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  • Review Article
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Genetics of common cerebral small vessel disease

Nature Reviews Neurology volume 18, pages 84–101 (2022)Cite this article

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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 does not manifest as clinical stroke, is highly prevalent in the general population, particularly 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 insights 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, multi-ancestry approaches and integration with other omics data.

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.

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Fig. 1: Potential clinical applications of cerebral small vessel disease genomics.
Fig. 2: Shared risk loci for cSVD phenotypes and associations with vascular risk factors.
Fig. 3: Associations between genetically predicted risk factors and the risk of cSVD.

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References

  1. Seshadri, S. & Wolf, P. A. Lifetime risk of stroke and dementia: current concepts, and estimates from the Framingham study. Lancet Neurol. 6, 1106–1114 (2007).

    PubMed  Google Scholar 

  2. Cuadrado-Godia, E. et al. Cerebral small vessel disease: a review focusing on pathophysiology, biomarkers, and machine learning strategies. J. Stroke 20, 302–320 (2018).

    PubMed  PubMed Central  Google Scholar 

  3. Wardlaw, J. M., Smith, C. & Dichgans, M. Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol. 12, 483–497 (2013).

    PubMed  Google Scholar 

  4. Greenberg, S. M. Small vessels, big problems. N. Engl. J. Med. 354, 1451–1453 (2006).

    CAS  PubMed  Google Scholar 

  5. Feigin, V. L., Lawes, C. M. M., Bennett, D. A., Barker-Collo, S. L. & Parag, V. Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review. Lancet Neurol. 8, 355–369 (2009).

    PubMed  Google Scholar 

  6. Mozaffarian, D. et al. Heart disease and stroke statistics–2015 update: a report from the American Heart Association. Circulation 131, e29–e322 (2015).

    PubMed  Google Scholar 

  7. Alber, J. et al. White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): knowledge gaps and opportunities. Alzheimers Dement. 5, 107–117 (2019).

    Google Scholar 

  8. Gladman, J. T. et al. Vascular contributions to cognitive impairment and dementia: research consortia that focus on etiology and treatable targets to lessen the burden of dementia worldwide. Alzheimers Dement. 5, 789–796 (2019).

    Google Scholar 

  9. Viswanathan, A., Rocca, W. A. & Tzourio, C. Vascular risk factors and dementia: how to move forward? Neurology 72, 368–374 (2009).

    PubMed  PubMed Central  Google Scholar 

  10. Kapasi, A., DeCarli, C. & Schneider, J. A. Impact of multiple pathologies on the threshold for clinically overt dementia. Acta Neuropathol. 134, 171–186 (2017).

    PubMed  PubMed Central  Google Scholar 

  11. Bos, D. et al. Cerebral small vessel disease and the risk of dementia: a systematic review and meta-analysis of population-based evidence. Alzheimers Dement. 14, 1482–1492 (2018).

    PubMed  Google Scholar 

  12. Wardlaw, J. M., Smith, C. & Dichgans, M. Small vessel disease: mechanisms and clinical implications. Lancet Neurol. 18, 684–696 (2019). An in-depth overview of the current knowledge of mechanisms of small vessel disease.

    PubMed  Google Scholar 

  13. Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 12, 822–838 (2013).

    PubMed  PubMed Central  Google Scholar 

  14. Debette, S. & Markus, H. S. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 341, c3666 (2010).

    PubMed  PubMed Central  Google Scholar 

  15. Maxwell, S. S. et al. Genetic associations with brain microbleeds: systematic review and meta-analyses. Neurology 77, 158–167 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Duperron, M.-G. et al. Burden of dilated perivascular spaces, an emerging marker of cerebral small vessel disease, is highly heritable. Stroke 49, 282–287 (2018).

    PubMed  Google Scholar 

  17. Dubost, F. et al. Enlarged perivascular spaces in brain MRI: automated quantification in four regions. NeuroImage 185, 534–544 (2019).

    PubMed  Google Scholar 

  18. van Veluw, S. J. et al. Detection, risk factors, and functional consequences of cerebral microinfarcts. Lancet Neurol. 16, 730–740 (2017).

    PubMed  PubMed Central  Google Scholar 

  19. van Veluw, S. J. et al. Different microvascular alterations underlie microbleeds and microinfarcts. Ann. Neurol. 86, 279–292 (2019).

    PubMed  PubMed Central  Google Scholar 

  20. Baykara, E. et al. A novel imaging marker for small vessel disease based on skeletonization of white matter tracts and diffusion histograms: novel SVD imaging marker. Ann. Neurol. 80, 581–592 (2016).

    PubMed  Google Scholar 

  21. Persyn, E. et al. Genome-wide association study of MRI markers of cerebral small vessel disease in 42,310 participants. Nat. Commun. 11, 2175 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Rutten-Jacobs, L. C. A. et al. Genetic study of white matter integrity in UK biobank (N=8448) and the overlap with stroke, depression, and dementia. Stroke 49, 1340–1347 (2018).

