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

Quantitative ultrasound criteria for risk stratification in clinical practice: a comparative assessment

This study aimed to compare two different classifications of the risk of fracture/osteoporosis (OP) based on quantitative ultrasound (QUS). Analyses were based on data from the Epidemiological Study on the Prevalence of Osteoporosis, a cross-sectional study conducted in 2000 aimed at assessing the risk of OP in a representative sample of the Italian population. Subjects were classified into 5 groups considering the cross-classification found in previous studies; logistic regression models were defined separately for women and men to study the fracture risk attributable to groups defined by the cross-classification, adjusting for traditional risk factors. Eight-thousand six-hundred eighty-one subjects were considered in the analyses. Logistic regression models revealed that the two classifications seem to be able to identify a common core of individuals at low and at high risk of fractures, and the importance of a multidimensional assessment in older patients to evaluate clinical risk factors together with a simple, inexpensive, radiation-free device such as QUS

Quantitative ultrasound criteria for risk stratification in clinical practice: a comparative assessment.

This study aimed to compare two different classifications of the risk of fracture/osteoporosis (OP) based on quantitative ultrasound (QUS). Analyses were based on data from the Epidemiological Study on the Prevalence of Osteoporosis, a cross-sectional study conducted in 2000 aimed at assessing the risk of OP in a representative sample of the Italian population. Subjects were classified into 5 groups considering the cross-classification found in previous studies; logistic regression models were defined separately for women and men to study the fracture risk attributable to groups defined by the cross-classification, adjusting for traditional risk factors. Eight-thousand six-hundred eighty-one subjects were considered in the analyses. Logistic regression models revealed that the two classifications seem to be able to identify a common core of individuals at low and at high risk of fractures, and the importance of a multidimensional assessment in older patients to evaluate clinical risk factors together with a simple, inexpensive, radiation-free device such as QUS

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