Phenome risk classification enables phenotypic imputation and gene discovery in developmental stuttering

Stuttering: Our Current Knowledge, Research Opportunities, and Ways to Address Critical Gaps

Our understanding of the neurobiological bases of stuttering remains limited, hampering development of effective treatments that are informed by basic science. Stuttering affects more than 5% of all preschool-age children and remains chronic in approximately 1% of adults worldwide. As a condition that affects a most fundamental human ability to engage in fluid and spontaneous verbal communication, stuttering can have substantial psychosocial, occupational, and educational impacts on those who are affected. This article summarizes invited talks and breakout sessions that were held in June 2023 as part of a 2-day workshop sponsored by the US National Institute on Deafness and Other Communication Disorders. The workshop encompassed topics including neurobiology, genetics, speech motor control, cognitive, social, and emotional impacts, and intervention. Updates on current research in these areas were summarized by each speaker, and critical gaps and priorities for future research were raised, and then discussed by participants. Research talks were followed by smaller, moderated breakout sessions intended to elicit diverse perspectives, including on the matter of defining therapeutic targets for stuttering. A major concern that emerged following participant discussion was whether priorities for treatment in older children and adults should focus on targeting core speech symptoms of stuttering, or on embracing effective communication regardless of whether the speaker exhibits overt stuttering. This article concludes with accumulated convergent points endorsed by most attendees on research and clinical priorities that may lead to breakthroughs with substantial potential to contribute to bettering the lives of those living with this complex speech disorder.

Genetic association studies using disease liabilities from deep neural networks

The case-control study is a widely used method for investigating the genetic underpinnings of binary traits. However, long-term, prospective cohort studies often grapple with absent or evolving health-related outcomes. Here, we propose two methods, liability and meta, for conducting genome-wide association studies (GWASs) that leverage disease liabilities calculated from deep patient phenotyping. Analyzing 38 common traits in ∼300,000 UK Biobank participants, we identified an increased number of loci in comparison to the number identified by the conventional case-control approach, and there were high replication rates in larger external GWASs. Further analyses confirmed the disease specificity of the genetic architecture; the meta method demonstrated higher robustness when phenotypes were imputed with low accuracy. Additionally, polygenic risk scores based on disease liabilities more effectively predicted newly diagnosed cases in the 2022 dataset, which were controls in the earlier 2019 dataset. Our findings demonstrate that integrating high-dimensional phenotypic data into deep neural networks enhances genetic association studies while capturing disease-relevant genetic architecture.

Adult stuttering prevalence I: Systematic review and identification of stuttering in large populations

PURPOSE

Stuttering epidemiology is reviewed with a primary goal of appraising methods used to identify stuttering in large populations. Secondary goals were to establish a best estimate of adult stuttering prevalence; identify data that could subgroup stuttering based upon childhood versus adult onset and covert versus over behaviour; and conduct a preliminary assessment of the degree to which stuttering features as a co-occurring diagnosis.

METHODS

Systematic review followed PRISMA guidelines. Quality assessment was based on the Joanna Briggs Institute Prevalence Critical Appraisal Tool, with criteria adjusted for appraisal of stuttering.

RESULTS

15 sets of data were assessed for quality, with three meeting criteria for inclusion. These estimated adult stuttering prevalence at 0.67% at age 14-17 years (Taghipour et al., 2013); 0.21% at age 16-20 years (Tsur et al., 2021); and 0.63% when aged over 21 years (Craig et al., 2002).

