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bioRxiv · 10.1101/819409

Computational Assessment of the Regulation-Modulating Potential for Noncoding Variants

Shi, F.-Y.·Wang, Y.·Huang, D.·Liang, Y.·Liang, N.·Chen, X.-W.·Gao, G.
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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 CARMENs 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.

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Shi, F.-Y., Wang, Y., Huang, D., Liang, Y., Liang, N., Chen, X.-W., Gao, G.. 2019-10-28. Computational Assessment of the Regulation-Modulating Potential for Noncoding Variants. https://doi.org/10.1101/819409

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