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- Mishra, A., et al.
(author)
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Stroke genetics informs drug discovery and risk prediction across ancestries
- 2022
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 611, s. 115-123
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Journal article (peer-reviewed)abstract
- 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-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.
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| 2. |
- Jaworek, T., et al.
(author)
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Contribution of Common Genetic Variants to Risk of Early-Onset Ischemic Stroke
- 2022
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In: Neurology. - : Ovid Technologies (Wolters Kluwer Health). - 0028-3878 .- 1526-632X. ; 99:16
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Journal article (peer-reviewed)abstract
- Background and Objectives Current genome-wide association studies of ischemic stroke have focused primarily on late-onset disease. As a complement to these studies, we sought to identify the contribution of common genetic variants to risk of early-onset ischemic stroke. Methods We performed a meta-analysis of genome-wide association studies of early-onset stroke (EOS), ages 18-59 years, using individual-level data or summary statistics in 16,730 cases and 599,237 nonstroke controls obtained across 48 different studies. We further compared effect sizes at associated loci between EOS and late-onset stroke (LOS) and compared polygenic risk scores (PRS) for venous thromboembolism (VTE) between EOS and LOS. Results We observed genome-wide significant associations of EOS with 2 variants in ABO, a known stroke locus. These variants tag blood subgroups O1 and A1, and the effect sizes of both variants were significantly larger in EOS compared with LOS. The odds ratio (OR) for rs529565, tagging O1, was 0.88 (95% confidence interval [CI]: 0.85-0.91) in EOS vs 0.96 (95% CI: 0.92-1.00) in LOS, and the OR for rs635634, tagging A1, was 1.16 (1.11-1.21) for EOS vs 1.05 (0.99-1.11) in LOS; p-values for interaction = 0.001 and 0.005, respectively. Using PRSs, we observed that greater genetic risk for VTE, another prothrombotic condition, was more strongly associated with EOS compared with LOS (p = 0.008). Discussion The ABO locus, genetically predicted blood group A, and higher genetic propensity for venous thrombosis are more strongly associated with EOS than with LOS, supporting a stronger role of prothrombotic factors in EOS.
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| 3. |
- Allen-Perkins, Alfonso, et al.
(author)
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CropPol : a dynamic, open and global database on crop pollination
- 2022
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In: Ecology. - : Wiley. - 0012-9658 .- 1939-9170. ; 103:3
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Journal article (peer-reviewed)abstract
- Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e. berry weight, number of fruits and kg per hectare, among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), Northern America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001-05 (21 studies), 2006-10 (40), 2011-15 (88), and 2016-20 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non-commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY-NC-SA). This article is protected by copyright. All rights reserved.
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| 5. |
- Leclair, V, et al.
(author)
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HLA-DRB1 ASSOCIATIONS WITH AUTOANTIBODY-DEFINED SUBGROUPS IN IDIOPATHIC INFLAMMATORY MYOPATHIES (IIM)
- 2022
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In: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 81, s. 104-105
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Conference paper (other academic/artistic)abstract