CONCLUSION

Systematic review indicates adult stuttering prevalence is between 0.6-0.7%. A false positive paradox follows from the low prevalence of stuttering in the general population, creating a need for very high specificity when measuring stuttering in the general population. Failure to achieve high specificity (99.9% is suggested) leads to loss of statistical power due to presence of false positives. A corollary of the false positive paradox is that sensitivity in measurement of stuttering can be relatively low (90% is suggested) before general population estimates of stuttering prevalence are appreciably affected. Despite this relaxation of measurement requirements regarding sensitivity, covert stuttering is likely to have been underestimated. Covert stuttering might be accounted for using data from prospective cohort studies, however such a revision seems unlikely to exceed the widely-accepted 1% adult stuttering prevalence estimate; see Gattie, Lieven & Kluk (2024 this issue) for an estimate at 0.96 %. When used to estimate stuttering prevalence, data reported by Tsur et al. (2021) are outlying, with the relatively low estimate possibly due to origin as military conscript data and/or generalised healthcare screening.

The clinical and genetic spectrum of paediatric speech and language disorders

Speech and language disorders are known to have a substantial genetic contribution. Although frequently examined as components of other conditions, research on the genetic basis of linguistic differences as separate phenotypic subgroups has been limited so far. Here, we performed an in-depth characterization of speech and language disorders in 52 143 individuals, reconstructing clinical histories using a large-scale data-mining approach of the electronic medical records from an entire large paediatric healthcare network. The reported frequency of these disorders was the highest between 2 and 5 years old and spanned a spectrum of 26 broad speech and language diagnoses. We used natural language processing to assess the degree to which clinical diagnoses in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be retrieved easily through ICD-10 diagnosis codes, whereas stuttering as a speech phenotype was coded in only 12% of individuals through appropriate ICD-10 codes. We found significant comorbidity of speech and language disorders in neurodevelopmental conditions (30.31%) and, to a lesser degree, with epilepsies (6.07%) and movement disorders (2.05%). The most common genetic disorders retrievable in our analysis of electronic medical records were STXBP1 (n = 21), PTEN (n = 20) and CACNA1A (n = 18). When assessing associations of genetic diagnoses with specific linguistic phenotypes, we observed associations of STXBP1 and aphasia (P = 8.57 × 10-7, 95% confidence interval = 18.62-130.39) and MYO7A with speech and language development delay attributable to hearing loss (P = 1.24 × 10-5, 95% confidence interval = 17.46-infinity). Finally, in a sub-cohort of 726 individuals with whole-exome sequencing data, we identified an enrichment of rare variants in neuronal receptor pathways, in addition to associations of UQCRC1 and KIF17 with expressive aphasia, MROH8 and BCHE with poor speech, and USP37, SLC22A9 and UMODL1 with aphasia. In summary, our study outlines the landscape of paediatric speech and language disorders, confirming the phenotypic complexity of linguistic traits and novel genotype-phenotype associations. Subgroups of paediatric speech and language disorders differ significantly with respect to the composition of monogenic aetiologies.

A Biopsychosocial Overview of Speech Disorders: Neuroanatomical, Genetic, and Environmental Insights

Speech disorders encompass a complex interplay of neuroanatomical, genetic, and environmental factors affecting individuals' communication ability. This review synthesizes current insights into the neuroanatomy, genetic underpinnings, and environmental influences contributing to speech disorders. Neuroanatomical structures, such as Broca's area, Wernicke's area, the arcuate fasciculus, and basal ganglia, along with their connectivity, play critical roles in speech production, comprehension, and motor coordination. Advances in the understanding of intricate brain networks involved in language offer insights into typical speech development and the pathophysiology of speech disorders. Genetic studies have identified key genes involved in neural migration and synaptic connectivity, further elucidating the role of genetic mutations in speech disorders, such as stuttering and speech sound disorders. Beyond the biological mechanisms, this review explores the profound impact of psychological factors, including anxiety, depression, and neurodevelopmental conditions, on individuals with speech disorders. Psychosocial comorbidities often exacerbate speech disorders, complicating diagnosis and treatment and underscoring the need for a holistic approach to managing these conditions. Future directions point toward leveraging genetic testing, digital technologies, and personalized therapies, alongside addressing the psychosocial dimensions, to improve outcomes for individuals with speech disorders. This comprehensive overview aims to inform future research and therapeutic advancements, particularly in treating fluency disorders like stuttering.

Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field

Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early.

Big data in sarcoidosis

PURPOSE OF REVIEW

This review provides an overview of recent advancements in sarcoidosis research, focusing on collaborative networks, phenotype characterization, and molecular studies. It highlights the importance of collaborative efforts, phenotype characterization, and the integration of multilevel molecular data for advancing sarcoidosis research and paving the way toward personalized medicine.

RECENT FINDINGS

Sarcoidosis exhibits heterogeneous clinical manifestations influenced by various factors. Efforts to define sarcoidosis endophenotypes show promise, while technological advancements enable extensive molecular data generation. Collaborative networks and biobanks facilitate large-scale studies, enhancing biomarker discovery and therapeutic protocols.

SUMMARY

Sarcoidosis presents a complex challenge due to its unknown cause and heterogeneous clinical manifestations. Collaborative networks, comprehensive phenotype delineation, and the utilization of cutting-edge technologies are essential for advancing our understanding of sarcoidosis biology and developing personalized medicine approaches. Leveraging large-scale epidemiological resources and biobanks and integrating multilevel molecular data offer promising avenues for unraveling the disease's heterogeneity and improving patient outcomes.

Developing a phenotype risk score for tic disorders in a large, clinical biobank

Tics are a common feature of early-onset neurodevelopmental disorders, characterized by involuntary and repetitive movements or sounds. Despite affecting up to 2% of children and having a genetic contribution, the underlying causes remain poorly understood. In this study, we leverage dense phenotype information to identify features (i.e., symptoms and comorbid diagnoses) of tic disorders within the context of a clinical biobank. Using de-identified electronic health records (EHRs), we identified individuals with tic disorder diagnosis codes. We performed a phenome-wide association study (PheWAS) to identify the EHR features enriched in tic cases versus controls (n = 1406 and 7030; respectively) and found highly comorbid neuropsychiatric phenotypes, including: obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, and anxiety (p < 7.396 × 10-5). These features (among others) were then used to generate a phenotype risk score (PheRS) for tic disorder, which was applied across an independent set of 90,051 individuals. A gold standard set of tic disorder cases identified by an EHR algorithm and confirmed by clinician chart review was then used to validate the tic disorder PheRS; the tic disorder PheRS was significantly higher among clinician-validated tic cases versus non-cases (p = 4.787 × 10-151; β = 1.68; SE = 0.06). Our findings provide support for the use of large-scale medical databases to better understand phenotypically complex and underdiagnosed conditions, such as tic disorders.

The clinical and genetic spectrum of paediatric speech and language disorders in 52,143 individuals

Speech and language disorders are known to have a substantial genetic contribution. Although frequently examined as components of other conditions, research on the genetic basis of linguistic differences as separate phenotypic subgroups has been limited so far. Here, we performed an in-depth characterization of speech and language disorders in 52,143 individuals, reconstructing clinical histories using a large-scale data mining approach of the Electronic Medical Records (EMR) from an entire large paediatric healthcare network. The reported frequency of these disorders was the highest between 2 and 5 years old and spanned a spectrum of twenty-six broad speech and language diagnoses. We used Natural Language Processing to assess to which degree clinical diagnosis in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be easily retrieved through ICD-10 diagnosis codes, while stuttering as a speech phenotype was only coded in 12% of individuals through appropriate ICD-10 codes. We found significant comorbidity of speech and language disorders in neurodevelopmental conditions (30.31%) and to a lesser degree with epilepsies (6.07%) and movement disorders (2.05%). The most common genetic disorders retrievable in our EMR analysis were STXBP1 (n=21), PTEN (n=20), and CACNA1A (n=18). When assessing associations of genetic diagnoses with specific linguistic phenotypes, we observed associations of STXBP1 and aphasia (P=8.57 × 10-7, CI=18.62-130.39) and MYO7A with speech and language development delay due to hearing loss (P=1.24 × 10-5, CI=17.46-Inf). Finally, in a sub-cohort of 726 individuals with whole exome sequencing data, we identified an enrichment of rare variants in synaptic protein and neuronal receptor pathways and associations of UQCRC1 with expressive aphasia and WASHC4 with abnormality of speech or vocalization. In summary, our study outlines the landscape of paediatric speech and language disorders, confirming the phenotypic complexity of linguistic traits and novel genotype-phenotype associations. Subgroups of paediatric speech and language disorders differ significantly with respect to the composition of monogenic aetiologies.

