| 1. |
- Romagnoni, A, et al.
(author)
-
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
-
In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
-
Journal article (peer-reviewed)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.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- Kostecka, A, et al.
(author)
-
High prevalence of somatic PIK3CA and TP53 pathogenic variants in the normal mammary gland tissue of sporadic breast cancer patients revealed by duplex sequencing
- 2022
-
In: NPJ breast cancer. - : Springer Science and Business Media LLC. - 2374-4677. ; 8:1, s. 76-
-
Journal article (peer-reviewed)abstract
- The mammary gland undergoes hormonally stimulated cycles of proliferation, lactation, and involution. We hypothesized that these factors increase the mutational burden in glandular tissue and may explain high cancer incidence rate in the general population, and recurrent disease. Hence, we investigated the DNA sequence variants in the normal mammary gland, tumor, and peripheral blood from 52 reportedly sporadic breast cancer patients. Targeted resequencing of 542 cancer-associated genes revealed subclonal somatic pathogenic variants of: PIK3CA, TP53, AKT1, MAP3K1, CDH1, RB1, NCOR1, MED12, CBFB, TBX3, and TSHR in the normal mammary gland at considerable allelic frequencies (9 × 10−2– 5.2 × 10−1), indicating clonal expansion. Further evaluation of the frequently damaged PIK3CA and TP53 genes by ultra-sensitive duplex sequencing demonstrated a diversified picture of multiple low-level subclonal (in 10−2–10−4 alleles) hotspot pathogenic variants. Our results raise a question about the oncogenic potential in non-tumorous mammary gland tissue of breast-conserving surgery patients.
|
|
| 7. |
- Razzaghian, Hamid Reza, et al.
(author)
-
Post-Zygotic and Inter-Individual Structural Genetic Variation in a Presumptive Enhancer Element of the Locus between the IL10Rβ and IFNAR1 Genes
- 2013
-
In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:9, s. e67752-
-
Journal article (peer-reviewed)abstract
- Although historically considered as junk-DNA, tandemly repeated sequence motifs can affect human phenotype. For example, variable number tandem repeats (VNTR) with embedded enhancers have been shown to regulate gene transcription. The post-zygotic variation is the presence of genetically distinct populations of cells in an individual derived from a single zygote, and this is an understudied aspect of genome biology. We report somatically variable VNTR with sequence properties of an enhancer, located upstream of IFNAR1. Initially, SNP genotyping of 63 monozygotic twin pairs and multiple tissues from 21 breast cancer patients suggested a frequent post-zygotic mosaicism. The VNTR displayed a repeated 32 bp core motif in the center of the repeat, which was flanked by similar variable motifs. A total of 14 alleles were characterized based on combinations of segments, which showed post-zygotic and inter-individual variation, with up to 6 alleles in a single subject. Somatic variation occurred in similar to 24% of cases. In this hypervariable region, we found a clustering of transcription factor binding sites with strongest sequence similarity to mouse Foxg1 transcription factor binding motif. This study describes a VNTR with sequence properties of an enhancer that displays post-zygotic and inter-individual genetic variation. This element is within a locus containing four related cytokine receptors: IFNAR2, IL10R beta, IFNAR1 and IFNGR2, and we hypothesize that it might function in transcriptional regulation of several genes in this cluster. Our findings add another level of complexity to the variation among VNTR-based enhancers. Further work may unveil the normal function of this VNTR in transcriptional control and its possible involvement in diseases connected with these receptors, such as autoimmune conditions and cancer.
|
|
| 8. |
- Chen, Hongjie, et al.
(author)
-
Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals
- 2021
-
In: Human Genetics and Genomics Advances. - : Cell Press. - 2666-2477. ; 2:3
-
Journal article (peer-reviewed)abstract
- Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis.
|
|
| 9. |
|
|
| 10. |
|
|
