Cheng, Yu‐Ching

BACKGROUND AND PURPOSE: Ischemic stroke is a heterogeneous complex disease that results from the interaction between numerous genetic and environmental risk factors. Stroke heritability is approximately 40% in the overall population, similar to that of cancer, although there may be important differences in the etiologies of young (<49 years old) versus older onset ischemic stroke. The goal of this study was to conduct a mechanistic genome-wide association (GWA) analysis contrasting the gene pathways involved in young- and old-onset stroke. METHODS: Our analysis included 946 young subjects (448 ischemic stroke subjects and 498 age and sex matched controls, mean age of stroke onset 41 years) and 2558 older subjects (1070 ischemic stroke subjects and 1488 controls, mean age of stroke onset 66.5 years). All subjects were of European ancestry. The young onset cases and controls were genotyped by Illumina Omni1-Quad array with imputation up to 1.8 M SNPs and the old onset cases and controls by a combination of 550 v1 and v3 as well as 610 Illumina arrays with imputation up to 5.0 M SNPs. Single SNP association testing was performed within each contributing study by logistic regression adjusting for age, sex and population structure. Numbers of genes assigned with significant SNPs (P<=0.0001) were 18 and 25 with young- and old-onset groups, respectively, which were used as an input to pathway analysis by the Pathway Studio (Ariadne Genomics, Co.) RESULTS: SNP-level analyses did not reveal any significant associations with each group that met Bonferroni adjusted thresholds for statistical significance. In analyzing the ‘suggestively associated SNPs’, protein-protein interaction pathway analysis in the young-onset group revealed a strong segregation of significant hub proteins (CAT, RUNX1, and EIF4E) with more than 100 connections, while in the old-onset group the interaction networks were dispersed without much segregation. We observed that protein-protein interaction networks have a power-law connectivity distribution in both young- and old-onset groups. We identified 3 significant and focused pathways, ROS metabolism, melanogenesis, and IGF1R->CdEBPA/FOXO1A signaling, enriched for young-onset stroke (P<0.04). In the old-onset group, 10 pathways were identified involving broader signaling pathways. CONCLUSIONS: Young-onset stroke may involve genetic predisposition characterized by a few distinct and finite pathways. Our data suggests that young onset stroke may involve a few pathways that relate to a small number of key hub proteins, including ROS metabolism, melanogenesis, and IGF1R->CEBPA/FOXO1A signaling. Our analysis is consistent with the hypothesis that different pathways may be involved in young versus old onset stroke.

Introduction: Estrogens have been associated with risk of stroke in both men and women. Estradiol and estrone are synthesized from testosterone via the aromatase enzyme in both men and postmenopausal women. Genetic variation affecting function of the aromatase gene (CYP19A1) has been linked to elevated estradiol levels. We tested the hypothesis that these variants, as well as a modifying gene, are associated with ischemic stroke in three cohorts: the Vitamin Intervention for Stroke Prevention trial (VISP), Women’s Health Initiative (WHI), and Genetic Susceptibility to Early-Onset Stroke study (GEOS). Methods: Genomic data for individuals of European descent with ischemic stroke was compared against an equal number of controls: VISP (1723 cases - 65% men), WHI (436 cases - all postmenopausal women), GEOS (557 cases - all men age <55). Genotyping was funded through the Genomics and Randomized Trials Network (GARNET) and Gene Environment Association Studies (GENEVA) consortia. All cohorts used the Illumina Omni1-Quad chip for genotyping with imputation as needed. Eight candidate single nucleotide polymorphisms (SNPs) in CYP19A1 on chromosome 15 and an additional regulatory SNP in a modifier locus on chromosome 8 were chosen for analysis due to coding status or previous association with estradiol levels. Analyses were adjusted for age, hormone therapy use (women), and the top three principal components. Association results were determined as odds ratios for individual SNPs (effect allele = minor), with significance level of p<0.05. Results: GWAS results are tabulated. There were no significant differences in allele frequency for four SNPs in CYP19A1 among stroke patients and controls. The additional four SNPs were monomorphic in all three populations. The modifier gene SNP (rs1864729) was less common among young men with prior stroke in GEOS, but not men in VISP or in women. Conclusions: Aromatase genetic variation was not associated with risk of stroke in these three cohorts. One SNP that modifies aromatase function was less common among young, but not older men with stroke. Replication in other studies is necessary.

Background and Purpose— Meta-analyses of extant genome-wide data illustrate the need to focus on subtypes of ischemic stroke for gene discovery. The National Institute of Neurological Disorders and Stroke SiGN (Stroke Genetics Network) contributes substantially to meta-analyses that focus on specific subtypes of stroke. Methods— The National Institute of Neurological Disorders and Stroke SiGN includes ischemic stroke cases from 24 genetic research centers: 13 from the United States and 11 from Europe. Investigators harmonize ischemic stroke phenotyping using the Web-based causative classification of stroke system, with data entered by trained and certified adjudicators at participating genetic research centers. Through the Center for Inherited Diseases Research, the Network plans to genotype 10 296 carefully phenotyped stroke cases using genome-wide single nucleotide polymorphism arrays and adds to these another 4253 previously genotyped cases, for a total of 14 549 cases. To maximize power for subtype analyses, the study allocates genotyping resources almost exclusively to cases. Publicly available studies provide most of the control genotypes. Center for Inherited Diseases Research–generated genotypes and corresponding phenotypes will be shared with the scientific community through the US National Center for Biotechnology Information database of Genotypes and Phenotypes, and brain MRI studies will be centrally archived. Conclusions— The Stroke Genetics Network, with its emphasis on careful and standardized phenotyping of ischemic stroke and stroke subtypes, provides an unprecedented opportunity to uncover genetic determinants of ischemic stroke.