Genetic syndromes frequently present with overlapping clinical features and inconclusive or ambiguous genetic findings which can confound accurate diagnosis and clinical management. An expanding number of genetic syndromes have been shown to have unique genomic DNA methylation patterns (called ''episignatures''). Peripheral blood episignatures can be used for diagnostic testing as well as for the interpretation of ambiguous genetic test results. We present here an approach to episignature mapping in 42 genetic syndromes, which has allowed the identification of 34 robust disease-specific episignatures. We examine emerging patterns of overlap, as well as similarities and hierarchical relationships across these episignatures, to highlight their key features as they are related to genetic heterogeneity, dosage effect, unaffected carrier status, and incomplete penetrance. We demonstrate the necessity of multiclass modeling for accurate genetic variant classification and show how disease classification using a single episignature at a time can sometimes lead to classification errors in closely related episignatures. We demonstrate the utility of this tool in resolving ambiguous clinical cases and identification of previously undiagnosed cases through mass screening of a large cohort of subjects with developmental delays and congenital anomalies. This study more than doubles the number of published syndromes with DNA methylation episignatures and, most significantly, opens new avenues for accurate diagnosis and clinical assessment in individuals affected by these disorders.

Conventional genetic testing of individuals with neurodevelopmental presentations and congenital anomalies (ND/CAs), i.e., the analysis of sequence and copy number variants, leaves a substantial proportion of them unexplained. Some of these cases have been shown to result from DNA methylation defects at a single locus (epi-variants), while others can exhibit syndrome-specific DNA methylation changes across multiple loci (epi-signatures). Here, we investigate the clinical diagnostic utility of genome-wide DNA methylation analysis of peripheral blood in unresolved ND/CAs. We generate a computational model enabling concurrent detection of 14 syndromes using DNA methylation data with full accuracy. We demonstrate the ability of this model in resolving 67 individuals with uncertain clinical diagnoses, some of whom had variants of unknown clinical significance (VUS) in the related genes. We show that the provisional diagnoses can be ruled out in many of the case subjects, some of whom are shown by our model to have other diseases initially not considered. By applying this model to a cohort of 965 ND/CA-affected subjects without a previous diagnostic assumption and a separate assessment of rare epi-variants in this cohort, we identify 15 case subjects with syndromic Mendelian disorders, 12 case subjects with imprinting and trinucleotide repeat expansion disorders, as well as 106 case subjects with rare epi-variants, a portion of which involved genes clinically or functionally linked to the subjects' phenotypes. This study demonstrates that genomic DNA methylation analysis can facilitate the molecular diagnosis of unresolved clinical cases and highlights the potential value of epigenomic testing in the routine clinical assessment of ND/CAs.

Purpose We describe the clinical implementation of genome-wide DNA methylation analysis in rare disorders across the EpiSign diagnostic laboratory network and the assessment of results and clinical impact in the first subjects tested. Methods We outline the logistics and data flow between an integrated network of clinical diagnostics laboratories in Europe, the United States, and Canada. We describe the clinical validation of EpiSign using 211 specimens and assess the test performance and diagnostic yield in the first 207 subjects tested involving two patient subgroups: the targeted cohort (subjects with previous ambiguous/inconclusive genetic findings including genetic variants of unknown clinical significance) and the screening cohort (subjects with clinical findings consistent with hereditary neurodevelopmental syndromes and no previous conclusive genetic findings). Results Among the 207 subjects tested, 57 (27.6%) were positive for a diagnostic episignature including 48/136 (35.3%) in the targeted cohort and 8/71 (11.3%) in the screening cohort, with 4/207 (1.9%) remaining inconclusive after EpiSign analysis. Conclusion This study describes the implementation of diagnostic clinical genomic DNA methylation testing in patients with rare disorders. It provides strong evidence of clinical utility of EpiSign analysis, including the ability to provide conclusive findings in the majority of subjects tested.

Background: ADNP syndrome is a rare Mendelian disorder characterized by global developmental delay, intellectual disability, and autism. It is caused by truncating mutations in ADNP, which is involved in chromatin regulation. We hypothesized that the disruption of chromatin regulation might result in specific DNA methylation patterns that could be used in the molecular diagnosis of ADNP syndrome. Results: We identified two distinct and partially opposing genomic DNA methylation episignatures in the peripheral blood samples from 22 patients with ADNP syndrome. The "epi-ADNP-1" episignature included~6000 mostly hypomethylated CpGs, and the "epi-ADNP-2" episignature included~1000 predominantly hypermethylated CpGs. The two signatures correlated with the locations of the ADNP mutations. Epi-ADNP-1 mutations occupy the N-and C-terminus, and epi-ADNP-2 mutations are centered on the nuclear localization signal. The episignatures were enriched for genes involved in neuronal system development and function. A classifier trained on these profiles yielded full sensitivity and specificity in detecting patients with either of the two episignatures. Applying this model to seven patients with uncertain clinical diagnosis enabled reclassification of genetic variants of uncertain significance and assigned new diagnosis when the primary clinical suspicion was not correct. When applied to a large cohort of unresolved patients with developmental delay (N = 1150), the model predicted three additional previously undiagnosed patients to have ADNP syndrome. DNA sequencing of these subjects, wherever available, identified pathogenic mutations within the gene domains predicted by the model. Conclusions: We describe the first Mendelian condition with two distinct episignatures caused by mutations in a single gene. These highly sensitive and specific DNA methylation episignatures enable diagnosis, screening, and genetic variant classifications in ADNP syndrome.