    PubMed  PubMed Central  Google Scholar 

  23. Debette, S., Schilling, S., Duperron, M.-G., Larsson, S. C. & Markus, H. S. Clinical significance of magnetic resonance imaging markers of vascular brain injury: a systematic review and meta-analysis. JAMA Neurol. 76, 81–94 (2019).

    PubMed  Google Scholar 

  24. Georgakis, M. K., Duering, M., Wardlaw, J. M. & Dichgans, M. WMH and long-term outcomes in ischemic stroke: a systematic review and meta-analysis. Neurology 92, e1298–e1308 (2019).

    PubMed  Google Scholar 

  25. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    CAS  PubMed  Google Scholar 

  26. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    CAS  PubMed  Google Scholar 

  27. Mancuso, M. et al. Monogenic cerebral small-vessel diseases: diagnosis and therapy. Consensus recommendations of the European Academy of Neurology. Eur. J. Neurol. 27, 909–927 (2020). Updated recommendations for the management of monogenic cSVD.

    CAS  PubMed  Google Scholar 

  28. Marini, S., Anderson, C. D. & Rosand, J. Genetics of cerebral small vessel disease. Stroke 51, 12–20 (2020).

    PubMed  Google Scholar 

  29. Falcone, G. J., Malik, R., Dichgans, M. & Rosand, J. Current concepts and clinical applications of stroke genetics. Lancet Neurol. 13, 405–418 (2014).

    PubMed  Google Scholar 

  30. Traylor, M. et al. Genetic architecture of lacunar stroke. Stroke 46, 2407–2412 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Woo, D. et al. Meta-analysis of genome-wide association studies identifies 1q22 as a susceptibility locus for intracerebral hemorrhage. Am. J. Hum. Genet. 94, 511–521 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Marini, S. et al. 17p12 influences hematoma volume and outcome in spontaneous intracerebral hemorrhage. Stroke 49, 1618–1625 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Chung, J. et al. Genome-wide association study of cerebral small vessel disease reveals established and novel loci. Brain 142, 3176–3189 (2019).

    PubMed  PubMed Central  Google Scholar 

  34. Traylor, M. et al. Genetic basis of lacunar stroke: a pooled analysis of individual patient data and genome-wide association studies. Lancet Neurol. 20, 351–361 (2021). The largest GWAS of lacunar stroke in which a multi-trait approach is used with white matter hyperintensities to discover additional lacunar stroke loci.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. von Berg, J. et al. Alternate approach to stroke phenotyping identifies a genetic risk locus for small vessel stroke. Eur. J. Hum. Genet. 28, 963–972 (2020).

    Google Scholar 

  36. Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018). The largest multi-ancestry GWAS meta-analysis of stroke and its subtypes; few loci were associated with small vessel stroke but follow-up bioinformatics analyses provided insights into the genetics of cSVD.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Traylor, M. et al. Genetic variation at 16q24.2 is associated with small vessel stroke. Ann. Neurol. 81, 383–394 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. NINDS Stroke Genetics Network & International Stroke Genetics Consortium. Loci associated with ischaemic stroke and its subtypes (SiGN): a genome-wide association study. Lancet Neurol. 15, 174–184 (2016).

  39. Rajajee, V. et al. Diagnosis of lacunar infarcts within 6 hours of onset by clinical and CT criteria versus MRI. J. Neuroimaging 18, 66–72 (2008).

    PubMed  Google Scholar 

  40. Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  PubMed  Google Scholar 

  42. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    PubMed  PubMed Central  Google Scholar 

  44. Sargurupremraj, M., Suzuki, H., Jian, X., Fornage, M. & Debette, S. Cerebral small vessel disease genomics and its implications across the lifespan. Nat. Commun. 11, 6285 (2020). The largest GWAS of WMHs, accounting for interaction with hypertension, providing important insights into the lifetime impact of genetic risk of cSVD.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Bin, B. H. et al. Molecular pathogenesis of spondylocheirodysplastic Ehlers-Danlos syndrome caused by mutant ZIP13 proteins. EMBO Mol. Med. 6, 1028–1042 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Ho, H. T., Dahlin, A. & Wang, J. Expression profiling of solute carrier gene families at the blood-CSF barrier. Front. Pharmacol. 3, 154 (2012).

    PubMed  PubMed Central  Google Scholar 

  47. Kusuhara, H. & Sugiyama, Y. Active efflux across the blood-brain barrier: role of the solute carrier family. NeuroRx 2, 73–85 (2005).

    PubMed  PubMed Central  Google Scholar 

  48. Geier, E. G. et al. Profiling solute carrier transporters in the human blood-brain barrier. Clin. Pharmacol. Ther. 94, 636–639 (2013).

    CAS  PubMed  Google Scholar 

  49. Wang, W. W., Gallo, L., Jadhav, A., Hawkins, R. & Parker, C. G. The druggability of solute carriers. J. Med. Chem. 63, 3834–3867 (2020).

