0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessUpstream open reading frames (uORFs) are tissue-specific cis-regulators of protein translation. Isolated reports have shown that variants that create or disrupt uORFs can cause disease. Here, in a systematic genome-wide study using 15,708 whole genome sequences, we show that variants that create new upstream start codons, and variants disrupting stop sites of existing uORFs, are under strong negative selection. This selection signal is significantly stronger for variants arising upstream of genes intolerant to loss-of-function variants. Furthermore, variants creating uORFs that overlap the coding sequence show signals of selection equivalent to coding missense variants. Finally, we identify specific genes where modification of uORFs likely represents an important disease mechanism, and report a novel uORF frameshift variant upstream of NF2 in neurofibromatosis. Our results highlight uORF-perturbing variants as an under-recognised functional class that contribute to penetrant human disease, and demonstrate the power of large-scale population sequencing data in studying non-coding variant classes.
Nicola Whiffin, Konrad J. Karczewski, Xiaolei Zhang, Sonia Chothani, Miriam J. Smith, D. Gareth Evans, Angharad M. Roberts, Nicholas M. Quaife, Sebastian Schäfer, Owen J. L. Rackham, Jessica Alföldi, Anne O’Donnell‐Luria, Laurent C. Francioli, Irina M. Armean, Eric Banks, Louis Bergelson, Kristian Cibulskis, Ryan L. Collins, Kristen M. Connolly, Miguel Covarrubias, Beryl B. Cummings, Mark J. Daly, Stacey Donnelly, Yossi Farjoun, Steven Ferriera, Stacey Gabriel, Laura D. Gauthier, Jeff Gentry, Namrata Gupta, Thibault Jeandet, Diane Kaplan, Kristen M. Laricchia, Christopher Llanwarne, Eric Vallabh Minikel, Ruchi Munshi, Benjamin M. Neale, Sam Novod, Nikelle Petrillo, Timothy Poterba, David Roazen, Valentín Ruano-Rubio, Andrea Saltzman, Kaitlin E. Samocha, Molly Schleicher, Cotton Seed, Matthew Solomonson, José Soto, Grace Tiao, Kathleen Tibbetts, Charlotte Tolonen, Christopher Vittal, Gordon Wade, Arcturus Wang, Qingbo S. Wang, Nicholas A. Watts, Ben Weisburd, Carlos A. Aguilar‐Salinas, Tariq Ahmad, Christine M. Albert, Diego Ardissino, Gil Atzmon, John Barnard, Laurent Beaugerie, Emelia Benjamin, Michael Boehnke, Lori L. Bonnycastle, Erwin P. Böttinger, Donald W. Bowden, Matthew J. Bown, John C. Chambers, Juliana C.N. Chan, Daniel I. Chasman, Judy H. Cho, Mina K. Chung, Bruce M. Cohen, Adolfo Correa, Dana Dabelea, Mark J. Daly, Dawood Darbar, Ravindranath Duggirala, Josée Dupuis, Patrick T. Ellinor, Roberto Elosúa, Jeanette Erdmann, Tõnu Esko, Martti Färkkilâ, José C. Florez, André Franke, Gad Getz, Benjamin Gläser, Stephen J. Glatt, David Goldstein, Clicerio González, Leif Groop, Christopher Haiman, Craig L. Hanis, Matthew B. Harms, Mikko Hiltunen, Matti Holi (2020). Characterising the loss-of-function impact of 5’ untranslated region variants in 15,708 individuals. , 11(1), DOI: https://doi.org/10.1038/s41467-019-10717-9.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2020
Authors
99
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1038/s41467-019-10717-9
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access
