The importance of deep speech phenotyping for neurodevelopmental and genetic disorders: a conceptual review

Abstract

BackgroundSpeech is the most common modality through which language is communicated, and delayed, disordered, or absent speech production is a hallmark of many neurodevelopmental and genetic disorders. Yet, speech is not often carefully phenotyped in neurodevelopmental disorders. In this paper, we argue that such deep phenotyping, defined as phenotyping that is specific to speech production and not conflated with language or cognitive ability, is vital if we are to understand how genetic variations affect the brain regions that are associated with spoken language.Speech is distinct from language, though the two are related behaviorally and share neural substrates. We present a brief taxonomy of developmental speech production disorders, with particular emphasis on the motor speech disorders childhood apraxia of speech (a disorder of motor planning) and childhood dysarthria (a set of disorders of motor execution). We review the history of discoveries concerning the KE family, in whom a hereditary form of communication impairment was identified as childhood apraxia of speech and linked to dysfunction in theFOXP2gene. The story demonstrates how instrumental deep phenotyping of speech production was in this seminal discovery in the genetics of speech and language. There is considerable overlap between the neural substrates associated with speech production and withFOXP2expression, suggesting that further genes associated with speech dysfunction will also be expressed in similar brain regions. We then show how a biologically accurate computational model of speech production, in combination with detailed information about speech production in children with developmental disorders, can generate testable hypotheses about the nature, genetics, and neurology of speech disorders.ConclusionsThough speech and language are distinct, specific types of developmental speech disorder are associated with far-reaching effects on verbal communication in children with neurodevelopmental disorders. Therefore, detailed speech phenotyping, in collaboration with experts on pediatric speech development and disorders, can lead us to a new generation of discoveries about how speech development is affected in genetic disorders.

Beyond the conservative hypothesis: a meta-analysis of lexical-semantic processing in Williams syndrome

“…Although heterogeneity is to some extent unavoidable in a meta-analysis, as the included studies differ in various aspects, the observed values were generally high. This heterogeneity could be explained, at least partially, by the fact that people with WS present very diverse cognitive and linguistic profiles (e.g., Mervis et al, 1999, 2000; Porter & Coltheart, 2005), and only part of this cognitive variability is explained by variation in genetics (Porter et al, 2012; Serrano-Juárez et al, 2018). Therefore, the results of our meta-analysis should be taken with caution, since they refer to WS as a whole, but certain people diagnosed with this syndrome could present a diverse pattern of lexical-semantic abilities.…”

Three children with different de novo BCL11A variants and diverse developmental phenotypes, but shared global motor discoordination and apraxic speech: Evidence for a functional gene network influencing the developing cerebellum and motor and auditory cortices

“…In the small sample presented here, the LoF mutation seen in Child 3 yielded a milder phenotypic impact in the areas of cognition and communication compared to the missense mutation in Child 1 and the microdeletion in Child 2, though it is noted that Child 3 had more significant medical issues. The differences observed among the children also highlight the importance of deep speech and language phenotyping (Chenausky & Tager-Flusberg, 2022). Thorough assessment and reporting of both speech and language skills supports accurate diagnosis and intervention as well as improved research outcomes, especially in the description of genotype-phenotype associations.…”

A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis

“…These breakthroughs have enabled whole-genome structure and variation analysis and human phenotypic analysis. Genome-based studies have highlighted the role of common genetic variability in influencing the risk for complicated disease development [ 8 ].…”