Deep learning approaches to decipher the impact of mobile element insertion on alternative splicing in neurological disorders

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The purpose of this training and research application is to study the functional impact of mobile element insertions (MEIs) in neurological disorders (NDs) using new developments in deep learning techniques. MEIs are transposable DNA fragments that are able to insert throughout the human genome. There are at least 124 independent MEIs associated with human diseases. Approximately 20% of these diseases represent a spectrum of NDs, yet the overall contribute of MEIs to the etiology of NDs has not been systematically estimated. To address this, we will (1) characterize functional MEIs in GTEx cohorts in healthy individuals; (2) build a comprehensive functional map of MEIs to determine tissue-specific and brain-specific impact; and (3) impute transcriptional changes on various NDs where whole-genome sequencing (WGS) data will be generated. The proposed application will also develop an extensive research program for Dr. Dadi Gao, a computational biologist and statistical geneticist who has trained in functional genomic studies of alternative splicing in neurodegenerative disorders and therapeutic targeting of a splicing defect that causes a severe neurodevelopmental disorder. He has developed novel methods to investigate regulation of the transcriptome and to facilitate analyses in drug development. He now seeks to expand his expertise by applying statistical and deep learning models on large cohorts of sequencing data from controls and cases with NDs from post-mortem tissues, then impute functional consequences of MEIs from WGS in large-scale disease cohorts. The training plan consists of two years of mentored research to learn new skills in genome analysis, MEI characterization, and advanced deep learning techniques, followed by three years of shaping an independent laboratory. The research plan is developed to comprehensively explore functional variation in the genome by decomposing transcriptomic changes against MEIs. Dr. Michael Talkowski at Massachusetts General Hospital, Harvard, and the Broad Institute will serve as the primary mentor, while Dr. Manolis Kellis at MIT and the MIT Computational Biology Group, and the Broad Institute will serve as a co-mentor and close collaborator. These mentors are recognized experts in genomic structural variants, functional genomics, the genetics of neurological disorders, and computational modeling to establish functional elements in the human genome. In addition, a team of independent investigators from basic and translational research will provide Dr. Gao with comprehensive feedback to keep both his science and career development on track. The highly collaborative environment in CGM, MGH, Harvard Medical School, the Broad Institute and the University of Michigan Medical School will prepare Dr. Gao for his transition to an independent investigator. This outstanding mentorship team and training program will facilitate the career development of Dr. Gao as he seeks to redefine the functional maps of MEIs in the human genome and to impute their impact in large-scale neurological disorders.