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

Abstract

Methylation; Splitting; Support vector machine

Metilació; Divisió; Màquina de vectors de suport

Metilación; División; Máquina de vectores de soporte

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 Methods We utilized an expanded cohort of 56 patients (22 previously unpublished individuals) with truncating variants in the MN1 gene and attempted different methods to assess plausible strategies to objectively delineate phenotypic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.

This work was supported by grants from the Society for the Relief of Disabled Children, Commissioned Paediatric Research at HKCH under The Health and Medical Research Fund (PR-HKU-4), the Agence Nationale de la Recherche “Investissements d’Avenir” program (ANR-10-IAHU-01), MSDAvenir (Devo-Decode project) and AXA (“Tête et Cœur” project). This work was supported by a Simons Foundation Autism Research Initiative (SFARI for RW) and an NSW Genomics Collaborative Grant (to AZ) and the NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54HD083091, Genetics Core). Bert Callewaert is a Senior Clinical Investigator of the Research Foundation – Flanders. E.B.O. was supported by the grant from Poznan University of Medical Sciences, Poland ProScience 2022 (502-14-11261860-11962). K.L. is supported by the National Institute for Health and Care Research Doctoral Research Fellowship 302303: The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. NIH P50HD103524 PI Sandra Juul, Genetics Core supported participant enrollment and clinical data collection for UW site. None of the sponsors had any role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.