A Genome-Wide Meta-Analysis

[Rationale] Ischemic stroke is among the leading causes of adult disability. Part of the variability in functional outcome after stroke has been attributed to genetic factors but no locus has been consistently associated with stroke outcome. [Objective] Our aim was to identify genetic loci influencing the recovery process using accurate phenotyping to produce the largest GWAS (genome-wide association study) in ischemic stroke recovery to date. [Methods and Results] A 12-cohort, 2-phase (discovery-replication and joint) meta-analysis of GWAS included anterior-territory and previously independent ischemic stroke cases. Functional outcome was recorded using 3-month modified Rankin Scale. Analyses were adjusted for confounders such as discharge National Institutes of Health Stroke Scale. A gene-based burden test was performed. The discovery phase (n=1225) was followed by open (n=2482) and stringent joint-analyses (n=1791). Those cohorts with modified Rankin Scale recorded at time points other than 3-month or incomplete data on previous functional status were excluded in the stringent analyses. Novel variants in PATJ (Pals1-associated tight junction) gene were associated with worse functional outcome at 3-month after stroke. The top variant was rs76221407 (G allele, β=0.40, P=1.70×10−9). [Conclusions] Our results identify a set of common variants in PATJ gene associated with 3-month functional outcome at genome-wide significance level. Future studies should examine the role of PATJ in stroke recovery and consider stringent phenotyping to enrich the information captured to unveil additional stroke outcome loci. Genetic contribution to functional outcome and disability after stroke (GODS) project, Fundació Marató-TV3 Grant 2011 (76/C/2011), Recercaixa’13; Generación Project, Instituto de Salud Carlos III; GENISIS (Genetics of Early Neurological InStability After Ischemic Stroke) project, National Institutes of Health (NIH); National Institute of Neurological Disorders and Stroke Stroke Genetics Network (SiGN) Project, NIH.