Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia

Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesized to have relevance to brain development and neuropsychiatric disease. However, the detection of individual somatic mobile element insertion (MEIs) presents a difficult signal-to-noise problem. Using a machine learning method (RetroSom) and deep whole genome sequencing, we analyzed L1 and Alu retrotransposition in sorted neurons and glia from human brains. We characterized two brain-specific L1 insertions in neurons and glia from a donor with schizophrenia. There was anatomical distribution of the L1 insertions in neurons and glia across both hemispheres, indicating retrotransposition occurred during early embryogenesis. Both insertions were within the introns of genes (CNNM2, FRMD4A) within genomic loci associated with neuropsychiatric disorders. Proof-of-principle experiments revealed these L1 insertions significantly reduced gene expression. These results demonstrate RetroSom has broad applications for studies of brain development and may provide insight into the possible pathological effects of somatic retrotransposition.