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Genome-wide association analysis of genetic variants and prostate cancer risk and prognosis

Principal Investigator
Name
Qingyi Wei
Institution
Duke Cancer Institute
Position Title
Prof.
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-124
Initial CDAS Request Approval
Dec 22, 2014
Title
Genome-wide association analysis of genetic variants and prostate cancer risk and prognosis
Summary
The purpose of the study is to identify novel genetic factors and confirm putative genes associated with prostate cancer risk, clinical features and survival. Previous genome-wide association studies have revealed multiple genetic variants associated with prostate cancer risk. However, these GWAS-level significant single nucleotide polymorphisms (SNPs) explain only a small proportion of heritable prostate cancer risk. Multiple approaches for secondary analysis of GWAS datasets have been proposed and supposed to identify additional novel variant with moderate but measurable effects, thereby extend our knowledge about molecular mechanisms of prostate cancer etiology. We have requested the PLCO GWAS data from dbGaP. In this project, we will firstly perform single-locus analysis, epistasis analysis, gene-environment interaction analysis and set-based analysis to prioritize associated genes and pathways using the PLCO data and other GWAS data. Considering the detailed clinical information available in PLCO, we will also explore the associations between genetic variants, clinical features, treatment regimen and prognosis of prostate cancer. These data will also be used to follow-up the findings from other candidate pathway analysis.
Aims

1. Identify multiple genetic variants associated with prostate cancer risk, clinical features and prognosis in the PLCO data. We will also use the PLCO data to validate the findings from our study.
2. Identify genes and pathways associated with prostate cancer risk and clinical features by performing set-based analysis in the PLCO data.
3. Develop risk prediction and prognostic models for prostate cancer using genetic variables and clinical variables.

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