Meta-analysis of developmental language disorder cases from electronic health records
Developmental language disorder (DLD) is a common pediatric language impairment that affects up to 7% of the population, that remains under-diagnosed in the population. Further, the underlying biology, etiology and risk factors especially at the population level are not well understood.
There are a few candidate gene studies, and population studies looking at the genetic basis of DLD, though small sample sizes tend to be a restrictive factor.
We aim to leverage genetic information from large EHRs and biobanks to conduct a heritability meta-analysis for DLD, and perform genetic correlations with other traits to study the genetic architecture of this neuro-developmental disorder. By doing so, we hope to better understand the polygenic nature of DLD and to further our understanding its biology.
We will use a newly developed algorithm to reliably identify instances of DLD in large biobanks to conduct well-powered GREML analysis of DLD.
The final goal of this project is to develop a gold standard DLD phenotype which can then be applied to multiple biobanks and also to study the genetic correlations between DLD and other traits as to better understand the genetic underpinnings of DLD.
Walters CE Jr, Nitin R, Margulis K, Boorom O, Gustavson DE, Bush CT, Davis LK, Below JE, Cox NJ, Camarata SM, Gordon RL. Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems. J Speech Lang Hear Res. 2020 Sep 15;63(9):3019-3035. doi: 10.1044/2020_JSLHR-19-00397. Epub 2020 Aug 11. PMID: 32791019; PMCID: PMC7890229.