Discordance between a deep learning model and clinical-grade variant pathogenicity classification in a rare disease cohort

Abstract Genetic testing has become an essential component in the diagnosis and management of a wide range of clinical conditions, from cancer to developmental disorders, especially in rare Mendelian diseases. Efforts to identify rare phenotype-associated variants have predominantly focused on protein-truncating variants, while the interpretation of missense variants presents a considerable challenge. Deep learning algorithms excel in various applications across biomedical tasks1,2, yet accurately distinguishing between pathogenic and benign genetic variants remains an elusive goal3-5. Specifically, even the most sophisticated models encounter difficulties in accurately assessing the pathogenicity of missense variants of uncertain significance (VUS). Our investigation of AlphaMissense (AM)5, the latest iteration of deep learning methods for predicting the potential functional impact of missense variants and assessing gene essentiality, reveals important limitations in its ability to identify pathogenic missense variants within a rare disease cohort. Indeed, AM struggles to accurately assess the pathogenicity of variants in intrinsically disordered regions (IDRs), leading to unreliable gene-level essentiality scores for certain genes containing IDRs. This limitation highlights the challenges in applying AM faces in the context of clinical genetics6. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study was supported by funding from the Intramural Research Program of the National Center for Advancing Translational Sciences (U01TR002623), the PrecisionLink Health Discovery and Children's Rare Disease Cohorts initiative of Boston Children's Hospital, R01NS129188 (SK), P30DK034854 (SBS), RC2DK122532 (SBS), the Helmsley Charitable Trust (SBS), the Wolpow Family Chair in IBD Research and Treatment (SBS), and the Egan Family Foundation Chair in Transitional Medicine (SBS). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the BCH Institutional Review Board under protocol number P00000159. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The individual-level phenotype and genotype data used in this study is available to approved researchers via Genomic Information Commons portal at: https://pl-gic.childrens.harvard.edu/.