The development of cancer diseases is driven by the accumulation of many oncogenesis-related genetic
alterations and tumorigenesis is triggered by complex networks of involved genes rather than independent
actions. The interactions among genetic factors are believed to play important roles in carcinogenesis and
contribute to the missing heritability. In this proposal, we designed a filtered gene-gene interaction analysis
aiming to identify the epistasis involving important oncogenesis-related genes. Besides the advantage of
dimensional reduction and increased power, this filtered interaction analysis will help us identify novel
oncogenes or tumor suppressor genes implicated in lung cancer development that will not be revealed in main
effect association analysis. It will also identify novel susceptibility variants, including those in intergenic and
non-coding regions, affecting lung cancer risk by interacting/modulating with oncogenesis-related genes. Lung
cancer is a heterogeneous disease and researchers have identified vast differences in genomic attributes.
However, the knowledge about epistatic features in lung cancer subtypes is limited. We will conduct a stratified
epistasis analysis by lung cancer histology subtype in the proposed study to reveal subtype-specific genetic
interactions and gene networks. The stratified analysis by histology will enhance our understanding about this
complicated disease mechanism in lung cancer and has the potential to contribute to precision medicine in
lung cancer treatment.
A main challenge in genetic association studies is to understand the functional consequences of identified
genetic variants. In this study, we proposed functional annotation analysis including eQTL gene expression
analysis, pathway and gene network analysis, and functional annotation of epistasis-involved SNPs. This
integrative epistasis and functional annotation analysis will help us pinpoint the causal epistasis and
characterize the epistasis-involved genes or regions in lung cancer risk development. It will be a pilot study to
explore how the regulatory non-coding variants impact lung cancer risk by interacting with oncogenesis-related
The proposed study will provide insights about the complicated biological interactions that are critical for
gene regulation, biochemical networks, and developmental pathways implicated in lung carcinogenesis. In
order to finish the proposed study, we collected the genotype data from three independent GWAS studies
including 24,037 lung cancer patients and 20,401 healthy controls from the Caucasian population. The
genotype and gene expression data in lung tissues from 409 individuals will be applied for eQTL gene
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