title: Importance of different types of prior knowledge in selecting genome-wide findings for follow-up.
abstract: 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 SNPs 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 SNPs. 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.
authors: Center for Biomedicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy. cosetta.minelli@eurac.edu
authors: Cosetta
authors: C
authors: Minelli
authors: Alessandro
authors: A
authors: De Grandi
authors: Christian X
authors: CX
authors: Weichenberger
authors: Martin
authors: M
authors: Gögele
authors: Mirko
authors: M
authors: Modenese
authors: John
authors: J
authors: Attia
authors: Jennifer H
authors: JH
authors: Barrett
authors: Michael
authors: M
authors: Boehnke
authors: Giuseppe
authors: G
authors: Borsani
authors: Giorgio
authors: G
authors: Casari
authors: Caroline S
authors: CS
authors: Fox
authors: Thomas
authors: T
authors: Freina
authors: Andrew A
authors: AA
authors: Hicks
authors: Fabio
authors: F
authors: Marroni
authors: Giovanni
authors: G
authors: Parmigiani
authors: Andrea
authors: A
authors: Pastore
authors: Cristian
authors: C
authors: Pattaro
authors: Arne
authors: A
authors: Pfeufer
authors: Fabrizio
authors: F
authors: Ruggeri
authors: Christine
authors: C
authors: Schwienbacher
authors: Daniel
authors: D
authors: Taliun
authors: Peter P
authors: PP
authors: Pramstaller
authors: Francisco S
authors: FS
authors: Domingues
authors: John R
authors: JR
authors: Thompson
mesh_terms: D019295:Computational Biology / Q000379:methods; D005500:Follow-Up Studies; D036281:Genetic Research; D055106:Genome-Wide Association Study; D006801:Humans; D015201:Meta-Analysis as Topic; D020641:Polymorphism, Single Nucleotide; D011336:Probability