Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.
作者： Xiaowei Zhu , Bo Zhou , Reenal Pattni , Kelly Gleason , Chunfeng Tan
DOI: 10.1038/S41593-020-00767-4
关键词:
摘要: Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesized to have relevance brain development and neuropsychiatric disease. However, detection of individual mobile element insertions presents a difficult signal-to-noise problem. Using machine-learning method (RetroSom) deep whole-genome sequencing, we analyzed L1 Alu retrotransposition sorted neurons glia from brains. We characterized two brain-specific donor with schizophrenia. There was anatomical distribution across both hemispheres, indicating occurred during early embryogenesis. Both were within introns genes (CNNM2 FRMD4A) inside genomic loci associated disorders. Proof-of-principle experiments revealed these significantly reduced gene expression. These results demonstrate that RetroSom has broad applications for studies may provide insight into possible pathological effects retrotransposition.
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