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Genome-wide association mapping of canopy wilting in diverse soybean genotypes

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Genome-wide association analysis identified 61 SNP markers for canopy wilting, which likely tagged 51 different loci. Based on the allelic effects of the significant SNPs, the slowest and fastest wilting genotypes were identified.

Abstract

Drought stress is a major global constraint for crop production, and slow canopy wilting is a promising trait for improving drought tolerance. The objective of this study was to identify genetic loci associated with canopy wilting and to confirm those loci with previously reported canopy wilting QTLs. A panel of 373 maturity group (MG) IV soybean genotypes was grown in four environments to evaluate canopy wilting. Statistical analysis of phenotype indicated wide variation for the trait, with significant effects of genotype (G), environment (E), and G × E interaction. Over 42,000 SNP markers were obtained from the Illumina Infinium SoySNP50K iSelect SNP Beadchip. After filtration for quality control, 31,260 SNPs with a minor allele frequency (MAF) ≥5% were used for association mapping using the Fixed and random model Circulating Probability Unification (FarmCPU) model. There were 61 environment-specific significant SNP-canopy wilting associations, and 21 SNPs that associated with canopy wilting in more than one environment. There were 34 significant SNPs associated with canopy wilting when averaged across environments. Together, these SNPs tagged 23 putative loci associated with canopy wilting. Six of the putative loci were located within previously reported chromosomal regions that were associated with canopy wilting through bi-parental mapping. Several significant SNPs were located within a gene or very close to genes that had a reported biological connection to transpiration or water transport. Favorable alleles from significant SNPs may be an important resource for pyramiding genes to improve drought tolerance and for identifying parental genotypes for use in breeding programs.

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References

  • Abdel-Haleem H, Carter TE Jr, Purcell LC, King CA, Ries LL, Chen PC, Schapaugh W Jr, Sinclair TR, Boerma HR (2012) Mapping of quantitative trait loci for canopy-wilting trait in soybean (Glycine max (L) Merr). Theor Appl Genet 125:837–846

    Article  CAS  PubMed  Google Scholar 

  • Blum A (2005) Drought resistance, water-use efficiency, and yield potential-are they compatible, dissonant, or mutually exclusive? Aust J Agric Res 56:1159–1168

    Article  Google Scholar 

  • Blum A (2011) Drought resistance—is it really a complex trait? Funct Plant Biol 38(10):753–757

    Article  Google Scholar 

  • Bondari K (2003) Statistical analysis of genotype × environment interaction in agricultural research. In: Paper SD15, SESUG: The Proceedings of the SouthEast SAS Users Group, St Pete Beach

  • Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635

    Article  CAS  PubMed  Google Scholar 

  • Carter TE Jr, Orf JH, Purcell LC, Specht JE, Chen P, Sinclair T, Rufty TW (2006) Tough times, tough plants—new soybean genes defend against drought and other stresses. In: Proceedings of the 33rd Soybean Seed Research Conference, Chicago, IL, 5–8 Dec 2006

  • Carter TE Jr, De Souza PI, Purcell LC (1999) Recent advances in breeding for drought and aluminum resistance in soybean. In: Kauffman H (ed) Proceedings of the sixth World Soybean Research Conference, Chicago, IL. Superior Printing, Champaign, pp 106–125

    Google Scholar 

  • Charlson DV, Bhatnagar S, King CA, Ray JD, Sneller CH, Carter TE Jr, Purcell LC (2009) Polygenic inheritance of canopy wilting in soybean [Glycine max (L) Merr]. Theor Appl Genet 119:587–594

    Article  PubMed  Google Scholar 

  • Clark SA, van der Werf J (2013) Genomic best linear unbiased prediction (gblup) for the estimation of genomic breeding values. Genome-wide association studies and genomic prediction. Springer, Berlin, pp 321–330

    Chapter  Google Scholar 

  • de Ollas C, Arbona V, Gomez-Cadenas A (2015) Jasmonic acid interacts with abscisic acid to regulate plant responses to water stress conditions. Plant Signal Behav 10:e1078953. doi:10.1080/15592324.2015.1078953

    Article  PubMed  PubMed Central  Google Scholar 

  • Devi MJ, Sinclair TR (2013) Nitrogen fixation drought tolerance of the slow-wilting soybean PI 471938. Crop Sci 53:2072–2078

    Article  CAS  Google Scholar 

  • Dhanapal AP, Ray JD, Singh SK, Hoyos-Villegas V, Smith JR, Purcell LC, King CA, Fritsch FB (2015a) Association mapping of total carotenoids in diverse soybean genotypes based on leaf extracts and high-throughput canopy spectral reflectance measurements. PLoS One 10(9):e0137213

