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

Kaushik Renganaath, Rocky Cheung, Laura Day, Sriram Kosuri, Leonid Kruglyak, Frank W. Albert

Research output: Contribution to journalArticlepeer-review

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 5832 natural DNA variants in the promoters of 2503 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.

Original languageEnglish (US)
Article numbere62669
Pages (from-to)1-35
Number of pages35
JournaleLife
Volume9
DOIs
StatePublished - Nov 12 2020

Bibliographical note

Funding Information:
We are grateful to Joshua Bloom, Chad Myers, and Henry Ward for input on data analysis. We thank Carl de Boer for help with applying his gene expression prediction model, Joshua Bloom for providing the BY/RM genotype data, and Liangke Gou for help with nucleosome data. We thank Suhua Feng for assistance with Illumina sequencing. We acknowledge resources and support from the Minnesota Supercomputing Institute.

Funding Information:
We are grateful to Joshua Bloom, Chad Myers, and Henry Ward for input on data analysis. We thank Carl de Boer for help with applying his gene expression prediction model, Joshua Bloom for provid-ing the BY/RM genotype data, and Liangke Gou for help with nucleosome data. We thank Suhua Feng for assistance with Illumina sequencing. We acknowledge resources and support from the Minnesota Supercomputing Institute. National Institutes of Health R35GM124676 Frank Wolfgang Albert Howard Hughes Medical Institute Pew Charitable Trusts Alfred P. Sloan Foundation Kinship Foundation Leonid Kruglyak Frank Wolfgang Albert Frank Wolfgang Albert 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.

Publisher Copyright:
© Renganaath et al.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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