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Thompson, J.R.* ; Gögele, M.* ; Weichenberger, C.X.* ; Modenese, M.* ; Attia, J.* ; Barrett, J.H.* ; Boehnke, M.* ; de Grandi, A.* ; Domingues, F.S.* ; Hicks, A.A.* ; Marroni, F.* ; Pattaro, C.* ; Ruggeri, F.* ; Borsani, G.* ; Casari, G.* ; Parmigiani, G.* ; Pastore, A.* ; Pfeufer, A. ; Schwienbacher, C.* ; Taliun, D.* ; CKDGen Consortium (*) ; Fox, C.S.* ; Pramstaller, P.P.* ; Minelli, C.*

SNP prioritization using a Bayesian probability of association.

Genet. Epidemiol. 37, 214-221 (2013)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Replication ; Prior Knowledge ; Genome-wide Studies; Genome-wide Association ; Chronic Kidney-disease ; Statistical-methods ; Positive Report ; False ; Epidemiology ; Discovery
Sprache englisch
Veröffentlichungsjahr 2013
HGF-Berichtsjahr 2013
ISSN (print) / ISBN 0741-0395
e-ISSN 1098-2272
Zeitschrift Genetic Epidemiology
Quellenangaben Band: 37, Heft: 2, Seiten: 214-221 Artikelnummer: , Supplement: ,
Verlag Wiley
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)
POF Topic(s) 30505 - New Technologies for Biomedical Discoveries
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503700-001
PubMed ID 23280596
DOI 10.1002/gepi.21704
WOS ID WOS:000313784000010
Scopus ID 84872395349
Erfassungsdatum 2013-02-14
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