Unravelling the genetic architecture of cerebral small vessel disease in the context of stroke

Abstract

Cerebral small vessel disease (cSVD) is a major contributor to stroke, dementia, and cognitive decline. Despite significant progress through large-scale genome-wide association studies (GWAS) for cSVD and stroke, the genetic architecture underlying these conditions remains poorly understood. This review highlights recent advancements in statistical tools and provides a comprehensive overview of current insights into the genetic underpinnings of cSVD and stroke. We focus on the relevance of non-additive effects, local heritability, and polygenicity in shaping these traits. While single nucleotide polymorphism (SNP)-based heritability estimates for stroke and cSVD traits remain lower than pedigree-based estimates, we explore challenges and opportunities in addressing this "missing heritability." In particular, we emphasize the importance of investigating both common and rare variants, to better characterize the genetic basis of cSVD. Furthermore, we discuss the role of negative selection in shaping complex disease traits and the relevance of the "omnigenic" model in the context of cSVD traits. In summary, we aim to provide a more nuanced understanding of cSVD and stroke genetics, paving the way for future research into their molecular mechanisms.

Keywords: Cerebral small vessel disease; GWAS; missing heritability; neuroimaging; polygenicity.

Figure 1.

Directed acyclic graph on the causal association of a given exposure (risk factor or protein levels) with an outcome. Genetic instruments (Z) satisfying the key MR assumptions: IV1 – Z strongly predictive of the exposure (X); IV2 – absence of correlated pleiotropy of Z through confounders (C); IV3 – absence of Z directly associated with the outcome (Y).

Figure 2.

Loci-level heritability of cSVD traits. Colored dots represent non-zero h² estimates (P < 0.05). Gene names correspond to loci with genome-wide significant (P < 2.94E-05) loci-level h². FA: Fractional anisotropy, MD: Mean diffusivity; WMH: white matter hyperintensities; WMPVS: white matter – perivascular space.

Figure 3.

Sample size requirements and predictions for future GWAS, including cSVD traits. (a) Effective (N_eff) sample size required for genome-wide significant SNPs to explain 50% or 90% of SNP heritability. (b) Predicted number of independent SNPs (M_GWAS) at %h²GWAS = 0.5. (c) Theoretical maximum PGS accuracy, defined as PGS r², at %h²GWAS = 0.5. MD: mean diffusivity; WMH: white matter hyperintensities; PVS: white matter –perivascular space; AFib: atrial fibrillation; CAD: coronary artery disease; BP: blood pressure; BMI: body mass index. (Reproduced with permission from Luke J. O’Connor, Connor L.J et al., 2021, for non cSVD traits).

Figure 4.

Comparison of polygenicity (M_e) estimates for stroke and cSVD traits with other complex-disease traits. Inf. Trait: Model trait with infinitesimal architecture. SCZ: schizophrenia; SMK: smoking; SBP: systolic blood pressure; IBD: inflammatory bowel disease; CVD: cardiovascular disease; HT: hypertension; BMI: body mass index; AS: all-cause stroke; WHR: waist-hip ratio; WMH: white matter hyperintensities; FA: fractional anisotropy; PVS-WM: white matter – perivascular spaces; RA: rheumatoid arthritis; AD-meta: Alzheimer’s disease including parental history; MD: mean diffusivity; T2D: type 2 diabetes; AID: auto-immune disorder.