Morphological deficits of glial cells in a transgenic mouse model for developmental stuttering

Vocal production involves intricate neural coordination across various brain regions. Stuttering, a common speech disorder, has genetic underpinnings, including mutations in lysosomal-targeting pathway genes. Using a Gnptab-mutant mouse model linked to stuttering, we examined neuron and glial cell morphology in vocal production circuits. Our findings revealed altered astrocyte and microglia processes in these circuits in Gnptab-mutant mice, while control regions remained unaffected. Our results shed light on the potential role of glial cells in stuttering pathophysiology and highlight their relevance in modulating vocal production behaviors.

A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems

It is estimated that as many as 1 in 16 people worldwide suffer from rare diseases. Rare disease patients face difficulty finding diagnosis and treatment for their conditions, including long diagnostic odysseys, multiple incorrect diagnoses, and unavailable or prohibitively expensive treatments. As a result, it is likely that large electronic health record (EHR) systems include high numbers of participants suffering from undiagnosed rare disease. While this has been shown in detail for specific diseases, these studies are expensive and time consuming and have only been feasible to perform for a handful of the thousands of known rare diseases. The bulk of these undiagnosed cases are effectively hidden, with no straightforward way to differentiate them from healthy controls. The ability to access them at scale would enormously expand our capacity to study and develop drugs for rare diseases, adding to tools aimed at increasing availability of study cohorts for rare disease. In this study, we train a deep learning transformer algorithm, RarePT (Rare-Phenotype Prediction Transformer), to impute undiagnosed rare disease from EHR diagnosis codes in 436,407 participants in the UK Biobank and validated on an independent cohort from 3,333,560 individuals from the Mount Sinai Health System. We applied our model to 155 rare diagnosis codes with fewer than 250 cases each in the UK Biobank and predicted participants with elevated risk for each diagnosis, with the number of participants predicted to be at risk ranging from 85 to 22,000 for different diagnoses. These risk predictions are significantly associated with increased mortality for 65% of diagnoses, with disease burden expressed as disability-adjusted life years (DALY) for 73% of diagnoses, and with 72% of available disease-specific diagnostic tests. They are also highly enriched for known rare diagnoses in patients not included in the training set, with an odds ratio (OR) of 48.0 in cross-validation cohorts of the UK Biobank and an OR of 30.6 in the independent Mount Sinai Health System cohort. Most importantly, RarePT successfully screens for undiagnosed patients in 32 rare diseases with available diagnostic tests in the UK Biobank. Using the trained model to estimate the prevalence of undiagnosed disease in the UK Biobank for these 32 rare phenotypes, we find that at least 50% of patients remain undiagnosed for 20 of 32 diseases. These estimates provide empirical evidence of a high prevalence of undiagnosed rare disease, as well as demonstrating the enormous potential benefit of using RarePT to screen for undiagnosed rare disease patients in large electronic health systems.

Stuttering as a spectrum disorder: A hypothesis

Childhood-onset fluency disorder, commonly referred to as stuttering, affects over 70 million adults worldwide. While stuttering predominantly initiates during childhood and is more prevalent in males, it presents consistent symptoms during conversational speech. Despite these common clinical manifestations, evidence suggests that stuttering, may arise from different etiologies, emphasizing the need for personalized therapy approaches. Current research models often regard the stuttering population as a singular, homogenous group, potentially overlooking the inherent heterogeneity. This perspective consolidates both historical and recent observations to emphasize that stuttering is a heterogeneous condition with diverse causes. As such, it is crucial that both therapeutic research and clinical practices consider the potential for varied etiologies leading to stuttering. Recognizing stuttering as a spectrum disorder embraces its inherent variability, allowing for a more nuanced categorization of individuals based on the underlying causes. This perspective aligns with the principles of precision medicine, advocating for tailored treatments for distinct subgroups of people who stutter, ultimately leading to personalized therapeutic approaches.

