Browsing by Author "Yang, Yi"

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    Bayesian Hierarchical Models For Multi-Variant and Multi-Trait Genome-Wide Association Studies
    (2020-06) Yang, Yi
    While genome-wide association studies (GWASs) have been widely used to identify associations between complex diseases and genetic variants, standard single-variant and single-trait analyses often have limited power when applied to scenarios in which variants are in linkage disequilibrium, occur at low frequency, or are associated with multiple correlated traits. In this dissertation, we propose three Bayesian hierarchical models for multi-variant and multi-trait GWASs based on the hierarchically structured variable selection (HSVS) framework: the generalized fused HSVS (HSVS-GF), the adaptive HSVS (HSVS-A), and the multivariate HSVS (HSVS-M). HSVS is a discrete mixture prior composed of a point mass at zero and a multivariate scale-mixing normal distribution for modeling the effects of variants. As an extension and development of the HSVS framework, the proposed methods have the flexibility to account for various correlation structures, which allows them to extensively borrow strength from multiple correlated variants and traits. As Bayesian methods, they can also integrate complex genetic information into the priors and thus boost the power by leveraging information from various sources. In addition to testing associations, the proposed methods in the Bayesian framework also produce posterior effect estimates for individual variants simultaneously, a distinctive and useful feature that most of the competing methods do not possess. Specifically, HSVS-GF is a pathway-based method that uses summary statistics and pathway structural information to identify the association of a disease with variants in a pathway. HSVS-A is a set-based method that tests the association of a continuous or dichotomous trait with rare variants in a set and estimates the effects of individual rare variants. HSVS-M is a multi-variant and multi-trait method that uses summary statistics both to test the association of variants in a gene with multiple correlated traits and to estimate the strength of association of the gene with each trait. Through analysis of simulated data in various scenarios and GWAS data from the Wellcome Trust Case Control Consortium and the Global Lipids Genetics Consortium, we show that the proposed methods can substantially outperform the competing methods and identify novel causal variants.
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    Community Pharmacists’ Awareness of Intimate Partner Violence: An Exploratory Study
    (University of Minnesota, College of Pharmacy, 2013) Barnard, Marie; West-Strum, Donna; Holmes, Erin; Yang, Yi; Swain, Kristen Alley
    Background: Intimate partner violence (IPV) is a serious public health problem, impacting more than 12 million people in the United States each year. The only know effective health care intervention is routine screening for IPV exposure; however, this intervention has been poorly adopted. Expansion of screening efforts to the community pharmacy setting provides an opportunity to have a substantial impact on the health and well-being of pharmacy patients. However, little is known about pharmacists’ knowledge, attitudes, and behaviors related to IPV. Objective: The objective of this study was to conduct an exploratory investigation of community pharmacists’ current level of knowledge, attitudes, behaviors, and intentions related to IPV and to IPV screening. Methods: A cross-sectional study using an online questionnaire was conducted. Surveys were distributed via email. Descriptive analyses of survey responses were conducted. Results: A total of 144 community pharmacists completed the survey. Results indicated most (67.4%) had no IPV education/training. Participants were significantly more willing to conduct screening with targeted patients compared to all patients. (X2=129.62; df=36; p<0.0001). There was strong agreement with interest in and willingness to participate in continuing education. Conclusions: Most respondents indicated relatively low levels of IPV knowledge and training and very little current IPV screening activity. Continuing education on IPV should be considered for pharmacists to increase knowledge and awareness of IPV.
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    A Unified Algorithm for Fitting Penalized Models with High Dimensional Data
    (2015-09) Yang, Yi
    In the light of high dimensional problems, research on the penalized model has received much interest. Correspondingly, several algorithms have been developed for solving penalized high dimensional models. In this thesis, we propose fast and efficient unified algorithms for computing the solution path for a collection of penalized models. In particular, we study the algorithm for solving l1 penalized learning problems and the algorithm for solving group-lasso learning problems. These algorithm take advantage of a majorization-minimization trick to make each update simple and efficient. The algorithms also enjoy a proven convergence property. To demonstrate the generality of our algorithms, we further extend these algorithms on a class of elastic net penalized large margin classification methods and the elastic net penalized Cox's proportional hazards model. These algorithms have been implemented in three R packages gglasso, gcdnet and fastcox, which are publicly available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/web/packages. On simulated and real data, our algorithms consistently outperform the existing software in speed for computing penalized models and often delivers better quality solutions.