Background: Genome-wide association studies (GWAS) require large sample
sizes to obtain adequate statistical power, but it may be possible to
increase the power by incorporating complementary data. In this study we
investigated the feasibility of automatically retrieving information
from the medical literature and leveraging this information in GWAS.
Methods: We developed a method that searches through PubMed abstracts
for pre-assigned keywords and key concepts, and uses this information to
assign prior probabilities of association for each single nucleotide
polymorphism (SNP) with the phenotype of interest - the Adjusting
Association Priors with Text (AdAPT) method. Association results from a
GWAS can subsequently be ranked in the context of these priors using the
Bayes False Discovery Probability (BFDP) framework. We initially tested
AdAPT by comparing rankings of known susceptibility alleles in a
previous lung cancer GWAS, and subsequently applied it in a two-phase
GWAS of oral cancer.
Results: Known lung cancer susceptibility SNPs were consistently ranked
higher by AdAPT BFDPs than by p-values. In the oral cancer GWAS, we
sought to replicate the top five SNPs as ranked by AdAPT BFDPs, of which
rs991316, located in the ADH gene region of 4q23, displayed a
statistically significant association with oral cancer risk in the
replication phase (per-rare-allele log additive p-value [p(trend)] =
2.5 x 10(-3)). The combined OR for having one additional rare allele was
0.83 (95% CI: 0.76-0.90), and this association was independent of
previously identified susceptibility SNPs that are associated with
overall UADT cancer in this gene region. We also investigated if
rs991316 was associated with other cancers of the upper aerodigestive
tract (UADT), but no additional association signal was found.
Conclusion: This study highlights the potential utility of
systematically incorporating prior knowledge from the medical literature
in genome-wide analyses using the AdAPT methodology. AdAPT is available
online (url: http://services.gate.ac.uk/lld/gwas/service/config)