{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/139122"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/139122","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Computational methods to dissect the genetic basis of human disease","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_html":"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_has_math":false,"creators":["Kim, Samuel Sungil"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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