Breeding-assisted genomics: Applying meta-GWAS for milling and baking quality in CIMMYT wheat breeding program

One of the biggest challenges for genetic studies on natural or unstructured populations is the unbalanced datasets where individuals are measured at different times and environments. This problem is also common in crop and animal breeding where many individuals are only evaluated for a single year...

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Published inPloS one Vol. 13; no. 11; p. e0204757
Main Authors Battenfield, Sarah D, Sheridan, Jaime L, Silva, Luciano D C E, Miclaus, Kelci J, Dreisigacker, Susanne, Wolfinger, Russell D, Peña, Roberto J, Singh, Ravi P, Jackson, Eric W, Fritz, Allan K, Guzmán, Carlos, Poland, Jesse A
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 29.11.2018
Public Library of Science (PLoS)
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Summary:One of the biggest challenges for genetic studies on natural or unstructured populations is the unbalanced datasets where individuals are measured at different times and environments. This problem is also common in crop and animal breeding where many individuals are only evaluated for a single year and large but unbalanced datasets can be generated over multiple years. Many wheat breeding programs have focused on increasing bread wheat (Triticum aestivum L.) yield, but processing and end-use quality are critical components when considering its use in feeding the rising population of the next century. The challenges with end-use quality trait improvements are high cost and seed amounts for testing, the latter making selection in early breeding populations impossible. Here we describe a novel approach to identify marker-trait associations within a breeding program using a meta-genome wide association study (GWAS), which combines GWAS analysis from multi-year unbalanced breeding nurseries, in a manner reflecting meta-GWAS in humans. This method facilitated mapping of processing and end-use quality phenotypes from advanced breeding lines (n = 4,095) of the CIMMYT bread wheat breeding program from 2009 to 2014. Using the meta-GWAS we identified marker-trait associations, allele effects, candidate genes, and can select using markers generated in this process. Finally, the scope of this approach can be broadly applied in 'breeding-assisted genomics' across many crops to greatly increase our functional understanding of plant genomes.
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Competing Interests: JLS and EWJ are employees of General Mills Inc. LDCES, KJM and RDW are employees of SAS Institute Inc. All other authors declare no conflict of interest. This work was supported by the United States Agency for International Development (USAID) through a specific cooperative agreement AID-OAA-A-13-00051 and the National Science Foundation under Grant No. (1339389). SB was supported through the Monsanto Beachell-Borlaug International Scholars program. General Mills Inc. and SAS Institute Inc. provided support in the form of salaries for authors JLS, EWJ, LDCES, KJM and RDW, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the U.S. Agency for International Development.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0204757