bioRxiv Science⌕ Search

Biology subjects

Romo, L.

Publications and source records attributed to Romo, L..

2 recordsLinked to original sources

Regulatory features aid interpretation of 3'UTR variants

Our ability to determine the clinical impact of variants in 3 untranslated regions (UTRs) of genes remains poor. We provide a thorough analysis of 3UTR variants from several datasets. Variants in putative regulatory elements including RNA-binding protein motifs, eCLIP peaks, and microRNA sites are up to 16 times more likely than other variants to have gene expression and phenotype associations. Heterozygous variants in regulatory motifs result in allele-specific protein binding in cell lines and allele-specific gene expression differences in population studies. In addition, variants in shared regions of alternatively polyadenylated isoforms and those proximal to polyA sites are more likely to affect gene expression and phenotype. Finally, pathogenic 3UTR variants in ClinVar are 20 times more likely than benign variants to fall in a regulatory site. We incorporated these findings into RegVar, a software tool that interprets regulatory elements and annotations for any 3UTR variant, and predicts whether the variant is likely to affect gene expression or phenotype. This tool will help prioritize variants for experimental studies and identify pathogenic variants in patients.

genomics↗

Quantifying negative selection in human 3' UTRs uncovers constrained targets of RNA-binding proteins

Many non-coding variants associated with phenotypes occur in 3 untranslated regions (3 UTRs) and may affect interactions with RNA-binding proteins (RBPs) to regulate post-transcriptional gene expression. However, identifying functional 3 UTR variants has proven difficult. We used allele frequencies from the Genome Aggregation Database (gnomAD) to identify classes of 3 UTR variants under strong negative selection in humans. We developed intergenic mutability-adjusted proportion singleton (iMAPS), a generalized measure related to MAPS, to quantify negative selection in non-coding regions. This approach, in conjunction with in vitro and in vivo binding data, identifies precise RBP binding sites, miRNA target sites, and polyadenylation signals (PASs) under strong selection. For each class of sites, we identified thousands of gnomAD variants under selection comparable to missense coding variants, and found that sites in core 3 UTR regions upstream of the most-used PAS are under strongest selection. Together, this work improves our understanding of selection on human genes and validates approaches for interpreting genetic variants in human 3 UTRs.

evolutionary biology↗