Bayesian Parametric and Nonparametric Methods for Multiple QTL Mapping and SNP-Set Analysis Public Deposited

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  • March 21, 2019
  • Chung, Wonil
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Many complex traits and human diseases, such as blood pressure and body weight, are known to change over time. The genetic basis of such traits can be better understood by repeatedly collecting data over time. The resulting longitudinal data provide us useful resources for studying the joint action of multiple time-dependent genetic factors. In the first part of the dissertation, we extend two existing Bayesian multiple quantitative trait loci (QTL) mapping methods from univariate traits to longitudinal traits. Our first approach focuses on mapping genes with main effects and two-way gene-gene and gene-environment interactions. Multiple QTL are selected by a variable selection procedure based on the composite model space framework. Our second approach presents a Bayesian Gaussian process method to map multiple QTL without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the nonparametric Bayesian method measures the importance of each QTL, regardless whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. We assign a Gaussian process prior to this unknown function. For the unstructured covariance matrix, both approaches employ a modified Cholesky decomposition. For data where phenotype measurements are not collected at a fixed set of time points across all samples, we propose a grid-based approach which parsimoniously approximates the covariance matrix of each subject as a function of a covariance matrix defined on a set of pre-selected time points. For most genome-wide association studies (GWAS), power to detect an association between a single genetic variant, such as a single nucleotide polymorphism (SNP) and a complex trait is extremely low. Alternative strategies, such as regional SNP-set analysis have overcome some of the limitations of the standard single SNP analysis. Our third topic develops a Bayesian regional SNP-set analysis which extends the nonparametric Gaussian process model and simultaneously models multiple groups of rare and/or common SNP variants. Instead of assigning each SNP a hyperparameter, we assign a common hyperparameter to every SNP within each set to measure the cumulative effect of all SNPs in that set.
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  • In Copyright
  • Zou, Fei
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill
Graduation year
  • 2013

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