    CAS  PubMed  Google Scholar 

  50. Liu, M., Xu, P., O’Brien, T. & Shen, S. Multiple roles of Ulk4 in neurogenesis and brain function. Neurogenesis 4, e1313646 (2017).

    PubMed  PubMed Central  Google Scholar 

  51. Liu, M. et al. Ulk4 deficiency leads to hypomyelination in mice. Glia 66, 175–190 (2018).

    PubMed  Google Scholar 

  52. Humphreys, C. A., Smith, C. & Wardlaw, J. M. Correlations in post‐mortem imaging‐histopathology studies of sporadic human cerebral small vessel disease: a systematic review. Neuropathol. Appl. Neurobiol. https://doi.org/10.1111/nan.12737 (2021).

    Article  PubMed  Google Scholar 

  53. Rajani, R. M. et al. Characterisation of early ultrastructural changes in the cerebral white matter of CADASIL small vessel disease using high-pressure freezing/freeze-substitution. Neuropathol. Appl. Neurobiol. 47, 694–704 (2021).

    CAS  PubMed  Google Scholar 

  54. Hadano, S. et al. A gene encoding a putative GTPase regulator is mutated in familial amyotrophic lateral sclerosis 2. Nat. Genet. 29, 166–173 (2001).

    CAS  PubMed  Google Scholar 

  55. Bindesbøll, C. et al. NBEAL1 controls SREBP2 processing and cholesterol metabolism and is a susceptibility locus for coronary artery disease. Sci. Rep. 10, 4528 (2020).

    PubMed  PubMed Central  Google Scholar 

  56. Biffi, A. et al. Variants at APOE influence risk of deep and lobar intracerebral hemorrhage. Ann. Neurol. 68, 934–943 (2010).

    PubMed  PubMed Central  Google Scholar 

  57. Rannikmäe, K. et al. Common variation in COL4A1/COL4A2 is associated with sporadic cerebral small vessel disease. Neurology 84, 918–926 (2015).

    PubMed  PubMed Central  Google Scholar 

  58. Rannikmäe, K. et al. COL4A2 is associated with lacunar ischemic stroke and deep ICH: meta-analyses among 21,500 cases and 40,600 controls. Neurology 89, 1829–1839 (2017).

    PubMed  PubMed Central  Google Scholar 

  59. Pelletier, A. et al. Age-related modifications of diffusion tensor imaging parameters and white matter hyperintensities as inter-dependent processes. Front. Aging Neurosci. 7, 255 (2015).

    PubMed  Google Scholar 

  60. Traylor, M. et al. Genome-wide meta-analysis of cerebral white matter hyperintensities in patients with stroke. Neurology 86, 146–153 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Armstrong, N. J. et al. Common genetic variation indicates separate causes for periventricular and deep white matter hyperintensities. Stroke 51, 2111–2121 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Fornage, M. et al. Genome-wide association studies of cerebral white matter lesion burden: the CHARGE consortium. Ann. Neurol. 69, 928–939 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Verhaaren, B. F. J. et al. Multiethnic genome-wide association study of cerebral white matter hyperintensities on MRI. Circ. Cardiovasc. Genet. 8, 398–409 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Knol, M. J. et al. Association of common genetic variants with brain microbleeds: a genome-wide association study. Neurology 95, e3331–e3343 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Chauhan, G. et al. Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting. Neurology 95, e3331–e3343 (2019).

    Google Scholar 

  66. Gonzalez, C. E. et al. Peripheral sphingolipids are associated with variation in white matter microstructure in older adults. Neurobiol. Aging 43, 156–163 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Sun, N., Keep, R. F., Hua, Y. & Xi, G. Critical role of the sphingolipid pathway in stroke: a review of current utility and potential therapeutic targets. Transl. Stroke Res. 7, 420–438 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Ohi, K. et al. DEGS2 polymorphism associated with cognition in schizophrenia is associated with gene expression in brain. Transl. Psychiatry 5, e550 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Wallis, M. et al. Surprisingly good outcome in antenatal diagnosis of severe hydrocephalus related to CCDC88C deficiency. Eur. J. Med. Genet. 61, 189–196 (2018).

    PubMed  Google Scholar 

  70. Jia, L., Piña-Crespo, J. & Li, Y. Restoring Wnt/β-catenin signaling is a promising therapeutic strategy for Alzheimer’s disease. Mol. Brain 12, 104 (2019).

    PubMed  PubMed Central  Google Scholar 

  71. Couffinhal, T., Dufourcq, P. & Duplàa, C. β-catenin nuclear activation. Circ. Res. 99, 1287–1289 (2006).

    CAS  PubMed  Google Scholar 

  72. Guo, F. et al. Canonical Wnt signaling in the oligodendroglial lineage–puzzles remain. Glia 63, 1671–1693 (2015).

    PubMed  Google Scholar 

  73. Zody, M. C. et al. Evolutionary toggling of the MAPT 17q21.31 inversion region. Nat. Genet. 40, 1076–1083 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Ikeda, M. et al. Genome-wide association study detected novel susceptibility genes for schizophrenia and shared trans-populations/diseases genetic effect. Schizophr. Bull. 45, 824–834 (2019).

