Using prior information from the medical literature in GWAS of oral cancer identifies novel susceptibility variant on chromosome 4--the AdAPT method.
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)] = 2.5×10(-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.


CONCLUSIONS
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).
Section of Genetics, International Agency for Research on Cancer (IARC), Lyon, France. johanssonm@iarc.fr
Mattias
M
Johansson
Angus
A
Roberts
Dan
D
Chen
Yaoyong
Y
Li
Manon
M
Delahaye-Sourdeix
Niraj
N
Aswani
Mark A
MA
Greenwood
Simone
S
Benhamou
Pagona
P
Lagiou
Ivana
I
Holcátová
Lorenzo
L
Richiardi
Kristina
K
Kjaerheim
Antonio
A
Agudo
Xavier
X
Castellsagué
Tatiana V
TV
Macfarlane
Luigi
L
Barzan
Cristina
C
Canova
Nalin S
NS
Thakker
David I
DI
Conway
Ariana
A
Znaor
Claire M
CM
Healy
Wolfgang
W
Ahrens
David
D
Zaridze
Neonilia
N
Szeszenia-Dabrowska
Jolanta
J
Lissowska
Eleonóra
E
Fabiánová
Ioan Nicolae
IN
Mates
Vladimir
V
Bencko
Lenka
L
Foretova
Vladimir
V
Janout
Maria Paula
MP
Curado
Sergio
S
Koifman
Ana
A
Menezes
Victor
V
Wünsch-Filho
Jose
J
Eluf-Neto
Paolo
P
Boffetta
Silvia
S
Franceschi
Rolando
R
Herrero
Leticia
L
Fernandez Garrote
Renato
R
Talamini
Stefania
S
Boccia
Pilar
P
Galan
Lars
L
Vatten
Peter
P
Thomson
Diana
D
Zelenika
Mark
M
Lathrop
Graham
G
Byrnes
Hamish
H
Cunningham
Paul
P
Brennan
Jon
J
Wakefield
James D
JD
McKay
D001499:Bayes Theorem; D002894:Chromosomes, Human, Pair 4; D019295:Computational Biology / Q000379:methods*; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D020407:Internet; D008175:Lung Neoplasms / Q000235:genetics; D009062:Mouth Neoplasms / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results