The European Genome-phenome Archive of human data consented for biomedical research

Lappalainen, Ilkka; Almeida-King, Jeff; Kumanduri, Vasudev; Senf, Alexander; Spalding, J. Dylan; Ur-Rehman, Saif; Saunders, Gary; Kandasamy, Jag; Cáccamo, Mario; Leinonen, Rasko; Vaughan, Brendan; Laurent, Thomas; Rowland, Francis; Marín-Garcia, Pablo; Barker, Jonathan; Jokinen, Petteri; Torres, Angel Carreño; Rambla, Jordi; Llobet, Oscar Martinez; Medina, Ignacio; Puy, Marc Sitges; Alberich, Mario; Torre, S. de La; Navarro, Arcadi; Paschall, Justin; Flicek, Paul

doi:10.1038/ng.3312

Cited by 307 publications

(249 citation statements)

References 24 publications

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“…We obtained VCF files for 3781 healthy individuals from the TwinsUK cohort (EGAD00001000741) and the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (EGAD00001000740) and 122 individuals with ciliopathies (EGAD00001000414) from the European Genome-phenome Archive (EGA) (24) under the UK10K data access agreement (25). We also obtained VCF files for human common variants from dbSNP (build146 GRCh37p13) (26).…”

Section: Methodsmentioning

confidence: 99%

Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource

Shim

Kim

et al. 2016

Nucleic Acids Res

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Whole exome sequencing (WES) accelerates disease gene discovery using rare genetic variants, but further statistical and functional evidence is required to avoid false-discovery. To complement variant-driven disease gene discovery, here we present function-driven disease gene discovery in zebrafish (Danio rerio), a promising human disease model owing to its high anatomical and genomic similarity to humans. To facilitate zebrafish-based function-driven disease gene discovery, we developed a genome-scale co-functional network of zebrafish genes, DanioNet (www.inetbio.org/danionet), which was constructed by Bayesian integration of genomics big data. Rigorous statistical assessment confirmed the high prediction capacity of DanioNet for a wide variety of human diseases. To demonstrate the feasibility of the function-driven disease gene discovery using DanioNet, we predicted genes for ciliopathies and performed experimental validation for eight candidate genes. We also validated the existence of heterozygous rare variants in the candidate genes of individuals with ciliopathies yet not in controls derived from the UK10K consortium, suggesting that these variants are potentially involved in enhancing the risk of ciliopathies. These results showed that an integrated genomics big data for a model animal of diseases can expand our opportunity for harnessing WES data in disease gene discovery.

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Section: Methodsmentioning

confidence: 99%

Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource

Shim

Kim

et al. 2016

Nucleic Acids Res

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“…Primary patient survival correlations for TNBC and molecular basal subtype breast cancer were performed using normalized gene expression data from METABRIC (29) and obtained from the publicly available European Genome-Phenome Archive (IDs EGAD00010000210 and EGAD0001000021) (47). Detailed analytical methods for the METABRIC comparisons are outlined in SI Appendix.…”

Section: Methodsmentioning

confidence: 99%

Integrin-β4 identifies cancer stem cell-enriched populations of partially mesenchymal carcinoma cells

Bierie

Pierce

Kroeger

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

286

233

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Neoplastic cells within individual carcinomas often exhibit considerable phenotypic heterogeneity in their epithelial versus mesenchymal-like cell states. Because carcinoma cells with mesenchymal features are often more resistant to therapy and may serve as a source of relapse, we sought to determine whether such cells could be further stratified into functionally distinct subtypes. Indeed, we find that a basal epithelial marker, integrin-β4 (ITGB4), can be used to enable stratification of mesenchymallike triple-negative breast cancer (TNBC) cells that differ from one another in their relative tumorigenic abilities. Notably, we demonstrate that ITGB4+ cancer stem cell (CSC)-enriched mesenchymal cells reside in an intermediate epithelial/mesenchymal phenotypic state. Among patients with TNBC who received chemotherapy, elevated ITGB4 expression was associated with a worse 5-year probability of relapse-free survival. Mechanistically, we find that the ZEB1 (zinc finger E-box binding homeobox 1) transcription factor activity in highly mesenchymal SUM159 TNBC cells can repress expression of the epithelial transcription factor TAp63α (tumor protein 63 isoform 1), a protein that promotes ITGB4 expression. In addition, we demonstrate that ZEB1 and ITGB4 are important in modulating the histopathological phenotypes of tumors derived from mesenchymal TNBC cells. Hence, mesenchymal carcinoma cell populations are internally heterogeneous, and ITGB4 is a mechanistically driven prognostic biomarker that can be used to identify the more aggressive subtypes of mesenchymal carcinoma cells in TNBC. The ability to rapidly isolate and mechanistically interrogate the CSC-enriched, partially mesenchymal carcinoma cells should further enable identification of novel therapeutic opportunities to improve the prognosis for high-risk patients with TNBC.cancer | heterogeneity | EMT | mesenchymal | ITGB4

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“…NCDS (Power and Elliot, 2005), UK Biobank (Sudlow et al 2015), UK Data Archive, 1 European Genome-phenome Archive (Lappalainen et al 2015).…”

Section: Access Methods Description Examplesmentioning

confidence: 99%

DataSHIELD – New Directions and Dimensions

Wilson

Butters

Avraam

et al. 2017

Data Science Journal

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In disciplines such as biomedicine and social sciences, sharing and combining sensitive individual-level data is often prohibited by ethical-legal or governance constraints and other barriers such as the control of intellectual property or the huge sample sizes. DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual-levEL Databases) is a distributed approach that allows the analysis of sensitive individual-level data from one study, and the co-analysis of such data from several studies simultaneously without physically pooling them or disclosing any data.Following initial proof of principle, a stable DataSHIELD platform has now been implemented in a number of epidemiological consortia. This paper reports three new applications of DataSHIELD including application to post-publication sensitive data analysis, text data analysis and privacy protected data visualisation. Expansion of DataSHIELD analytic functionality and application to additional data types demonstrate the broad applications of the software beyond biomedical sciences.

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The European Genome-phenome Archive of human data consented for biomedical research

Cited by 307 publications

References 24 publications

Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource

Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource

Integrin-β4 identifies cancer stem cell-enriched populations of partially mesenchymal carcinoma cells

DataSHIELD – New Directions and Dimensions

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