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XiaofeiZhou_Thesis.pdf (7.88 MB)

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Bayesian Lasso for Detecting Rare Genetic Variants Associated with Common Diseases

Zhou, Xiaofei
http://rave.ohiolink.edu/etdc/view?acc_num=osu1563455460578675

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Statistics.
Rare genetic variants are one of the key factors in understanding the etiology of common diseases. Although much focus on the literature has been on rare single nucleotide variants (rSNVs), rare haplotype variants (rHTVs) offer perhaps even greater biological relevance, as variants on the same chromosomes are often passed jointly. Several methods have been developed to detect rHTV effects on common diseases based on the Bayesian Lasso methodology for binary and quantitative traits (LBL) and greater power has been demonstrated over a number of rSNV-based methods. To further extend the capability of LBL, I work on two additional methods for detecting rHTVs associated with common diseases. The first method jointly analyzes independent case-control and family trio data, and is referred to as Combined Logistic Bayesian Lasso (cLBL). cLBL gains higher power than the single-design-based Bayesian Lassos because it achieves larger sample size by combining these two types of data. The likelihood used in cLBL is retrospective, making the method more efficient in identifying the rare haplotypes that are associated with diseases. The second method focuses on survival traits and is called Survival Bayesian Lasso (SBL). The current implementation of SBL is based on the accelerated failure time framework. SBL utilizes Weibull, loglogistic, and lognormal distributions to accommodate various types of potential hazards and interpretation schemes. A selection procedure is implemented in SBL to choose the most appropriate distribution. While SBL mainly focuses on rHTVs main effects, it can also evaluate environmental covariates as well as their interactions with HTVs. I applied SBL to The Cancer Genome Atlas breast cancer dataset and identified a risk rHTV that resides in the tumor suppressor CDH1 gene. I also conducted extensive simulations to gauge the performance of SBL.
Shili Lin (Advisor)
Eloise Kaizar (Committee Member)
Guy Brock (Committee Member)
131 p.
Statistics
Bayesian LASSO; common diseases; haplotype; rare variant; genetic analysis; combined design; survival analysis; GWAS; SNP

Recommended Citations

Citations

  • Zhou, X. (2019). Bayesian Lasso for Detecting Rare Genetic Variants Associated with Common Diseases [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563455460578675

    APA Style (7th edition)

  • Zhou, Xiaofei. Bayesian Lasso for Detecting Rare Genetic Variants Associated with Common Diseases. 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1563455460578675.

    MLA Style (8th edition)

  • Zhou, Xiaofei. "Bayesian Lasso for Detecting Rare Genetic Variants Associated with Common Diseases." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563455460578675

    Chicago Manual of Style (17th edition)

osu1563455460578675
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This open access ETD is published by The Ohio State University and OhioLINK.