Genetics of common cerebral small vessel disease

C Bordes, M Sargurupremraj, A Mishra… - Nature Reviews …, 2022 - nature.com
Nature Reviews Neurology, 2022nature.com
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 …
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.
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