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Family-Based Bayesian LASSO for Detecting Association of Rare Haplotypes with Common Diseases

Wang, Meng
http://rave.ohiolink.edu/etdc/view?acc_num=osu1398896091

Abstract Details

2014, Doctor of Philosophy, Ohio State University, Statistics.
In recent years, there has been an increasing interest in using common SNPs amassed in GWAS to investigate rare haplotype effects on complex diseases. Evidence has suggested that rare haplotypes may tag rare causal single nucleotide variants, making SNP-based rare haplotype analysis not only cost effective, but also more valuable for detecting causal variants. Although a number of methods for detecting rare haplotype association have been proposed in recent years, they are population based and thus susceptible to population stratification. In this dissertation we propose family-based logistic Bayesian Lasso (famLBL) for estimating the effect of haplotypes on complex diseases using SNP data. By choosing appropriate prior distribution, effect size of an unassociated haplotype can be shrunk toward zero, allowing for more precise estimation of associated haplotypes, especially those that are rare, thereby achieving greater detection power. We evaluate famLBL using simulation to gauge its type I error and power. Comparison with its population counterpart LBL highlights famLBL's robustness property in the presence of population substructure. Further investigation by comparing famLBL with traditional family based association test (FBAT) reveals its advantage for detecting rare haplotype association. Compared with first order collapsing methods like Combined Multivariate and Collapsing (CMC) and the single variant version of FBAT (fbat-v0), as well as the second order collapsing methods such as sequence kernel association test (SKAT), famLBL is more consistent in its ability to detect across different settings. To demonstrate the practical utility of famLBL, we applied it to the Framingham Heart Study data in the hope of identifying haplotypes associated with high blood pressure. Focusing on common SNVs identified by another method, we were able to locate rare haplotypes associated with the trait that potentially tag causal rare variants. Future work on famLBL includes extending the method to extended pedigrees, as well as incorporating environmental factors and modeling quantitative traits.
Shili Lin (Advisor)
Christopher Bartlett (Committee Member)
Asuman Turkmen (Committee Member)
114 p.
Statistics

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Citations

  • Wang, M. (2014). Family-Based Bayesian LASSO for Detecting Association of Rare Haplotypes with Common Diseases [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398896091

    APA Style (7th edition)

  • Wang, Meng. Family-Based Bayesian LASSO for Detecting Association of Rare Haplotypes with Common Diseases. 2014. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1398896091.

    MLA Style (8th edition)

  • Wang, Meng. "Family-Based Bayesian LASSO for Detecting Association of Rare Haplotypes with Common Diseases." Doctoral dissertation, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398896091

    Chicago Manual of Style (17th edition)

osu1398896091
344
© 2014, some rights reserved.Creative Commons License Family-Based Bayesian LASSO for Detecting Association of Rare Haplotypes with Common Diseases by Meng Wang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.