POCUS: mining genomic sequence annotation to predict disease genes

Abstract

Here we present POCUS (prioritization of candidate genes using statistics), a novel computational approach to prioritize candidate disease genes that is based on over-representation of functional annotation between loci for the same disease. We show that POCUS can provide high (up to 81-fold) enrichment of real disease genes in the candidate-gene shortlists it produces compared with the original large sets of positional candidates. In contrast to existing methods, POCUS can also suggest counterintuitive candidates.

Figure 1

POCUS results per locus for positive control sets of disease genes: the percentage of loci for each of three outcomes is plotted against locus size (100, 500, and 1,000 IDs) at two threshold scores (0.5 and 0.8). The outcome 'No genes exceed threshold' corresponds to the rate of false negatives, 'Only non-disease genes exceed threshold' corresponds to the rate of false positives, and 'Disease gene exceeds threshold' corresponds to the rate of true positives.