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CHARACTERIZING GLOBAL REGULATORY PATTERNS OF TRANSCRIPTION FACTORS ON SYSTEMS-WIDE SCALE USING MULTI-OMICS DATASETS AND MACHINE LEARNING

Patel, Neel R
http://rave.ohiolink.edu/etdc/view?acc_num=case1626284802198267

Abstract Details

2021, Doctor of Philosophy, Case Western Reserve University, Systems Biology and Bioinformatics.
Transcription factors(TFs) are specialized DNA binding proteins, that regulate target gene (TG) expression by driving the process of transcription. Disruption of TF binding sites can cause significant changes in TG expression, which has been shown to be associated with several diseases. Moreover, besides binding of TFs, which is a local/cis regulatory mechanism, trans-acting mechanisms such as cooperativity among different combinations of TFs and co-regulation of multiple TGs by the same set of TFs can also influence TG expression. Integrative approaches that can incorporate information from these mechanisms, obtained from different data sources, are needed to comprehend TF based TG expression regulation on a systems-wide level. Furthermore, there is also a need to integrate this regulatory information in tests for statistical associations of genetic variants with complex disease traits to unravel mechanisms responsible for causing these diseases, while also discovering novel risk TGs. In this dissertation, I develop an integrative gene regulatory network based approach utilizing information from different cis and trans regulatory mechanisms to model TG expression using machine learning algorithms. Furthermore, I use these models to calculate effect estimates of individual TFs as well as of combinations of TFs forming TF regulatory modules. Lastly, I build neural networks to quantify influence of non-coding variants on TF binding and integrate them with effect estimates of the TFs in order to derive their impact on TG regulation. I utilize these aggregated scores, as weights for common variants (allele frequency > 5%), to build TG expression prediction models based on individual level genotype information to perform transcriptome wide association study(TWAS) within my novel framework TFXcan. I show that such models are more accurate compared to state-of-the-art TWAS models using broad epigenetic priors as variant weights. Furthermore, I describe a novel weighted kernel association test TFKin, which uses kinship matrix computed for individuals based on TF regulatory scores of rare variants. I show that this kind of a weighting approach, is better at TG-expression association, compared to conventional allele frequency derived weights. Both TFXcan and TFKin can be utilized to derive influence of common and rare variants on TF based TG expression regulation.
William Bush (Advisor)
David Lodowski (Committee Chair)
Mark Cameron (Committee Member)
Rong Xu (Committee Member)
161 p.
Bioinformatics; Genetics; Molecular Biology
Machine learning, Transcription factors, Deep learning, Gene expression, Multi-omics, Gene regulation

Recommended Citations

Citations

  • Patel, N. R. (2021). CHARACTERIZING GLOBAL REGULATORY PATTERNS OF TRANSCRIPTION FACTORS ON SYSTEMS-WIDE SCALE USING MULTI-OMICS DATASETS AND MACHINE LEARNING [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1626284802198267

    APA Style (7th edition)

  • Patel, Neel. CHARACTERIZING GLOBAL REGULATORY PATTERNS OF TRANSCRIPTION FACTORS ON SYSTEMS-WIDE SCALE USING MULTI-OMICS DATASETS AND MACHINE LEARNING. 2021. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1626284802198267.

    MLA Style (8th edition)

  • Patel, Neel. "CHARACTERIZING GLOBAL REGULATORY PATTERNS OF TRANSCRIPTION FACTORS ON SYSTEMS-WIDE SCALE USING MULTI-OMICS DATASETS AND MACHINE LEARNING." Doctoral dissertation, Case Western Reserve University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1626284802198267

    Chicago Manual of Style (17th edition)

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This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.