Deciphering the Impact of Genetic Variation on Human Polyadenylation using APARENT2

Genetic variants that disrupt polyadenylation can cause or contribute to genetic disorders. Yet, due to the complex cis-regulation of polyadenylation, variant interpretation remains challenging. Here, we introduce a residual neural network model, APARENT2, that can infer 3'-cleavage and polyadenylation from DNA sequence more accurately than any previous model. This model generalizes to the case of alternative polyadenylation (APA) for a variable number of polyadenylation signals. We demonstrate APARENT2's performance on several variant datasets, including functional reporter data and human 3' aQTLs from GTEx. We apply neural network interpretation methods to gain insights into disrupted or protective higher-order features of polyadenylation. We fine-tune APARENT2 on human tissue-resolved transcriptomic data to elucidate tissue-specific variant effects. By combining APARENT2 with models of mRNA stability, we extend aQTL effect size predictions to the entire 3' untranslated region. Finally, we perform in-silico saturation mutagenesis of all human polyadenylation signals and compare the predicted effects of >44 million variants against gnomAD. While loss-of-function variants were generally selected against, we also find specific clinical conditions linked to gain-of-function mutations. For example, we detect an association between gain-of-function mutations in the 3'-end and Autism Spectrum Disorder. To experimentally validate APARENT2's predictions, we assayed clinically relevant variants in multiple cell lines, including microglia-derived cells. References and variant APA libraries were cloned into a reporter. APA profiling data was obtained from RNA-seq of 3 different cell lines (HEK293T, SK-N-SH, and HMC3) with 2 replicates of each library. Polyadenylation cleavage position was determined for mapped reads, and then UMIs were collapsed to determine measured variant log odds ratio of cleavage. Overall design: To experimentally validate APARENT2 predictions, we measured the impact of 100 clinically relevant variants (including Autism case variants) in a plasmid reporter MPRA in HEK293T, SK-N-SH and HMC3 cell lines.