Non-coding region variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms

Wright, Caroline F.; Quaife, Nicholas M; Ramos-Hernández, Laura; Danecek, Petr; Ferla, Matteo P.; Samocha, Kaitlin E.; Kaplanis, Joanna; Gardner, Eugene J.; Eberhardt, Ruth Y; Chao, Katherine R.; Karczewski, Konrad J.; Morales, Joannella; Gallone, Giuseppe; Balasubramanian, Meena; Banka, Siddharth; Gompertz, Lianne; Kerr, Bronwyn; Kirby, Amelia; Lynch, Sally Ann; Morton, Jenny; Pinz, Hailey; Sansbury, Francis H.; Stewart, Helen; Zuccarelli, Britton; Cook, Stuart A.; Taylor, Jenny C; Juusola, Jane; Retterer, Kyle; Firth, Helen V.; Hurles, Matthew E.; Lara‐Pezzi, Enrique; Barton, Paul J.R.; Whiffin, Nicola

doi:10.1016/j.ajhg.2021.04.025

Cited by 51 publications

(52 citation statements)

References 62 publications

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“…The performance of TADA improves compared to the initial ranking analysis of rare variants. However, we reasoned that a model solely focusing on coding regions would not reflect currently emerging and future CNV effects well as multiple instances of non-coding pathogenic variation are being identified [ 5 , 42 ]. The presented and published model therefore also includes features accounting for the CNV’s non-coding regulatory environment.…”

Section: Resultsmentioning

confidence: 99%

TADA—a machine learning tool for functional annotation-based prioritisation of pathogenic CNVs

et al. 2022

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Few methods have been developed to investigate copy number variants (CNVs) based on their predicted pathogenicity. We introduce TADA, a method to prioritise pathogenic CNVs through assisted manual filtering and automated classification, based on an extensive catalogue of functional annotation supported by rigourous enrichment analysis. We demonstrate that our classifiers are able to accurately predict pathogenic CNVs, outperforming current alternative methods, and produce a well-calibrated pathogenicity score. Our results suggest that functional annotation-based prioritisation of pathogenic CNVs is a promising approach to support clinical diagnostics and to further the understanding of mechanisms controlling the disease impact of larger genomic alterations.

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Section: Resultsmentioning

confidence: 99%

TADA—a machine learning tool for functional annotation-based prioritisation of pathogenic CNVs

et al. 2022

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“…First, we scrutinized the GS data of the ATM locus. We looked for rare or novel complex structural variants ( Maroilley et al, 2021 ), mobile element insertions ( Tarailo-Graovac et al, 2017a ), 5′ and 3′ UTR ( Whiffin et al, 2020 ; Wright et al, 2021 ; Zhang et al, 2021 ), deep exonic ( Casadei et al, 2019 ), and deep intronic variants either inherited from father or absent from both parents. These analyses led to the identification of a paternal deep intronic variant NG_009830.1(NM_000051.3): c.1803-270T > G. The variant is absent from all the population databases tested, including our in-house database, gnomAD (v2 and v3) and TopMed database freeze 8.…”

Section: Resultsmentioning

confidence: 99%

Case Report: Biallelic Loss of Function ATM due to Pathogenic Synonymous and Novel Deep Intronic Variant c.1803-270T > G Identified by Genome Sequencing in a Child With Ataxia–Telangiectasia

et al. 2022

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Ataxia–telangiectasia (AT) is a complex neurodegenerative disease with an increased risk for bone marrow failure and malignancy. AT is caused by biallelic loss of function variants in ATM, which encodes a phosphatidylinositol 3-kinase that responds to DNA damage. Herein, we report a child with progressive ataxia, chorea, and genome instability, highly suggestive of AT. The clinical ataxia gene panel identified a maternal heterozygous synonymous variant (NM_000051.3: c.2250G > A), previously described to result in exon 14 skipping. Subsequently, trio genome sequencing led to the identification of a novel deep intronic variant [NG_009830.1(NM_000051.3): c.1803-270T > G] inherited from the father. Transcript analyses revealed that c.1803-270T > G results in aberrant inclusion of 56 base pairs of intron 11. In silico tests predicted a premature stop codon as a consequence, suggesting non-functional ATM; and DNA repair analyses confirmed functional loss of ATM. Our findings highlight the power of genome sequencing, considering deep intronic variants in undiagnosed rare disease patients.

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“… 14 , 30 Finally, large amounts of “off‐target” sequencing data, for example intronic and 3′/5′ untranslated regions, are generated by ES yet are often filtered by cES bioinformatic pipelines despite increasing evidence of their significance in Mendelian disorders and improved in silico tools for evaluating their pathogenicity. 19 , 21 , 31 …”

Section: Discussionmentioning

confidence: 99%

Deep clinicopathological phenotyping identifies a previously unrecognized pathogenic EMD splice variant

Calame

Fatih

Herman

et al. 2021

Ann Clin Transl Neurol

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Exome sequencing (ES) has revolutionized rare disease management, yet only ~25%–30% of patients receive a molecular diagnosis. A limiting factor is the quality of available phenotypic data. Here, we describe how deep clinicopathological phenotyping yielded a molecular diagnosis for a 19‐year‐old proband with muscular dystrophy and negative clinical ES. Deep phenotypic analysis identified two critical data points: (1) the absence of emerin protein in muscle biopsy and (2) clinical features consistent with Emery‐Dreifuss muscular dystrophy. Sequencing data analysis uncovered an ultra‐rare, intronic variant in EMD, the gene encoding emerin. The variant, NM_000117.3: c.188‐6A > G, is predicted to impact splicing by in silico tools. This case thus illustrates how better integration of clinicopathologic data into ES analysis can enhance diagnostic yield with implications for clinical practice.

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Non-coding region variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms

Cited by 51 publications

References 62 publications

TADA—a machine learning tool for functional annotation-based prioritisation of pathogenic CNVs

TADA—a machine learning tool for functional annotation-based prioritisation of pathogenic CNVs

Case Report: Biallelic Loss of Function ATM due to Pathogenic Synonymous and Novel Deep Intronic Variant c.1803-270T > G Identified by Genome Sequencing in a Child With Ataxia–Telangiectasia

Deep clinicopathological phenotyping identifies a previously unrecognized pathogenic EMD splice variant

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