    PubMed  Google Scholar 

  76. Kouri, N. et al. Genome-wide association study of corticobasal degeneration identifies risk variants shared with progressive supranuclear palsy. Nat. Commun. 6, 7247 (2015).

    CAS  PubMed  Google Scholar 

  77. Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Pain, O. et al. Novel insight into the etiology of autism spectrum disorder gained by integrating expression data with genome-wide association statistics. Biol. Psychiatry 86, 265–273 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Simón-Sánchez, J. et al. Genome-wide association study reveals genetic risk underlying Parkinson’s disease. Nat. Genet. 41, 1308–1312 (2009).

    PubMed  PubMed Central  Google Scholar 

  80. Davies, G. et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 9, 2098 (2018).

    PubMed  PubMed Central  Google Scholar 

  81. Koolen, D. A. et al. A new chromosome 17q21.31 microdeletion syndrome associated with a common inversion polymorphism. Nat. Genet. 38, 999–1001 (2006).

    CAS  PubMed  Google Scholar 

  82. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Sanchez-Roige, S. et al. Genome-wide association study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts. Am. J. Psychiatry 176, 107–118 (2019).

    PubMed  Google Scholar 

  84. Ikram, M. A. et al. Common variants at 6q22 and 17q21 are associated with intracranial volume. Nat. Genet. 44, 539–544 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Jonsson, B. A. et al. Brain age prediction using deep learning uncovers associated sequence variants. Nat. Commun. 10, 5409 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Zhao, B. et al. Common genetic variation influencing human white matter microstructure. Science 372, eabf3736 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Barbu, M. C. et al. Expression quantitative trait loci-derived scores and white matter microstructure in UK Biobank: a novel approach to integrating genetics and neuroimaging. Transl. Psychiatry 10, 55 (2020). Analysis of unexplored relationships between gene expression and heritable in vivo measures of human brain structural connectivity by combining expression quantitative trait loci and genome-wide genotype data.

    PubMed  PubMed Central  Google Scholar 

  88. Boettger, L. M., Handsaker, R. E., Zody, M. C. & McCarroll, S. A. Structural haplotypes and recent evolution of the human 17q21.31 region. Nat. Genet. 44, 881–885 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Steinberg, K. M. et al. Structural diversity and African origin of the 17q21.31 inversion polymorphism. Nat. Genet. 44, 872–880 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Tabara, Y. et al. Association of Chr17q25 with cerebral white matter hyperintensities and cognitive impairment: the J-SHIPP study. Eur. J. Neurol. 20, 860–862 (2013).

    CAS  PubMed  Google Scholar 

  91. Verhaaren, B. F. J. et al. Replication study of chr17q25 with cerebral white matter lesion volume. Stroke 42, 3297–3299 (2011).

    PubMed  Google Scholar 

  92. Adib-Samii, P. et al. 17q25 locus is associated with white matter hyperintensity volume in ischemic stroke, but not with lacunar stroke status. Stroke 44, 1609–1615 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Jian, X. et al. Exome chip analysis identifies low-frequency and rare variants in MRPL38 for white matter hyperintensities on brain magnetic resonance imaging. Stroke 49, 1812–1819 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Hara, K. et al. Association of HTRA1 mutations and familial ischemic cerebral small-vessel disease. N. Engl. J. Med. 360, 1729–1739 (2009).

    CAS  PubMed  Google Scholar 

  95. Dabertrand, F. et al. Potassium channelopathy-like defect underlies early-stage cerebrovascular dysfunction in a genetic model of small vessel disease. Proc. Natl Acad. Sci. USA 112, E796–E805 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Joutel, A., Haddad, I., Ratelade, J. & Nelson, M. T. Perturbations of the cerebrovascular matrisome: a convergent mechanism in small vessel disease of the brain? J. Cereb. Blood Flow. Metab. 36, 143–157 (2016). A description of the concept that cerebrovascular matrisome perturbations are a convergent pathological pathway in monogenic SVD and are likely to be relevant in cSVD.

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell. Proteom. 11, M111.014647 (2012).

    Google Scholar 

  98. Mehta, P. & Piao, X. Adhesion G-protein coupled receptors and extracellular matrix proteins: roles in myelination and glial cell development. Dev. Dyn. 246, 275–284 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Monk, K. R., Oshima, K., Jörs, S., Heller, S. & Talbot, W. S. Gpr126 is essential for peripheral nerve development and myelination in mammals. Development 138, 2673–2680 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Chang, A. et al. Cortical remyelination: a new target for repair therapies in multiple sclerosis. Ann. Neurol. 72, 918–926 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Diamantopoulou, E. et al. Identification of compounds that rescue otic and myelination defects in the zebrafish adgrg6 (gpr126) mutant. eLife 8, e44889 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Paavola, K. J., Sidik, H., Zuchero, J. B., Eckart, M. & Talbot, W. S. Type IV collagen is an activating ligand for the adhesion G protein-coupled receptor GPR126. Sci. Signal. 7, ra76 (2014).

