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Deciphering the Impact of Genetic Variation on Human Polyadenylation

View ORCID ProfileJohannes Linder, View ORCID ProfileAnshul Kundaje, View ORCID ProfileGeorg Seelig
doi: https://doi.org/10.1101/2022.05.09.491198
Johannes Linder
bDepartment of Genetics, Stanford University
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  • For correspondence: jlinder2@stanford.edu
Anshul Kundaje
bDepartment of Genetics, Stanford University
cDepartment of Computer Science, Stanford University
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Georg Seelig
dPaul G. Allen School of Computer Science and Engineering, University of Washington
eDepartment of Electrical and Computer Engineering, University of Washington
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  • ORCID record for Georg Seelig
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Abstract

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. 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, using APARENT2’s predictions we detect an association between gain-of-function mutations in the 3’-end and Autism Spectrum Disorder.

Competing Interest Statement

A.K. is a scientific co-founder of Ravel Biotechnology Inc., is on the SAB of PatchBio Inc., SerImmune Inc., AINovo Inc., TensorBio Inc. and OpenTargets, is a consultant with Illumina Inc. and owns shares in DeepGenomics Inc., Immuni Inc. and Freenome Inc. G.S. is a co-founder of Parse Biosciences and is on the SAB of Modulus Therapeutics.

Footnotes

  • ↵a Web tool: https://apa.cs.washington.edu; Github: https://github.com/johli/aparent-resnet

  • https://github.com/johli/aparent-resnet

  • https://apa.cs.washington.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 10, 2022.
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Deciphering the Impact of Genetic Variation on Human Polyadenylation
Johannes Linder, Anshul Kundaje, Georg Seelig
bioRxiv 2022.05.09.491198; doi: https://doi.org/10.1101/2022.05.09.491198
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Deciphering the Impact of Genetic Variation on Human Polyadenylation
Johannes Linder, Anshul Kundaje, Georg Seelig
bioRxiv 2022.05.09.491198; doi: https://doi.org/10.1101/2022.05.09.491198

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