4.7 Article Data Paper

A catalogue of 863 Rett-syndrome-causing MECP2 mutations and lessons learned from data integration

Journal

SCIENTIFIC DATA
Volume 8, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41597-020-00794-7

Keywords

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Funding

  1. ELIXIR (European Commission within the Research Infrastructures programme of Horizon 2020), the research infrastructure for life-science data (MolData2)
  2. Dutch Rett Syndrome Foundation (Stichting Terre)
  3. EXCELERATE (H2020) [676559]
  4. RD-Connect, European Union Seventh Framework Programme (FP7/2007-2013) [305444]
  5. European Union's Horizon 2020 Research and Innovation Program [825575]
  6. NWO in project VWData [400.17.605]
  7. BBMRI-NL (NWO, National Roadmap for Large-Scale Research Facilities) [184.033.111]
  8. INB Grant [PT17/0009/0001]

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Research on MECP2 in Rett syndrome can improve understanding of disease mechanisms and faster diagnosis of variants, but the lack of interoperability between genotype-phenotype databases currently hinders this process. Integrating a large amount of MECP2 variant data revealed variants that can cause both RTT and be benign, indicating additional factors contributing to disease development in these cases.
Rett syndrome (RTT) is a rare neurological disorder mostly caused by a genetic variation in MECP2. Making new MECP2 variants and the related phenotypes available provides data for better understanding of disease mechanisms and faster identification of variants for diagnosis. This is, however, currently hampered by the lack of interoperability between genotype-phenotype databases. Here, we demonstrate on the example of MECP2 in RTT that by making the genotype-phenotype data more Findable, Accessible, Interoperable, and Reusable (FAIR), we can facilitate prioritization and analysis of variants. In total, 10,968 MECP2 variants were successfully integrated. Among these variants 863 unique confirmed RTT causing and 209 unique confirmed benign variants were found. This dataset was used for comparison of pathogenicity predicting tools, protein consequences, and identification of ambiguous variants. Prediction tools generally recognised the RTT causing and benign variants, however, there was a broad range of overlap Nineteen variants were identified that were annotated as both disease-causing and benign, suggesting that there are additional factors in these cases contributing to disease development.

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