Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data

Insights into the ANKRD11 variants and short-stature phenotype through literature review and ClinVar database search

Ankyrin repeat domain containing-protein 11 (ANKRD11), a transcriptional factor predominantly localized in the cell nucleus, plays a crucial role in the expression regulation of key genes by recruiting chromatin remodelers and interacting with specific transcriptional repressors or activators during numerous biological processes. Its pathogenic variants are strongly linked to the pathogenesis and progression of multisystem disorder known as KBG syndrome. With the widespread application of high-throughput DNA sequencing technologies in clinical medicine, numerous pathogenic variants in the ANKRD11 gene have been reported. Patients with KBG syndrome usually exhibit a broad phenotypic spectrum with a variable degree of severity, even if having identical variants. In addition to distinctive dental, craniofacial and neurodevelopmental abnormalities, patients often present with skeletal anomalies, particularly postnatal short stature. The relationship between ANKRD11 variants and short stature is not well-understood, with limited knowledge regarding its occurrence rate or underlying biological mechanism involved. This review aims to provide an updated analysis of the molecular spectrum associated with ANKRD11 variants, investigate the prevalence of the short stature among patients harboring these variants, evaluate the efficacy of recombinant human growth hormone in treating children with short stature and ANKRD11 variants, and explore the biological mechanisms underlying short stature from both scientific and clinical perspectives. Our investigation indicated that frameshift and nonsense were the most frequent types in 583 pathogenic or likely pathogenic variants identified in the ANKRD11 gene. Among the 245 KBGS patients with height data, approximately 50% displayed short stature. Most patients showed a positive response to rhGH therapy, although the number of patients receiving treatment was limited. ANKRD11 deficiency potentially disrupts longitudinal bone growth by affecting the orderly differentiation of growth plate chondrocytes. Our review offers crucial insights into the association between ANKRD11 variants and short stature and provides valuable guidance for precise clinical diagnosis and treatment of patients with KBG syndrome.

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.

Deep phenotyping and whole-exome sequencing improved the diagnostic yield for nuclear pedigrees with neurodevelopmental disorders

Background

Neurodevelopmental disorders, a group of early‐onset neurological disorders with significant clinical and genetic heterogeneity, remain a diagnostic challenge for clinical genetic evaluation. Therefore, we assessed the diagnostic yield by combining standard phenotypes and whole‐exome sequencing in families with these disorders that were “not yet diagnosed” by the traditional testing methods.

Methods

Using a standardized vocabulary of phenotypic abnormalities from human phenotype ontology (HPO), we performed deep phenotyping for 45 “not yet diagnosed” pedigrees to characterize multiple clinical features extracted from Chinese electronic medical records (EMRs). By matching HPO terms with known human diseases and phenotypes from model organisms, together with whole‐exome sequencing data, we prioritized candidate mutations/genes. We made probable genetic diagnoses for the families.

Results

We obtained a diagnostic yield of 29% (13 out of 45) with probably genetic diagnosis, of which compound heterozygosity and de novo mutations accounted for 77% (10/13) of the diagnosis. Of note, these pedigrees are accompanied by a more significant number of non‐neurological features.

Conclusions

Deep phenotyping and whole‐exome sequencing improve the etiological evaluation for neurodevelopmental disorders in the clinical setting.

Genetic and Phenotypic Spectrum of KBG Syndrome: A Report of 13 New Chinese Cases and a Review of the Literature