    PubMed  PubMed Central  Google Scholar 

  103. Rajani, R. M. et al. Reversal of endothelial dysfunction reduces white matter vulnerability in cerebral small vessel disease in rats. Sci. Transl. Med. 10, eaam9507 (2018).

    PubMed  Google Scholar 

  104. Corder, E. H. et al. Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat. Genet. 7, 180–184 (1994).

    CAS  PubMed  Google Scholar 

  105. Jun, G. R. et al. Transethnic genome-wide scan identifies novel Alzheimer’s disease loci. Alzheimers Dement. 13, 727–738 (2017).

    PubMed  PubMed Central  Google Scholar 

  106. Maddison, D. C. & Giorgini, F. The kynurenine pathway and neurodegenerative disease. Semin. Cell Dev. Biol. 40, 134–141 (2015).

    CAS  PubMed  Google Scholar 

  107. Strang, K. H., Golde, T. E. & Giasson, B. I. MAPT mutations, tauopathy, and mechanisms of neurodegeneration. Lab. Invest. 99, 912–928 (2019).

    PubMed  PubMed Central  Google Scholar 

  108. Harold, D. et al. Interaction between the ADAM12 and SH3MD1 genes may confer susceptibility to late-onset Alzheimer’s disease. Am. J. Med. Genet. B Neuropsychiatr. Genet. 144B, 448–452 (2007).

    CAS  PubMed  Google Scholar 

  109. Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Wu, Y. et al. Identification of the primate-specific gene BTN3A2 as an additional schizophrenia risk gene in the MHC loci. EBioMedicine 44, 530–541 (2019).

    PubMed  PubMed Central  Google Scholar 

  111. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    PubMed Central  Google Scholar 

  112. Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Xue, A. et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9, 2941 (2018).

    PubMed  PubMed Central  Google Scholar 

  114. Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Wootton, R. E. et al. Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study. Psychol. Med. 50, 2435–2443 (2020).

    PubMed  Google Scholar 

  116. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Holliday, E. G. et al. Genetic overlap between diagnostic subtypes of ischemic stroke. Stroke 46, 615–619 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Mishra, A. et al. Association of variants in HTRA1 and NOTCH3 with MRI-defined extremes of cerebral small vessel disease in older subjects. Brain 142, 1009–1023 (2019). In this study, whole-exome sequencing in a population-based setting provided evidence that a composite, extreme MRI-derived phenotype for cSVD is useful for identification of rare variants, adding to evidence that NOTCH3 mutations that cause CADASIL are more common than previously suspected.

    PubMed  PubMed Central  Google Scholar 

  119. Narayan, S. K., Gorman, G., Kalaria, R. N., Ford, G. A. & Chinnery, P. F. The minimum prevalence of CADASIL in northeast England. Neurology 78, 1025–1027 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Razvi, S. S. M., Davidson, R., Bone, I. & Muir, K. W. The prevalence of cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy (CADASIL) in the west of Scotland. J. Neurol. Neurosurg. Psychiatry 76, 739–741 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Verdura, E. et al. Heterozygous HTRA1 mutations are associated with autosomal dominant cerebral small vessel disease. Brain 138, 2347–2358 (2015).

    PubMed  Google Scholar 

  122. Siitonen, M. et al. Multi-infarct dementia of Swedish type is caused by a 3’UTR mutation of COL4A1. Brain 140, e29 (2017).

    PubMed  PubMed Central  Google Scholar 

  123. Lemmens, R. et al. Novel COL4A1 mutations cause cerebral small vessel disease by haploinsufficiency. Hum. Mol. Genet. 22, 391–397 (2013).

    CAS  PubMed  Google Scholar 

  124. Li, J. et al. Replication of top loci from COL4A1/2 associated with white matter hyperintensity burden in patients with ischemic stroke. Stroke 51, 3751–3755 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Malik, R. et al. Genome-wide meta-analysis identifies 3 novel loci associated with stroke: MEGASTROKE and UK Biobank GWAS. Ann. Neurol. 84, 934–939 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. Simon, A. J. et al. Mutations in STN1 cause Coats plus syndrome and are associated with genomic and telomere defects. J. Exp. Med. 213, 1429–1440 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Schmidt, H. et al. Genetic variants of the NOTCH3 gene in the elderly and magnetic resonance imaging correlates of age-related cerebral small vessel disease. Brain 134, 3384–3397 (2011).

