4.8 Article

Annotation of uORFs in the OMIM genes allows to reveal pathogenic variants in 5′UTRs

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NUCLEIC ACIDS RESEARCH
卷 51, 期 3, 页码 1229-1244

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OXFORD UNIV PRESS
DOI: 10.1093/nar/gkac1247

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An increasing number of studies highlight the significance of non-coding variants in hereditary diseases. However, the interpretation of such variants in clinical genetic testing remains challenging due to limited knowledge of their pathogenicity mechanisms. This study manually annotated upstream translation initiation sites (TISs) in human disease-associated genes and identified numerous TISs related to upstream open reading frames (uORFs). A machine-learning algorithm was also developed to predict TISs in other human genes.
An increasing number of studies emphasize the role of non-coding variants in the development of hereditary diseases. However, the interpretation of such variants in clinical genetic testing still remains a critical challenge due to poor knowledge of their pathogenicity mechanisms. It was previously shown that variants in 5 '-untranslated regions (5 ' UTRs) can lead to hereditary diseases due to disruption of upstream open reading frames (uORFs). Here, we performed a manual annotation of upstream translation initiation sites (TISs) in human disease-associated genes from the OMIM database and revealed similar to 4.7 thousand of TISs related to uORFs. We compared our TISs with the previous studies and provided a list of 'high confidence' uORFs. Using a luciferase assay, we experimentally validated the translation of uORFs in the ETFDH, PAX9, MAST1, HTT, TTN,GLI2 and COL2A1 genes, as well as existence of N-terminal CDS extension in the ZIC2 gene. Besides, we created a tool to annotate the effects of genetic variants located in uORFs. We revealed the variants from the HGMD and ClinVar databases that disrupt uORFs and thereby could lead to Mendelian disorders. We also showed that the distribution of uORFs-affecting variants differs between pathogenic and population variants. Finally, drawing on manually curated data, we developed a machine-learning algorithm that allows us to predict the TISs in other human genes.

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