Evolutionary and functional genomics of DNA methylation in maize domestication and improvement

Gen Xu, Jing Lyu, Qing Li, Han Liu, Dafang Wang, Mei Zhang, Nathan M. Springer, Jeffrey Ross-Ibarra, Jinliang Yang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

DNA methylation is a ubiquitous chromatin feature, present in 25% of cytosines in the maize genome, but variation and evolution of the methylation landscape during maize domestication remain largely unknown. Here, we leverage whole-genome sequencing (WGS) and whole-genome bisulfite sequencing (WGBS) data on populations of modern maize, landrace, and teosinte (Zea mays ssp. parviglumis) to estimate epimutation rates and selection coefficients. We find weak evidence for direct selection on DNA methylation in any context, but thousands of differentially methylated regions (DMRs) are identified population-wide that are correlated with recent selection. For two trait-associated DMRs, vgt1-DMR and tb1-DMR, HiChIP data indicate that the interactive loops between DMRs and respective downstream genes are present in B73, a modern maize line, but absent in teosinte. Our results enable a better understanding of the evolutionary forces acting on patterns of DNA methylation and suggest a role of methylation variation in adaptive evolution.

Original languageEnglish (US)
Article number5539
JournalNature communications
Volume11
Issue number1
DOIs
StatePublished - Dec 1 2020

Bibliographical note

Funding Information:
J.Y. is supported by the Agriculture and Food Research Initiative Grant number 2019-67013-29167 from the USDA National Institute of Food and Agriculture, the National Science Foundation under award number OIA-1557417 for Center for Root and Rhi-zobiome Innovation (CRRI), Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under Grant number P20GM103476, and the University of Nebraska-Lincoln Start-up fund and the Layman seed award. J.R.-I. is supported by NSF grant 1546719 and USDA Hatch project CA-D-PLS-2066-H. This work was conducted using the Holland Computing Center of the University of Nebraska-Lincoln Start-up, which receives supports from the Nebraska Research Initiative. We thank Mike May for help in developing the MCMC approach used here, the helpful discussion in J.R.-I.’s REHAB, and constructive suggestions from anonymous reviewers.

Publisher Copyright:
© 2020, The Author(s).

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