Importance of Different Types of Prior Knowledge in Selecting Genome‐Wide Findings for Follow‐Up

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

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.