title: SNP prioritization using a Bayesian probability of association.
abstract: Prioritization is the process whereby a set of possible candidate genes or SNPs is ranked so that the most promising can be taken forward into further studies. In a genome-wide association study, prioritization is usually based on the P-values alone, but researchers sometimes take account of external annotation information about the SNPs such as whether the SNP lies close to a good candidate gene. Using external information in this way is inherently subjective and is often not formalized, making the analysis difficult to reproduce. Building on previous work that has identified 14 important types of external information, we present an approximate Bayesian analysis that produces an estimate of the probability of association. The calculation combines four sources of information: the genome-wide data, SNP information derived from bioinformatics databases, empirical SNP weights, and the researchers' subjective prior opinions. The calculation is fast enough that it can be applied to millions of SNPS and although it does rely on subjective judgments, those judgments are made explicit so that the final SNP selection can be reproduced. We show that the resulting probability of association is intuitively more appealing than the P-value because it is easier to interpret and it makes allowance for the power of the study. We illustrate the use of the probability of association for SNP prioritization by applying it to a meta-analysis of kidney function genome-wide association studies and demonstrate that SNP selection performs better using the probability of association compared with P-values alone.
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, United Kingdom. john.thompson@le.ac.uk
authors.forename: John R
authors.initials: JR
authors.lastname: Thompson
authors.forename: Martin
authors.initials: M
authors.lastname: Gögele
authors.forename: Christian X
authors.initials: CX
authors.lastname: Weichenberger
authors.forename: Mirko
authors.initials: M
authors.lastname: Modenese
authors.forename: John
authors.initials: J
authors.lastname: Attia
authors.forename: Jennifer H
authors.initials: JH
authors.lastname: Barrett
authors.forename: Michael
authors.initials: M
authors.lastname: Boehnke
authors.forename: Alessandro
authors.initials: A
authors.lastname: De Grandi
authors.forename: Francisco S
authors.initials: FS
authors.lastname: Domingues
authors.forename: Andrew A
authors.initials: AA
authors.lastname: Hicks
authors.forename: Fabio
authors.initials: F
authors.lastname: Marroni
authors.forename: Cristian
authors.initials: C
authors.lastname: Pattaro
authors.forename: Fabrizio
authors.initials: F
authors.lastname: Ruggeri
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Borsani
authors.forename: Giorgio
authors.initials: G
authors.lastname: Casari
authors.forename: Giovanni
authors.initials: G
authors.lastname: Parmigiani
authors.forename: Andrea
authors.initials: A
authors.lastname: Pastore
authors.forename: Arne
authors.initials: A
authors.lastname: Pfeufer
authors.forename: Christine
authors.initials: C
authors.lastname: Schwienbacher
authors.forename: Daniel
authors.initials: D
authors.lastname: Taliun
authors.forename: Caroline S
authors.initials: CS
authors.lastname: Fox
authors.forename: Peter P
authors.initials: PP
authors.lastname: Pramstaller
authors.forename: Cosetta
authors.initials: C
authors.lastname: Minelli
mesh_terms: D001499:Bayes Theorem; D030541:Databases, Genetic; D055106:Genome-Wide Association Study; D006801:Humans; D007668:Kidney / Q000502:physiology; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011336:Probability