Determining acquisition and cessation antecedents to adolescent cigarette smoking for development of intervention strategies

Our national focus and emphasis on the promotion of healthy behavior choices regarding tobacco and other drugs continues to target adolescents. Multiple studies have shown that adolescence is the optimum period for the prevention of substance use initiation as life-long patterns of health behaviors are established during this critical developmental stage. Tobacco use is associated with an increase in morbid and mortal health conditions of which prevalence increases throughout the lifespan. Attention to the antecedents of preventable health conditions aims to modify the risks and identify health promotion factors. Modifying antecedent factors for tobacco initiation in youth and identifying protective factors for successful smoking cessation has major public health implications across the lifespan. Of foremost interest are those risk factors and resultant behaviors that predict a youth's probability of initiating cigarette use and their cessation of cigarette use. Specifically, this dissertation supports previous results identifying intervention variables on the initiation/cessation continuum model especially with the established predictors of smoking (decisional balance and susceptibility) and with more recently identified predictors of smoking (nicotine dependence and withdrawal symptoms) in current and former smokers in a sample of high school students in Austin and Houston, Texas. These results offer insight for the development of appropriate intervention program strategies for our youth. ^

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^