BACKGROUND AND PURPOSE: The contribution of genetics to stroke risk, and whether this differs for different stroke subtypes, remainsuncertain. Genomewide complex trait analysis allows heritability to be assessed from genomewide association study (GWAS) data. Previous candidate gene studies have identified many associations with stoke but whether these are important requires replication in large independent data sets. GWAS data sets provide a powerful resource to perform replication studies. METHODS: We applied genomewide complex trait analysis to a GWAS data set of 3752 ischemic strokes and 5972 controls and determined heritability for all ischemic stroke and the most common subtypes: large-vessel disease, small-vessel disease, and cardioembolic stroke. By systematic review we identified previous candidate gene and GWAS associations with stroke and previous GWAS associations with related cardiovascular phenotypes (myocardial infarction, atrial fibrillation, and carotid intima-media thickness). Fifty associations were identified. RESULTS: For all ischemic stroke, heritability was 37.9%. Heritability varied markedly by stroke subtype being 40.3% for large-vessel disease and 32.6% for cardioembolic but lower for small-vessel disease (16.1%). No previously reported candidate gene was significant after rigorous correction for multiple testing. In contrast, 3 loci from related cardiovascular GWAS studies were significant: PHACTR1 in large-vessel disease (P=2.63e(-6)), PITX2 in cardioembolic stroke (P=4.78e(-8)), and ZFHX3 in cardioembolic stroke (P=5.50e(-7)). CONCLUSIONS: There is substantial heritability for ischemic stroke, but this varies for different stroke subtypes. Previous candidate gene associations contribute little to this heritability, but GWAS studies in related cardiovascular phenotypes are identifying robust associations. The heritability data, and data from GWAS, suggest detecting additional associations will depend on careful stroke subtyping.
BACKGROUND AND PURPOSE: The contribution of genetics to stroke risk, and whether this differs for different stroke subtypes, remainsuncertain. Genomewide complex trait analysis allows heritability to be assessed from genomewide association study (GWAS) data. Previous candidate gene studies have identified many associations with stoke but whether these are important requires replication in large independent data sets. GWAS data sets provide a powerful resource to perform replication studies. METHODS: We applied genomewide complex trait analysis to a GWAS data set of 3752 ischemic strokes and 5972 controls and determined heritability for all ischemic stroke and the most common subtypes: large-vessel disease, small-vessel disease, and cardioembolic stroke. By systematic review we identified previous candidate gene and GWAS associations with stroke and previous GWAS associations with related cardiovascular phenotypes (myocardial infarction, atrial fibrillation, and carotid intima-media thickness). Fifty associations were identified. RESULTS: For all ischemic stroke, heritability was 37.9%. Heritability varied markedly by stroke subtype being 40.3% for large-vessel disease and 32.6% for cardioembolic but lower for small-vessel disease (16.1%). No previously reported candidate gene was significant after rigorous correction for multiple testing. In contrast, 3 loci from related cardiovascular GWAS studies were significant: PHACTR1 in large-vessel disease (P=2.63e(-6)), PITX2 in cardioembolic stroke (P=4.78e(-8)), and ZFHX3 in cardioembolic stroke (P=5.50e(-7)). CONCLUSIONS: There is substantial heritability for ischemic stroke, but this varies for different stroke subtypes. Previous candidate gene associations contribute little to this heritability, but GWAS studies in related cardiovascular phenotypes are identifying robust associations. The heritability data, and data from GWAS, suggest detecting additional associations will depend on careful stroke subtyping.
Authors: Rainer Malik; Therese Dau; Maria Gonik; Anirudh Sivakumar; Daniel J Deredge; Evgeniia V Edeleva; Jessica Götzfried; Sander W van der Laan; Gerard Pasterkamp; Nathalie Beaufort; Susana Seixas; Steve Bevan; Lisa F Lincz; Elizabeth G Holliday; Annette I Burgess; Kristiina Rannikmäe; Jens Minnerup; Jennifer Kriebel; Melanie Waldenberger; Martina Müller-Nurasyid; Peter Lichtner; Danish Saleheen; Peter M Rothwell; Christopher Levi; John Attia; Cathie L M Sudlow; Dieter Braun; Hugh S Markus; Patrick L Wintrode; Klaus Berger; Dieter E Jenne; Martin Dichgans Journal: Proc Natl Acad Sci U S A Date: 2017-03-06 Impact factor: 11.205
Authors: Carla A Ibrahim-Verbaas; Myriam Fornage; Joshua C Bis; Seung Hoan Choi; Bruce M Psaty; James B Meigs; Madhu Rao; Mike Nalls; Joao D Fontes; Christopher J O'Donnell; Sekar Kathiresan; Georg B Ehret; Caroline S Fox; Rainer Malik; Martin Dichgans; Helena Schmidt; Jari Lahti; Susan R Heckbert; Thomas Lumley; Kenneth Rice; Jerome I Rotter; Kent D Taylor; Aaron R Folsom; Eric Boerwinkle; Wayne D Rosamond; Eyal Shahar; Rebecca F Gottesman; Peter J Koudstaal; Najaf Amin; Renske G Wieberdink; Abbas Dehghan; Albert Hofman; André G Uitterlinden; Anita L Destefano; Stephanie Debette; Luting Xue; Alexa Beiser; Philip A Wolf; Charles Decarli; M Arfan Ikram; Sudha Seshadri; Thomas H Mosley; W T Longstreth; Cornelia M van Duijn; Lenore J Launer Journal: Stroke Date: 2014-01-16 Impact factor: 7.914