Multiple developmental and congenital disorders due to genetic variants or environmental exposures are associated with unique genome-wide alterations in DNA methylation (DNAm). Consequently, these patterns referred to as DNAm signatures, can be leveraged for diagnostic purposes by developing artificial intelligence (AI) models that enable molecular subclassification of individuals. Notably, DNAm signature application has been particularly successful for diagnosing individuals affected by developmental disorders and congenital anomalies, especially those with defects in genes encoding the Mendelian epigenetic machinery. So far, over 100 DNAm distinctive signatures have been reported for these disorders. However, the translation into diagnostic practice remains challenging not only because of the scarcity of samples from these (ultra)rare disorders needed to train DNAm AI models, but also due to the privacy regulations that restrict the sharing of affected individuals’ data, lack of methods for standardization, limited replication across different centers, and the emergence of commercial entities with competing interests.

In this study, we show that ‘synthetic’ cases, meaning in silico cases generated from publicly available DNAm data from unaffected individuals, and summarized data derived from anonymized study cohorts of affected individuals with certain disorders, can be used to train DNAm classifiers. We demonstrate that these DNAm classifiers trained on a large cohort of synthetic cases have an improved performance compared to previously published classifiers trained on cohorts of affected individuals only, which typically are limited in size due to the rarity of these conditions. Furthermore, they improve the classification of variants with intermediate effect and mosaic cases and do not require any private affected individual data for training. Finally, to facilitate dissemination of these models, we release 169 synthetic cases-trained DNAm classifiers for 89 disorders with MethaDory, an open-access tool for simultaneous testing of these DNAm signatures.

The authors have declared no competing interest.

This research was supported by funding from the Netherlands Organisation for Health Research and Development (ZonMw) (grant number TK, 91718310)

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:

Data was obtained from the GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi). Respective study codes are available in the supplementary of the table

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

Publicly available studies used in this work can be found in GEO using the identifiers listed in Supplementary Table 1. The MethaDory tool is available at: https://github.com/f-ferraro/MethaDory.