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<p>Neurodevelopmental disorders (NDDs) exhibit complex genotype-phenotype associations that frequently result in inconclusive variant interpretations, contributing to suboptimal diagnostic yields (~ 40%). Koolen-de Vries syndrome (KdVS), an autosomal dominant NDD caused by <i>KANSL1</i> haploinsufficiency, exemplifies this diagnostic challenge with its multisystem manifestations and lack of systematic genotype-phenotype associations. To address this gap, we constructed a comprehensive KdVS genotype-phenotype repository by systematically integrating all molecularly confirmed cases from global literature. Comprehensive phenotypic analysis revealed that core KdVS features include developmental delay/intellectual disability, characteristic craniofacial dysmorphism, hypotonia, and multisystem abnormalities. Phenotypic association analysis identified 249 significant correlations, demonstrating that KdVS clinical manifestations are highly interconnected rather than representing isolated features, such as the association between strabismus and hydrocephalus (OR = 14.26). Application of this repository to screen a Chinese rare disease cohort identified 53 <i>KANSL1</i> variants. Among these, one de novo nonsense variant (NM_001193466.2: c.902T &gt; G, p.Leu301Ter) was classified as pathogenic in a Chinese boy with classic KdVS features. The remaining 52 variants were categorized as variants of uncertain significance (VUS), approximately half of which were absent from gnomAD databases. Each VUS was comprehensively annotated with detailed clinical profiles to facilitate phenotype-driven reinterpretation. In conclusion, this study establishes KdVS as a highly interconnected multisystem disorder and demonstrates that deep phenotypic association analysis enhanced genetic diagnosis. This disease-specific repository approach provides a scalable framework for improving molecular diagnostics across rare NDDs.</p>

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Improving variant interpretation and diagnosis in Koolen-de Vries syndrome through a curated genotype–phenotype repository

  • 期刊论文

  • 发表日期：

    2025年12月29日

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  • Hailin Huang,
  • Jia Geng,
  • Yang Long,
  • Wenyu Xiong,
  • Xiaolu Wang,
  • Chao Wang,
  • Qian Zhang,
  • Ting Tang,
  • Yuxin Chen,
  • Yu Zhao,
  • Jing Cheng,
  • Yu Lu,
  • Fengxiao Bu,
  • Huijun Yuan

摘要

Neurodevelopmental disorders (NDDs) exhibit complex genotype-phenotype associations that frequently result in inconclusive variant interpretations, contributing to suboptimal diagnostic yields (~ 40%). Koolen-de Vries syndrome (KdVS), an autosomal dominant NDD caused by KANSL1 haploinsufficiency, exemplifies this diagnostic challenge with its multisystem manifestations and lack of systematic genotype-phenotype associations. To address this gap, we constructed a comprehensive KdVS genotype-phenotype repository by systematically integrating all molecularly confirmed cases from global literature. Comprehensive phenotypic analysis revealed that core KdVS features include developmental delay/intellectual disability, characteristic craniofacial dysmorphism, hypotonia, and multisystem abnormalities. Phenotypic association analysis identified 249 significant correlations, demonstrating that KdVS clinical manifestations are highly interconnected rather than representing isolated features, such as the association between strabismus and hydrocephalus (OR = 14.26). Application of this repository to screen a Chinese rare disease cohort identified 53 KANSL1 variants. Among these, one de novo nonsense variant (NM_001193466.2: c.902T > G, p.Leu301Ter) was classified as pathogenic in a Chinese boy with classic KdVS features. The remaining 52 variants were categorized as variants of uncertain significance (VUS), approximately half of which were absent from gnomAD databases. Each VUS was comprehensively annotated with detailed clinical profiles to facilitate phenotype-driven reinterpretation. In conclusion, this study establishes KdVS as a highly interconnected multisystem disorder and demonstrates that deep phenotypic association analysis enhanced genetic diagnosis. This disease-specific repository approach provides a scalable framework for improving molecular diagnostics across rare NDDs.