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- Mishra, A., et al.
(författare)
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Stroke genetics informs drug discovery and risk prediction across ancestries
- 2022
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 611, s. 115-123
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Tidskriftsartikel (refereegranskat)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. |
- Ramdas, S., et al.
(författare)
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A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids
- 2022
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Ingår i: American Journal of Human Genetics. - : Elsevier BV. - 0002-9297 .- 1537-6605. ; 109:8, s. 1366-1387
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Tidskriftsartikel (refereegranskat)abstract
- A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.
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| 8. |
- Carnohan, Shane, et al.
(författare)
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Next generation application of DPSIR for sustainable policy implementation
- 2023
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Ingår i: Current Research in Environmental Sustainability. - : Elsevier B.V.. - 2666-0490. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- As our societies and natural systems are becoming ever more interconnected, it is critical that sustainable management can adapt to new knowledge from both the ecological and the social domains, and act on it in a timely and effective manner. This need is amplifying in the Anthropocene as we are approaching the limit for humanity's safe operating space, leading to irreversible change to ecosystem function. This urgently requires increased attention and concern regarding the information feedbacks between the silos of science, policy and society. A web of policies is in place to protect the health of people and the planet, but to ensure that they are effective we need frameworks to make sense of real-world complexities and interlinkages between multiple factors. The Drivers-Pressures-State-Impacts-Response (DPSIR) framework was created for this purpose, however, its' implicit focus on 1) analytical and 2) procedural aspects must be made explicit, to enable coordination across silos and studies. Continued creation of new DPSIR derivatives may limit its impact, while more explicit coordination between these two aspects can improve the effectiveness of DPSIR while retaining its flexibility. We thus propose five elements to support sustainable policy development and implementation using DPSIR: 1) iteration; 2) risk, uncertainty and analytical bias; 3) flexible integration; 4) use of quantitative methods, and; 5) clear and standard definitions for DPSIR. We illustrate these elements in four cases: Three highlight missing feedbacks when DPSIR elements are not made explicit and a fourth case – on per-and-polyfluorinated alkyl substances (PFAS) – showing a potential roadmap to successful policy implementation using DPSIR. © 2022 The Authors
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