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Detection of mobile elements insertions for routine clinical diagnostics in targeted sequencing data
Author(s) -
Demidov German,
Park Joohyun,
ArmeanuEbinger Sorin,
Roggia Cristiana,
Faust Ulrike,
Cordts Isabell,
Blandfort Maria,
Haack Tobias B.,
Schroeder Christopher,
Ossowski Stephan
Publication year - 2021
Publication title -
molecular genetics and genomic medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.765
H-Index - 29
ISSN - 2324-9269
DOI - 10.1002/mgg3.1807
Subject(s) - medicine , computational biology , computer science , data science , biology
Background Targeted sequencing approaches such as gene panel or exome sequencing have become standard of care for the diagnosis of rare and common genetic disease. The detection and interpretation of point mutations, small insertions and deletions, and even exon‐level copy number variants are well established in clinical genetic testing. Other types of genetic variation such as mobile elements insertions (MEIs) are technically difficult to detect. In addition, their downstream clinical interpretation is more complex compared to point mutations due to a larger genomic footprint that can not only predict a clear loss of protein function but might disturb gene regulation and splicing even when located within the non‐coding regions. As a consequence, the contribution of MEIs to disease and tumor development remains largely unexplored in routine diagnostics. Methods In this study, we investigated the occurrence of MEIs in 7,693 exome datasets from individuals with rare diseases and healthy relatives as well as 788 cancer patients analyzed by panel sequencing. Results We present several exemplary cases highlighting the diagnostic value of MEIs and propose a strategy for the detection, prioritization, and clinical interpretation of MEIs in routine clinical diagnostics. Conclusion In this paper, we state that detection and interpretation of MEIs in clinical practice in targeted NGS data can be performed relatively easy despite the fact that MEIs very rarely occur in coding parts of the human genome. Large scale reanalysis of MEIs in existing cohorts may solve otherwise unsolvable cases.

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