Methods for unraveling the phenotypic consequences of regulatory variation [electronic resource]
- Konrad J. Karczewski.
- Physical description
- 1 online resource.
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|3781 2013 K||In-library use|
- Karczewski, Konrad J.
- Montgomery, Stephen, 1979- primary advisor.
- Snyder, Michael, primary advisor.
- Altman, Russ advisor.
- Stanford University Program in Biomedical Informatics.
- Most human variation lies outside of coding regions, where molecular functions are more difficult to determine than for variants in coding regions. As most disease-associated variants lie in non-coding regions, characterizing the role of regulatory variation is crucial to understanding the molecular mechanisms of these diseases and methods to do so are currently limited. Recently, a number of research efforts, including the ENCODE project, have produced a wealth of data on transcription factor binding sites and other regulatory information, in addition to increasingly available whole genome sequence and transcriptomics data. These data will crucial to understanding the molecular basis of many diseases, but their scale and disparate nature, as well as high levels of noise, require clever informatics methods to integrate and properly apply this information. In this thesis, I describe methods to address some of the major informatics challenges to characterize the role of regulatory variants in phenotypes and disease. In particular, I have shown that I can (1) detect cooperativity among transcription factors using human variation data, (2) associate transcription factors to functional modules and thus discover new TF interactions and disease associations, (3) provide molecular mechanisms for disease-associated non-coding variants, and (4) explore regulatory functional mechanisms using long-range interactions in the human genome.
- Publication date
- Submitted to the Program in Biomedical Informatics.
- Thesis (Ph.D.)--Stanford University, 2013.
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