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- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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| 2. |
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| 3. |
- Niemi, MEK, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 4. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 5. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 8. |
- Jaworek, T., et al.
(författare)
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Contribution of Common Genetic Variants to Risk of Early-Onset Ischemic Stroke
- 2022
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Ingår i: Neurology. - : Ovid Technologies (Wolters Kluwer Health). - 0028-3878 .- 1526-632X. ; 99:16
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Tidskriftsartikel (refereegranskat)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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| 9. |
- Sherratt, K., et al.
(författare)
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Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
- 2023
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Ingår i: eLIFE. - : eLife Sciences Publications Ltd. - 2050-084X. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance.Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models.Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks.
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| 10. |
- Ades, M., et al.
(författare)
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Global Climate : in State of the climate in 2019
- 2020
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Ingår i: Bulletin of The American Meteorological Society - (BAMS). - : American Meteorological Society. - 0003-0007 .- 1520-0477. ; 101:8, s. S17-S127
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Tidskriftsartikel (refereegranskat)
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