Computational methods to dissect the genetic basis of human disease

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.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Kim, Samuel Sungil

Advisors dc:contributor.advisor

• Price, Alkes L.
• Kellis, Manolis

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139122

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139122