Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross... Show morePrevious genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). 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 analysis(3), 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 approach(4), 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 ancestry(5). 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. Show less
Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions... Show moreCommon single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries. Show less
Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here... Show moreGlycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets. Show less
Roessel, S. van; Strijker, M.; Steyerberg, E.W.; Groen, J.V.; Mieog, J.S.; Groot, V.P.; ... ; Besselink, M.G. 2020
Background: The objective of this study was to validate and update the Amsterdam prediction model including tumor grade, lymph node ratio, margin status and adjuvant therapy, for prediction of... Show moreBackground: The objective of this study was to validate and update the Amsterdam prediction model including tumor grade, lymph node ratio, margin status and adjuvant therapy, for prediction of overall survival (OS) after pancreatoduodenectomy for pancreatic cancer.Methods: We included consecutive patients who underwent pancreatoduodenectomy for pancreatic cancer between 2000 and 2017 at 11 tertiary centers in 8 countries (USA, UK, Germany, Italy, Sweden, the Netherlands, Korea, Australia). Model performance for prediction of OS was evaluated by calibration statistics and Uno's C-statistic for discrimination. Validation followed the TRIPOD statement.Results: Overall, 3081 patients (53% male, median age 66 years) were included with a median OS of 24 months, of whom 38% had N2 disease and 77% received adjuvant chemotherapy. Predictions of 3-year OS were fairly similar to observed OS with a calibration slope of 0.72. Statistical updating of the model resulted in an increase of the C-statistic from 0.63 to 0.65 (95% CI 0.64-0.65), ranging from 0.62 to 0.67 across different countries. The area under the curve for the prediction of 3 -year OS was 0.71 after updating. Median OS was 36, 25 and 15 months for the low, intermediate and high risk group, respectively (P < 0.001).Conclusions: This large international study validated and updated the Amsterdam model for survival prediction after pancreatoduodenectomy for pancreatic cancer. The model incorporates readily available variables with a fairly accurate model performance and robustness across different countries, while novel markers may be added in the future. The risk groups and web-based calculator www pancreascalculaior. corn may facilitate use in daily practice and future trials. (C) 2019 Elsevier Ltd, BASO The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved. Show less
Malik, R.; Chauhan, G.; Traylor, M.; Sargurupremraj, M.; Okada, Y.; Mishra, A.; ... ; MEGASTROKE Consortium 2018
Background Whether triglyceride-mediated pathways are causally relevant to coronary heart disease is uncertain. We studied a genetic variant that regulates triglyceride concentration to help judge... Show moreBackground Whether triglyceride-mediated pathways are causally relevant to coronary heart disease is uncertain. We studied a genetic variant that regulates triglyceride concentration to help judge likelihood of causality. Methods We assessed the -1131T>C (rs662799) promoter polymorphism of the apolipoprotein A5 (APOA5) gene in relation to triglyceride concentration, several other risk factors, and risk of coronary heart disease. We compared disease risk for genetically-raised triglyceride concentration (20 842 patients with coronary heart disease, 35 206 controls) with that recorded for equivalent differences in circulating triglyceride concentration in prospective studies (302 430 participants with no history of cardiovascular disease; 12 785 incident cases of coronary heart disease during 2.79 million person-years at risk). We analysed -1131T>C in 1795 people without a history of cardiovascular disease who had information about lipoprotein concentration and diameter obtained by nuclear magnetic resonance spectroscopy. Findings The minor allele frequency of -1131T>C was 8% (95% CI 7-9). -1131T>C was not significantly associated with several non-lipid risk factors or LDL cholesterol, and it was modestly associated with lower HDL cholesterol (mean difference per C allele 3.5% [95% CI 2.6-4.6]; 0.053 mmol/L [0.039-0.068]), lower apolipoprotein AI (1.3% [0.3-2.3]; 0.023 g/L [0.005-0.041]), and higher apolipoprotein B (3.2% [1.3-5.1]; 0.027 g/L [0.011-0.043]). By contrast, for every C allele inherited, mean triglyceride concentration was 16.0% (95% CI 12.9-18.7), or 0.25 mmol/L (0.20-0.29), higher (p=4.4x10(-24)). The odds ratio for coronary heart disease was 1.18 (95% CI 1.11-1.26; p=2.6x10(-7)) per C allele, which was concordant with the hazard ratio of 1.10 (95% CI 1.08-1.12) per 16% higher triglyceride concentration recorded in prospective studies. -1131T>C was significantly associated with higher VLDL particle concentration (mean difference per C allele 12.2 nmol/L [95% CI 7.7-16.7]; p=9.3x10(-8)) and smaller HDL particle size (0.14 nm [0.08-0.20]; p=7.0x10(-5)), factors that could mediate the effects of triglyceride. Interpretation These data are consistent with a causal association between triglyceride-mediated pathways and coronary heart disease. Show less
