Systematic identification of cis-regulatory variants that cause gene expression differences in a yeast cross

  1. Kaushik Renganaath
  2. Rockie Chong
  3. Laura Day
  4. Sriram Kosuri
  5. Leonid Kruglyak  Is a corresponding author
  6. Frank Wolfgang Albert  Is a corresponding author
  1. University of Minnesota, United States
  2. University of California, Los Angeles, United States

Abstract

Sequence variation in regulatory DNA alters gene expression and shapes genetically complex traits. However, the identification of individual, causal regulatory variants is challenging. Here, we used a massively parallel reporter assay to measure the cis-regulatory consequences of 5,832 natural DNA variants in the promoters of 2,503 genes in the yeast Saccharomyces cerevisiae. We identified 451 causal variants, which underlie genetic loci known to affect gene expression. Several promoters harbored multiple causal variants. In five promoters, pairs of variants showed non-additive, epistatic interactions. Causal variants were enriched at conserved nucleotides, tended to have low derived allele frequency, and were depleted from promoters of essential genes, which is consistent with the action of negative selection. Causal variants were also enriched for alterations in transcription factor binding sites. Models integrating these features provided modest, but statistically significant, ability to predict causal variants. This work revealed a complex molecular basis for cis-acting regulatory variation.

Data availability

Raw data and barcode assignments to oligos are available under GEO accession GSE155944. Source Data is provided for Figures 2, 3, 4, 5, and 6. Additional processed data and the MPRA design are available as Supplementary Files.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Kaushik Renganaath

    Department of Genetics, Cell Biology, & Development, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1010-3604
  2. Rockie Chong

    Department of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Laura Day

    Department of Human Genetics, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Sriram Kosuri

    Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4661-0600
  5. Leonid Kruglyak

    Department of Human Genetics, University of California, Los Angeles, Los Angeles, United States
    For correspondence
    LKruglyak@mednet.ucla.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8065-3057
  6. Frank Wolfgang Albert

    Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, United States
    For correspondence
    falbert@umn.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1380-8063

Funding

National Institutes of Health (R35GM124676)

  • Frank Wolfgang Albert

Howard Hughes Medical Institute

  • Leonid Kruglyak

Pew Charitable Trusts

  • Frank Wolfgang Albert

Alfred P. Sloan Foundation

  • Frank Wolfgang Albert

Kinship Foundation

  • Sriram Kosuri

Department of Energy, Labor and Economic Growth (DE-FC02-02ER63421)

  • Sriram Kosuri

National Institutes of Health (R01GM102308)

  • Leonid Kruglyak

National Institutes of Health (DP2GM114829)

  • Sriram Kosuri

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

Copyright

© 2020, Renganaath 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

  • 2,904
    views
  • 301
    downloads
  • 22
    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. Kaushik Renganaath
  2. Rockie Chong
  3. Laura Day
  4. Sriram Kosuri
  5. Leonid Kruglyak
  6. Frank Wolfgang Albert
(2020)
Systematic identification of cis-regulatory variants that cause gene expression differences in a yeast cross
eLife 9:e62669.
https://doi.org/10.7554/eLife.62669

Share this article

https://doi.org/10.7554/eLife.62669

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Daniel Hui, Scott Dudek ... Marylyn D Ritchie
    Research Article

    Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI–covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

    1. Genetics and Genomics
    Sedigheh Delmaghani, Aziz El-Amraoui
    Insight

    The DYRK1A enzyme is a pivotal contributor to frequent and severe episodes of otitis media in Down syndrome, positioning it as a promising target for therapeutic interventions.