Psychomotor retardation with a 1q42.11-q42.12 deletion

A Gene-Based Algorithm for Identifying Factors That May Affect a Speaker's Voice

Over the past decades, many machine-learning- and artificial-intelligence-based technologies have been created to deduce biometric or bio-relevant parameters of speakers from their voice. These voice profiling technologies have targeted a wide range of parameters, from diseases to environmental factors, based largely on the fact that they are known to influence voice. Recently, some have also explored the prediction of parameters whose influence on voice is not easily observable through data-opportunistic biomarker discovery techniques. However, given the enormous range of factors that can possibly influence voice, more informed methods for selecting those that may be potentially deducible from voice are needed. To this end, this paper proposes a simple path-finding algorithm that attempts to find links between vocal characteristics and perturbing factors using cytogenetic and genomic data. The links represent reasonable selection criteria for use by computational by profiling technologies only, and are not intended to establish any unknown biological facts. The proposed algorithm is validated using a simple example from medical literature-that of the clinically observed effects of specific chromosomal microdeletion syndromes on the vocal characteristics of affected people. In this example, the algorithm attempts to link the genes involved in these syndromes to a single example gene (FOXP2) that is known to play a broad role in voice production. We show that in cases where strong links are exposed, vocal characteristics of the patients are indeed reported to be correspondingly affected. Validation experiments and subsequent analyses confirm that the methodology could be potentially useful in predicting the existence of vocal signatures in naïve cases where their existence has not been otherwise observed.

Two Novel Variants of WDR26 in Chinese Patients with Intellectual Disability

Skraban-Deardorff syndrome is a rare autosomal dominant genetic disease caused by variants in the WDR26 gene. Here, we report two Chinese patients diagnosed with Skraban-Deardorff syndrome caused by novel de novo, heterozygous pathogenic WDR26 variants c.977delA (p. 12 N326Ifs*2) and c.1020-2A>G (p. R340Sfs*29). Their clinical features were characterized by intellectual disability (ID), developmental delay, abnormal facial features and the absence of early-onset seizure, which expands the phenotype spectrum associated with Skraban-Deardorff syndrome. By comparing our cases with current reported cases of WDR26-related intellectual disability, we suggest that developmental delay, particularly in speech, and facial features including rounded palpebral fissures, depressed nasal root, full nasal tip and abnormal gums, represent the prominent clinical phenotypes for diagnosis of Skraban-Deardorff syndrome. Together, WDR26 variants and 1q41q42 deletions should feature prominently on the differential diagnosis of ID with distinctive facial features.

1q42.12q42.2 Deletion in a Child with Midline Defects and Hypoplasia of the Corpus Callosum

Chromosome 1q42.12q42.2 deletions are documented as "disease causing" and show overlapping phenotypes depending on the genes involved in the deletion. In this report, we detected a 5.8-Mb deletion encompassing the chromosome 1q42.12q42.2 region in a 4-year-old boy with hypoplastic corpus callosum, epilepsy, developmental delay, microcephaly, cataract, cleft palate, and skeletal changes. The deletion was de novo. Genotype-phenotype correlations suggest that the major features of 1q42.12q42.2 microdeletion were attributed to the genes with a high probability of loss-of-function intolerance score in this deletion, namely LBR, ENAH, ACBD3, LIN9, ITPKB, CDC42BPA, ARF1, TAF5L, GALNT2, SPRTN, and EGLN1 along with GNPAT.