Computational methods to dissect the genetic basis of human disease
Name
Kim-sungil-PhD-EECS-2021-thesis.pdf
Description
Thesis PDF
Size
50.91 MB
Format
Adobe PDF
Checksum (MD5)
e2fe3ca3a02b44a7dd6f34754e7b78e9
Author(s)
Kim, Samuel Sungil
Advisor(s)
Price, Alkes L.
Kellis, Manolis
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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