1. |
- Alonso, Lorena, et al.
(författare)
-
TIGER : The gene expression regulatory variation landscape of human pancreatic islets
- 2021
-
Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 37:2
-
Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D.
|
|
2. |
- Duran-Ferrer, Marti, et al.
(författare)
-
The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome
- 2020
-
Ingår i: NATURE CANCER. - : Springer Nature. - 2662-1347. ; 1:11, s. 1066-1081
-
Tidskriftsartikel (refereegranskat)abstract
- We report a systematic analysis of the DNA methylation variability in 1,595 samples of normal cell subpopulations and 14 tumor subtypes spanning the entire human B-cell lineage. Differential methylation among tumor entities relates to differences in cellular origin and to de novo epigenetic alterations, which allowed us to build an accurate machine learning-based diagnostic algorithm. We identify extensive individual-specific methylation variability in silenced chromatin associated with the proliferative history of normal and neoplastic B cells. Mitotic activity generally leaves both hyper- and hypomethylation imprints, but some B-cell neoplasms preferentially gain or lose DNA methylation. We construct a DNA-methylation-based mitotic clock, called epiCMIT, whose lapse magnitude represents a strong independent prognostic variable in B-cell tumors and is associated with particular driver genetic alterations. Our findings reveal DNA methylation as a holistic tracer of B-cell tumor developmental history, with implications in differential diagnosis and the prediction of clinical outcome. Martin-Subero and colleagues analyze DNA methylation patterns in B-cell tumors and their normal cells of origin, and develop epiCMIT, a methylation-based mitotic clock with prognostic relevance.
|
|