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Deciphering the impact of genetic variation on human polyadenylation using APARENT2

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Linder, Johannes, Koplik, Samantha E., Kundaje, Anshul, Seelig, Georg·2022·Genome Biology
DOI 10.1186/s13059-022-02799-4PMC PMC9636789
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Abstract

Background: 3′-end processing by cleavage and polyadenylation is an important and finely tuned regulatory process during mRNA maturation. Numerous genetic variants are known to cause or contribute to human disorders by disrupting the cis-regulatory code of polyadenylation signals. Yet, due to the complexity of this code, variant interpretation remains challenging. Results: 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$${>}43$$>43million 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. Conclusions: A sequence-to-function model based on deep residual learning enables accurate functional interpretation of genetic variants in polyadenylation signals and, when coupled with large human variation databases, elucidates the link between functional 3′-end mutations and human health.

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  • Sequence-based models of post-transcriptional regulation rely on genome annotations and transformed measurements extracted from RNA-seq to isolate each regulatory mechanism.

    “Similarly, sequence-based models of post-transcriptional regulation rely on genome annotations and transformed measurements extracted from RNA-seq to isolate each regulatory mechanism (for example, percent spliced-in for splicing)14–23.”

    From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [22]

    The Methods sections on endogenous datasets and tissue-specific modeling describe mapping RNA-seq reads to annotated polyadenylation sites and normalizing read counts into isoform proportions. The paper thus supports this approach for modeling polyadenylation, but its APARENT2 training data also come from a massively parallel reporter assay; it does not establish that sequence-based models of post-transcriptional regulation generally rely on genome annotations and RNA-seq transformations to isolate each mechanism. No page numbers are available in our copy. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Borzoi was consistently more accurate than APARENT2 at classifying paQTLs from matched negatives.

    “Compared to variant scores predicted by the APARENT2 model22, Borzoi was consistently more accurate (Fig. 6f).”

    From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [22]

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Cited by

Sorted by citations

Authors
Linder, J., Koplik, S. E., Kundaje, A., & Seelig, G.
Affiliations
  • Stanford University (US)
  • University of Washington (US)
Corresponding author
Linder, Johannes
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2022-11-05
Volume
23
Issue
1
Pages
232-232
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-022-02799-4
PMC
PMC9636789
OpenAlex
W4308292989
Cited by
49
References
8
Citations by year
2026: 8 · 2025: 18 · 2024: 18 · 2023: 4 · 2021: 1
FWCI
3.25
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health (U01HG012069, 5R21HG010945)
  • National Science Foundation (EF 2021552, DGE-2140004)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords
polyadenylation, 3' end processing, alternative polyadenylation, polyadenylation signal, variant interpretation, ResNet, 3' untranslated region, mRNA stability, gain-of-function mutations, autism spectrum disorder, microglia, saturation mutagenesis, gnomAD
MeSH terms
Autism Spectrum Disorder, Humans, Genetic Variation, 3' Untranslated Regions, RNA Stability, Polyadenylation, Transcriptome

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