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- Bryois, J., et al.
(author)
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Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease
- 2020
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In: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 52:5, s. 482-493
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Journal article (peer-reviewed)abstract
- Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson’s disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson’s disease. © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
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| 2. |
- Wightman, D. P., et al.
(author)
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A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease
- 2021
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In: Nature Genetics. - : Springer Nature. - 1061-4036 .- 1546-1718. ; 53:9, s. 1276-1282
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Journal article (peer-reviewed)abstract
- Late-onset Alzheimer’s disease is a prevalent age-related polygenic disease that accounts for 50–70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer’s disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer’s disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer’s disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer’s disease to identify further genetic variants that contribute to Alzheimer’s pathology.
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| 3. |
- Johnsen, M. B., et al.
(author)
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Development and validation of a prediction model for incident hand osteoarthritis in the HUNT study
- 2020
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In: Osteoarthritis and Cartilage. - : Elsevier BV. - 1063-4584. ; 28:7, s. 932-940
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Journal article (peer-reviewed)abstract
- Objective: To develop and externally validate prediction models for incident hand osteoarthritis (OA) in a large population-based cohort of middle aged and older men and women. Design: We included 17,153 men and 18,682 women from a population-based cohort, aged 35–70 years at baseline (1995–1997). Incident hand OA were obtained from diagnostic codes in the Norwegian National Patient Register (1995–2018). We studied whether a range of self-reported and clinically measured predictors could predict hand OA, using the Area Under the receiver-operating Curve (AUC) from logistic regression. External validation of an existing prediction model for male hand OA was tested on discrimination in a sample of men. Bootstrapping was used to avoid overfitting. Results: The model for men showed modest discriminatory ability (AUC = 0.67, 95% CI 0.62–0.71). Adding a genetic risk score did not improve prediction. Similar discrimination was observed in the model for women (AUC = 0.62, 95% CI 0.59–0.64). Prediction was not improved by adding a genetic risk score or hormonal and reproductive factors. Applying external validation, similar results were observed among men in HUNT (The Nord-Trøndelag Health Study) as in the developmental sample (AUC = 0.62, 95% CI 0.57–0.65). Conclusion: We developed prediction models for incident hand OA in men and women. For women, the model included body mass index (BMI), heavy physical work, high physical activity and perceived poor health. The model showed moderate discrimination. For men, we have shown that a prediction model including BMI, education and information on sleep can predict incident hand OA in several populations with moderate discriminative ability.
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