelian randomization can generate more reliable evidence regarding the association of the exposure with disease and therefore provide indirect evidence for the development of interventions that could benefit health.

A set of genetic variants — referred to as a genetic instrument — can be used as a proxy for a given modifiable exposure if they are robustly associated with the risk of exposure. For example, a set of genetic variants associated with high blood pressure can be used as a proxy for exposure to high blood pressure. The instrumental variable must not be associated with Y independently of X (Figure).

Mendelian randomization can be performed at the individual data level (one-sample Mendelian randomization)¹³³ or at the summary statistics level (two-sample Mendelian randomization)¹³⁴. In two-sample Mendelian randomization, data from two study samples are used to compare the association between the instrument and the exposure with the association between the instrument and the outcome across multiple single-nucleotide polymorphisms to estimate the causal role of the exposure in the outcome. Given that GWAS summary statistics are largely made publicly available, two-sample Mendelian randomization has become extremely popular.

Several notes of caution are required in relation to Mendelian randomization studies. Weak genetic instruments — for example, those that are pleiotropic and/or are directly associated with the outcome — will lead to regression dilution bias towards the null¹⁸⁸, so that the estimated causal effects are underestimations. Construction of instrumental variables from well-powered GWAS helps to generate stronger genetic instruments¹⁸⁹, but this approach also increases the likelihood of adding pleiotropic SNPs. Avoidance of this risk requires application of various methods that avoid pleiotropy¹⁹⁰.
# Glossary

## Genetic variant

A specific region of the genome that differs between individuals in the population.

## Single nucleotide polymorphism

A single nucleotide variation in the DNA sequence.

## Alleles

Two or more versions of a polymorphic genetic site; for single nucleotide polymorphisms, alleles correspond to two alternative nucleotides at the given position.

## Linkage disequilibrium

The non-random association of alleles at two nearby genetic loci, reflecting haplotypes that descend from a common ancestor.

## White matter hyperintensity of presumed vascular origin (WMH)

An abnormality of variable size in the white matter seen on MRI as a hyperintensity on T2-weighted images such as fluid-attenuated inversion recovery without cavitation (with a signal different from the cerebrospinal fluid).

## Fractional anisotropy

A scalar measure derived from diffusion tensor imaging that quantifies the overall directionality of water diffusion in brain tissue. The measure is greater in organized white matter tracts and lower in disorganized tissues, such as CSF.

## Mean diffusivity

A scalar measure obtained from diffusion tensor imaging that quantifies the average mobility of water molecules regardless of its direction within the brain tissue. It is used to study microstructural properties and the structural integrity of brain tissue.

## Cerebral microbleed
A small area (usually 2–5 mm in diameter) of blood leakage that appears as a signal void with associated blooming on T2*-weighted MRI or susceptibility-weighted imaging.

**Perivascular spaces**

Fluid-filled spaces that surround perforating vessels in the brain, with a signal intensity similar to that of cerebrospinal fluid on all MRI sequences (diameter generally smaller than 3 mm).

**Lacune of presumed vascular origin**

A subcortical, round, fluid-filled cavity (3–15 mm diameter) consistent with a previous acute small subcortical infarct or haemorrhage in the territory of one perforating arteriole.

**Pleiotropy**

A phenomenon in which genes or genetic variants affect multiple, apparently unrelated, phenotypes.

**Inversion polymorphism**

A type of DNA structural variant that changes the orientation of a genomic segment.

In this Review, the authors provide an overview of the genetics of common small vessel disease, the insight into causal genes and the biological pathways involved, the overlap with monogenic small vessel disease, and the therapeutic implications.