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

Shaw, Douglas M.; Polikowsky, Hannah P.; Pruett, Dillon; Chen, Hung‐Hsin; Petty, Lauren E.; Viljoen, Kathryn Z.; Beilby, Janet M.; Jones, Robin M.; Kraft, Shelly Jo; Below, Jennifer E.

doi:10.1016/j.ajhg.2021.11.004

Cited by 20 publications

(15 citation statements)

References 64 publications

(99 reference statements)

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“… 49 , 50 The electronic health record at Vanderbilt University Medical Center offers de-identified demographic data, clinical notes, electronic orders, laboratory measurements, ICD-9 CM/ICD-10 disease diagnosis codes, and CPT codes. Using electronic health records, individuals with diagnoses of developmental, speech, or language disorders as identified via ICD-9 and ICD-10 codes ( Table S1 ) or a phenome risk classifier, 51 52 and individuals under age 18 years were excluded as potential control subjects. To select ancestry-matched control subjects, we calculated eigenvectors and eigenvalues through principal component analysis (PCA) run on PLINKv.1.90.…”

Section: Methodsmentioning

confidence: 99%

Population-based genetic effects for developmental stuttering

Polikowsky

Shaw

Petty

et al. 2022

Human Genetics and Genomics Advances

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Summary 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.

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Section: Methodsmentioning

confidence: 99%

Population-based genetic effects for developmental stuttering

Polikowsky

Shaw

Petty

et al. 2022

Human Genetics and Genomics Advances

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“…Phecodes are a representation of clinical phenotypes that can be used to identify cases and controls based on the mapping of International Classification of Diseases codes present in individuals' EMR (Bastarache, 2021). The classifier enabled identification of 9239 individuals predicted to have stammering from their research biorepository, empowering a common variant genome‐wide association study that identified two loci (Shaw et al, 2021).…”

Section: The Challenge Of Representing Important Absent Phenotypesmentioning

confidence: 99%

“…However, Pruett et al (2021) found it to be under-reported in EMR billing code data from the Vanderbilt University Medical Center, given the 1%−3% prevalence in the general population. We too have found empowering a common variant genome-wide association study that identified two loci (Shaw et al, 2021).…”

Section: Domain-specific Biomedical Ontologies Can Be Improved Throug...mentioning

confidence: 99%

Computational analysis of neurodevelopmental phenotypes: Harmonization empowers clinical discovery

et al. 2022

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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.

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“…The goal of extending these lines of research to other speech, language, and reading traits highlights the need for coordinated efforts toward collecting and meta-analyzing largescale data in cohorts that have been able to link language-related traits to genotypes. [e.g., GenLang consortium (genlang.org): Eising et al, 2021, BioRXiv, Lancaster et al, in prep.; International Stuttering Project (theinternationalstutteringproject.com): Polikowsky et al, 2021;Shaw et al, 2021].…”

Section: Introductionmentioning

confidence: 99%

“…[e.g., GenLang consortium ( genlang.org ): Eising et al, 2021 , BioRXiv , Lancaster et al, in prep. ; International Stuttering Project ( theinternationalstutteringproject.com ): Polikowsky et al, 2021 ; Shaw et al, 2021 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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

Cited by 20 publications

References 64 publications

Population-based genetic effects for developmental stuttering

Population-based genetic effects for developmental stuttering

Computational analysis of neurodevelopmental phenotypes: Harmonization empowers clinical discovery

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

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