Functional annotation of noncoding sequence variants

Abstract

Identifying functionally relevant variants against the background of ubiquitous genetic variation is a major challenge in human genetics. For variants in protein-coding regions, our understanding of the genetic code and splicing allows us to identify likely candidates, but interpreting variants outside genic regions is more difficult. Here we present genome-wide annotation of variants (GWAVA), a tool that supports prioritization of noncoding variants by integrating various genomic and epigenomic annotations.

Figure 1

Mean receiver operating characteristic (ROC) curves for 10 fold cross-validation experiments on each of the three training sets. The area under the curve (AUC) statistics illustrate that all classifiers are able to discriminate between the disease variants and controls, though performance depends on how well the variants in the training sets are matched.

Figure 2

Classifier scores for recurrent (n = 812) vs. non-recurrent (n = 185,435) non-coding somatic mutations from COSMIC. The AUC for discriminating between these two classes of mutation is 0.67. The P-value is calculated using a two-sided Mann-Whitney U test. The whiskers include scores within 1.5 × IQR of the upper and lower quartiles (the default in the R package).