Sex steroid hormones and sex hormone binding globulin levels, CYP17 MSP AI (−34 T:C) and CYP19 codon 39 (Trp:Arg) variants in children with developmental stuttering

Mohammadi, Hiwa; Joghataei, Mohammad Taghi; Rahimi, Zohreh; Faghihi, Faezeh; Khazaie, Habibolah; Farhangdoost, Hashem; Mehrpour, Masoud

doi:10.1016/j.bandl.2017.09.004

Cited by 12 publications

(14 citation statements)

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“…50 Little is known about the neuronal basis of developmental stuttering, although imaging studies have demonstrated that patients who stutter show abnormal function in the form of overactivity in the cortical motor and pre-motor areas associated with speech, as well as disruptions in the basal ganglia and dopaminergic systems. [57][58][59] Although the previous linkage analyses have identified candidate genes, including DRD2 (MIM: 126450), AP4E1, CYP17A1 (MIM: 609300), GNPTAB, GNPTG, and NAGPA, 13,[60][61][62] the mechanisms of action remain uncertain, although both GNPTAB and GNPTG are active in lysosomal enzyme-targeting pathways and energy metabolism. 63 We checked for replication within these genes but failed to demonstrate any significant findings (see Table S4).…”

Section: Stuttering Classification Model Development and Application To Biovumentioning

confidence: 99%

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

Shaw¹,

Polikowsky²,

Pruett³

et al. 2021

The American Journal of Human Genetics

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Developmental stuttering is a speech disorder characterized by disruption in the forward movement of speech. This disruption includes part-word and single-syllable repetitions, prolongations, and involuntary tension that blocks syllables and words, and the disorder has a life-time prevalence of 6-12%. Within Vanderbilt's electronic health record (EHR)-linked biorepository (BioVU), only 142 individuals out of 92,762 participants (0.15%) are identified with diagnostic ICD9/10 codes, suggesting a large portion of people who stutter do not have a record of diagnosis within the EHR. To identify individuals affected by stuttering within our EHR, we built a PheCode-driven Gini impurity-based classification and regression tree model, PheML, by using comorbidities enriched in individuals affected by stuttering as predicting features and imputing stuttering status as the outcome variable. Applying PheML in BioVU identified 9,239 genotyped affected individuals (a clinical prevalence of $10%) for downstream genetic analysis. Ancestry-stratified GWAS of PheML-imputed affected individuals and matched control individuals identified rs12613255, a variant near CYRIA on chromosome 2 (B ¼ 0.323; p value ¼ 1.31 3 10 À8 ) in European-ancestry analysis and rs7837758 (B ¼ 0.518; p value ¼ 5.07 3 10 À8 ), an intronic variant found within the ZMAT4 gene on chromosome 8, in African-ancestry analysis. Polygenic-risk prediction and concordance analysis in an independent clinically ascertained sample of developmental stuttering cases validate our GWAS findings in PheML-imputed affected and control individuals and demonstrate the clinical relevance of our population-based analysis for stuttering risk.

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Section: Stuttering Classification Model Development and Application To Biovumentioning

confidence: 99%

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

Shaw¹,

Polikowsky²,

Pruett³

et al. 2021

The American Journal of Human Genetics

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“… 18 , 19 , 20 Despite extensive research on the psychological and economic consequences of this speech disorder, the etiology of developmental stuttering remains elusive. Current evidence postulates neurological, 21 biological, 22 and genetic underpinnings for stuttering, 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 though few causal associations have been identified to date. Even though multiple studies in the past few decades 12 , 23 , 30 , 31 , 32 , 33 evince a genetic predisposition for developmental stuttering, its genetic etiology and architecture largely remain evasive.…”

Section: Introductionmentioning

confidence: 99%

“…To date, published literature investigating genetic contributions to developmental stuttering has primarily drawn on family-based analyses and studies of population isolates. 23 , 24 , 25 , 26 , 27 , 29 , 30 , 31 , 33 , 45 Linkage and other family-based approaches have been successful at identifying rare and private causal variants with large genetic effects in the absence of genetic heterogeneity. For developmental stuttering, identifying the causal gene(s) within and across families has proven challenging.…”

Section: Introductionmentioning

confidence: 99%

“… 46 performed Sanger sequencing and homozygosity mapping for 25 Iranian families afflicted by developmental stuttering and identified an additional 3 variants in GNPTAB and GNPTG that co-segregated with stuttering. Additional studies have revealed several regions across the genome linked with the trait but only identified three candidate risk genes: DRD2 31 (MIM: 126450 ), AP4E1 33 (MIM: 607244 ), and CYP17A1 30 (MIM: 609300 ). Lan et al.…”

Section: Introductionmentioning

confidence: 99%

“…In 2017, Mohammadi et al. 30 performed a case-control study of the Kurdish population aged 3 to 9 years from Western Iran, specifically focusing on the dimorphic nature of stuttering, and identified an allelic polymorphism associated with stuttering susceptibility in CYP17A1 , a gene integral for the synthesis of steroid hormones. As reported by Frigerio Domingues et al.…”

Section: Introductionmentioning

confidence: 99%

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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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Morphological and hemispheric and sex differences of the anterior ascending ramus and the horizontal ascending ramus of the lateral sulcus

Wang

Zhou

et al. 2022

Brain Struct Funct

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Broca’s area is composed of the pars opercularis (PO) and the pars triangularis (PTR) of the inferior frontal gyrus; the anterior ascending ramus of the lateral sulcus (aals) separates the PO from the PTR, and the horizontal ascending ramus of the lateral sulcus (hals) separates the PTR from the pars orbitalis. The morphometry of these two sulci maybe has potential effects on the various functions of Broca’s area. Exploring the morphological variations, hemispheric differences and sex differences of these two sulci contributed to a better localization of Broca's area. BrainVISA was used to reconstruct and parameterize these two sulci based on data from 3D MR images of 90 healthy right-handed subjects. The 3D anatomic morphologies of these two sulci were investigated using 4 sulcal parameters: average depth (AD), average width (AW), outer length (OL) and inner length (IL). The aals and hals could be identified in 98.89% and 98.33%, respectively, of the hemispheres evaluated. The morphological patterns of these two sulci were categorized into four typical types. There were no statistically significant interhemispheric or sex differences in the frequency of the morphological patterns. There was statistically significant interhemispheric difference in the IL of the aals. Significant sex differences were found in the AD and the IL of the aals and OL of the hals. Our results not only provide a structural basis for functional studies related to Broca’s area but also are helpful in determining the precise position of Broca’s area in neurosurgery.

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Sex steroid hormones and sex hormone binding globulin levels, CYP17 MSP AI (−34 T:C) and CYP19 codon 39 (Trp:Arg) variants in children with developmental stuttering

Cited by 12 publications

References 86 publications

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

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

Population-based genetic effects for developmental stuttering

Morphological and hemispheric and sex differences of the anterior ascending ramus and the horizontal ascending ramus of the lateral sulcus

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