Nature CommunicationsGenomicsPeer-reviewed journal article
Characterising the loss-of-function impact of 5’ untranslated region variants in 15,708 individuals
Nicola WhiffiniD, Konrad J. KarczewskiiD, Xiaolei ZhangiD, Sonia Pankaj ChothaniiD, Miriam Jane SmithiD, D. Gareth EvansiD, Angharad M. RobertsiD, Nicholas M. QuaifeiD, Sebastian SchäferiD, Owen J. L. RackhamiD, Jessica AlföldiiD, Anne O’Donnell‐LuriaiD, Laurent C. FrancioliiD, Genome Aggregation Database Production Team, Irina M. ArmeaniD, Eric BanksiD, Louis Bergelson, Kristian CibulskisiD, Ryan L. CollinsiD, Kristen M. Connolly, Miguel Covarrubias, Beryl B. CummingsiD, Mark J. DalyiD, Stacey DonnellyiD, Yossi FarjouniD, , Clicerio GonzáleziD
Abstract
Upstream open reading frames (uORFs) are important tissue-specific cis-regulators of protein translation. Although isolated case reports have shown that variants that create or disrupt uORFs can cause disease, genetic sequencing approaches typically focus on protein-coding regions and ignore these variants. Here, we describe a systematic genome-wide study of variants that create and disrupt human uORFs, and explore their role in human disease using 15,708 whole genome sequences collected by the Genome Aggregation Database (gnomAD) project. We show that 14,897 variants that create new start codons upstream of the canonical coding sequence (CDS), and 2,406 variants disrupting the stop site of existing uORFs, are under strong negative selection. Furthermore, variants creating uORFs that overlap the CDS show signals of selection equivalent to coding loss-of-function variants, and uORF-perturbing variants are under strong selection when arising upstream of known disease genes and genes intolerant to loss-of-function variants. Finally, we identify specific genes where perturbation of uORFs is likely to represent an important disease mechanism, and report a novel uORF frameshift variant upstream of NF2 in families with neurofibromatosis. Our results highlight uORF-perturbing variants as an important and under-recognised functional class that can contribute to penetrant human disease, and demonstrate the power of large-scale population sequencing data to study the deleteriousness of specific classes of non-coding variants.
- organism: human
- assay: whole-genome sequencing
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 180
- FWCI
- 14.63
- Field percentile
- top 1%
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