Moreover, all local hospitals annually provided lists of stroke discharges .... autopsy evidence excluding a hemorrhage, or in the absence of such direct evidence (in
Supplementary webappendix This webappendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors. Supplement to: Traylor M, Farrall M, Holliday EG, et al, on behalf of the International Stroke Genetics Consortium. Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE Collaboration): a meta-analysis of genome-wide association studies. Lancet Neurol 2012; published online Oct 5. http://dx.doi.org/10.1016/S14744422(12)70234-X.
Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE Collaboration): a meta-analysis of genome-wide association studies Web Extra Material
Clinical phenotyping and stroke subtyping In two discovery centers (Rotterdam, HPS) and three replication centers (Copenhagen, VISP, Portugal) data on subtypes was not available and these cohorts were only included in analyses of all ischaemic stroke and sex specific analyses. Statistical Analysis across studies Analysis was restricted to SNPs that met the QC criteria in at least six centers for the young stroke analysis as well as the LVD, CE, and SVD subtypes. All centres provided data for the all ischaemic stroke and sex specific analyses, so SNPs present in at least eight centers were considered for analysis. The number of SNPs remaining after QC in each analysis was approximately 2!4M, roughly corresponding to the SNPs from the HapMap II SNP set. Genomic Inflation factor correction was used per center to correct for over-dispersion. After meta-analysis, statistical heterogeneity was evaluated using Cochran’s test (Q-test). Manhattan and QQ-plots were generated using the R statistical software package (http://www.R-project.org). Plots of the –log10(p-values) by genomic position for associations of statistical significance were generated using LocusZoom (http://csg.sph.umich.edu/locuszoom/). Discovery analysis Four of the discovery cohorts used ancestry-informative prinicipal components as covariates to correct for population stratification (ISGS/SWISS, GEOS, ASGC, BRAINS). Age and sex were included as covariates in two centres (ISGS/SWISS and BRAINS), sex was used as a covariate in one centre (MGH-GASROS) and one center used recruitment phase (1 or 2) as a covariate (GEOS). In all other centres no covariates were included. Replication analysis For all centers, SNPs were removed that failed testing for Hardy-Weinberg equilibrium at p