    PubMed  PubMed Central  Google Scholar 

  128. Rutten, J. W. et al. Archetypal NOTCH3 mutations frequent in public exome: implications for CADASIL. Ann. Clin. Transl. Neurol. 3, 844–853 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Rutten, J. W. et al. Broad phenotype of cysteine-altering NOTCH3 variants in UK Biobank: CADASIL to nonpenetrance. Neurology 95, e1835–e1843 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Cho, B. P. H. et al. NOTCH3 variants are more common than expected in the general population and associated with stroke and vascular dementia: an analysis of 200 000 participants. J. Neurol. Neurosurg. Psychiatry 92, 694–701 (2021). Using UK Biobank data, this study provides evidence that NOTCH3 mutations that cause CADASIL are more common than expected in the general population and describes their phenotypic spectrum.

    PubMed  Google Scholar 

  131. Chauhan, G. et al. Identification of additional risk loci for stroke and small vessel disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 15, 695–707 (2016). This study identified a novel risk locus for any stroke and subclinical cerebral small vessel disease in a population-based setting, with experimental follow-up in mice and zebrafish implicating FOXF2.

    Google Scholar 

  132. French, C. R. et al. Mutation of FOXC1 and PITX2 induces cerebral small-vessel disease. J. Clin. Invest. 124, 4877–4881 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Whitesell, T. R. et al. foxc1 is required for embryonic head vascular smooth muscle differentiation in zebrafish. Dev. Biol. 453, 34–47 (2019).

    CAS  PubMed  Google Scholar 

  134. Siegenthaler, J. A. et al. Foxc1 is required by pericytes during fetal brain angiogenesis. Biol. Open 2, 647–659 (2013).

    PubMed  PubMed Central  Google Scholar 

  135. Reyahi, A. et al. Foxf2 is required for brain pericyte differentiation and development and maintenance of the blood-brain barrier. Dev. Cell 34, 19–32 (2015).

    CAS  PubMed  Google Scholar 

  136. He, W. et al. FOXF2 acts as a crucial molecule in tumours and embryonic development. Cell Death Dis. 11, 424 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Smith, G. D. & Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003).

    PubMed  Google Scholar 

  138. Georgakis, M. K. et al. Genetically determined blood pressure, antihypertensive drug classes, and risk of stroke subtypes. Neurology 95, e353–e361 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Yuan, S., Tang, B., Zheng, J. & Larsson, S. C. Circulating lipoprotein lipids, apolipoproteins and ischemic stroke. Ann. Neurol. 88, 1229–1236 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Georgakis, M. K. et al. Genetic determinants of blood lipids and cerebral small vessel disease: role of high-density lipoprotein cholesterol. Brain 143, 597–610 (2020).

    PubMed  PubMed Central  Google Scholar 

  141. Falcone, G. J. et al. Genetically elevated LDL associates with lower risk of intracerebral hemorrhage. Ann. Neurol. 88, 56–66 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Valdes-Marquez, E. et al. Relative effects of LDL-C on ischemic stroke and coronary disease: a Mendelian randomization study. Neurology 92, e1176–e1187 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Pan, Y., Li, H., Wang, Y. & Meng, X. Causal effect of Lp(a) [lipoprotein(a)] level on ischemic stroke and Alzheimer disease: a Mendelian randomization study. Stroke 50, 3532–3539 (2019).

    CAS  PubMed  Google Scholar 

  144. Georgakis, M. K. et al. Diabetes mellitus, glycemic traits, and cerebrovascular disease: a Mendelian randomization study. Neurology 96, e1732–e1742 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Larsson, S. C. et al. Type 2 diabetes, glucose, insulin, BMI, and ischemic stroke subtypes: Mendelian randomization study. Neurology 89, 454–460 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. Liu, J., Rutten-Jacobs, L., Liu, M., Markus, H. S. & Traylor, M. Causal impact of type 2 diabetes mellitus on cerebral small vessel disease: a Mendelian randomization analysis. Stroke 49, 1325–1331 (2018).

    PubMed  PubMed Central  Google Scholar 

  147. Lu, H., Wu, P. F., Li, R. Z., Zhang, W. & Huang, G. X. Sleep duration and stroke: a Mendelian randomization study. Front. Neurol. 11, 976 (2020).

    PubMed  PubMed Central  Google Scholar 

  148. Dale, C. E. et al. Causal associations of adiposity and body fat distribution with coronary heart disease, stroke subtypes, and type 2 diabetes mellitus: a Mendelian randomization analysis. Circulation 135, 2373–2388 (2017).

    PubMed  PubMed Central  Google Scholar 

  149. Marini, S. et al. Mendelian randomization study of obesity and cerebrovascular disease. Ann. Neurol. 87, 516–524 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Qian, Y. et al. Coffee consumption and risk of stroke: a Mendelian randomization study. Ann. Neurol. 87, 525–532 (2020).

    PubMed  Google Scholar 

  151. Wang, M. et al. Higher tea consumption is associated with decreased risk of small vessel stroke. Clin. Nutr. 40, 1430–1435 (2021).

    CAS  PubMed  Google Scholar 

  152. Qian, Y. et al. Role of cigarette smoking in the development of ischemic stroke and its subtypes: a Mendelian randomization study. Clin. Epidemiol. 11, 725–731 (2019).

