| 1. |
- Romagnoni, A, et al.
(författare)
-
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
-
Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
-
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.
|
|
| 2. |
- Craddock, Nick, et al.
(författare)
-
Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls
- 2010
-
Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 464:7289, s. 713-720
-
Tidskriftsartikel (refereegranskat)abstract
- Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed,19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated similar to 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease-IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetes-although in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.
|
|
| 3. |
|
|
| 4. |
- Joy, K. H., et al.
(författare)
-
Timing of geological events in the lunar highlands recorded in shocked zircon-bearing clasts from Apollo 16
- 2020
-
Ingår i: Royal Society Open Science. - : Royal Society. - 2054-5703. ; 7:6
-
Tidskriftsartikel (refereegranskat)abstract
- Apollo 16 soil-like regolith breccia 65745,7 contains two zircon-bearing clasts. One of these clasts is a thermally annealed silica-rich rock, which mineralogically has affinities with the High Alkali Suite (Clast 1), and yields zircon dates ranging from 4.08 to 3.38 Ga. The other clast is a KREEP-rich impact melt breccia (Clast 2) and yields zircon dates ranging from 3.97 to 3.91 Ga. The crystalline cores of both grains, which yield dates ofca3.9 Ga, have undergone shock pressure modification at less than 20 GPa. We interpret that the U-Pb chronometer in these zircon grains has been partially reset by the Imbrium basin-forming event when the clasts were incorporated into the Cayley Plains ejecta blanket deposit. The zircon grains in Clast 1 have been partially decomposed, resulting in a breakdown polymineralic texture, with elevated U, Pb and Th abundances compared with those in the crystalline zircon. These decomposed areas exhibit younger dates around 3.4 Ga, suggesting a secondary high-pressure, high-temperature event, probably caused by an impact in the local Apollo 16 highlands area.
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
- Curran, N. M., et al.
(författare)
-
The early geological history of the Moon inferred from ancient lunar meteorite Miller Range 13317
- 2019
-
Ingår i: Meteoritics and Planetary Science. - : John Wiley & Sons, Ltd (10.1111). - 1086-9379 .- 1945-5100. ; 54:7, s. 1401-1430
-
Tidskriftsartikel (refereegranskat)abstract
- Abstract Miller Range (MIL) 13317 is a heterogeneous basalt-bearing lunar regolith breccia that provides insights into the early magmatic history of the Moon. MIL 13317 is formed from a mixture of material with clasts having an affinity to Apollo ferroan anorthosites and basaltic volcanic rocks. Noble gas data indicate that MIL 13317 was consolidated into a breccia between 2610 ± 780 Ma and 1570 ± 470 Ma where it experienced a complex near-surface irradiation history for ~835 ± 84 Myr, at an average depth of ~30 cm. The fusion crust has an intermediate composition (Al2O3 15.9 wt%; FeO 12.3 wt%) with an added incompatible trace element (Th 5.4 ppm) chemical component. Taking the fusion crust to be indicative of the bulk sample composition, this implies that MIL 13317 originated from a regolith that is associated with a mare-highland boundary that is KREEP-rich (i.e., K, rare earth elements, and P). A comparison of bulk chemical data from MIL 13317 with remote sensing data from the Lunar Prospector orbiter suggests that MIL 13317 likely originated from the northwest region of Oceanus Procellarum, east of Mare Nubium, or at the eastern edge of Mare Frigoris. All these potential source areas are on the near side of the Moon, indicating a close association with the Procellarum KREEP Terrane. Basalt clasts in MIL 13317 are from a very low-Ti to low-Ti (between 0.14 and 0.32 wt%) source region. The similar mineral fractionation trends of the different basalt clasts in the sample suggest they are comagmatic in origin. Zircon-bearing phases and Ca-phosphate grains in basalt clasts and matrix grains yield 207Pb/206Pb ages between 4344 ± 4 and 4333 ± 5 Ma. These ancient 207Pb/206Pb ages indicate that the meteorite has sampled a range of Pre-Nectarian volcanic rocks that are poorly represented in the Apollo, Luna, and lunar meteorite collections. As such, MIL 13317 adds to the growing evidence that basaltic volcanic activity on the Moon started as early as ~4340 Ma, before the main period of lunar mare basalt volcanism at ~3850 Ma.
|
|
| 8. |
- Lorenzini, Luigi, et al.
(författare)
-
The Open-Access European Prevention of Alzheimer?s Dementia (EPAD) MRI dataset and processing workflow
- 2022
-
Ingår i: NeuroImage. - : Elsevier. - 2213-1582. ; 35
-
Tidskriftsartikel (refereegranskat)abstract
- The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer's Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI pre-processing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features - i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features - Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) - were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses
|
|
