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SNP Prioritization Using a B ayesian Probability of Association
Author(s) -
Thompson John R.,
Gögele Martin,
Weichenberger Christian X.,
Modenese Mirko,
Attia John,
Barrett Jennifer H.,
Boehnke Michael,
De Grandi Alessandro,
Domingues Francisco S.,
Hicks Andrew A.,
Marroni Fabio,
Pattaro Cristian,
Ruggeri Fabrizio,
Borsani Giuseppe,
Casari Giorgio,
Parmigiani Giovanni,
Pastore Andrea,
Pfeufer Arne,
Schwienbacher Christine,
Taliun Daniel,
Consortium CKDGen,
Fox Caroline S.,
Pramstaller Peter P.,
Minelli Cosetta
Publication year - 2013
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21704
Subject(s) - snp , computer science , prioritization , selection (genetic algorithm) , single nucleotide polymorphism , association (psychology) , genetic association , statistical power , set (abstract data type) , genome wide association study , computational biology , data mining , genetics , biology , statistics , gene , mathematics , machine learning , genotype , philosophy , epistemology , programming language , management science , economics
Prioritization is the process whereby a set of possible candidate genes or SNP s 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 SNP s 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 B ayesian 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.