| 1. |
- Kanai, M, et al.
(författare)
-
- 2023
-
swepub:Mat__t
|
|
| 2. |
|
|
| 3. |
- Franke, Andre, et al.
(författare)
-
Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci
- 2010
-
Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 42:12, s. 1118-1125
-
Tidskriftsartikel (refereegranskat)abstract
- We undertook a meta-analysis of six Crohn's disease genome-wide association studies (GWAS) comprising 6,333 affected individuals (cases) and 15,056 controls and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent-offspring trios. We identified 30 new susceptibility loci meeting genome-wide significance (P < 5 × 10⁻⁸). A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, these results identify 71 distinct loci with genome-wide significant evidence for association with Crohn's disease.
|
|
| 4. |
- Fuchsberger, Christian, et al.
(författare)
-
The genetic architecture of type 2 diabetes
- 2016
-
Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 536:7614, s. 41-47
-
Tidskriftsartikel (refereegranskat)abstract
- The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.
|
|
| 5. |
- Gaulton, Kyle J, et al.
(författare)
-
Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.
- 2015
-
Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 47:12, s. 1415-1415
-
Tidskriftsartikel (refereegranskat)abstract
- We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.
|
|
| 6. |
|
|
| 7. |
- McGovern, Dermot P B, et al.
(författare)
-
Genome-wide association identifies multiple ulcerative colitis susceptibility loci
- 2010
-
Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 42:4, s. 332-337
-
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.
|
|
| 8. |
|
|
| 9. |
- Rivas, Manuel A., et al.
(författare)
-
Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease
- 2011
-
Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 43:11, s. 1066-U50
-
Tidskriftsartikel (refereegranskat)abstract
- More than 1,000 susceptibility loci have been identified through genome-wide association studies (GWAS) of common variants; however, the specific genes and full allelic spectrum of causal variants underlying these findings have not yet been defined. Here we used pooled next-generation sequencing to study 56 genes from regions associated with Crohn's disease in 350 cases and 350 controls. Through follow-up genotyping of 70 rare and low-frequency protein-altering variants in nine independent case-control series (16,054 Crohn's disease cases, 12,153 ulcerative colitis cases and 17,575 healthy controls), we identified four additional independent risk factors in NOD2, two additional protective variants in IL23R, a highly significant association with a protective splice variant in CARD9 (P < 1 x 10(-16), odds ratio approximate to 0.29) and additional associations with coding variants in IL18RAP, CUL2, C1orf106, PTPN22 and MUC19. We extend the results of successful GWAS by identifying new, rare and probably functional variants that could aid functional experiments and predictive models.
|
|
| 10. |
- 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.
|
|
