ArticleAmerican journal of human genetics2021
Detecting cryptic clinically relevant structural variation in exome-sequencing data increases diagnostic yield for developmental disorders.
Article in American journal of human genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Identifying the genetic causes of developmental disorders and intellectual disability in Africa: a systematic literature review.Frontiers in genetics · 2023Pooled it
- Systematic analysis of homozygous autosomal copy number losses in exomes improves diagnostic yield and uncovers ultra-rare recessive disorders.European journal of human genetics : EJHG · 2026Article
- Complex de novo structural variants are an underestimated cause of rare disorders.Nature communications · 2025Article
- The Genomic Landscape of Wilson Disease in a Pan India Disease Cohort and Population-Scale Data.Movement disorders clinical practice · 2025Article
- Structural variant calling and clinical interpretation in 6224 unsolved rare disease exomes.European journal of human genetics : EJHG · 2024Article
- The impact of inversions across 33,924 families with rare disease from a national genome sequencing project.American journal of human genetics · 2024Article
- Severity of Autism Spectrum Disorder Symptoms Associated with de novo Variants and Pregnancy-Induced Hypertension.Journal of autism and developmental disorders · 2024Article
- Mobile element insertions in rare diseases: a comparative benchmark and reanalysis of 60,000 exome samples.European journal of human genetics : EJHG · 2024Article
- In-Depth Genomic Analysis: The New Challenge in Congenital Heart Disease.International journal of molecular sciences · 2024Review
- Exome sequencing of UK birth cohorts.Wellcome open research · 2024Article
- Genomic Diagnosis of Rare Pediatric Disease in the United Kingdom and Ireland.The New England journal of medicine · 2023Article
- Cryptic mutations of PLC family members in brain disorders: recent discoveries and a deep-learning-based approach.Brain : a journal of neurology · 2023Review
- A collection of read depth profiles at structural variant breakpoints.Scientific data · 2023Article
- Incorporating CNV analysis improves the yield of exome sequencing for rare monogenic disorders-an important consideration for resource-constrained settings.Frontiers in genetics · 2023Review
- Detection of SmallFrontiers in endocrinology · 2022Article
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
Abstract
Structural variation (SV) describes a broad class of genetic variation greater than 50 bp in size. SVs can cause a wide range of genetic diseases and are prevalent in rare developmental disorders (DDs). Individuals presenting with DDs are often referred for diagnostic testing with chromosomal microarrays (CMAs) to identify large copy-number variants (CNVs) and/or with single-gene, gene-panel, or exome sequencing (ES) to identify single-nucleotide variants, small insertions/deletions, and CNVs. However, individuals with pathogenic SVs undetectable by conventional analysis often remain undiagnosed. Consequently, we have developed the tool InDelible, which interrogates short-read sequencing data for split-read clusters characteristic of SV breakpoints. We applied InDelible to 13,438 probands with severe DDs recruited as part of the Deciphering Developmental Disorders (DDD) study and discovered 63 rare, damaging variants in genes previously associated with DDs missed by standard SNV, indel, or CNV discovery approaches. Clinical review of these 63 variants determined that about half (30/63) were plausibly pathogenic. InDelible was particularly effective at ascertaining variants between 21 and 500 bp in size and increased the total number of potentially pathogenic variants identified by DDD in this size range by 42.9%. Of particular interest were seven confirmed de novo variants in MECP2, which represent 35.0% of all de novo protein-truncating variants in MECP2 among DDD study participants. InDelible provides a framework for the discovery of pathogenic SVs that are most likely missed by standard analytical workflows and has the potential to improve the diagnostic yield of ES across a broad range of genetic diseases.
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