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

Poneh Adib‐Samii(Faculty of 1000 (United Kingdom)), William J. Devan(Faculty of 1000 (United Kingdom)), Matthew Traylor(Faculty of 1000 (United Kingdom)), Silvia Lanfranconi(Faculty of 1000 (United Kingdom)), Cathy R. Zhang(Faculty of 1000 (United Kingdom)), Lisa Cloonan(Faculty of 1000 (United Kingdom)), Guido J. Falcone(Faculty of 1000 (United Kingdom)), Farid Radmanesh(Faculty of 1000 (United Kingdom)), Kaitlin Fitzpatrick(Faculty of 1000 (United Kingdom)), Allison Kanakis(Faculty of 1000 (United Kingdom)), Peter M. Rothwell(Faculty of 1000 (United Kingdom)), Cathie Sudlow(Faculty of 1000 (United Kingdom)), Giorgio B. Boncoraglio(Faculty of 1000 (United Kingdom)), James F. Meschia(Jacksonville College), Christopher Levi(Faculty of 1000 (United Kingdom)), Martin Dichgans(Faculty of 1000 (United Kingdom)), Steve Bevan(Faculty of 1000 (United Kingdom)), Jonathan Rosand(Harvard University Press), Natalia S. Rost(Faculty of 1000 (United Kingdom)), Hugh S. Markus(Faculty of 1000 (United Kingdom))
Stroke
December 31, 2014
Cited by 29Open Access
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Abstract

BACKGROUND AND PURPOSE: Epidemiological studies suggest that 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 nonhypertensive 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 (SNP heritability [HSNP]) and tested the hypothesis that WMH heritability differs between hypertensive and nonhypertensive individuals. 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 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). HSNP estimates were higher among hypertensive individuals (HSNP=0.45; P=7.99×10(-5)); this increase was greater than expected by chance (P=0.012). In contrast, estimates were lower, and nonsignificant, in nonhypertensive individuals (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 nonhypertensives. Future WMHV GWAS studies may gain power by accounting for this interaction.


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