Genetic Architecture of White Matter Hyperintensities Differs in Hypertensive and Nonhypertensive Ischemic Stroke

Abstract

Background and Purpose Epidemiological studies suggest white matter hyperintensities (WMH) are extremely heritable but the underlying genetic variants are largely unknown. Pathophysiological heterogeneity is known to reduce the power of genome-wide association studies (GWAS). Hypertensive and non-hypertensive individuals with WMH might have different underlying pathologies. We used GWAS data to calculate the variance in WMH volume (WMHV) explained by common single nucleotide polymorphisms (SNPs) as a measure of heritability (HSNP), and tested the hypothesis that WMH heritability differs between hypertensive and non-hypertensives. Methods WMHV was measured on MRI in the stroke-free cerebral hemisphere of 2336 ischemic stroke cases with GWAS data. After adjustment for age and intracranial volume, we determined which cardiovascular risk factors were independent predictors of WMHV. Using the genome-wide complex trait analysis (GCTA) tool to estimate HSNP for WMHV overall, and within subgroups stratified by risk factors found to be significant in multivariate analyses. Results A significant proportion of the variance of WMHV was attributable to common SNPs after adjustment for significant risk factors (HSNP=0.23,p=0.0026). When stratified by hypertension, SNP heritability estimates were higher amongst hypertensive individuals; (HSNP=0.45, p=7.99e-5); this increase was greater than expected by chance (p=0.012). In contrast estimates were lower, and non-significant, in non-hypertensives (HSNP=0.13, p=0.13). Conclusions A quarter of variance is attributable to common SNPs but this estimate was greater in hypertensive individuals. These findings suggest that the genetic architecture of WMH in ischemic stroke differs between hypertensives and non-hypertensives. Future WMHV GWAS studies may gain power by accounting for this interaction.

Genome-Wide Genotyping Demonstrates a Polygenic Risk Score Associated With White Matter Hyperintensity Volume in CADASIL

“…10 In the current study, we found no association signal with variants at 17q25. This might relate to differences in the protocols used for measuring WMH burden or to a differential impact of 17q25 region on WMH burden in normal elderly subjects and CADASIL.…”

“…However, differentiating lacunes from other imaging findings, such as dilated Virchow-Robin spaces, may be difficult, 22 and a recent study in patients with acute ischemic stroke showed that the signals for WMH and lacunes do not necessarily overlap. 10 In conclusion, we found a polygenic risk score to be associated with WMH volume in CADASIL. Our results suggest that multiple genetic variants with small effect sizes influence WMH burden.…”

“…3,7,8 In contrast, a recent report using genome-wide approaches provided evidence for an association between a locus on chromosome 17q25 and WMH burden, which was subsequently replicated in an independent study. 9,10 The identification of genetic determinants accounting for the high degree of heritability of WMH has been challenging, despite large sample sizes available for study. A presumed explanation has been heterogeneity within study samples and frequent coexistence of age-related pathologies and vascular risk factors.…”

Emerging Evidence for Pathogenesis of Sporadic Cerebral Small Vessel Disease

“…31 Discrepancy exists between high heritability estimates (>50%) for WMH obtained in twin and family history studies 32 and relatively low estimates (~25%) derived from genome-wide association studies. 33 A factor influencing such missing heritability is gene-environment interactions; thus, measuring environment rigorously and analyzing it against genome-wide association studies data could be a useful strategy. In fact, interaction between VRFs and gene expression could help explain part of the missing heritability, as WMH volume attributable to common genetic variants has been estimated to be 45% for hypertensives against 13% for nonhypertensives.…”

Shared genetic contribution to ischemic stroke and Alzheimer's disease

“…These approaches use distant relatedness between individuals to infer heritability and coheritability of complex traits and have been used to provide insights into and between many diseases 35, 36, 37. We performed a bivariate GREML analysis in two large GWAS cohorts consisting of AD and IS cases and controls to assess evidence of a shared genetic contribution to the two diseases.…”