Stroke genetics informs drug discovery and risk prediction across ancestries.

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

Unsupervised hierarchical clustering identifies a metabolically challenged subgroup of hypertensive individuals.

The current classification of hypertension does not reflect the heterogeneity in characteristics or cardiovascular outcomes of hypertensive individuals. Our objective was to identify distinct phenotypes of hypertensive individuals with potentially different cardiovascular risk profiles using data-driven cluster analysis. We performed clustering, a procedure that identifies groups with similar characteristics, in 3726 individuals (mean age 59.4 years, 49% women) with grade 2 hypertension (blood pressure ≥160/100 mmHg or antihypertensive medication) selected from FINRISK 1997, 2002, and 2007 cohorts. We computed clusters based on eight factors associated with hypertension: mean arterial pressure, pulse pressure, non-high-density lipoprotein cholesterol, blood glucose, BMI, C-reactive protein, estimated glomerular filtration rate, and alcohol. After that, we used Cox regression models adjusted for age and sex to assess the relative risk of cardiovascular disease (CVD) outcomes between the clusters and a reference group of 11 020 individuals. We observed two comparable clusters in both men and women. The Metabolically Challenged (MC) cluster was characterized by high blood glucose (Z-score 4.4 ± 1.1 vs 0.2 ± 0.8, men; 3.5 ± 1.1 vs 0.0 ± 0.6, women) and elevated BMI (30.4 ± 4.1 vs 28.9 ± 4.3, men; 32.7 ± 4.9 vs 29.3 ± 5.5, women). Over a 10-year follow-up (1034 CVD events), MC had 1.6-fold (95% CI 1.1-2.4) CVD risk compared to non-MC and 2.5-fold (95% CI 1.7-3.7) CVD risk compared to the reference group (P ≤ .009 for both). Using unsupervised hierarchical clustering, we found two phenotypically distinct hypertension subgroups with different risks of CVD complications. This substratification could be used to design studies that explore the differential effects of antihypertensive therapies among subgroups of hypertensive individuals.