Ground-breaking research, led by scientists at Vanderbilt University Medical Center, has reported the discovery of new genetic variants associated with developmental stuttering. Their findings have been published in The American Journal of Human Genetics.
Developmental stuttering
More than 70 million people worldwide suffer from developmental stuttering, which is approximately 1% of the population. Developmental stuttering is a speech disorder that is characterised by the disruption in forward movement of speech. This includes part-word and single-syllable word repetitions, sound prolongations and involuntary breaks. This chronic condition arises in early childhood and negatively impacts education, job performance and employability into adulthood. There are several risk factors for developmental stuttering including sex (males demonstrate increased risk) and family history.
Heritability estimates of developmental stuttering have varied across studies, ranging from 0.42 to 0.84 in the two largest twin studies. Despite this variation, there is clear evidence that developmental stuttering has a genetic component. As such, several linkage-based genetic analyses have identified significant hits within several genes, including GNPTAB, GNPTG, NAGPA and AP4E1. However, there is little concordance in identified loci across studies, which indicates that these results might be specific to certain families.
Most genetic research to date has provided little information about the biological mechanisms that contribute to the stuttering phenotype. A key challenge in studying this condition is acquiring large numbers of cases for genetic studies to be well powered.
Phenotypic imputation and gene discovery
To overcome these challenges, researchers used Vanderbilt University’s large electronic health record (EHR)-linked DNA database (BioVU) – one of the world’s largest repositories of human genetic information linked to searchable, electronic health information. However, only 142 individuals out of 92,762 were identified with a stuttering diagnostic code. This indicates that a large proportion of people who stutter do not have a record of diagnosis within the EHR.
To identify individuals affected by stuttering, the team built an artificial intelligence tool using comorbidities enriched in individuals affected by stuttering, such as ADHD. The team used the presence of these phenotypes recorded in the EHR to predict those likely to stutter. The team applied this tool (PheML) in BioVU and were able to positively predict the presence of stuttering more than 80% of the time.
By conducting a GWAS of PheML-imputed affected individuals, the researchers identified a variant near CYRIA on chromosome 2 in European-ancestry analysis and an intronic variant within the ZMAT4 gene on chromosome 8 in African-ancestry analysis.
The team hope that by establishing genetic connections between developmental stuttering and other traits, these findings could open up avenues for treating both conditions at the same time.
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