SNP Prioritization Using a Bayesian Probability of Association

John R. Thompson, Martin Gögele, Christian X. Weichenberger, Mirko Modenese, John Attia, Jennifer H. Barrett, Michael Boehnke, Alessandro De Grandi, Francisco S. Domingues, Andrew A. Hicks, Fabio Marroni, Cristian Pattaro, Fabrizio Ruggeri, Giuseppe Borsani, Giorgio Casari, Giovanni Parmigiani, Andrea Pastore, Arne Pfeufer, Christine Schwienbacher, Daniel TaliunCkdgen Consortium, Caroline S. Fox, Peter P. Pramstaller, Cosetta Minelli

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)214-221
Number of pages8
JournalGenetic Epidemiology
Issue number2
StatePublished - Feb 2013
Externally publishedYes


  • Genome-wide studies
  • Prior knowledge
  • Replication

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology


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