Researchers Develop Phenome-Risk Classification Machine Learning Algorithm to Identify Patients Who Are at High Risk for Developmental Stuttering; Results Presented at ASHG 2020 Virtual Annual Meeting (October 27-30)

On Day 3 (Thursday, October 29) of the American Society of Human Genetics (ASHG) 2020 Virtual Annual Meeting (https://www.ashg.org/meetings/2020meeting/), one of the most interesting presentations was on the subject of developmental stuttering. Douglas M. Shaw (a graduate student in the Vanderbilt Genetics institute, Vanderbilt University) gave talk entitled “Applying a Phenome Risk Classifying Model to Identify Undiagnosed Developmental Stuttering Cases in a Biobank for Genome Wide Association Analysis.” In the abstract to his talk, Shaw described “developmental stuttering” as a speech disorder characterized by a disturbance in fluency and speech pattern, with an adult prevalence of 1-3% in the US. Despite twin-based studies showing ~50% heritability, the genetic etiology of stuttering is still largely unknown. No population-based genome wide association analysis (GWAS) has yielded variants that reach genome-wide significance, Shaw and colleagues wrote. Shaw noted that within Vanderbilt’s Electronic Health Record-linked biorepository (BioVU), only 142 cases of stuttering have diagnostic ICD9/10 (ICD9-307.0, ICD10-F98.5, ICD9-315.35, ICD10-F80.81, ICD10-R47.82) codes out of 92,762 genotyped samples, suggesting a large portion of people who stutter are not well-captured within the EHR. To address this case acquisition issue and provide a large enough sample set to power a GWAS, Shaw and colleagues developed a phenome-risk classification machine learning algorithm to identify patients who are at high risk for developmental stuttering.
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