Ranking of non-coding pathogenic variants and putative essential regions of the human genome

Alex Wells, David Heckerman, Ali Torkamani, Li Yin, Jonathan Sebat, Bing Ren, Amalio Telenti () and Julia Iulio ()
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Alex Wells: Stanford University
David Heckerman: University of California Los Angeles
Ali Torkamani: Scripps Research Translational Institute
Li Yin: Scripps Research Translational Institute
Jonathan Sebat: University of California San Diego
Bing Ren: Ludwig Institute for Cancer Research
Amalio Telenti: Scripps Research Translational Institute
Julia Iulio: Scripps Research Translational Institute

Nature Communications, 2019, vol. 10, issue 1, 1-9

Abstract: Abstract A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. Here we train a machine learning model using functional, mutational and structural features, including new genome essentiality metrics, 3D genome organization and enhancer reporter data to identify deleterious variants in non-coding regions. We assess the model for functional correlates by using data from tiling-deletion-based and CRISPR interference screens of activity of cis-regulatory elements in over 3 Mb of genome sequence. Finally, we explore two user cases that involve indels and the disruption of enhancers associated with a developmental disease. We rank variants in the non-coding genome according to their predicted deleteriousness. The model prioritizes non-coding regions associated with regulation of important genes and with cell viability, an in vitro surrogate of essentiality.

Date: 2019
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DOI: 10.1038/s41467-019-13212-3

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