Artificial intelligence-driven genotype–epigenotype–phenotype approaches to resolve challenges in syndrome diagnosticsResearch in context

Artificial intelligence-driven genotype–epigenotype–phenotype approaches to resolve challenges in syndrome diagnosticsResearch in context

Summary: Background: Decisions to split two or more phenotypic manifestations related to genetic variations within the same gene can be challenging, especially during the early stages of syndrome discovery. Genotype-based diagnostics with artificial intelligence (AI)-driven approaches using next-ge...

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Main Authors: Christopher C.Y. Mak, Hannah Klinkhammer, Sanaa Choufani, Nikola Reko, Angela K. Christman, Elise Pisan, Martin M.C. Chui, Mianne Lee, Fiona Leduc, Jennifer C. Dempsey, Pedro A. Sanchez-Lara, Hannah M. Bombei, John A. Bernat, Laurence Faivre, Frederic Tran Mau-Them, Irene Valenzuela Palafoll, Natalie Canham, Ajoy Sarkar, Yuri A. Zarate, Bert Callewaert, Ewelina Bukowska-Olech, Aleksander Jamsheer, Andreas Zankl, Marjolaine Willems, Laura Duncan, Bertrand Isidor, Benjamin Cogne, Odile Boute, Clémence Vanlerberghe, Alice Goldenberg, Elliot Stolerman, Karen J. Low, Vianney Gilard, Jeanne Amiel, Angela E. Lin, Christopher T. Gordon, Dan Doherty, Peter M. Krawitz, Rosanna Weksberg, Tzung-Chien Hsieh, Brian H.Y. Chung
Format: Article
Language:English
Published: Elsevier 2025-05-01
Series:EBioMedicine
Subjects:
Splitting
GestaltMatcher
Methylation
Support vector machine
MN1
MCTT
Online Access:http://www.sciencedirect.com/science/article/pii/S2352396425001215
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Summary:Summary: Background: Decisions to split two or more phenotypic manifestations related to genetic variations within the same gene can be challenging, especially during the early stages of syndrome discovery. Genotype-based diagnostics with artificial intelligence (AI)-driven approaches using next-generation phenotyping (NGP) and DNA methylation (DNAm) can be utilized to expedite syndrome delineation within a single gene. Methods: We utilized an expanded cohort of 56 patients (22 previously unpublished individuals) with truncating variants in the MN1 gene and attempted different methods to assess plausible strategies to objectively delineate phenotypic differences between the C-Terminal Truncation (CTT) and N-Terminal Truncation (NTT) groups. This involved transcriptomics analysis on available patient fibroblast samples and AI-assisted approaches, including a new statistical method of GestaltMatcher on facial photos and blood DNAm analysis using a support vector machine (SVM) model. Findings: RNA-seq analysis was unable to show a significant difference in transcript expression despite our previous hypothesis that NTT variants would induce nonsense mediated decay. DNAm analysis on nine blood DNA samples revealed an episignature for the CTT group. In parallel, the new statistical method of GestaltMatcher objectively distinguished the CTT and NTT groups with a low requirement for cohort number. Validation of this approach was performed on syndromes with known DNAm signatures of SRCAP, SMARCA2 and ADNP to demonstrate the effectiveness of this approach. Interpretation: We demonstrate the potential of using AI-based technologies to leverage genotype, phenotype and epigenetics data in facilitating splitting decisions in diagnosis of syndromes with minimal sample requirement. Funding: The specific funding of this article is provided in the acknowledgements section.
ISSN:2352-3964

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