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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. |
- Yuen, RKC, et al.
(författare)
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Genome-wide characteristics of de novo mutations in autism
- 2016
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Ingår i: NPJ genomic medicine. - : Springer Science and Business Media LLC. - 2056-7944. ; 1, s. 160271-1602710
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Tidskriftsartikel (refereegranskat)abstract
- De novo mutations (DNMs) are important in autism spectrum disorder (ASD), but so far analyses have mainly been on the ~1.5% of the genome encoding genes. Here, we performed whole-genome sequencing (WGS) of 200 ASD parent–child trios and characterised germline and somatic DNMs. We confirmed that the majority of germline DNMs (75.6%) originated from the father, and these increased significantly with paternal age only (P=4.2×10−10). However, when clustered DNMs (those within 20 kb) were found in ASD, not only did they mostly originate from the mother (P=7.7×10−13), but they could also be found adjacent to de novo copy number variations where the mutation rate was significantly elevated (P=2.4×10−24). By comparing with DNMs detected in controls, we found a significant enrichment of predicted damaging DNMs in ASD cases (P=8.0×10−9; odds ratio=1.84), of which 15.6% (P=4.3×10−3) and 22.5% (P=7.0×10−5) were non-coding or genic non-coding, respectively. The non-coding elements most enriched for DNM were untranslated regions of genes, regulatory sequences involved in exon-skipping and DNase I hypersensitive regions. Using microarrays and a novel outlier detection test, we also found aberrant methylation profiles in 2/185 (1.1%) of ASD cases. These same individuals carried independently identified DNMs in the ASD-risk and epigenetic genes DNMT3A and ADNP. Our data begins to characterize different genome-wide DNMs, and highlight the contribution of non-coding variants, to the aetiology of ASD.
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