Department of Bioinformatics - BiGCaT, NUTRIM School of Nutrition and Translational Research in Metabolism, MHeNS School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.
GKC - Rett Expertise Centre, Maastricht University Medical Center, Maastricht, The Netherlands.
Sci Data. 2021 Jan 15;8(1):10. doi: 10.1038/s41597-020-00794-7.
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.
雷特综合征（RTT）是一种罕见的神经发育障碍，主要由 MECP2 中的基因突变引起。生成新的 MECP2 变体和相关表型变体，为更好地理解疾病机制和更快地识别诊断变体提供了数据。然而，目前由于基因型-表型数据库之间缺乏互操作性而受到阻碍。在这里，我们以 RTT 中的 MECP2 为例，证明通过使基因型-表型数据更加可发现、可访问、可互操作和可重用（FAIR），我们可以促进变体的优先级排序和分析。总共成功整合了 10968 个 MECP2 变体。在这些变体中，发现了 863 个独特的确认导致 RTT 的变体和 209 个独特的确认良性变体。该数据集用于比较致病性预测工具、蛋白质后果和识别模棱两可的变体。预测工具通常可以识别导致 RTT 的变体和良性变体，但存在广泛的重叠，有 19 个变体被注释为既致病又良性，这表明在这些情况下，还有其他因素导致疾病的发展。
