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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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- Carpten, JD, et al.
(författare)
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HRPT2, encoding parafibromin, is mutated in hyperparathyroidism-jaw tumor syndrome
- 2002
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1546-1718 .- 1061-4036. ; 32:4, s. 676-680
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Tidskriftsartikel (refereegranskat)abstract
- We report here the identification of a gene associated with the hyperparathyroidism-jaw tumor (HPT-JT) syndrome. A single locus associated with HPT-JT (HRPT2) was previously mapped to chromosomal region 1q25-q32. We refined this region to a critical interval of 12 cM by genotyping in 26 affected kindreds. Using a positional candidate approach, we identified thirteen different heterozygous, germline, inactivating mutations in a single gene in fourteen families with HPT-JT. The proposed role of HRPT2 as a tumor suppressor was supported by mutation screening in 48 parathyroid adenomas with cystic features, which identified three somatic inactivating mutations, all located in exon 1. None of these mutations were detected in normal controls, and all were predicted to cause deficient or impaired protein function. HRPT2 is a ubiquitously expressed, evolutionarily conserved gene encoding a predicted protein of 531 amino acids, for which we propose the name parafibromin. Our findings suggest that HRPT2 is a tumor-suppressor gene, the inactivation of which is directly involved in predisposition to HPT-JT and in development of some sporadic parathyroid tumors.
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