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3′ untranslated region (3′UTR) variants are strongly associated with human traits and diseases, yet few have been causally identified. We developed the massively parallel reporter assay for 3′UTRs (MPRAu) to sensitively assay 12,173 3′UTR variants. We applied MPRAu to six human cell lines, focusing on genetic variants associated with genome-wide association studies (GWAS) and human evolutionary adaptation. MPRAu expands our understanding of 3′UTR function, suggesting that simple sequences predominately explain 3′UTR regulatory activity. We adapt MPRAu to uncover diverse molecular mechanisms at base pair resolution, including an adenylate-uridylate (AU)-rich element of LEPR linked to potential metabolic evolutionary adaptations in East Asians. We nominate hundreds of 3′UTR causal variants with genetically fine-mapped phenotype associations. Using endogenous allelic replacements, we characterize one variant that disrupts a miRNA site regulating the viral defense gene TRIM14 and one that alters PILRB abundance, nominating a causal variant underlying transcriptional changes in age-related macular degeneration. [Display omitted] • Assayed thousands of GWAS and adaptation associated 3′UTR variants in 6 cell lines • Nominated hundreds of causal GWAS variants with functional evidence of activity • Characterized mechanistic regulatory motifs at base pair resolution • Used allelic replacement on causal variants for macular degeneration and viral defense Massively parallel reporter assay for 3′UTRs measures individual regulatory effects of over 12,000 3′UTR variants associated with human disease and evolutionary selection in many cell types, nominating functional genetic variation. [ABSTRACT FROM AUTHOR]

Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over 3 million APA reporters. APARENT's predictions are highly accurate when tasked with inferring APA in synthetic and human 3′UTRs. Visualizing features learned across all network layers reveals that APARENT recognizes sequence motifs known to recruit APA regulators, discovers previously unknown sequence determinants of 3′ end processing, and integrates these features into a comprehensive, interpretable, cis -regulatory code. We apply APARENT to forward engineer functional polyadenylation signals with precisely defined cleavage position and isoform usage and validate predictions experimentally. Finally, we use APARENT to quantify the impact of genetic variants on APA. Our approach detects pathogenic variants in a wide range of disease contexts, expanding our understanding of the genetic origins of disease. • Trained a neural network to predict APA using data from over 3 million reporters • Visualized learned features to reveal a rich cis -regulatory code for APA • Developed and tested an algorithm to accurately engineer polyadenylation signals • Predicted and experimentally characterized over 12,000 human APA variants A deep neural network enables precise engineering of polyadenylation signals, identifies human genetic variants that act through mis-regulating APA, and learns a comprehensive model of the cis -regulatory APA code. [ABSTRACT FROM AUTHOR]