Evaluation of DNA Methylation Episignatures for Diagnosis and Phenotype Correlations in 42 Mendelian Neurodevelopmental Disorders

Abstract

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.

Keywords: DNA methylation; EpiSign; VUS classification; episignature; molecular diagnostics; uncertain clinical cases.

Figure 1

Relationships across Various Syndromes and Their Subtypes The plot shows clustering analysis with heatmap using probes specific to the DNA methylation of one syndrome (or its subtype) as compared with another. Rows indicate probes and columns indicate samples. The top pane colors indicate the classes. The heatmap color scale from gold to red represents the level of methylation from 0–1. (A) Probes differentially methylated in Kabuki 1 (KMT2D) and controls do not provide distinction between subjects with Kabuki 1 and Kabuki 2 (KDM6A), although they differentiate both of them from the controls. (B) The same pattern is observed when Kabuki-2-specific probes are used. (C) Probes differentially methylated between individuals with Hunter McAlpine syndrome (HMA) (harboring duplication of NSD1) and controls generate a hypermethylation pattern in the HMA individuals. The same probes generate a mirror hypomethylation pattern in individuals with Sotos syndrome (loss of function of NSD1). (D) The same mirror effect is observed when probes selected for Sotos syndrome are used.

Figure 2

DNA Methylation Episignature of One Syndrome in Others The top two dimensions of multidimensional scaling plots (x axis = dim1, y axis = dim2) representing the pairwise distance across the samples with various episignatures: (A) Sotos-syndrome-specific probes distinguish Sotos syndrome samples from controls, but they do not differentiate alpha-thalassemia mental retardation syndrome (ATRX) samples from the controls. (B) ATRX-specific probes differentiate both Sotos syndrome and ATRX samples both from controls and from each other. (C) Kabuki-syndrome-specific probes differentiate Kabuki syndrome samples from controls, but they do not distinguish the BAFopathy samples from controls. (D) BAFopathy-specific probes generate an intermediate pattern for the Kabuki syndrome subjects between the BAFopathies and controls.

Figure 3

Distance and Hierarchical Orders across 34 Episignatures The dendrogram shows the distance and hierarchical orders of 34 episignatures. The y axis is the measure of distance or dissimilarity of either individual data points or clusters. The vertical position of the split in the dendrogram indicates the distance between every two points or clusters. The major splits are shown in different colors. Syndromes with very strong hypomethylation patterns are clustered together on the right, whereas those with hypermethylation episignatures are placed in a great distance to those in the left. As seen, BAFopathies are the most similar episignature to the controls, being consistent with their very mild DNA methylation changes.

Figure 4

Dimensionality Reduction of DNA Methylation Data from 34 Peripheral Blood DNA Methylation Episignatures The members of the three major clusters identified in the dendrogram in Figure 3 were projected in three separate two-dimensional plots (A–C) using a t-distributed stochastic neighbor embedding (t-SNE). Despite similarities observed across some, the use of all of the probes from all episignatures together provides enough distinctions between them. A small subgrouping is observed for BAFopathies and CHARGE syndrome. This observation is not explained by the genes involved, mutation coordinates, mutation type, clinical presentations, age, or sex. It is also not replicated when probes specific to each of these conditions are used for this analysis.

Figure 5

The Challenge of Disease Classification Using Closely Related Episignatures The plot shows an attempt at disease classification of three subjects using DNA methylation data through unsupervised analysis. (A) Multidimensional scaling of DNA methylation data from probes specific to RSTS episignature provides enough distinction between the Rubinstein-Taybi syndrome (RSTS) subjects and controls. The addition of two samples from individuals suspected to have RSTS (purple) clusters one of them with controls and the other with RSTS subjects. Another sample from an individual suspected to have Cornelia de Lange syndrome (CdLS; orange), however, is also situated closer to RSTS subjects than to controls. (B) The addition of the CdLS samples to the analysis using the RSTS-specific probes demonstrates that these samples show an intermediate pattern between RSTS and controls. (C) Incorporation of probes specific to CdLS in the analysis demonstrates that CdLS subjects are indeed distinct from RSTS cases. The uncertain sample from the individual suspected of having CdLS now clearly clusters with the other confirmed CdLS subjects.

Figure 6

A Multiclass Classification Algorithm for Concurrent Classification of 34 Episignatures Concurrent classification of the 34 episignatures is performed using 34 individual support vector machine (SVM) classifiers trained to distinguish each episignature from all others and from the methylation profile of the controls. For any given subject, each of which is represented with a point here, 34 models will generate 34 scores between 0 and 1 (y axis) representing the chance that the subject has a methylation profile similar to each of the 34 episignatures (x axis). The default cutoff of 0.5 is used for determining the class. However, most samples received scores close to 0 or 1, and thus for visualization, the points are jittered. Gray represents samples used in the training, and blue indicates those that were not used for training. The top two panels illustrate samples from the training and testing dataset with Cornelia de Lange syndrome (CdLS) and Rubinstein-Taybi syndrome (RSTS). These two categories were selected as examples among the 34 categories due to the challenge presented earlier in their unsupervised classification (Figure 5). As seen, each sample has received high scores only for the episignature it is supposed to have, and very low scores for all others. Samples with RSTS and CdLS have not been classified as one another. The third panel shows a trial performed for a large cohort of individuals from the general population (n = 2,315) as well as those with other developmental disorders not in the list of our episignatures (n = 442), all of which are scored close to zero. The final panel illustrates a cohort of unresolved subjects (n = 965) with various congenital anomalies among which a total of nine have been classified as potential cases of some of the syndromes in the study.