Disfluency-Affirming Therapy for Young People Who Stutter: Unpacking Ableism in the Therapy Room

PURPOSE

Ableist messages are conveyed early in the life of a stutterer and are amplified throughout the school-age years. Increased recognition of the benefits of acceptance-based therapies for positive long-term outcomes has changed the narrative about stuttering and stutterers. Speech-language therapists are resonating with the ideas that "it is okay to stutter," socioemotional aspects of stuttering must be considered, and support and community are valuable. Despite the shift in understanding, messages conveyed to students and parents commonly encourage suppression of stuttering and masking of one's stuttering identity. The purpose of this article was to (a) expose unintentional ableist messages that perpetuate stigma and feeling "othered" in the therapy relationship and (b) offer suggestions for congruent messaging in stuttering therapy.

CONCLUSIONS

Ableism in stuttering therapy contributes to the physical struggle and socioemotional challenges experienced by stutterers. The author offers ideas for revisioning therapy outcomes, language, and messaging to students to encourage a congruent, disfluency-affirming culture in schools and community. Introspection and advocacy by both therapists and the broader professional community, in collaboration with young people who stutter, will serve to reduce the stigma that fuels many of the daily challenges faced by school-age children who stutter.

Computational analysis of neurodevelopmental phenotypes: Harmonization empowers clinical discovery

Making a specific diagnosis in neurodevelopmental disorders is traditionally based on recognizing clinical features of a distinct syndrome, which guides testing of its possible genetic etiologies. Scalable frameworks for genomic diagnostics, however, have struggled to integrate meaningful measurements of clinical phenotypic features. While standardization has enabled generation and interpretation of genomic data for clinical diagnostics at unprecedented scale, making the equivalent breakthrough for clinical data has proven challenging. However, increasingly clinical features are being recorded using controlled dictionaries with machine readable formats such as the Human Phenotype Ontology (HPO), which greatly facilitates their use in the diagnostic space. Improving the tractability of large-scale clinical information will present new opportunities to inform genomic research and diagnostics from a clinical perspective. Here, we describe novel approaches for computational phenotyping to harmonize clinical features, improve data translation through revising domain-specific dictionaries, quantify phenotypic features, and determine clinical relatedness. We demonstrate how these concepts can be applied to longitudinal phenotypic information, which represents a critical element of developmental disorders and pediatric conditions. Finally, we expand our discussion to clinical data derived from electronic medical records, a largely untapped resource of deep clinical information with distinct strengths and weaknesses.

Self-reported impact of developmental stuttering across the lifespan

AIM

To examine the phenomenology of stuttering across the lifespan in the largest prospective cohort to date.

METHOD

Participants aged 7 years and older with a history of developmental stuttering were recruited. Self-reported phenotypic data were collected online including stuttering symptomatology, co-occurring phenotypes, genetic predisposition, factors associated with stuttering severity, and impact on anxiety, education, and employment.

RESULTS

A total of 987 participants (852 adults: 590 males, 262 females, mean age 49 years [SD = 17 years 10 months; range = 18-93 years] and 135 children: 97 males, 38 females, mean age 11 years 4 months [SD = 3 years; range = 7-17 years]) were recruited. Stuttering onset occurred at age 3 to 6 years in 64.0%. Blocking (73.2%) was the most frequent phenotype; 75.9% had sought stuttering therapy and 15.5% identified as having recovered. Half (49.9%) reported a family history. There was a significant negative correlation with age for both stuttering frequency and severity in adults. Most were anxious due to stuttering (90.4%) and perceived stuttering as a barrier to education and employment outcomes (80.7%).

