Genome-wide Association Study Identifies Novel Risk Loci for Apical Periodontitis.

INTRODUCTION: Apical periodontitis (AP) is a common consequence of root canal infection leading to periapical bone resorption. Microbial and host genetic factors and their interactions have been shown to play a role in AP development and progression. Variations in a few genes have been reported in association with AP; however, the lack of genome-wide studies has hindered progress in understanding the molecular mechanisms involved. Here, we report the first genome-wide association study of AP in a large and well-characterized population. METHODS: Male and female adults (n = 932) presenting with deep caries and AP (cases), or deep caries without AP (controls) were included. Genotyping was performed using the Illumina Expanded Multi-Ethnic Genotyping Array (MEGA). Single-variant association testing was performed adjusting for sex and 5 principal components. Subphenotype association testing, analyses of genetically regulated gene expression, polygenic risk score, and phenome-wide association (PheWAS) analyses were also conducted. RESULTS: Eight loci reached near genome-wide significant association with AP (P < 5 × 10-6); gene-focused analyses replicated 3 previously reported associations (P < 8.9 × 10-5). Sex-specific and subphenotype-specific analyses revealed additional significant associations with variants genome-wide. Functionally oriented gene-based analyses revealed 8 genes significantly associated with AP (P < 5 × 10-5), and PheWAS analysis revealed 33 phecodes associated with AP risk score (P < 3.08 × 10-5). CONCLUSIONS: This study identified novel genes/loci contributing to AP and specific contributions to AP risk in men and women. Importantly, we identified additional systemic conditions significantly associated with AP risk. Our findings provide strong evidence for host-mediated effects on AP susceptibility.

Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping.

Importance: Developmental language disorder (DLD) is a common (with up to 7% prevalence) yet underdiagnosed childhood disorder whose underlying biological profile and comorbidities are not fully understood, especially at the population level. Objective: To identify clinically relevant conditions that co-occur with DLD at the population level. Design, Setting, and Participants: This case-control study used an electronic health record (EHR)-based population-level approach to compare the prevalence of comorbid health phenotypes between DLD cases and matched controls. These cases were identified using the Automated Phenotyping Tool for Identifying Developmental Language Disorder algorithm of the Vanderbilt University Medical Center EHR, and a phenome enrichment analysis was used to identify comorbidities. An independent sample was selected from the Geisinger Health System EHR to test the replication of the phenome enrichment using the same phenotyping and analysis pipeline. Data from the Vanderbilt EHR were accessed between March 2019 and October 2020, while data from the Geisinger EHR were accessed between January and March 2022. Main Outcomes and Measures: Common and rare comorbidities of DLD at the population level were identified using EHRs and a phecode-based enrichment analysis. Results: Comorbidity analysis was conducted for 5273 DLD cases (mean [SD] age, 16.8 [7.2] years; 3748 males [71.1%]) and 26 353 matched controls (mean [SD] age, 14.6 [5.5] years; 18 729 males [71.1%]). Relevant phenotypes associated with DLD were found, including learning disorder, delayed milestones, disorders of the acoustic nerve, conduct disorders, attention-deficit/hyperactivity disorder, lack of coordination, and other motor deficits. Several other health phenotypes not previously associated with DLD were identified, such as dermatitis, conjunctivitis, and weight and nutrition, representing a new window into the clinical complexity of DLD. Conclusions and Relevance: This study found both rare and common comorbidities of DLD. Comorbidity profiles may be leveraged to identify risk of additional health challenges, beyond language impairment, among children with DLD.

Polygenic risk impacts PDGFRA mutation penetrance in non-syndromic cleft lip and palate.

Non-syndromic cleft lip with or without cleft palate (NSCL/P) is a common, severe craniofacial malformation that imposes significant medical, psychosocial and financial burdens. NSCL/P is a multifactorial disorder with genetic and environmental factors playing etiologic roles. Currently, only 25% of the genetic variation underlying NSCL/P has been identified by linkage, candidate gene and genome-wide association studies. In this study, whole-genome sequencing and genome-wide genotyping followed by polygenic risk score (PRS) and linkage analyses were used to identify the genetic etiology of NSCL/P in a large three-generation family. We identified a rare missense variant in PDGFRA (c.C2740T; p.R914W) as potentially etiologic in a gene-based association test using pVAAST (P = 1.78 × 10-4) and showed decreased penetrance. PRS analysis suggested that variant penetrance was likely modified by common NSCL/P risk variants, with lower scores found among unaffected carriers. Linkage analysis provided additional support for PRS-modified penetrance, with a 7.4-fold increase in likelihood after conditioning on PRS. Functional characterization experiments showed that the putatively causal variant was null for signaling activity in vitro; further, perturbation of pdgfra in zebrafish embryos resulted in unilateral orofacial clefting. Our findings show that a rare PDGFRA variant, modified by additional common NSCL/P risk variants, have a profound effect on NSCL/P risk. These data provide compelling evidence for multifactorial inheritance long postulated to underlie NSCL/P and may explain some unusual familial patterns.

