Abstract

Little is known about co-transcriptional or post-transcriptional regulatory mechanisms linking noncoding variation to variation in organismal traits. To begin addressing this gap, we used 3' Seq to study the impact of genetic variation on alternative polyadenylation (APA) in the nuclear and total mRNA fractions of 52 HapMap Yoruba human lymphoblastoid cell lines. We mapped 602 APA quantitative trait loci (apaQTLs) at 10% FDR, of which 152 were nuclear specific. Effect sizes at intronic apaQTLs are negatively correlated with eQTL effect sizes. These observations suggest genetic variants can decrease mRNA expression levels by increasing usage of intronic PAS. We also identified 24 apaQTLs associated with protein levels, but not mRNA expression. Finally, we found that 19% of apaQTLs can be associated with disease. Thus, our work demonstrates that APA links genetic variation to variation in gene expression, protein expression, and disease risk, and reveals uncharted modes of genetic regulation.

Data availability

Fastq files and PAS annotations are available at GEO under accession GSE138197. All reproducible scripts and software versions can be found at through Zenodo with doi:10.5281/zenodo.3905372

The following data sets were generated

Article and author information

Author details

  1. Briana E Mittleman

    Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4979-4652
  2. Sebastian Pott

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4118-6150
  3. Shane Warland

    Section of Genetic Medicine, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Tony Zeng

    Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Zepeng Mu

    Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7717-3247
  6. Mayher Kaur

    Section of Genetic Medicine, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Yoav Gilad

    Department of Medicine, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8284-8926
  8. Yang Li

    Department of Medicine, Department of Human Genetics, University of Chicago, Chicago, United States
    For correspondence
    yangili1@uchicago.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0736-251X

Funding

National Institutes of Health (T32 GM09197)

  • Briana E Mittleman

National Institutes of Health (F31HL149259)

  • Briana E Mittleman

National Institutes of Health (R01GM130738)

  • Yang Li

National Institutes of Health (K12 HL119995)

  • Sebastian Pott

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2020, Mittleman et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,929
    views
  • 548
    downloads
  • 50
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Briana E Mittleman
  2. Sebastian Pott
  3. Shane Warland
  4. Tony Zeng
  5. Zepeng Mu
  6. Mayher Kaur
  7. Yoav Gilad
  8. Yang Li
(2020)
Alternative polyadenylation mediates genetic regulation of gene expression
eLife 9:e57492.
https://doi.org/10.7554/eLife.57492

Share this article

https://doi.org/10.7554/eLife.57492

Further reading

    1. Genetics and Genomics
    Silvia Diz-de Almeida, Raquel Cruz ... Ángel Carracedo
    Research Article

    The genetic basis of severe COVID-19 has been thoroughly studied, and many genetic risk factors shared between populations have been identified. However, reduced sample sizes from non-European groups have limited the discovery of population-specific common risk loci. In this second study nested in the SCOURGE consortium, we conducted a genome-wide association study (GWAS) for COVID-19 hospitalization in admixed Americans, comprising a total of 4702 hospitalized cases recruited by SCOURGE and seven other participating studies in the COVID-19 Host Genetic Initiative. We identified four genome-wide significant associations, two of which constitute novel loci and were first discovered in Latin American populations (BAZ2B and DDIAS). A trans-ethnic meta-analysis revealed another novel cross-population risk locus in CREBBP. Finally, we assessed the performance of a cross-ancestry polygenic risk score in the SCOURGE admixed American cohort. This study constitutes the largest GWAS for COVID-19 hospitalization in admixed Latin Americans conducted to date. This allowed to reveal novel risk loci and emphasize the need of considering the diversity of populations in genomic research.

    1. Genetics and Genomics
    Shaohua Gu, Yuanzhe Shao ... Zhiyuan Li
    Research Article

    Microbial secondary metabolites are a rich source for pharmaceutical discoveries and play crucial ecological functions. While tools exist to identify secondary metabolite clusters in genomes, precise sequence-to-function mapping remains challenging because neither function nor substrate specificity of biosynthesis enzymes can accurately be predicted. Here, we developed a knowledge-guided bioinformatic pipeline to solve these issues. We analyzed 1928 genomes of Pseudomonas bacteria and focused on iron-scavenging pyoverdines as model metabolites. Our pipeline predicted 188 chemically different pyoverdines with nearly 100% structural accuracy and the presence of 94 distinct receptor groups required for the uptake of iron-loaded pyoverdines. Our pipeline unveils an enormous yet overlooked diversity of siderophores (151 new structures) and receptors (91 new groups). Our approach, combining feature sequence with phylogenetic approaches, is extendable to other metabolites and microbial genera, and thus emerges as powerful tool to reconstruct bacterial secondary metabolism pathways based on sequence data.