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Sökning: WFRF:(Jewell DP)

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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.
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3.
  • Popat, S, et al. (författare)
  • Variation in the CTLA4/CD28 gene region confers an increased risk of coeliac disease
  • 2002
  • Ingår i: Annals of Human Genetics. - : Wiley. - 1469-1809 .- 0003-4800. ; 66:2, s. 125-137
  • Tidskriftsartikel (refereegranskat)abstract
    • Susceptibility to coeliae disease involves HLA and non-HLA-linked genes. The CTLA4/CD28 gene region encodes immune regulatory T-cell surface molecules and is a strong candidate as a susceptibility locus. We evaluated CTLA4/CD28 in coeliac disease by genetic linkage and association and combined Our findings with published studies through a meta-analysis. 116 multiplex families were genotyped across CTLA4/CD28 using eight markers. The contribution of CTLA4/CD28 to coeliac disease was assessed by non-parametric linkage and association analyses. Seven studies were identified that had evaluated the relationship between CTLA4/CD28 and coeliac disease and a pooled analysis of data undertaken. In our study there was evidence for a relationship between variation in the CTLA4/CD28 region and coeliae disease by linkage and association analyses. However. the findings did not attain formal statistical significance (p=0.004 and 0.039. respectively). Pooling findings with published results showed significant evidence for linkage (504 families) and association (910 families) : p values. 0.0001 and 0.0014 at D2S2214. respectively. and 0.0008 and 0.0006 at D2S116, respectively. These findings suggest that variation in the CD28/CTLA4 gene region is a determinant of coeliac disease susceptibility. Dissecting the sequence variation underlying this relationship will depend on further analyses utilising denser sets of markers.
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