KBG syndrome (KBGS) is a rare autosomal dominant inherited disease that involves multiple systems and is associated with variations in the ankyrin repeat domain 11 (ANKRD11) gene. We report the clinical and genetic data for 13 Chinese KBGS patients diagnosed by genetic testing and retrospectively analyse the genotypes and phenotypes of previously reported KBGS patients. The 13 patients in this study had heterozygous variations in the ANKRD11 gene, including seven frameshift variations, three nonsense variations, and three missense variations. They carried 11 variation sites, of which eight were previously unreported. The clinical phenotype analysis of these 13 patients and 240 previously reported patients showed that the occurrence rates of craniofacial anomalies, dental anomalies, global developmental delays, intellectual disability/learning difficulties, limb anomalies, and behavioural anomalies were >70%. The occurrence rates of short stature, delayed bone age, and spinal vertebral body anomalies were >50%. The frequency of global developmental delays and intellectual disability/learning difficulties in patients with truncated ANKRD11 gene variation was higher than that in patients with missense variation in the ANKRD11 gene (p < 0.05). Collectively, this study reported the genotypic and phenotypic characteristics of the largest sample of KBGS patients from China and discovered eight new ANKRD11 gene variations, which enriched the variation spectrum of the ANKRD11 gene. Variation in the ANKRD11 gene mainly caused craniofacial anomalies, growth and developmental anomalies, skeletal system anomalies, and nervous system anomalies. Truncated variation in the ANKRD11 gene is more likely to lead to global growth retardation and intellectual disability/learning difficulties than missense variation in ANKRD11.

Genetic pathways involved in human speech disorders

Rare genetic variants that disrupt speech development provide entry points for deciphering the neurobiological foundations of key human capacities. The value of this approach is illustrated by FOXP2, a transcription factor gene that was implicated in speech apraxia, and subsequently investigated using human cell-based systems and animal models. Advances in next-generation sequencing, coupled to de novo paradigms, facilitated discovery of etiological variants in additional genes in speech disorder cohorts. As for other neurodevelopmental syndromes, gene-driven studies show blurring of boundaries between diagnostic categories, with some risk genes shared across speech disorders, intellectual disability and autism. Convergent evidence hints at involvement of regulatory genes co-expressed in early human brain development, suggesting that etiological pathways could be amenable for investigation in emerging neural models such as cerebral organoids.

Genomically Aided Diagnosis of Severe Developmental Disorders

Our ability to make accurate and specific genetic diagnoses in individuals with severe developmental disorders has been transformed by data derived from genomic sequencing technologies. These data reveal both the patterns and rates of different mutational mechanisms and identify regions of the human genome with fewer mutations than would be expected. In outbred populations, the most common identifiable cause of severe developmental disorders is de novo mutation affecting the coding region in one of approximately 500 different genes, almost universally showing constraint. Simply combining the location of a de novo genomic event with its predicted consequence on the gene product gives significant diagnostic power. Our knowledge of the diversity of phenotypic consequences associated with comparable diagnostic genotypes at each locus is improving. Computationally useful phenotype data will improve diagnostic interpretation of ultrarare genetic variants and, in the long run, indicate which specific embryonic processes have been perturbed.

Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm

Untargeted metabolomics may become a standard approach to address diagnostic requests, but, at present, data interpretation is very labor-intensive. To facilitate its implementation in metabolic diagnostic screening, we developed a method for automated data interpretation that preselects the most likely inborn errors of metabolism (IEM). The input parameters of the knowledge-based algorithm were (1) weight scores assigned to 268 unique metabolites for 119 different IEM based on literature and expert opinion, and (2) metabolite Z-scores and ranks based on direct-infusion high resolution mass spectrometry. The output was a ranked list of differential diagnoses (DD) per sample. The algorithm was first optimized using a training set of 110 dried blood spots (DBS) comprising 23 different IEM and 86 plasma samples comprising 21 different IEM. Further optimization was performed using a set of 96 DBS consisting of 53 different IEM. The diagnostic value was validated in a set of 115 plasma samples, which included 58 different IEM and resulted in the correct diagnosis being included in the DD of 72% of the samples, comprising 44 different IEM. The median length of the DD was 10 IEM, and the correct diagnosis ranked first in 37% of the samples. Here, we demonstrate the accuracy of the diagnostic algorithm in preselecting the most likely IEM, based on the untargeted metabolomics of a single sample. We show, as a proof of principle, that automated data interpretation has the potential to facilitate the implementation of untargeted metabolomics for metabolic diagnostic screening, and we provide suggestions for further optimization of the algorithm to improve diagnostic accuracy.