Genomically Aided Diagnosis of Severe Developmental Disorders

Variation in morpho-lexical development within and between diagnoses in children with neurodevelopmental disorders

While primary diagnosis is only one aspect of the presentation of a child with neurodevelopmental delay/disorder, the degree to which early expressive language reflects diagnostic divisions must be understood in order to reduce the risk of obscuring clinically important differences and similarities across diagnoses. We present original data from the New Zealand MacArthur-Bates Communicative Development Inventory (NZCDI) from 88 English-speaking children aged 2;6 to 5;6 years receiving multidisciplinary intervention within a single family-centered program. The children had one of six pediatrician-assigned genetic or behaviorally determined diagnoses: Down syndrome (DS); motor disorders (cerebral palsy and developmental coordination disorder); global development delay; disorders of relating and communicating (R&C); other genetically defined diagnoses; or language delay due to premature (PREM) birth. Morphological and lexical development were compared within and across diagnostic groups, using both data visualization and mixed-effects modeling. Groups varied in the amount of variation within and between them, but only prematurity reached significance, in interaction with age, as a predictor of morpho-lexical scores. Further analysis of longitudinal data available from a subset of the sample (n = 62) suggested that individual trajectories of vocabulary growth could not be reliably predicted by diagnosis. Moreover, the distribution of word types (nouns, predicates, etc.) only distinguished PREM children with language delay from those with DS and those in the R&C group. There were strong similarities in early morpho-lexical development across these clinical populations, with some differences. These findings align with research and clinical approaches which accommodate individual variation within diagnosis, and broad similarities across diagnostic groups.

IMPROVE-DD: Integrating multiple phenotype resources optimizes variant evaluation in genetically determined developmental disorders

Diagnosing rare developmental disorders using genome-wide sequencing data commonly necessitates review of multiple plausible candidate variants, often using ontologies of categorical clinical terms. We show that Integrating Multiple Phenotype Resources Optimizes Variant Evaluation in Developmental Disorders (IMPROVE-DD) by incorporating additional classes of data commonly available to clinicians and recorded in health records. In doing so, we quantify the distinct contributions of sex, growth, and development in addition to Human Phenotype Ontology (HPO) terms and demonstrate added value from these readily available information sources. We use likelihood ratios for nominal and quantitative data and propose a classifier for HPO terms in this framework. This Bayesian framework results in more robust diagnoses. Using data systematically collected in the Deciphering Developmental Disorders study, we considered 77 genes with pathogenic/likely pathogenic variants in ≥10 individuals. All genes showed at least a satisfactory prediction by receiver operating characteristic when testing on training data (AUC ≥ 0.6), and HPO terms were the best predictor for the majority of genes, though a minority (13/77) of genes were better predicted by other phenotypic data types. Overall, classifiers based upon multiple integrated phenotypic data sources performed better than those based upon any individual source, and importantly, integrated models produced notably fewer false positives. Finally, we show that IMPROVE-DD models with good predictive performance on cross-validation can be constructed from relatively few individuals. This suggests new strategies for candidate gene prioritization and highlights the value of systematic clinical data collection to support diagnostic programs.