Open Access Research article

Complexity in the genetic architecture of leukoaraiosis in hypertensive sibships from the GENOA Study

Jennifer A Smith1*, Stephen T Turner2, Yan V Sun1, Myriam Fornage3, Reagan J Kelly1, Thomas H Mosley4, Clifford R Jack5, Iftikhar J Kullo6 and Sharon LR Kardia1

Author Affiliations

1 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA

2 Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic and Foundation, Rochester, MN, USA

3 Human Genetics Center and Institute of Molecular Medicine, University of Texas-Houston Health Science Center, Houston, TX, USA

4 Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA

5 Department of Diagnostic Radiology, Mayo Clinic and Foundation, Rochester, MN, USA

6 Division of Cardiovascular Diseases, Department of Internal Medicine, Mayo Clinic and Foundation, Rochester, MN, USA

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BMC Medical Genomics 2009, 2:16  doi:10.1186/1755-8794-2-16

Published: 7 April 2009

Abstract

Background

Subcortical white matter hyperintensity on magnetic resonance imaging (MRI) of the brain, referred to as leukoaraiosis, is associated with increased risk of stroke and dementia. Hypertension may contribute to leukoaraiosis by accelerating the process of arteriosclerosis involving penetrating small arteries and arterioles in the brain. Leukoaraiosis volume is highly heritable but shows significant inter-individual variability that is not predicted well by any clinical covariates (except for age) or by single SNPs.

Methods

As part of the Genetics of Microangiopathic Brain Injury (GMBI) Study, 777 individuals (74% hypertensive) underwent brain MRI and were genotyped for 1649 SNPs from genes known or hypothesized to be involved in arteriosclerosis and related pathways. We examined SNP main effects, epistatic (gene-gene) interactions, and context-dependent (gene-environment) interactions between these SNPs and covariates (including conventional and novel risk factors for arteriosclerosis) for association with leukoaraiosis volume. Three methods were used to reduce the chance of false positive associations: 1) false discovery rate (FDR) adjustment for multiple testing, 2) an internal replication design, and 3) a ten-iteration four-fold cross-validation scheme.

Results

Four SNP main effects (in F3, KITLG, CAPN10, and MMP2), 12 SNP-covariate interactions (including interactions between KITLG and homocysteine, and between TGFB3 and both physical activity and C-reactive protein), and 173 SNP-SNP interactions were significant, replicated, and cross-validated. While a model containing the top single SNPs with main effects predicted only 3.72% of variation in leukoaraiosis in independent test samples, a multiple variable model that included the four most highly predictive SNP-SNP and SNP-covariate interactions predicted 11.83%.

Conclusion

These results indicate that the genetic architecture of leukoaraiosis is complex, yet predictive, when the contributions of SNP main effects are considered in combination with effects of SNP interactions with other genes and covariates.