Spoken language is a near universal feature of human behavior that binds individuals into social groups and provides vital tools for interpersonal interaction, learning, problem solving and commerce. Most children acquire the spoken language of their community quickly and with little conscious effort on their part or on the part of their caregivers. However, not all children accomplish this with equal success. Language impairment (LI), one contributor to this societal problem, is a neurodevelopmental condition that leads to linguistic deficits in children where development is otherwise normal (i.e. other conditions such as autism or hearing impairment have been ruled out). LI can have profound and lasting effects on social relations, employment, and mental health. LI is known to have a substantial genetic component, but the technologies used in the past decade of genetic research have only been able to implicate a few specific genes. We propose to use whole genome sequencing (WGS) to produce the most comprehensive catalog to date of genetic variation in language impaired and language proficient individuals (aim 1). Using this catalog, we will use computational approaches to infer the most likely functional impact for each genetic variant. These putatively functional variants will be grouped together into pathways and gene sets for which there is a plausible role in LI, and we will test (aim 2) for enrichment of functionl genetic variation in cases (language impaired) vs. controls (language proficient). Furthermore, we will investigate the role of potentially functional non-coding genetic variation by using a nove test for positional enrichment of genetic variation near regulatory landmarks, such as genomic binding sites of the language-associated transcription factor FOXP2. Finally, we will test the hypothesis that LI shares a genetic basis with the language deficits present in some individuals with autism spectrum disorders (ASDs). This will be accomplished through performing a gene network analysis that looks for regions of concentrated genetic burden of functional variants (network modules). Modules based on LI genetic variants will be compared with modules based on ASD variants, in terms of the significance of their proximity and overlap. This analysis will further illuminate, on a molecular level, the relationship between these conditions that share some similar clinical features. This project has the potential to transform the genetics of LI and illuminate its underlying biology and connection to other neurodevelopmental conditions. We will gain a more concrete understanding of any shared genetic liability between LI and ASD. Further, this would be the first LI genetics study to use WGS, with the goal of an integrated analysis of all major modes of genetic variation (single nucleotide variants, indels, and structura variants) while capturing the full frequency spectrum - rare and common alleles alike. By increasing our understanding of the genes at play in LI, we will move closer to being able to use genetic findings as evidence to support early interventions for improved outcomes.

Public Health Relevance

Language impairment (LI) is the most common developmental disorder, and it can persist well into adulthood, contributing to poor literacy, educational attainment, and mental health. Our proposed research will sequence the genomes of those affected with the disorder, with the goal of finding genes and genetic variants that contribute to the condition. Knowledge of which genes play a role in language impairment will eventually help in improving diagnosis and uncovering new avenues for therapy.

National Institute of Health (NIH)
National Institute on Deafness and Other Communication Disorders (NIDCD)
Research Project (R01)
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Study Section
Language and Communication Study Section (LCOM)
Program Officer
Cooper, Judith
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University of Iowa
Schools of Medicine
Iowa City
United States
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Vervier, Kévin; Michaelson, Jacob J (2018) TiSAn: estimating tissue-specific effects of coding and non-coding variants. Bioinformatics 34:3061-3068
SPARK Consortium. Electronic address: pfeliciano@simonsfoundation.org; SPARK Consortium (2018) SPARK: A US Cohort of 50,000 Families to Accelerate Autism Research. Neuron 97:488-493
Michaelson, Jacob J (2017) Genetic Approaches to Understanding Psychiatric Disease. Neurotherapeutics 14:564-581
Bahl, Ethan; Koomar, Tanner; Michaelson, Jacob J (2017) cerebroViz: an R package for anatomical visualization of spatiotemporal brain data. Bioinformatics 33:762-763
Geisheker, Madeleine R; Heymann, Gabriel; Wang, Tianyun et al. (2017) Hotspots of missense mutation identify neurodevelopmental disorder genes and functional domains. Nat Neurosci 20:1043-1051
Vervier, Kévin; Michaelson, Jacob J (2016) SLINGER: large-scale learning for predicting gene expression. Sci Rep 6:39360