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Title: Using Prior Information from the Medical Literature in GWAS of Oral Cancer Identifies Novel Susceptibility Variant on Chromosome 4 - the AdAPT Method
Author: Johansson, Mattias
Roberts, Angus
Chen, Dan
Li, Yaoyong
Delahaye-Sourdeix, Manon
Aswani, Niraj
Greenwood, Mark A.
Benhamou, Simone
Lagiou, Pagona
Holcátová, Ivana
Richiardi, Lorenzo
Kjaerheim, Kristina
Agudo, Antonio
Castellsagué, 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
Fabianova, Eleonora
Mates, Ioan Nicolae
Bencko, Vladimir
Foretova, Lenka
Janout, Vladimir
Curado, Maria Paula
Koifman, Sergio
Menezes, Ana
Wünsch-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.
Keywords: Genòmica
Càncer de pulmó
Càncer de pulmó
Lung cancer
Issue Date: 25-May-2012
Publisher: Public Library of Science (PLoS)
Abstract: 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:
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It is part of: PLoS One, 2012, vol. 7, num. 5, p. e36888
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Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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