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    Rare variant analysis on UK Biobank

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    KAUST_Thesis_Dissertation_LaTeX_5Sept2016gamma_YangThesis (1) (2).pdf
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    7.522Mb
    Format:
    PDF
    Description:
    MS Thesis
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    Type
    Thesis
    Authors
    Liu, Yang cc
    Advisors
    Hoehndorf, Robert cc
    Committee members
    Hauser, Charlotte cc
    Gojobori, Takashi cc
    Program
    Bioengineering
    KAUST Department
    Biological and Environmental Science and Engineering (BESE) Division
    Date
    2022-04-17
    Permanent link to this record
    http://hdl.handle.net/10754/676336
    
    Metadata
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    Abstract
    Genome-wide Association Studies (GWAS) is the study used to associate common variants and phenotypes and has uncovered thousands of disease-associated variants. However, there is limited research on the contribution of a rare variant. The UK Biobank (UKB) contains detailed medical records and genetic information for nearly 500,000 individuals and offers a great opportunity for genetic association studies on rare variants. Here we focused on the role of rare protein-coding variants on UKB phenotypes. We selected three diseases for analysis: breast cancer, hypothyroidism and type II diabetes. We defined criteria for qualifying variants and pruned the control group to reduce interference signals from similar phenotypes. We identified the most known biomarkers for those diseases, such as BRCA1 and BRCA2 gene for breast cancer, TG and TSHR gene for hypothyroidism and GCK for type II diabetes. This result supports the model validity and clarifies the contribution of rare variants to diseases. Moreover, we also tried the geneset based collapsing method to aggregate information across genes to strengthen the signal from rare variants and build a diagnosis model that only relies on the genetic information. Our model could achieve great performance with an AUC of more than 20% improvement for type II diabetes and breast cancer and more than 90% accuracy for hypothyroidism.
    Citation
    Liu, Y. (2022). Rare variant analysis on UK Biobank. KAUST Research Repository. https://doi.org/10.25781/KAUST-T66H1
    DOI
    10.25781/KAUST-T66H1
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-T66H1
    Scopus Count
    Collections
    Bioengineering Program; Biological and Environmental Science and Engineering (BESE) Division; MS Theses

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