- There is a gap between how IIM patients are classified in practice and current validated classification criteria1. Also, different associations with genetic variations in HLA can inform about different T-cell mechanisms involved in disease pathogenesis.ObjectivesWe aimed to systematically study associations between HLA-DRB1 alleles, clinical manifestations, and autoantibody-defined IIM subgroups.MethodsWe included 1348 IIM patients from five European countries. An unsupervised cluster analysis was performed using 14 autoantibodies: anti-Jo1, -PL7, -PL12, -EJ, -OJ, -SRP, -U1RNP, -Ro52, -Mi2, -TIF1γ, -MDA5, -PMScl, -SAE1, and -NXP2 to identify patients’ subgroups. Logistic regressions were used to estimate the associations between HLA-DRB1 alleles, clinical manifestations and the identified subgroups.ResultsEight subgroups were defined by the autoantibody status (Table 1). Three of the subgroups (1, 2 and 6) have overlapping autoantibodies, while four are almost monospecific (3,4,5 and 7), and one (8) has patients negative for tested autoantibodies. Figure 1 represents the significant associations between HLA-DRB1 alleles and the eight subgroups. Heliotrope rash and Gottron’s sign were significantly more frequent in subgroups 3 (OR:2.2 95%CI:[1.1-4.8], OR:2.6 95%CI:[1.3-5.9], respectively), 4 (OR:12 95%CI:[3.6-75], OR:7.8 95%CI:[2.8-33], respectively) and 7 (OR:22 95%CI:[4.5-385], OR:10 95%CI:[3.1-65], respectively), and Raynaud’s phenomenon was significantly more frequent in subgroup 6 (OR:3.3 95%CI:[1.2-11]).Table 1.Autoantibody-defined subgroups using an unsupervised cluster analysis.Subgroups/ MedoidsVariables1 Ro522 U1RNP3 PMScl4 Mi25 Jo16 Jo1/Ro527 TIF18 None*Alln (%)137 (10)183 (14)107 (8)65 (5)119 (9)140 (10)78 (6)519 (39)1348 (100)Female (%)93 (68)116 (63)79 (74)45 (69)76 (64)96 (69)64 (82)313 (60)882 (65)Age at diagnosis, median (IQR)56 (16)51.5 (23)51 (25)57 (22.5)47.5 (23.25)52 (19.5)53.5 (21.75)58 (22)55 (23)AutoantibodiesAnti-Jo106 (3)01 (2)119 (100)140 (100)00266 (20)Anti-PL77 (5)13 (7)00000020 (1.5)Anti-PL125 (4)3 (2)1 (1)01 (1)00010 (0.7)Anti-EJ2 (2)00000002 (0.1)Anti-OJ07 (4)0000007 (0.5)Anti-TIF110 (7)2 (1)2 (2)00078 (100)092 (7)Anti-Mi21 (1)1 (1)1 (1)65 (100)02 (1)0070 (5)Anti-SAE18 (6)23 (13)00000031 (2)Anti-NXP21 (1)23 (13)1 (1)0000025 (2)Anti-MDA59 (7)10 (6)1 (1)1 (2)01 (1)0022 (2)Anti-SRP8 (6)32 (18)00000040 (3)Anti-Ro52137 (100)16 (9)000140 (100)00293 (22)Anti-PMScl11 (8)1 (1)107 (100)00000119 (9)Anti-U1RNP079 (43)0003 (2)0082 (6)*IIM patients negative for the tested autoantibodies.Figure 1.Forest plot of significant associations of HLA. *DRB1 alleles with autoantibody-defined subgroups. Scandinavia includes patients from Denmark, Norway, and Sweden.ConclusionOur study reveals that certain subgroups of IIM patients are characterized by overlap of myositis -specific and -associated autoantibodies, which in turn are associated with different HLA-DRB1 alleles including potential novel associations. These results point to different disease mechanisms in the subgroups, as well as suggest that IIM classification could be improved by integrating broader serological and genetic data.References[1]Parker MJS, Oldroyd A, Roberts ME, et al. The performance of the European League Against Rheumatism/American College of Rheumatology idiopathic inflammatory myopathies classification criteria in an expert-defined 10 year incident cohort. Rheumatology (Oxford). 2019;58(3):468-475.AcknowledgementsWe thank all the patients who participated in the study.Disclosure of InterestsValerie Leclair: None declared, Angeles Shunashy Galindo-Feria: None declared, Simon Rothwell: None declared, Olga Kryštůfková: None declared, Heřman Mann: None declared, Louise Pyndt Diederichsen: None declared, helena andersson: None declared, Martin Klein: None declared, Sarah Tansley: None declared, Neil McHugh: None declared, Janine Lamb: None declared, Jiří Vencovský Speakers bureau: Abbvie, Biogen, Boehringer, Eli Lilly, Gilead, MSD, Novartis, Pfizer, Roche, Sanofi, UCB, Werfen, Consultant of: Abbvie, Argenx, Boehringer, Eli Lilly, Gilead, Octapharma, Pfizer, UCB, Grant/research support from: Abbvie, Hector Chinoy: None declared, Marie Holmqvist: None declared, Leonid Padyukov: None declared, Ingrid E. Lundberg Shareholder of: Roche and Novartis, Consultant of: Corbus Pharmaceuticals Inc, Astra Zeneca, Bristol Myer´s Squibb, Corbus Pharmaceutical, EMD Serono Research & Development Institute, Argenx, Octapharma, Kezaar, Orphazyme, and Janssen, Grant/research support from: Astra Zeneca, Lina M. Diaz-Gallo: None declared
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