Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies.

ABSTRACT:

Background

Bladder cancer is common disease with a complex etiology that is likely due to many different genetic and environmental factors. The goal of this study was to embrace this complexity using a bioinformatics analysis pipeline designed to use machine learning to measure synergistic interactions between single nucleotide polymorphisms (SNPs) in two genome-wide association studies (GWAS) and then to assess their enrichment within functional groups defined by Gene Ontology. The significance of the results was evaluated using permutation testing and those results that replicated between the two GWAS data sets were reported.

Results

In the first step of our bioinformatics pipeline, we estimated the pairwise synergistic effects of SNPs on bladder cancer risk in both GWAS d

SUBMITTER: Cheng S 

PROVIDER: S-EPMC5154053 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature