Publication details

Using Prior Information from the Medical Literature in GWAS of Oral Cancer Identifies Novel Susceptibility Variant on Chromosome 4-the AdAPT Method

Authors	JOHANSSON Mattias ROBERTS Angus CHEN Dan LI Yaoyong DELAHAYE-SOURDEIX Manon ASWANI Niraj GREENWOOD Mark A. BENHAMOU Simone LAGIOU Pagona HOLCATOVA Ivana RICHIARDI Lorenzo KJAERHEIM Kristina AGUDO Antonio CASTELLSAGUE Xavier MACFARLANE Tatiana V. BARZAN Luigi CANOVA Cristina THAKKER Nalin S. CONWAY David I. ZNAOR Ariana HEALY Claire M. AHRENS Wolfgang ZARIDZE David SZESZENIA-DABROWSKA Neonilia LISSOWSKA Jolanta FABIANOVÁ Eleonora MATES Ioan Nicolae BENCKO Vladimír FORETOVÁ Lenka JANOUT Vladimír CURADO Maria Paula KOIFMAN Sergio MENEZES Ana WUENSCH-FILHO Victor ELUF-NETO Jose BOFFETTA Paolo FRANCESCHI Silvia HERRERO Rolando FERNANDEZ GARROTE Leticia TALAMINI Renato BOCCIA Stefania GALAN Pilar VATTEN Lars THOMSON Peter ZELENIKA Diana LATHROP Mark BYRNES Graham CUNNINGHAM Hamish BRENNAN Paul WAKEFIELD Jon MCKAY James D
Year of publication	2012
Type	Article in Periodical
Magazine / Source	Plos One
MU Faculty or unit	Faculty of Medicine
Citation
Doi	http://dx.doi.org/10.1371/journal.pone.0036888
Field	Oncology and hematology
Keywords	GENOME-WIDE ASSOCIATION; LUNG-CANCER; POOLED ANALYSIS; 15Q25; LOCUS; DISEASES; EUROPE; GENES; RISK
Description	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).