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RevisedDissertation_Terry3.pdf (3.36 MB)

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ASSESSING GENETIC IMPUTATION QUALITY AND ITS APPLICATION IN RARE VARIANT ANALYSIS FOR GENOME-WIDE ASSOCIATION STUDIES: A SIMULATION-POWERED APPROACH

Cui, Zuxi
http://orcid.org/0000-0002-3962-7711
http://rave.ohiolink.edu/etdc/view?acc_num=case1722319825147714

Abstract Details

2024, Doctor of Philosophy, Case Western Reserve University, Epidemiology and Biostatistics.
This dissertation provides a study in the realm of genome-wide association studies (GWAS), with a specific focus on genetic imputation quality and its impact on uncommon and rare variant analysis. This research encompasses the development of an advanced whole-genome GWAS data simulation methodology using the SLiM program to enhance imputation and trait loci discovery. This approach marks a significant progression from previous methods limited to regional or chromosome-level simulations. Key aspects of this research includes the exploration of unique quality control metrics for genetic imputation, thus underscoring the influence of minor allele frequency (MAF) on imputation accuracy. This investigation is crucial for enhancing the precision of imputation techniques, especially for rare genetic variants, which are often challenging to analyze due to their low frequency. As an applied example, this research has led to the identification of three new loci marginally associated with prostate cancer (PrCa), contributing significantly to the understanding of its genetic architecture. This discovery has potential implications for future prostate cancer research and treatment strategies. Overall, this dissertation represents a substantial contribution to the field of genetic research in GWAS. It provides valuable evaluations of new imputation approaches and demonstrates their effectiveness in discovering new disease- related genetic variants, paving the way for future studies in cancer GWAS.
Xiaofeng Zhu (Committee Chair)
Fredrick Schumacher (Advisor)
Thomas LaFramboise (Committee Member)
Hao Feng (Committee Member)
Jessica Cooke Bailey (Committee Member)
128 p.
Biostatistics; Epidemiology; Genetics
genetic simulation; GWAS; genetic imputation; random mating; small-group originating model

Recommended Citations

Citations

  • Cui, Z. (2024). ASSESSING GENETIC IMPUTATION QUALITY AND ITS APPLICATION IN RARE VARIANT ANALYSIS FOR GENOME-WIDE ASSOCIATION STUDIES: A SIMULATION-POWERED APPROACH [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1722319825147714

    APA Style (7th edition)

  • Cui, Zuxi. ASSESSING GENETIC IMPUTATION QUALITY AND ITS APPLICATION IN RARE VARIANT ANALYSIS FOR GENOME-WIDE ASSOCIATION STUDIES: A SIMULATION-POWERED APPROACH. 2024. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1722319825147714.

    MLA Style (8th edition)

  • Cui, Zuxi. "ASSESSING GENETIC IMPUTATION QUALITY AND ITS APPLICATION IN RARE VARIANT ANALYSIS FOR GENOME-WIDE ASSOCIATION STUDIES: A SIMULATION-POWERED APPROACH." Doctoral dissertation, Case Western Reserve University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=case1722319825147714

    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.