A strong genetic component exists among individuals with autism spectrum disorder (ASD), with an estimated heritability up to 80–90 percent; however, our existing knowledge can only explain approximately 25 percent of ASD cases. Furthermore, our current genetic analyses for ASD have been largely confined to coding sequences, accounting for approximately 1.5 percent of the human genome, which leaves the vast noncoding component largely unexplored. Identifying pathogenic noncoding mutations associated with ASD will require deep profiling of the brain epigenome in specific cell types across representative developmental stages.

Jingjing Li and Arnold Kriegstein aim to generate reference maps of the “encyclopedia” of active noncoding elements in specific brain cell types along the trajectory of brain development, followed by fine-mapping of pathogenic regulatory mutations leveraging whole-genome sequencing data from thousands of individuals with ASD.

Specifically, the team will study the regulatory landscapes in different brain cell types at different developmental stages. The team will then analyze large-scale ASD genomes and map genomic mutations onto the identified noncoding regulatory elements, followed by the development of machine-learning algorithms that can identify pathogenic mutations that lead to dysregulated gene expression in the brain. The team will further aggregate noncoding and coding sequence mutations for a direct translation from personal genomes to clinical traits.

Taken together, by identifying cell-type-specific regulatory elements along the trajectory of brain development, this study will expand our view of the ASD genetic architecture from coding sequences to the vast noncoding genome, which will enable us to understand exactly when and where noncoding mutations exert their regulatory effects on modulating brain development.