    Article  PubMed  PubMed Central  Google Scholar 

  • Dhanapal AP, Ray JD, Singh SK, Hoyos-Villegas V, Smith JR, Purcell LC, King CA, Cregan PB, Song Q, Fritsch FB (2015b) Genome-wide association study (GWAS) of carbon isotope ratio (δ13C) in diverse soybean [Glycine max (L.) Merr.] Genotypes. Theor Appl Genet 128:73–91

    Article  CAS  PubMed  Google Scholar 

  • Dixit S, Huang BE, Sta Cruz MT, Maturan PT, Ontoy JCE, Kumar A (2014) QTLs for tolerance of drought and breeding for tolerance of abiotic and biotic stress: an integrated approach. PLoS One 9:e109574

    Article  PubMed  PubMed Central  Google Scholar 

  • Du W, Yu D, Fu S (2009) Detection of quantitative trait loci for yield and drought tolerance traits in soybean using a recombinant inbred line population. J Integr Plant Biol 51:868–878

    Article  CAS  PubMed  Google Scholar 

  • Fletcher AL, Sinclair TR, Allen LH Jr (2007) Transpiration responses to vapor pressure deficit in well-watered ‘slow-wilting’ and commercial soybean. Environ Exp Bot 61:145–151

    Article  CAS  Google Scholar 

  • Fox CM, Cary TR, Colgrove AL, Nafziger ED, Haudenshiel JS, Hartman GL, Specht JE, Diers BW (2013) Estimating soybean genetic gain for yield in the northern United States-Influence of cropping history. Crop Sci 53:2473–2482

    Article  Google Scholar 

  • Hao D, Cheng H, Yin Z, Cui S, Zhang D, Wang H, Yu D (2012) Identification of single nucleotide polymorphisms and haplotypes associated with yield and yield components in soybean (Glycine max) landraces across multiple environments. Theor Appl Genet 124:447–458

    Article  CAS  PubMed  Google Scholar 

  • Hill WG, Weir BS (1988) Variances and covariance of squared linkage disequilibria in finite populations. Theor Popul Biol 33:54–78

    Article  CAS  PubMed  Google Scholar 

  • Hwang E, Song Q, Jia G, Specht JE, Hyten DL, Costa J, Cregan PB (2014) A genome-wide association study of seed protein and oil content in soybean. PLoS Genet 15:1

    Google Scholar 

  • Hwang S, King CA, Chen P, Ray JD, Cregan PB, Carter TE Jr, Li Z, Abdel-Haleem H, Matson KW, Schapaugh W Jr, Purcell LC (2015) Confirmation of delayed canopy wilting QTLs from multiple soybean mapping populations. Theor Appl Genet 128:2047–2065

    Article  PubMed  Google Scholar 

  • Hwang S, King CA, Chen P, Ray JD, Cregan PB, Carter TE Jr, Li Z, Abdel-Haleem H, Matson KW, Schapaugh W Jr, Purcell LC (2016) Meta-analysis to refine map position and reduce confidence intervals for delayed-canopy-wilting QTLs in soybean. Mol Breed 36:91

    Article  Google Scholar 

  • Irwin S, Good D (2015) Forming expectations for the 2015 U.S. Average soybean yield: what does history teach us? Farmdoc daily (5):51, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, March 19, 2015

  • King CA, Purcell LC, Brye KR (2009) Differential wilting among soybean genotypes in response to water deficit. Crop Sci 49:290–298

    Article  Google Scholar 

  • Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397–2399

    Article  CAS  PubMed  Google Scholar 

  • Liu X, Huang M, Fan B, Buckler ES, Zhang Z (2016) Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet 12(2):e1005767

    Article  PubMed  PubMed Central  Google Scholar 

  • McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JPA, Hirschhorn JN (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Rev Genet 9(5):356–369

    Article  CAS  PubMed  Google Scholar 

  • Money D, Gardner K, Migicovsky Z, Schwaninger H, Zhong GY, Myles S (2015) LinkImpute fast and accurate genotype imputation for non-model organisms. G3 5(11):2383–2390

    Article  PubMed  PubMed Central  Google Scholar 

  • Nordborg M, Borevitz J, Bergelsom J, Berry C, Chory J, Hagenbland J, Kreitman M, Maloof J, Noyes T, Oefner P, Stahl E, Weigel D (2002) The extent of linkage disequilibrium in Arabidopsis thaliana. Nat Genet 30(2):190–193

