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Genome-wide association mapping of flooding tolerance in soybean

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Abstract

Flooding threatens soybean production and limits soybean yields worldwide. The most effective and economic approach to decrease loss of yield due to flooding is to develop flood-tolerant soybean cultivars. The objective of this study was to identify genetic loci and candidate genes associated with flooding tolerance. A panel of 384 soybean plant introductions (PIs) was evaluated for flooding tolerance in two consecutive years in the field. The plant foliar damage score was used to index soybean response to flooding stress. A total of 42,291 SNP markers were obtained from the Illumina Infinium SoySNP50K BeadChip database. After filtration for quality control, 31,125 SNPs were used for genome-wide association mapping utilizing four different models (regression linear model (GLM), mixed linear model (MLM), compressed mixed linear model (CMLM), and enriched compressed mixed linear model (ECMLM)). Fourteen SNPs were identified to be associated with flooding tolerance across all environments and models at a significance level of −Log10 (P) ≥ 2.5. Five SNPs were located within the coding regions of five candidate genes. Several PIs with lower best linear unbiased prediction (BLUPs) of the breeding values and a large number of favorable flood-tolerant alleles were found as new genetic sources for use in soybean breeding programs.

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Abbreviations

GWAS:

Genome-wide association study

SNP:

Single nucleotide polymorphism

SSR:

Simple sequence repeat

QTL:

Quantitative trait loci

PI:

Plant introduction

MAS:

Marker-assisted selection

Chr:

Chromosome

RIL:

Recombinant inbred line

FDS:

Foliar damage score

V5:

Soybean vegetative growth stage with five nodes on the main stem, beginning with the unifoliate node, with a fully developed leaf

R1:

Soybean reproductive growth stage with first flower at any node

LOD:

Log of the odds

GLM:

Regression linear model

MLM:

Mixed linear model

CMLM:

Compressed mixed linear model

ECMLM:

Enriched compressed mixed linear model

BLUP:

Best linear unbiased prediction

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Acknowledgments

We appreciate the field support from Jonathan McCoy and David Hendrix at the Rice Research and Extension Center in Stuttgart, AR.

Funding

This study is financially supported by the United Soybean Board and the Arkansas Soybean Promotion Board.

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Authors and Affiliations

  1. Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, 72701, USA

    Chengjun Wu, Leandro A. Mozzoni, David Moseley & Wade Hummer

  2. Division of Plant Sciences, University of Missouri, 1-31 Ag Building, Columbia, MO, 65201, USA

    Heng Ye & Henry Nguyen

  3. Division of Plant Sciences, Delta Center, University of Missouri, P.O. Box 160, Portageville, MO, 63873, USA

    Pengyin Chen & Grover Shannon

Authors
  1. Chengjun Wu
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  2. Leandro A. Mozzoni
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  3. David Moseley
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  4. Wade Hummer
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  5. Heng Ye
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  6. Pengyin Chen
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  7. Grover Shannon
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  8. Henry Nguyen
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Contributions

CW performed the all experiments and data analysis, and wrote the paper. LAM critically revised the manuscript and supervised the project. DM performed data analysis and revised the manuscript. WH participated in the field experiments. HY provided materials and revised the manuscript. PC designed experiment and supervised the project. GS and HN provided materials and supervised the project. All authors read and approved the final manuscript.

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Correspondence to Leandro A. Mozzoni.

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Wu, C., Mozzoni, L.A., Moseley, D. et al. Genome-wide association mapping of flooding tolerance in soybean. Mol Breeding 40, 4 (2020). https://doi.org/10.1007/s11032-019-1086-0

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