TY - JOUR
T1 - Using prior information from the medical literature in GWAS of oral cancer identifies novel susceptibility variant on chromosome 4--the AdAPT method
AU - Johansson, Mattias
AU - Roberts, Angus
AU - Chen, Dan
AU - Li, Yaoyong
AU - Delahaye-Sourdeix, Manon
AU - Aswani, Niraj
AU - Greenwood, Mark A.
AU - Benhamou, Simone
AU - Lagiou, Pagona
AU - Holcátová, Ivana
AU - Richiardi, Lorenzo
AU - Kjaerheim, Kristina
AU - Agudo, Antonio
AU - Castellsagué, Xavier
AU - Macfarlane, Tatiana V.
AU - Barzan, Luigi
AU - Canova, Cristina
AU - Thakker, Nalin S.
AU - Conway, David I.
AU - Znaor, Ariana
AU - Healy, Claire M.
AU - Ahrens, Wolfgang
AU - Zaridze, David
AU - Szeszenia-Dabrowska, Neonilia
AU - Lissowska, Jolanta
AU - Fabiánová, Eleonóra
AU - Mates, Ioan Nicolae
AU - Bencko, Vladimir
AU - Foretova, Lenka
AU - Janout, Vladimir
AU - Curado, Maria Paula
AU - Koifman, Sergio
AU - Menezes, Ana
AU - Wünsch-Filho, Victor
AU - Eluf-Neto, Jose
AU - Boffetta, Paolo
AU - Franceschi, Silvia
AU - Herrero, Rolando
AU - Garrote, Leticia Fernandez
AU - Talamini, Renato
AU - Boccia, Stefania
AU - Galan, Pilar
AU - Vatten, Lars
AU - Thomson, Peter
AU - Zelenika, Diana
AU - Lathrop, Mark
AU - Byrnes, Graham
AU - Cunningham, Hamish
AU - Brennan, Paul
AU - Wakefield, Jon
AU - Mckay, James D.
PY - 2012
Y1 - 2012
N2 - 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).
AB - 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).
KW - Bayes Theorem
KW - Chromosomes, Human, Pair 4
KW - Computational Biology
KW - Genetic Predisposition to Disease
KW - Genome-Wide Association Study
KW - Humans
KW - Lung Neoplasms
KW - Mouth Neoplasms
KW - POOLED ANALYSIS
KW - Polymorphism, Single Nucleotide
KW - Reproducibility of Results
KW - Bayes Theorem
KW - Chromosomes, Human, Pair 4
KW - Computational Biology
KW - Genetic Predisposition to Disease
KW - Genome-Wide Association Study
KW - Humans
KW - Lung Neoplasms
KW - Mouth Neoplasms
KW - POOLED ANALYSIS
KW - Polymorphism, Single Nucleotide
KW - Reproducibility of Results
UR - http://hdl.handle.net/10807/39954
U2 - 10.1371/journal.pone.0036888
DO - 10.1371/journal.pone.0036888
M3 - Article
SN - 1932-6203
VL - 7
SP - e36888-e36888
JO - PLoS One
JF - PLoS One
ER -