Systematic Search for Gene-Gene Interaction Effect on Prostate Cancer Risk

Abstract

Prostate cancer (PCa) is the most common cancer and the second leading cause of cancer death among men in the US. PCa development requires the coordination of many genes and it is expected that simultaneous evaluation of multiple genetic variants can improve the statistical power to detect additional PCa risk variants. We hypothesized that multiple sequence variants in the genome may interact to increase PCa risk. These variants may or may not have known main effect on PCa risk and can be better detected by systematically evaluating gene-gene interactions for SNPs in the genome. We utilized data from the CGEMS study to systematically discover gene-gene interactions in the genome. We also evaluated the gene-gene interactions in two additional independent populations, a population based PCa case-control study from Sweden and a PCa patient population from Johns Hopkins Hospital. We identified 35 pairs of SNPs that significantly interact with the 32 known risk SNPs on PCa risk at a P-value of 1E-05 in the combined analysis of three populations. The most significant interaction detected was between rs12418451 in MYEOV and rs784411 in CEP152, with a P(sub interaction) of 1.15E-07 in the metaanalysis. In addition, we emphasized two pairs of interactions with potential biological implication, including an interaction between rs7127900 near IGF2/IGF2AS and rs12628051 in TNRC6B, with a P interaction of 3.39E-06; and an interaction between rs7679763 near TET2 and rs290258 in SYK, with a P interaction of 1.49E-06. Those results show statistical evidence for novel loci interacting with known risk-associated SNPs to modify PCa risk. The interacting loci identified provide hints on the underlying molecular mechanism of the associations with PCa risk for the known risk-associated SNPs.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2012
Accession Number
ADA564269

Entities

People

  • Jianfeng Xu
  • Jielin Sun
  • Siqun L. Zheng

Organizations

  • Wake Forest University

Tags

DTIC Thesaurus Topics

  • Biomedical Research
  • Cancer
  • Cells
  • Computer Programming
  • Computer Programs
  • Data Analysis
  • Department Of Defense
  • Diseases And Disorders
  • Genes
  • Genetics
  • Genome
  • Health Services
  • Neoplasms
  • Prostate
  • Prostate Cancer
  • Statistical Analysis
  • Test And Evaluation

Fields of Study

  • Biology

Readers

  • Molecular and genetic basis of cancer.
  • Prostate Cancer Biology.

Technology Areas

  • Biotechnology