Browsing by Subject "Genome-wide association studies"
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Item Bayesian Hierarchical Models For Multi-Variant and Multi-Trait Genome-Wide Association Studies(2020-06) Yang, YiWhile 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.Item Data related to Genetic diversity fuels gene discovery for tobacco and alcohol use(2022-10-13) Saunders, Gretchen R B; Wang, Xingyan; Chen, Fang; Jang, Seon-Kyeong; Liu, Mengzhen; Wang, Chen; Liu, Dajiang J; Vrieze, Scott; saund247@umn.edu; Saunders, Gretchen R BWe conducted a meta-analysis of 60 genome wide association studies (GWAS) in up to 3.4 million participants from four major ancestries on nicotine and substance use. Specifically, we targeted different stages and kinds of substance use from initiation (smoking initiation and age of regular smoking initiation) to regular use (drinks per week and cigarettes per day) to cessation (smoking cessation). Here we present the final set of filtered meta-analysis summary statistics and polygenic risk score weights excluding 23andMe. As per requirement and to ease dissemination of our results for other scientific endeavors, we are sharing our results here to facilitate downloading.Item Detection of complex genetic effects in genome-wide association studies.(2010-07) Ma, LiThe large number of single nucleotide polymorphisms (SNPs) available provides a powerful molecular resource for identifying complex genetic interactions associated with complex traits or diseases but also presents unprecedented data analysis challenges. In this work we developed new quantitative genetics methods and parallel computing tools to detect complex interactive SNP effects underlying complex traits or diseases using genome-wide association studies (GWAS). The new quantitative genetics methods allow detection of novel interactions between genes, sex and environment including second order and third order gene-gene, gene-sex, gene-environment interactions, where each gene may have additive, dominance or parent-of-origin effects. The parallel computing tools allow such complex analysis to be conducted in a timely manner for any large scale GWAS and can be scalable to meet growing data analysis challenges in the future. The analytical and computing methods were applied to the analysis of a Holstein cattle GWAS data set and the Framingham Heart Study (FHS) data. Significant epistasis and single-locus effects were detected affecting human cholestoral levels and dairy production, fertility and body traits. The analytical methods and computing tools will significantly facilitate the discovery of complex mechanisms underlying phenotypes using GWAS.