2020
DOI: 10.1038/s41436-020-0749-x
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Mobile element insertion detection in 89,874 clinical exomes

Abstract: Purpose: Exome sequencing (ES) is increasingly used for the diagnosis of rare genetic disease. However, some pathogenic sequence variants within the exome go undetected due to the technical difficulty of identifying them. Mobile element insertions (MEIs) are a known cause of genetic disease in humans but have been historically difficult to detect via ES and similar targeted sequencing methods. Methods: We developed and applied a novel MEI detection method prospectively to samples received for clinical ES begin… Show more

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Cited by 46 publications
(54 citation statements)
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“…Our study is limited in that only one tool was used to identify MEIs. Though the overall performance of MELT outperformed existing MEI discovery tools (Gardner et al 2017) and it has been successfully used in several large-scale studies (Gardner et al 2017(Gardner et al , 2019Feusier et al 2019;Werling et al 2018;Torene et al 2020), but the detection power could be compromised by modest sequencing depth and incompetence in complex genomic regions of short-read WGS etc. In addition, the overall genotyping accuracy by MELT v2 was 87.95% for non-reference Alus (not excluding MEIs in low complexity regions), when compared with PCR generated genotypes (Goubert et al 2020).…”
Section: Discussionmentioning
confidence: 99%
“…Our study is limited in that only one tool was used to identify MEIs. Though the overall performance of MELT outperformed existing MEI discovery tools (Gardner et al 2017) and it has been successfully used in several large-scale studies (Gardner et al 2017(Gardner et al , 2019Feusier et al 2019;Werling et al 2018;Torene et al 2020), but the detection power could be compromised by modest sequencing depth and incompetence in complex genomic regions of short-read WGS etc. In addition, the overall genotyping accuracy by MELT v2 was 87.95% for non-reference Alus (not excluding MEIs in low complexity regions), when compared with PCR generated genotypes (Goubert et al 2020).…”
Section: Discussionmentioning
confidence: 99%
“…Since intronic TEIs can affect gene function through various mechanisms, such as altering RNA expression and splicing 1; 8 , TEIs contributing to ASD may present a phenotype different from known phenotypes caused by LoF coding mutations or large CNVs in these genes. Including TEIs and structural variants in standard clinical genetic analyses for ASD will continue to expand our knowledge of non-coding mutations and could increase the rates of genetic diagnoses 17 . Our work also presents important advances in scalable bioinformatic processing and identification of TEIs, which by their nature represent a challenging form of genomic variation to study.…”
Section: Main Textmentioning
confidence: 99%
“…The majority are Alu insertions 17 and only a few SVA (n = 16). Recently, large studies have evaluated the impact of MEI in patients between 0.04 and 0.15% of the cases 18,19 …”
Section: Discussionmentioning
confidence: 99%