    PubMed  PubMed Central  Google Scholar 

  153. Larsson, S. C., Burgess, S. & Michaëlsson, K. Smoking and stroke: a mendelian randomization study. Ann. Neurol. 86, 468–471 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. Harshfield, E. L., Georgakis, M. K., Malik, R., Dichgans, M. & Markus, H. S. Modifiable lifestyle factors and risk of stroke: a Mendelian randomization analysis. Stroke 52, 931–936 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Wang, T. et al. Birth weight and stroke in adult life: genetic correlation and causal inference with genome-wide association data sets. Front. Neurosci. 14, 479 (2020).

    PubMed  PubMed Central  Google Scholar 

  156. Cai, H. et al. Major depression and small vessel stroke: a Mendelian randomization analysis. J. Neurol. 266, 2859–2866 (2019).

    CAS  PubMed  Google Scholar 

  157. Maners, J. et al. A Mendelian randomization of γ’ and total fibrinogen levels in relation to venous thromboembolism and ischemic stroke. Blood 136, 3062–3069 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Georgakis, M. K. et al. Genetically determined levels of circulating cytokines and risk of stroke. Circulation 139, 256–268 (2019). This study demonstrates how Mendelian randomization can be used to provide evidence for causal involvement of circulating biomarkers for stroke (MCP-1), leveraging summary data from GWAS.

    CAS  PubMed  PubMed Central  Google Scholar 

  159. Lin, J., Wang, Y. & Pan, Y. Inflammatory biomarkers and risk of ischemic stroke and subtypes: a 2-sample Mendelian randomization study. Neurol. Res. 42, 118–125 (2020).

    PubMed  Google Scholar 

  160. Yuan, S., Lin, A., He, Q. Q., Burgess, S. & Larsson, S. C. Circulating interleukins in relation to coronary artery disease, atrial fibrillation and ischemic stroke and its subtypes: a two-sample Mendelian randomization study. Int. J. Cardiol. 313, 99–104 (2020).

    PubMed  PubMed Central  Google Scholar 

  161. Larsson, S. C., Traylor, M. & Markus, H. S. Homocysteine and small vessel stroke: a Mendelian randomization analysis. Ann. Neurol. 85, 495–501 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  162. Mulvey, B., Lagunas, T. & Dougherty, J. D. Massively parallel reporter assays: defining functional psychiatric genetic variants across biological contexts. Biol. Psychiatry 89, 76–89 (2021).

    CAS  PubMed  Google Scholar 

  163. Sanseau, P. et al. Use of genome-wide association studies for drug repositioning. Nat. Biotechnol. 30, 317–320 (2012).

    CAS  PubMed  Google Scholar 

  164. Gill, D. et al. Use of genetic variants related to antihypertensive drugs to inform on efficacy and side effects. Circulation 140, 270–279 (2019). In this study, genetic variants were used to explore the efficacy and side effects of different antihypertensive drug classes for stroke prevention, providing proof-of-concept that genomic data can provide genetic support for drug effects.

    CAS  PubMed  PubMed Central  Google Scholar 

  165. Wardlaw, J. M. et al. ESO guideline on covert cerebral small vessel disease. Eur. Stroke J. 6, CXI–CLXII (2021).

    PubMed  PubMed Central  Google Scholar 

  166. Abraham, G. & Inouye, M. Genomic risk prediction of complex human disease and its clinical application. Curr. Opin. Genet. Dev. 33, 10–16 (2015).

    CAS  PubMed  Google Scholar 

  167. Habes, M. et al. The brain chart of aging: machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans. Alzheimers Dement. 17, 89–102 (2021).

    CAS  PubMed  Google Scholar 

  168. Abraham, G. et al. Genomic prediction of coronary heart disease. Eur. Heart J. 37, 3267–3278 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  169. Escott-Price, V., Shoai, M., Pither, R., Williams, J. & Hardy, J. Polygenic score prediction captures nearly all common genetic risk for Alzheimer’s disease. Neurobiol. Aging 49, 214.e7–214.e11 (2017).

    Google Scholar 

  170. Lambert, S. A., Abraham, G. & Inouye, M. Towards clinical utility of polygenic risk scores. Hum. Mol. Genet. 28, R133–R142 (2019).

    CAS  PubMed  Google Scholar 

  171. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  172. Abraham, G. et al. Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke. Nat. Commun. 10, 5819 (2019). Abraham et al. developed a new polygenic risk score (PRS) approach based on the combination of PRS from various traits (metaGRS) and applied it to predict ischaemic stroke in the UK biobank.

    CAS  PubMed  PubMed Central  Google Scholar 

  173. Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  174. Mok, V. et al. Race-ethnicity and cerebral small vessel disease–comparison between Chinese and White populations. Int. J. Stroke 9(Suppl A100), 36–42 (2014).

