Computational Assessment of the Regulation-Modulating Potential for Noncoding Variants

Abstract

Large-scale genome-wide association and expression quantitative trait loci studies have identified multiple noncoding variants associated with genetic diseases via affecting gene expression. However, effectively and efficiently pinpointing causal variants remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional noncoding expression-modulating variants. Multiple evaluations demonstrated CARMEN’s superior performance over state-of-the-art tools. Its higher sensitivity and low false discovery rate enable CARMEN to identify multiple causal expression-modulating variants that other tools simply missed. Meanwhile, benefitting from extensive annotations generated, CARMEN provides mechanism hints on predicted expression-modulating variants, enabling effectively characterizing functional variants involved in gene expression and disease-related phenotypes. CARMEN scales well with the massive datasets and is available online as a Web server at http://carmen.gao-lab.org.