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- Kurki, MI, et al.
(författare)
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FinnGen provides genetic insights from a well-phenotyped isolated population
- 2023
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 613:7944, s. 508-
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Tidskriftsartikel (refereegranskat)abstract
- Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10–11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.
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| 18. |
- Ma, Q, et al.
(författare)
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ZnT8 loss-of-function accelerates functional maturation of hESC-derived β cells and resists metabolic stress in diabetes
- 2022
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1, s. 4142-
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Tidskriftsartikel (refereegranskat)abstract
- Human embryonic stem cell-derived β cells (SC-β cells) hold great promise for treatment of diabetes, yet how to achieve functional maturation and protect them against metabolic stresses such as glucotoxicity and lipotoxicity remains elusive. Our single-cell RNA-seq analysis reveals that ZnT8 loss of function (LOF) accelerates the functional maturation of SC-β cells. As a result, ZnT8 LOF improves glucose-stimulated insulin secretion (GSIS) by releasing the negative feedback of zinc inhibition on insulin secretion. Furthermore, we demonstrate that ZnT8 LOF mutations endow SC-β cells with resistance to lipotoxicity/glucotoxicity-triggered cell death by alleviating endoplasmic reticulum (ER) stress through modulation of zinc levels. Importantly, transplantation of SC-β cells with ZnT8 LOF into mice with preexisting diabetes significantly improves glycemia restoration and glucose tolerance. These findings highlight the beneficial effect of ZnT8 LOF on the functional maturation and survival of SC-β cells that are useful as a potential source for cell replacement therapies.
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| 19. |
- Mei, J, et al.
(författare)
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Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
- 2020
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 10:12, s. e044028-
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Tidskriftsartikel (refereegranskat)abstract
- This study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.DesignRetrospective cohort study.SettingFive designated tertiary hospitals for COVID-19 in Hubei province, China.ParticipantsWe routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19.MethodsThe model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers’ D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted.Main outcome measuresThe primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.ResultsThe full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort.ConclusionThe prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification.
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