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. 2018 Nov 29;13(11):e0204757.
doi: 10.1371/journal.pone.0204757. eCollection 2018.

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

Sarah D Battenfield  1 , Jaime L Sheridan  2 , Luciano D C E Silva  3 , Kelci J Miclaus  3 , Susanne Dreisigacker  4 , Russell D Wolfinger  3 , Roberto J Peña  4 , Ravi P Singh  4 , Eric W Jackson  5 , Allan K Fritz  6 , Carlos Guzmán  4 , Jesse A Poland  7
Affiliations

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

Sarah D Battenfield et al. PLoS One. .

Abstract

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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Conflict of interest statement

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.

Figures

Fig 1
Fig 1. Demonstration and quantification of grain, dough and loaf volume tests.
a) Grain samples for protein testing and milling, b) Alveograph example demonstrating that P/L is the height to width ratio which measures extensibility and W, area under the curve measures dough strength. These are measured on c) dough tested in forced air method of Alveograph. Loaf volume test is represented in d) with breeding lines of various volumes demonstrated.
Fig 2
Fig 2. Distributions of phenotypic traits over time for thousand kernel weight (TKW, no data available in 2010), grain protein, Alveograph W and P/L, and loaf volume in a) 2010, b) 2011, c) 2012, d) 2013, and e) 2014.
Fig 3
Fig 3. Manhattan plots of thousand kernel weight (TKW), grain protein (GRNPRO), Alveograph W (ALVW) and P/L (ALVPL), and loaf volume (LOFVOL) traits.
Homeologous chromosomes are identified by number, and color separates the genome where purple is A, green is B, and yellow is D.
Fig 4
Fig 4. Meta-marker trait associations for seven significant multi-trait associations.
Meta effect of each haplotype is displayed with marker frequencies. Effects are demonstrated for thousand kernel weight (TKW), grain protein (GRNPRO), Alveograph W (ALVW) and P/L (ALVPL), and loaf volume (LOFVOL) traits.
See this image and copyright information in PMC

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