INTERPRETATION

The frequent persistence of stuttering and the high proportion with a family history suggest that stuttering is a complex trait that does not often resolve, even with therapy. These data provide new insights into the phenotype and prognosis of stuttering, information that is critically needed to encourage the development of more effective speech therapies.

WHAT THIS PAPER ADDS

Half of the study cohort had a family history of stuttering. While 75.9% of participants had sought stuttering therapy, only 15.5% identified as having recovered. There was a significant negative correlation between age and stuttering frequency and severity in adults.

Test of Prosody via Syllable Emphasis ("TOPsy"): Psychometric Validation of a Brief Scalable Test of Lexical Stress Perception

Prosody perception is fundamental to spoken language communication as it supports comprehension, pragmatics, morphosyntactic parsing of speech streams, and phonological awareness. A particular aspect of prosody: perceptual sensitivity to speech rhythm patterns in words (i.e., lexical stress sensitivity), is also a robust predictor of reading skills, though it has received much less attention than phonological awareness in the literature. Given the importance of prosody and reading in educational outcomes, reliable and valid tools are needed to conduct large-scale health and genetic investigations of individual differences in prosody, as groundwork for investigating the biological underpinnings of the relationship between prosody and reading. Motivated by this need, we present the Test of Prosody via Syllable Emphasis ("TOPsy") and highlight its merits as a phenotyping tool to measure lexical stress sensitivity in as little as 10 min, in scalable internet-based cohorts. In this 28-item speech rhythm perception test [modeled after the stress identification test from Wade-Woolley (2016)], participants listen to multi-syllabic spoken words and are asked to identify lexical stress patterns. Psychometric analyses in a large internet-based sample shows excellent reliability, and predictive validity for self-reported difficulties with speech-language, reading, and musical beat synchronization. Further, items loaded onto two distinct factors corresponding to initially stressed vs. non-initially stressed words. These results are consistent with previous reports that speech rhythm perception abilities correlate with musical rhythm sensitivity and speech-language/reading skills, and are implicated in reading disorders (e.g., dyslexia). We conclude that TOPsy can serve as a useful tool for studying prosodic perception at large scales in a variety of different settings, and importantly can act as a validated brief phenotype for future investigations of the genetic architecture of prosodic perception, and its relationship to educational outcomes.

Population-based genetic effects for developmental stuttering

Despite a lifetime prevalence of at least 5%, developmental stuttering, characterized by prolongations, blocks, and repetitions of speech sounds, remains a largely idiopathic speech disorder. Family, twin, and segregation studies overwhelmingly support a strong genetic influence on stuttering risk; however, its complex mode of inheritance combined with thus-far underpowered genetic studies contribute to the challenge of identifying and reproducing genes implicated in developmental stuttering susceptibility. We conducted a trans-ancestry genome-wide association study (GWAS) and meta-analysis of developmental stuttering in two primary datasets: The International Stuttering Project comprising 1,345 clinically ascertained cases from multiple global sites and 6,759 matched population controls from the biobank at Vanderbilt University Medical Center (VUMC), and 785 self-reported stuttering cases and 7,572 controls ascertained from The National Longitudinal Study of Adolescent to Adult Health (Add Health). Meta-analysis of these genome-wide association studies identified a genome-wide significant (GWS) signal for clinically reported developmental stuttering in the general population: a protective variant in the intronic or genic upstream region of SSUH2 (rs113284510, protective allele frequency = 7.49%, Z = -5.576, p = 2.46 × 10-8) that acts as an expression quantitative trait locus (eQTL) in esophagus-muscularis tissue by reducing its gene expression. In addition, we identified 15 loci reaching suggestive significance (p < 5 × 10-6). This foundational population-based genetic study of a common speech disorder reports the findings of a clinically ascertained study of developmental stuttering and highlights the need for further research.