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Computational methods to dissect the genetic basis of human disease

Author(s)
Kim, Samuel Sungil
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Advisor
Price, Alkes L.
Kellis, Manolis
Terms of use
In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/
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Abstract
Genome-wide association studies (GWAS) have been successful in identifying disease-associated genetic variants. However, the path from GWAS to biological insight remains challenging, notably in identifying relevant biological pathways, explaining mechanistic links between diseases, and nominating disease-critical tissues and cell types. In this thesis, I introduce computational methods to dissect the genetic basis of human disease by integrating GWAS with functional data. In the first chapter, I integrate the GWAS with biological pathways and gene networks to elucidate biological mechanisms. I identify significantly associated pathways and highlight the importance of accounting for regulatory annotations in pathway enrichment and gene network analyses. In the second chapter, I investigate the shared genetic architecture between Mendelian disease and common disease by developing a machine learning framework to impute and denoise Mendelian disease-derived pathogenicity scores. I assess the informativeness of Mendelian pathogenicity scores for common disease and improve upon existing scores. In the third chapter, I prioritize disease-critical cell types by integrating GWAS with single-cell gene expression and chromatin accessibility profiling of fetal and adult brains. I show that identified disease-cell type associations recapitulates known biology while informing future analyses of disease mechanisms.
Date issued
2021-06
URI
https://hdl.handle.net/1721.1/139122
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology

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