    PubMed  Google Scholar 

  175. Keene, K. L. et al. Genome-wide association study meta-analysis of stroke in 22 000 individuals of African descent identifies novel associations with stroke. Stroke 51, 2454–2463 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  176. Carty, C. L. et al. Meta-analysis of genome-wide association studies identifies genetic risk factors for stroke in African Americans. Stroke 46, 2063–2068 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  177. Márquez-Luna, C., Loh, P. R. & Price, A. L. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol. 41, 811–823 (2017).

    PubMed  PubMed Central  Google Scholar 

  178. Montaner, J. et al. Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke. Nat. Rev. Neurol. 16, 247–264 (2020).

    PubMed  Google Scholar 

  179. Woo, D. et al. Top research priorities for stroke genetics. Lancet Neurol. 17, 663–665 (2018).

    PubMed  Google Scholar 

  180. Adams, H. P. Jr et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 24, 35–41 (1993).

    PubMed  Google Scholar 

  181. Ay, H. et al. A computerized algorithm for etiologic classification of ischemic stroke: the Causative Classification of Stroke System. Stroke 38, 2979–2984 (2007).

    PubMed  Google Scholar 

  182. Lin, M. K. et al. HTRA1, an age-related macular degeneration protease, processes extracellular matrix proteins EFEMP1 and TSP1. Aging Cell 17, e12710 (2018).

    PubMed  PubMed Central  Google Scholar 

  183. Zagryazhskaya-Masson, A. et al. Intersection of TKS5 and FGD1/CDC42 signaling cascades directs the formation of invadopodia. J. Cell Biol. 219, e201910132 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  184. Leonardo, C. C., Eakin, A. K., Ajmo, J. M. & Gottschall, P. E. Versican and brevican are expressed with distinct pathology in neonatal hypoxic-ischemic injury. J. Neurosci. Res. 86, 1106–1114 (2008).

    CAS  PubMed  Google Scholar 

  185. Menezes, M. J. et al. The extracellular matrix protein laminin α2 regulates the maturation and function of the blood-brain barrier. J. Neurosci. 34, 15260–15280 (2014).

    PubMed  PubMed Central  Google Scholar 

  186. Fukada, T. et al. The zinc transporter SLC39A13/ZIP13 is required for connective tissue development; its involvement in BMP/TGF-β signaling pathways. PLoS ONE 3, e3642 (2008).

    PubMed  PubMed Central  Google Scholar 

  187. Yoneshiro, T. et al. BCAA catabolism in brown fat controls energy homeostasis through SLC25A44. Nature 572, 614–619 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  188. Bowden, J. & Holmes, M. V. Meta-analysis and Mendelian randomization: a review. Res. Synth. Methods 10, 486–496 (2019).

    PubMed  PubMed Central  Google Scholar 

  189. Burgess, S. & Thompson, S. G. Avoiding bias from weak instruments in Mendelian randomization studies. Int. J. Epidemiol. 40, 755–764 (2011).

    PubMed  Google Scholar 

  190. Hemani, G., Bowden, J. & Davey Smith, G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum. Mol. Genet. 27, R195–R208 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  191. Pierce, B. L., Ahsan, H. & Vanderweele, T. J. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int. J. Epidemiol. 40, 740–752 (2011).

    PubMed  Google Scholar 

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Acknowledgements

We thank S. 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 agreement numbers 643417, 640643, 667375, and 754517.

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 time points 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 × 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 r² < 0.1); for correlated SNPs (r² > 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.

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Authors and Affiliations

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

    Constance Bordes, Muralidharan Sargurupremraj, Aniket Mishra & Stéphanie Debette

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

    Muralidharan Sargurupremraj

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

    Stéphanie Debette

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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.

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Glossary

White matter hyperintensity (WMH) of presumed vascular origin

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, but as isointense or hypointense on T1-weighted images (although not as hypointense as cerebrospinal fluid).

Lacunes of presumed vascular origin

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

Cerebral microbleeds

A small area (usually 2–5 mm in diameter) that appears as a signal void with associated blooming on T2*-weighted MRI or susceptibility-weighted imaging, which is likely to mostly reflect vascular leakage of blood cells.

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).

Genetic variants

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

Alleles

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

Fractional anisotropy

A scalar measure derived from diffusion tensor imaging that quantifies the overall directionality of water diffusion in brain tissue. The measure is greatest in organized white matter tracts and lowest in the case of free water movement, such as in cerebrospinal fluid. Reduced fractional anisotropy in white matter is seen in cerebral small vessel disease.

Mean diffusivity

A scalar measure obtained from diffusion tensor imaging that quantifies the magnitude of water diffusion regardless of the direction. It is used to study microstructural properties and the structural integrity of brain tissue. Increased mean diffusivity in white matter is seen in cerebral small vessel disease.

Linkage disequilibrium

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

Inversion polymorphism

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

Single nucleotide polymorphisms

A single nucleotide variation in the DNA sequence.

Pleiotropy

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

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Bordes, C., Sargurupremraj, M., Mishra, A. et al. Genetics of common cerebral small vessel disease. Nat Rev Neurol 18, 84–101 (2022). https://doi.org/10.1038/s41582-021-00592-8

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