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Effect of population size and unbalanced data sets on QTL detection using genome-wide association mapping in barley breeding germplasm

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Abstract

Over the past two decades many quantitative trait loci (QTL) have been detected; however, very few have been incorporated into breeding programs. The recent development of genome-wide association studies (GWAS) in plants provides the opportunity to detect QTL in germplasm collections such as unstructured populations from breeding programs. The overall goal of the barley Coordinated Agricultural Project was to conduct GWAS with the intent to couple QTL detection and breeding. The basic idea is that breeding programs generate a vast amount of phenotypic data and combined with cheap genotyping it should be possible to use GWAS to detect QTL that would be immediately accessible and used by breeding programs. There are several constraints to using breeding program-derived phenotype data for conducting GWAS namely: limited population size and unbalanced data sets. We chose the highly heritable trait heading date to study these two variables. We examined 766 spring barley breeding lines (panel #1) grown in balanced trials and a subset of 384 spring barley breeding lines (panel #2) grown in balanced and unbalanced trials. In panel #1, we detected three major QTL for heading date that have been detected in previous bi-parental mapping studies. Simulation studies showed that population sizes greater than 384 individuals are required to consistently detect QTL. We also showed that unbalanced data sets from panel #2 can be used to detect the three major QTL. However, unbalanced data sets resulted in an increase in the false-positive rate. Interestingly, one-step analysis performed better than two-step analysis in reducing the false-positive rate. The results of this work show that it is possible to use phenotypic data from breeding programs to detect QTL, but that careful consideration of population size and experimental design are required.

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Acknowledgments

This research was supported by USDA-CSREES-NRI Grant No. 2006-55606-16722 and USDA-NIFA Grant No. 2009-85606-05701, “Barley Coordinated Agricultural Project: Leveraging Genomics, Genetics, and Breeding for Gene Discovery and Barley Improvement”.

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Authors and Affiliations

  1. Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA

    Hongyun Wang, Kevin P. Smith, Emily Combs & Gary J. Muehlbauer

  2. Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, 59717, USA

    Tom Blake

  3. Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108, USA

    Richard D. Horsley

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  1. Hongyun Wang
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  5. Richard D. Horsley
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  6. Gary J. Muehlbauer
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Correspondence to Gary J. Muehlbauer.

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Communicated by R. Waugh.

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Wang, H., Smith, K.P., Combs, E. et al. Effect of population size and unbalanced data sets on QTL detection using genome-wide association mapping in barley breeding germplasm. Theor Appl Genet 124, 111–124 (2012). https://doi.org/10.1007/s00122-011-1691-8

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