2026-09-19T13:53:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1391222022-01-15T03:39:21Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Price, Alkes L.
Kellis, Manolis
Kim, Samuel Sungil
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-01-14T14:51:15Z
2022-01-14T14:51:15Z
2021-06
2021-06-23T19:38:07.441Z
https://hdl.handle.net/1721.1/139122
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.
Ph.D.
Massachusetts Institute of Technology
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http://rightsstatements.org/page/InC-EDU/1.0/
Computational methods to dissect the genetic basis of human disease
Thesis
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Doctoral
Doctor of Philosophy
Publication
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unknown
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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.</Abstract>
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