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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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| 2. |
- McGovern, Dermot P B, et al.
(författare)
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Genome-wide association identifies multiple ulcerative colitis susceptibility loci
- 2010
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 42:4, s. 332-337
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Tidskriftsartikel (refereegranskat)abstract
- Ulcerative colitis is a chronic, relapsing inflammatory condition of the gastrointestinal tract with a complex genetic and environmental etiology. In an effort to identify genetic variation underlying ulcerative colitis risk, we present two distinct genome-wide association studies of ulcerative colitis and their joint analysis with a previously published scan, comprising, in aggregate, 2,693 individuals with ulcerative colitis and 6,791 control subjects. Fifty-nine SNPs from 14 independent loci attained an association significance of P < 10(-5). Seven of these loci exceeded genome-wide significance (P < 5 x 10(-8)). After testing an independent cohort of 2,009 cases of ulcerative colitis and 1,580 controls, we identified 13 loci that were significantly associated with ulcerative colitis (P < 5 x 10(-8)), including the immunoglobulin receptor gene FCGR2A, 5p15, 2p16 and ORMDL3 (orosomucoid1-like 3). We confirmed association with 14 previously identified ulcerative colitis susceptibility loci, and an analysis of acknowledged Crohn's disease loci showed that roughly half of the known Crohn's disease associations are shared with ulcerative colitis. These data implicate approximately 30 loci in ulcerative colitis, thereby providing insight into disease pathogenesis.
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