| 1. |
- Aad, G, et al.
(författare)
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- 2015
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swepub:Mat__t
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| 2. |
- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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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. |
- Aad, G., et al.
(författare)
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- 2010
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swepub:Mat__t
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| 5. |
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| 6. |
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- 2019
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Tidskriftsartikel (refereegranskat)
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| 7. |
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| 8. |
- Brownstein, Catherine A., et al.
(författare)
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An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge
- 2014
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Ingår i: Genome Biology. - : Springer Science and Business Media LLC. - 1465-6906 .- 1474-760X. ; 15:3, s. R53-
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Tidskriftsartikel (refereegranskat)abstract
- Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
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| 9. |
- Wahl, Simone, et al.
(författare)
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Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity
- 2017
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Ingår i: Nature. - : NATURE PUBLISHING GROUP. - 0028-0836 .- 1476-4687. ; 541:7635, s. 81-
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Tidskriftsartikel (refereegranskat)abstract
- Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type (2) diabetes, cardiovascular disease and related metabolic and inflammatory disturbances(1,2). Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation(3-6), a key regulator of gene expression and molecular phenotype(7). Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 x 10(-7), range P = 9.2 x 10(-8) to 6.0 x 10(-46); n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 x 10(-6), range P = 5.5 x 10(-6) to 6.1 x 10(-35), n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07-2.56); P = 1.1 x 10(-54)). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.
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