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- Postmus, I., et al.
(författare)
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Meta-analysis of genome-wide association studies of HDL cholesterol response to statins
- 2016
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Ingår i: Journal of Medical Genetics. - : BMJ. - 0022-2593 .- 1468-6244. ; 53:12, s. 835-45
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Tidskriftsartikel (refereegranskat)abstract
- Background In addition to lowering low density lipoprotein cholesterol (LDL-C), statin therapy also raises high density lipoprotein cholesterol (HDL-C) levels. Interindividual variation in HDL-C response to statins may be partially explained by genetic variation. Methods and results We performed a meta-analysis of genome-wide association studies (GWAS) to identify variants with an effect on statin-induced high density lipoprotein cholesterol (HDL-C) changes. The 123 most promising signals with p<1x10(-4) from the 16 769 statin-treated participants in the first analysis stage were followed up in an independent group of 10 951 statin-treated individuals, providing a total sample size of 27 720 individuals. The only associations of genome-wide significance (p<5x10(-8)) were between minor alleles at the CETP locus and greater HDL-C response to statin treatment. Conclusions Based on results from this study that included a relatively large sample size, we suggest that CETP may be the only detectable locus with common genetic variants that influence HDL-C response to statins substantially in individuals of European descent. Although CETP is known to be associated with HDL-C, we provide evidence that this pharmacogenetic effect is independent of its association with baseline HDL-C levels.
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- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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- Edelson, R. A., et al.
(författare)
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Multiwavelength observations of short-timescale variability in NGC 4151. IV. Analysis of multiwavelength continuum variability
- 1996
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Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 470:1, s. 364-377377
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Tidskriftsartikel (refereegranskat)abstract
- For pt.III see ibid., vol.470, no.1, p.349-63 (1996). Combines data from the three preceding papers in order to analyze the multi wave-band variability and spectral energy distribution of the Seyfert 1 galaxy NGC 4151 during the 1993 December monitoring campaign. The source, which was near its peak historical brightness, showed strong, correlated variability at X-ray, ultraviolet, and optical wavelengths. The strongest variations were seen in medium-energy (~1.5 keV) X-rays, with a normalized variability amplitude (NVA) of 24%. Weaker (NVA=6%) variations (uncorrelated with those at lower energies) were seen at soft gamma-ray energies of ~100 keV. No significant variability was seen in softer (0.1-1 keV) X-ray bands. In the ultraviolet/optical regime, the NVA decreased from 9% to 1% as the wavelength increased from 1275 to 6900 Aring. These data do not probe extreme ultraviolet (1200 Aring to 0.1 keV) or hard X-ray (250 keV) variability. The phase differences between variations in different bands were consistent with zero lag, with upper limits of lsim0.15 day between 1275 Aring and the other ultraviolet bands, lsim0.3 day between 1275 Aring and 1.5 keV, and lsim1 day between 1275 and 5125 Aring. These tight limits represent more than an order of magnitude improvement over those determined in previous multi-wave-band AGN monitoring campaigns. The ultraviolet fluctuation power spectra showed no evidence for periodicity, but were instead well fitted with a very steep, red power law (ales-2.5)
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