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<p>Neurodevelopmental disorders (NDDs) often have unknown genetic causes. Current efforts in identifying disease-related genetic variants using exome or genome sequencing still lead to an excessive number of variants of uncertain significance (VUS). There is an increasing interest in transcriptomics and, more recently, proteomics for variant detection and interpretation. In this study, we integrated quantitative liquid chromatography-mass spectrometry proteomics, RNA sequencing, and exome reanalysis to resolve VUS and detect novel causal variants in 34 patients with undiagnosed NDDs, using the software PROTRIDER and DROP to detect protein outliers and RNA outliers, respectively. We obtained a diagnosis in 11 cases (32%) resulting from the increased amount of information provided by the two additional levels of omics (<i>n</i> = 5) and the updated literature evidence (<i>n</i> = 6). Our experience suggests the potential of this outlier-detection multi-omics workflow for improving diagnostic yield in NDDs and other rare disorders.</p>

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An outlier approach: advancing diagnosis of neurological diseases through integrating proteomics into multi-omics guided exome reanalysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月3日

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  • Martin Man-Chun Chui,
  • Anna Ka-Yee Kwong,
  • Hiu Yu Cherie Leung,
  • Chingyiu Pang,
  • Ines F. Scheller,
  • Sheila Suet-Na Wong,
  • Cheuk-Wing Fung,
  • Vicente A. Yépez,
  • Julien Gagneur,
  • Christopher Chun-Yu Mak,
  • Brian Hon-Yin Chung

摘要

Neurodevelopmental disorders (NDDs) often have unknown genetic causes. Current efforts in identifying disease-related genetic variants using exome or genome sequencing still lead to an excessive number of variants of uncertain significance (VUS). There is an increasing interest in transcriptomics and, more recently, proteomics for variant detection and interpretation. In this study, we integrated quantitative liquid chromatography-mass spectrometry proteomics, RNA sequencing, and exome reanalysis to resolve VUS and detect novel causal variants in 34 patients with undiagnosed NDDs, using the software PROTRIDER and DROP to detect protein outliers and RNA outliers, respectively. We obtained a diagnosis in 11 cases (32%) resulting from the increased amount of information provided by the two additional levels of omics (n = 5) and the updated literature evidence (n = 6). Our experience suggests the potential of this outlier-detection multi-omics workflow for improving diagnostic yield in NDDs and other rare disorders.