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Meta-analysis to refine map position and reduce confidence intervals for delayed-canopy-wilting QTLs in soybean

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Abstract

Slow canopy wilting in soybean has been identified as a potentially beneficial trait for ameliorating drought effects on yield. Previous research identified QTLs for slow wilting from two different biparental populations, and this information was combined with data from three other populations to identify nine QTL clusters for slow wilting on Gm02, Gm05, Gm11, Gm 14, Gm17, and Gm19. The QTL cluster on Gm14 was eliminated because these QTLs appeared to be false positives. In the present research, QTLs from these remaining eight clusters were compiled onto the soybean consensus map for meta-QTL analysis. Five model selection criteria were used to determine the most appropriate number of meta-QTLs at these eight chromosomal regions. For a QTL cluster on Gm02, two meta-QTLs were identified, whereas for the remaining seven QTL clusters the single meta-QTL model was most appropriate. Thus, the analysis identified nine meta-QTLs associated with slow wilting. Meta-analysis decreased the confidence intervals from an average of 21.4 cM for the eight QTL clusters to 10.8 cM for the meta-QTLs. Averaged R2 values of the nine meta-QTLs in eight QTL clusters were 0.13 and ranged from 0.09 to 0.22. Meta-QTLs on Gm11 and Gm19 had the highest R2 values (0.22 and 0.20, respectively).

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Acknowledgments

The authors gratefully acknowledge financial support for this research from the United Soybean Board. This work was also supported in part by the United States Department of Agriculture–Agriculture Research Service (USDA-ARS) project number 6066-21220-012-00D.

Authors contribution

SH designed the experiment and performed the statistical analysis. CAK, TEC, HAH, WS, and LCP conducted field experiments and collected phenotypic data. PC, CAK, HAH, ZL, KWM, and LCP developed the populations. JDR, PBC, ZL, and KWM collected the genotypic data. SH and LCP co-wrote the manuscript. JDR, TEC, PC, ZL, and KWM critically revised the manuscript. LCP coordinated and supervised the project. All authors read and approved the final manuscript.

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Authors and Affiliations

  1. Department of Crop, Soil, and Environmental Sciences, University of Arkansas, 1336 Altheimer Drive, Fayetteville, AR, 72704, USA

    Sadal Hwang, C. Andy King, Pengyin Chen & Larry C. Purcell

  2. Crop Genetics and Production Research Unit, USDA-ARS, Stoneville, MS, 38776, USA

    Jeffery D. Ray

  3. Soybean Genomics and Improvement Laboratory, USDA-ARR, BARC-West, Beltsville, MD, 20705-2350, USA

    Perry B. Cregan

  4. Department of Crop Science, North Carolina State University, USDA-ARS, Raleigh, NC, 27695, USA

    Thomas E. Carter Jr.

  5. Institute for Plant Breeding, Genetics and Genomics, Center for Applied Genetic Technologies, The University of Georgia, 111 Riverbend Rd., Athens, GA, 30602-6810, USA

    Zenglu Li

  6. US Arid-Land Agricultural Research Center, USDA-ARS, 21881 North Cardon Lane, Maricopa, AZ, 85138, USA

    Hussein Abdel-Haleem

  7. Global Soybean Breeding, Monsanto Company, St. Louis, MO, 63167, USA

    Kevin W. Matson

  8. Department of Agronomy, Kansas State University, 2004C Throckmorton Hall, Manhattan, KS, 6506-5501, USA

    William Schapaugh Jr.

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  1. Sadal Hwang
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  2. C. Andy King
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  3. Pengyin Chen
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  7. Zenglu Li
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  8. Hussein Abdel-Haleem
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  11. Larry C. Purcell
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Correspondence to Larry C. Purcell.

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Hwang, S., King, C.A., Chen, P. et al. Meta-analysis to refine map position and reduce confidence intervals for delayed-canopy-wilting QTLs in soybean. Mol Breeding 36, 91 (2016). https://doi.org/10.1007/s11032-016-0516-5

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