Authors:
Ekta Khurana, Yao Fu, Vincenza Colonna, Xinmeng Jasmine Mu, Hyun Min Kang, Tuuli Lappalainen, Andrea Sboner, Lucas Lochovsky, Jieming Chen, Arif Harmanci, Jishnu Das, Alexej Abyzov, Suganthi Balasubramanian, Kathryn Beal, Dimple Chakravarty, Daniel Challis, Yuan Chen, Declan Clarke, Laura Clarke, Fiona Cunningham, Uday S. Evani, Paul Flicek, Robert Fragoza, Erik Garrison, Richard Gibbs, Zeynep H. Gümüş, Javier Herrero, Naoki Kitabayashi, Yong Kong, Kasper Lage, Vaja Liluashvili, Steven M. Lipkin, Daniel G. MacArthur, Gabor Marth, Donna Muzny, Tune H. Pers, Graham R. S. Ritchie, Jeffrey A. Rosenfeld, Cristina Sisu, Xiaomu Wei1, Michael Wilson, Yali Xue, Fuli Yu, 1000 Genomes Project Consortium, Emmanouil T. Dermitzakis, Haiyuan Yu, Mark A. Rubin, Chris Tyler-Smith, & Mark Gerstein
Summary:
Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations (“ultrasensitive”) and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, “motif-breakers”). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.
Source:
Science; Vol. 342, No. 6154 (10/04/13)