Registration of the S2MET barley mapping population for multi-environment genomewide selection

Jeffrey L. Neyhart, Daniel Sweeney, Mark Sorrells, Christian Kapp, Kenneth D. Kephart, Jamie Sherman, Eric J. Stockinger, Scott Fisk, Patrick Hayes, Sintayehu Daba, Mohsen Mohammadi, Nia Hughes, Lewis Lukens, Pablo González Barrios, Lucia Gutiérrez, Kevin P. Smith

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Market changes in the malting and brewing industries have increased the demand for locally produced barley (Hordeum vulgare L.) in many regions across North America. Breeding for productive barley cultivars in diverse growing environments is complicated by genotype × environment interactions (GEIs), which can make selection for broad adaptation difficult but may be exploited to select optimal cultivars for each environment. Genomewide selection has recently become a useful tool to make efficient selections on individuals using genomewide marker data. To support the use of genomewide selection to breed locally adapted barley cultivars, the University of Minnesota barley breeding program is publicly releasing a panel of two-row barley lines, and accompanying data, called the S2MET (Spring Two-Row Multi-Environment Trial) (Reg. No. MP-2, NSL 526938 MAP). The S2MET includes 233 breeding lines grouped into a 183-line training population and a 50-line validation population. The entire panel was genotyped using genotyping-by-sequencing and phenotyped for 14 important traits in 44 location-year environments between 2015 and 2017. All data are freely available at the Triticeae Toolbox (https://, and we describe several on-tap projects and breeding advances that are exploiting this resource. We believe this panel and dataset will be useful for answering important breeding questions related to genomewide selection and GEIs and developing locally superior barley cultivars.

Original languageEnglish (US)
Pages (from-to)270-280
Number of pages11
JournalJournal of Plant Registrations
Issue number2
StatePublished - May 2019

Bibliographical note

Funding Information:
Many hands went into the development of the S2MET panel and contributed to the success of this project. We thank our additional collaborators: Aaron Mills, Chad Sellmer, Richard Horsley, Martin Hochhalter, Jean Goudet, Heather Darby, Carl Duley, Chris Evans, and Gongshe Hu. We also thank Ed Schiefelbein, Guillermo Velasquez, and Karen Beaubien for technical support during population development, field trial management (Minnesota), and genotyping. Special thanks go to Shiaoman Chao (USDA-ARS, Fargo, ND) for performing DNA extractions and generating sequencing data. Celeste Falcon, Alexandrea Ollhoff, and Tyler Tiede were instrumental in selecting the S2MET training population lines. The first author thanks Peyton Ginakes for assistance during seed packing and Ian McNish for helpful comments on earlier drafts of this manuscript. Thanks go to Austin Case, Jo Heuschele, John Hill Price, Becky Zhong, Alexander Susko, Lu Yin, and Lauren Sexton for assistance, advice, and encouragement. We acknowledge Liz Elmore from Montana State University and Lisa Trumble from the University of Guelph for data collection. Resources from the Minnesota Supercomputing Institute were used to complete this project. This research received support from the US Department of Agriculture (USDA) cooperative project with the US Wheat & Barley Scab Initiative under Agreement No. (59-0206-2-020), the Minnesota Department of Agriculture, Rahr Malting Company, the Brewers Association, the American Malting Barley Association, USDA National Institute of Food and Agriculture (NIFA) Hatch Project 149-447, and USDA-NIFA Grant #2018-67011-28075. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the US Department of Agriculture.

Publisher Copyright:
© 2019 Crop Science Society of America. All rights reserved.


Dive into the research topics of 'Registration of the S2MET barley mapping population for multi-environment genomewide selection'. Together they form a unique fingerprint.

Cite this