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.

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

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 = 0.323; p value = 1.31 × 10-8) in European-ancestry analysis and rs7837758 (B = 0.518; p value = 5.07 × 10-8), 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.

Identifying developmental stuttering and associated comorbidities in electronic health records and creating a phenome risk classifier.

PURPOSE: This study aimed to identify cases of developmental stuttering and associated comorbidities in de-identified electronic health records (EHRs) at Vanderbilt University Medical Center, and, in turn, build and test a stuttering prediction model. METHODS: A multi-step process including a keyword search of medical notes, a text-mining algorithm, and manual review was employed to identify stuttering cases in the EHR. Confirmed cases were compared to matched controls in a phenotype code (phecode) enrichment analysis to reveal conditions associated with stuttering (i.e., comorbidities). These associated phenotypes were used as proxy variables to phenotypically predict stuttering in subjects within the EHR that were not otherwise identifiable using the multi-step identification process described above. RESULTS: The multi-step process resulted in the manually reviewed identification of 1,143 stuttering cases in the EHR. Highly enriched phecodes included codes related to childhood onset fluency disorder, adult-onset fluency disorder, hearing loss, sleep disorders, atopy, a multitude of codes for infections, neurological deficits, and body weight. These phecodes were used as variables to create a phenome risk classifier (PheRC) prediction model to identify additional high likelihood stuttering cases. The PheRC prediction model resulted in a positive predictive value of 83 %. CONCLUSIONS: This study demonstrates the feasibility of using EHRs in the study of stuttering and found phenotypic associations. The creation of the PheRC has the potential to enable future studies of stuttering using existing EHR data, including investigations into the genetic etiology.

Host genetic effects in pneumonia.

Given the coronavirus disease 2019 (COVID-19) pandemic, investigations into host susceptibility to infectious diseases and downstream sequelae have never been more relevant. Pneumonia is a lung disease that can cause respiratory failure and hypoxia and is a common complication of infectious diseases, including COVID-19. Few genome-wide association studies (GWASs) of host susceptibility and severity of pneumonia have been conducted. We performed GWASs of pneumonia susceptibility and severity in the Vanderbilt University biobank (BioVU) with linked electronic health records (EHRs), including Illumina Expanded Multi-Ethnic Global Array (MEGAEX)-genotyped European ancestry (EA, n= 69,819) and African ancestry (AA, n = 15,603) individuals. Two regions of large effect were identified: the CFTR locus in EA (rs113827944; OR = 1.84, p value = 1.2 × 10-36) and HBB in AA (rs334 [p.Glu7Val]; OR = 1.63, p value = 3.5 × 10-13). Mutations in these genes cause cystic fibrosis (CF) and sickle cell disease (SCD), respectively. After removing individuals diagnosed with CF and SCD, we assessed heterozygosity effects at our lead variants. Further GWASs after removing individuals with CF uncovered an additional association in R3HCC1L (rs10786398; OR = 1.22, p value = 3.5 × 10-8), which was replicated in two independent datasets: UK Biobank (n = 459,741) and 7,985 non-overlapping BioVU subjects, who are genotyped on arrays other than MEGAEX. This variant was also validated in GWASs of COVID-19 hospitalization and lung function. Our results highlight the importance of the host genome in infectious disease susceptibility and severity and offer crucial insight into genetic effects that could potentially influence severity of COVID-19 sequelae.

Differential Pharmacology and Binding of mGlu2 Receptor Allosteric Modulators.

Allosteric modulation of metabotropic glutamate receptor 2 (mGlu2) has demonstrated efficacy in preclinical rodent models of several brain disorders, leading to industry and academic drug discovery efforts. Although the pharmacology and binding sites of some mGlu2 allosteric modulators have been characterized previously, questions remain about the nature of the allosteric mechanism of cooperativity with glutamate and whether structurally diverse allosteric modulators bind in an identical manner to specific allosteric sites. To further investigate the in vitro pharmacology of mGlu2 allosteric modulators, we developed and characterized a novel mGlu2 positive allosteric modulator (PAM) radioligand in parallel with functional studies of a structurally diverse set of mGlu2 PAMs and negative allosteric modulators (NAMs). Using an operational model of allosterism to analyze the functional data, we found that PAMs affect both the affinity and efficacy of glutamate at mGlu2, whereas NAMs predominantly affect the efficacy of glutamate in our assay system. More importantly, we found that binding of a novel mGlu2 PAM radioligand was inhibited by multiple structurally diverse PAMs and NAMs, indicating that they may bind to the mGlu2 allosteric site labeled with the novel mGlu2 PAM radioligand; however, further studies suggested that these allosteric modulators do not all interact with the radioligand in an identical manner. Together, these findings provide new insights into the binding sites and modes of efficacy of different structurally and functionally distinct mGlu2 allosteric modulators and suggest that different ligands either interact with distinct sites or adapt different binding poses to shared allosteric site(s).