Stroke genetics informs drug discovery and risk prediction across ancestries

Stroke genetics informs drug discovery and risk prediction across ancestries · SciRah

^aBordeaux Population Health Research Center, University of Bordeaux, Inserm, UMR 1219, Bordeaux, France
^bInstitute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
^cIwate Tohoku Medical Megabank Organization, Iwate Medical University, Iwate, Japan
^dEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
^eInstitute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
^fDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
^gMassachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
^hDivision of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
ⁱTIMI Study Group, Boston, MA, USA
^jDivision of Molecular Pathology, Institute of Medical Sciences, The University of Tokyo, Tokyo, Japan
^kLaboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
^lCenter for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
^mProgram in Medical and Population Genetics, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
ⁿDepartment of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan
^oDepartment of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung, Taiwan
^pLaboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
^qDepartment of Internal Medicine, University of Turku, Turku, Finland
^rDepartment of Public Health and Welfare, Finnish Institute for Health and Welfare, Turku, Finland
^sNuffield Department of Population Health, University of Oxford, Oxford, UK
^tDepartment of Neurology, Oslo University Hospital, Oslo, Norway
^uDepartment of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
^vK. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
^wDepartment of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
^xDepartment of Neurology and Cerebrovascular Disease Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
^yRajendra Institute of Medical Sciences, Ranchi, India
^zDepartment of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
^aaThrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
^abDepartment of Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
^acCenter for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria
^adNeuroscience and Ageing Research Unit Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Nigeria
^aeDepartment of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
^afDepartment of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
^agThe Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
^ahDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
^aiDepartment of Clinical Biochemistry, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
^ajDepartment of Clinical Biochemistry, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
^akDepartment of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, VA, USA
^alDepartment of Public Health Sciences, College of Medicine, The Pennsylvania State University, State College, PA, USA

Abstract

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. A cross-ancestry meta-analysis of genome-wide association studies identifies association signals for stroke and its subtypes at 89 (61 new) independent loci, reveals putative causal genes, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as potential drug targets, and provides cross-ancestry integrative risk prediction.

TL;DRA cross-ancestry meta-analysis of genome-wide association studies identifies association signals for stroke and its subtypes at 89 independent loci, reveals putative causal genes, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as potential drug targets, and provides cross-ancestry integrative risk prediction. (Semantic Scholar)

Stroke genetics informs drug discovery and risk prediction across ancestries

Abstract

Plain-language summary

Discussion