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Importance of Different Types of Prior Knowledge in Selecting Genome‐Wide Findings for Follow‐Up
Author(s) -
Minelli Cosetta,
De Grandi Alessandro,
Weichenberger Christian X.,
Gögele Martin,
Modenese Mirko,
Attia John,
Barrett Jennifer H.,
Boehnke Michael,
Borsani Giuseppe,
Casari Giorgio,
Fox Caroline S.,
Freina Thomas,
Hicks Andrew A.,
Marroni Fabio,
Parmigiani Giovanni,
Pastore Andrea,
Pattaro Cristian,
Pfeufer Arne,
Ruggeri Fabrizio,
Schwienbacher Christine,
Taliun Daniel,
Pramstaller Peter P.,
Domingues Francisco S.,
Thompson John R.
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.21705
Subject(s) - snp , single nucleotide polymorphism , weighting , genome wide association study , biology , genetic association , trait , computational biology , meta analysis , genetics , gene , computer science , genotype , medicine , radiology , programming language
Biological plausibility and other prior information could help select genome‐wide association ( GWA ) findings for further follow‐up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts’ opinions and empirical evidence to estimate the relative importance of 15 types of information at the single‐nucleotide polymorphism ( SNP ) and gene levels. Opinions were elicited from 10 experts using a two‐round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNP s established as being associated with seven disease traits through GWA meta‐analysis and independent replication, with the corresponding frequency in a randomly selected set of SNP s. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta‐analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research.