    Article  CAS  PubMed  Google Scholar 

  • Oya T, Nepomuceno AL, Numaier N, Farias JRB, Tobita S, Ito S (2004) Drought tolerance characteristics of Brazilian cultivars—evaluation and characterization of drought tolerance of various Brazilian soybean cultivars in the field. Plant Prod Sci 7:129–137

    Article  Google Scholar 

  • Ray JD, Dhanapal AP, Singh SK, Hoyos-Villegas V, Smith JR, Purcell LC, King CA, Boykin D, Cregan PB, Song Q, Fritschi FB (2015) Genome-wide association study of ureide concentration in diverse maturity group IV soybean [Glycine max (L.) Merr.] accessions. G3 5(11):2391–2403

    Article  PubMed  PubMed Central  Google Scholar 

  • Ries LL, Purcell LC, Carter TE Jr, Edwards JT, King CA (2012) Physiological traits contributing to differential canopy wilting in soybean under drought. Crop Sci 52:272–281

    Article  Google Scholar 

  • Sadok W, Sinclair TR (2009) Genetic variability of transpiration response to vapor pressure deficit among soybean [Glycine max (L.) Merr.] cultivars. Crop Sci 49:955–960

    Article  Google Scholar 

  • SAS Institute (2013) The SAS System for Windows. Version 9.3. SAS Inst. Inc., Cary, NC

  • Sinclair TR, Zwieniecki MA, Holbrook NM (2008) Low leaf hydraulic conductance associated with drought tolerance in soybean. Physiol Plant 132:446–451

    Article  CAS  PubMed  Google Scholar 

  • Sloane RJ, Patterson RP, Carter TE Jr (1990) Field drought tolerance of a soybean plant introduction. Crop Sci 30:118–123

    Article  Google Scholar 

  • Song Q, Hyten DL, Jia G, Quigley CV, Fickus EW, Nelson RL, Cregan PB (2013) Development and evaluation of SoySNP50K, a high-density genotyping array for soybean. PLoS One 8(1):e54985

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tuberosa R, Salvi S, Giuliani S, Sanguineti MC, Bellotti M, Conti S, Landi P (2007) Genome-wide approaches to investigate and improve maize response to drought. Crop Sci 47:120–141

    Article  Google Scholar 

  • USDA-FAS (2016) https://apps.fas.usda.gov/psdonline/circulars/production.pdf. Accessed 17 July 2017

  • Wen Z, Tan R, Yuan J, Bales C, Du W (2014) Genome-wide association mapping of quantitative resistance to sudden death syndrome in soybean. BMC Genom 15:809

    Article  Google Scholar 

  • Yu J, Pressoir G, Briggs WH, Vroh BI, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208

    Article  CAS  PubMed  Google Scholar 

  • Zhang Z, Todhunter RJ, Buckler ES, Van Vleck LD (2007) Technical note: use of marker-based relationships with multiple-trait derivative-free restricted maximal likelihood. J Anim Sci 85:881–885

    Article  CAS  PubMed  Google Scholar 

  • Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang J, Song Q, Cregan PB, Nelson RL, Wang X, Wu J, Jiang GL (2015) Genome-wide association study for flowering time, maturity dates and plant height in early maturing soybean (Glycine max) germplasm. BMC Genom 16:217

    Article  CAS  Google Scholar 

  • Zhu C, Gore MA, Buckler ES, Yu J (2008) Status and prospects of association mapping in plants. Plant Genom 1:5–20

    Article  CAS  Google Scholar 

  • Zipper SC, Qiu J, Kucharik CJ (2016) Drought effects on US maize and soybean production: spatiotemporal patterns and historical changes. Environ Res Lett 11:094021

    Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge partial funding of this research from the United Soybean Board. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. The USDA is an equal opportunity provider and employer. Excellent technical support from Marilynn Davies, Jody Hedge, and Linda Martin is gratefully acknowledged.

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Authors and Affiliations

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

    Avjinder S. Kaler, C. Andy King & Larry C. Purcell

  2. Crop Genetics Research Unit, USDA-ARS, 141 Experimental Station Road, Stoneville, MS, 38776, USA

    Jeffery D. Ray

  3. Department of Agronomy, Kansas State University, Manhattan, KS, 66506, USA

    William T. Schapaugh

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  1. Avjinder S. Kaler
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  2. Jeffery D. Ray
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  5. Larry C. Purcell
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Correspondence to Larry C. Purcell.

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Communicated by Volker Hahn.

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Kaler, A.S., Ray, J.D., Schapaugh, W.T. et al. Genome-wide association mapping of canopy wilting in diverse soybean genotypes. Theor Appl Genet 130, 2203–2217 (2017). https://doi.org/10.1007/s00122-017-2951-z

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  • DOI: https://doi.org/10.1007/s00122-017-2951-z

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