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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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- Kamm, C, et al.
(författare)
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The fragile X tremor ataxia syndrome in the differential diagnosis of multiple system atrophy: data from the EMSA Study Group
- 2005
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Ingår i: Brain. - : Oxford University Press (OUP). - 1460-2156 .- 0006-8950. ; 128:8, s. 1855-1860
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Tidskriftsartikel (refereegranskat)abstract
- The recent identification of fragile X-associated tremor ataxia syndrome (FXTAS) associated with premutations in the FMR1 gene and the possibility of clinical overlap with multiple system atrophy (MSA) has raised important questions, such as whether genetic testing for FXTAS should be performed routinely in MSA and whether positive cases might affect the specificity of current MSA diagnostic criteria. We genotyped 507 patients with clinically diagnosed or pathologically proven MSA for FMR1 repeat length. Among the 426 clinically diagnosed cases, we identified four patients carrying FMR1 premutations (0.94%). Within the subgroup of patients with probable MSA-C, three of 76 patients (3.95%) carried premutations. We identified no premutation carriers among 81 patients with pathologically proven MSA and only one carrier among 622 controls (0.16%). Our results suggest that, with proper application of current diagnostic criteria, FXTAS is very unlikely to be confused with MSA. However, slowly progressive disease or predominant tremor are useful red flags and should prompt the consideration of FXTAS. On the basis of our data, the EMSA Study Group does not recommend routine FMR1 genotyping in typical MSA patients.
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