Methods to Unveil the Genetic Architecture for Nicotine Dependence via NGS data

Nicotine dependence and addiction have strong relevance to public health, partly due to their induced risk on cancer and cardiovascular disease risk. Nicotine use has strong genetic component as shown in genome-wide association studies and family based studies. Unveiling the genetic basis for nicotine dependence is greatly important for regulating the tobacco use, and practicing individualized treatment. Next generation sequencing has enabled large scale investigation on the genetic and genomic basis for nicotine dependence. Multiple large scale studies have focused on detecting the associations with direct measurements of nicotine intake, or with smoking frequency data such as cigarettes per day. There is a compelling need to integrate these resources with functional genomic data from ENCODE, GTEx etc, and develop methods that can advance our understanding on the genetic architecture. In this application, we propose novel methods for understanding genetic architecture through modeling the genetic effect distribution, variant causality for each functional class of variants (Aim 1). To aid in functional interpretation of genotype-phenotype associations in-silico, we also propose methods for integrating the analysis of nicotine metabolites and smoking frequency data (Aim 2). There methods will be implemented in efficient and user-friendly tools, which not only facilitate our proposed research, but will also aid in the studies in a broader research community. These projects have the potential to bring a paradigm shift to the genetic analysis of nicotine dependence. The methods and tools will also be valuable for studying other similar complex traits.