Molecular Breeding

, 38:50 | Cite as

Association mapping identifies loci for canopy coverage in diverse soybean genotypes

  • Avjinder S. Kaler
  • Jeffery D. Ray
  • William T. Schapaugh
  • Marilynn K. Davies
  • C. Andy King
  • Larry C. Purcell


Rapid establishment of canopy coverage decreases soil evaporation relative to transpiration, improves water use efficiency and light interception, and increases soybean competitiveness against weeds. The objective of this study was to identify genomic loci associated with canopy coverage (CC). Canopy coverage was evaluated using a panel of 373 MG IV soybean genotypes that was grown in five environments. Digital image analysis was used to determine canopy coverage two times (CC1 and CC2) during vegetative development approximately 8 to 16 days apart for each environment. After filtration for quality control, 31,260 SNPs with a minor allele frequency (MAF) ≥ 5% were used for association mapping with the FarmCPU model. Analysis identified significant SNP-canopy coverage associations including 36 for CC1 and 56 for CC2. Five SNPs for CC1 and 11 SNPs for CC2 were present in at least two environments. The significant SNP associations likely tagged 33 (CC1) and 50 (CC2) different quantitative trait loci (QTLs). Eleven putative loci were identified in which chromosomal regions associated were coincident for CC1 and CC2. Candidate genes identified using these significant SNPs included those with reported functions associated with growth, developmental, and light responses. Favorable alleles from significant SNPs may be an important resource for pyramiding genes to improve canopy coverage and for identifying parental genotypes for use in breeding programs.


Soybean Association mapping Light interception 



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.

Supplementary material

11032_2018_810_MOESM1_ESM.docx (62 kb)
ESM 1 (DOCX 62 kb).
11032_2018_810_MOESM2_ESM.docx (1.5 mb)
ESM 2 (DOCX 1522 kb).
11032_2018_810_MOESM3_ESM.xlsx (87 kb)
ESM 3 (XLSX 86 kb).


  1. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. Irrigation and Drainage Paper No. 56, Food and Agriculture Organization of the United Nations, Rome, ItalyGoogle Scholar
  2. 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 BeachGoogle Scholar
  3. Bussan AJ, Burnside OC, Orf JH, Ristau EA, Puettmann KJ (1997) Field evaluation of soybean (Glycine max) genotype for weed competitiveness. Weed Sci 45:31–37Google Scholar
  4. Campillo C, Prieto MH, Daza C, Moñino MJ, García MI (2008) Using digital images to characterize canopy coverage and light interception in a processing tomato crop. Hort Science 43:1780–1786.35Google Scholar
  5. Clauw P, Coppens F, De Beuf K, Dhondt S, Van Daele T, Maleux K, Storme V, Clement L, Gonzalez N, Inzé D (2015) Leaf responses to mild drought stress in natural variants of Arabidopsis. Plant Physiol 167:800–816CrossRefPubMedPubMedCentralGoogle Scholar
  6. De Bruin JL, Pedersen P (2008) Soybean seed yield response to planting date and seeding rate in the upper Midwest. Agron J 100:696–703CrossRefGoogle Scholar
  7. 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):e0137213CrossRefPubMedPubMedCentralGoogle Scholar
  8. 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 128:73–91CrossRefPubMedGoogle Scholar
  9. Edward JT, Purcell LC, Karcher DE (2005) Soybean yield and biomass responses to increasing plant population among diverse maturity groups. II Light interception and utilization Crop Sci 45:1778–1785Google Scholar
  10. Edwards JT, Purcell LC (2005) Soybean yield and biomass responses to increasing plant population among diverse maturity groups: I Agronomic characteristics. Crop Sci 45:1770–1777CrossRefGoogle Scholar
  11. Fickett ND, Boerboom CM, Stoltenberg DE (2013) Soybean yield loss potential associated with EarlySeason weed competition across 64 site-years. Weed Sci 61:500–507CrossRefGoogle Scholar
  12. Fiorani F, Rascher U, Jahnke S, Schurr U (2012) Imaging plants dynamics in heterogenic environments. Curr Opin Biotech 23:227–235CrossRefPubMedGoogle Scholar
  13. Gaspar AP, Conley SP (2015) Responses of canopy reflectance, light interception, and soybean seed yield to replanting suboptimal stands. Crop Sci 55(1):377–385CrossRefGoogle Scholar
  14. Gifford RM, Thorne JH, Hitz WD, Giaquinta RT (1984) Crop productivity and photoassimilate partitioning. Science 225:801–808CrossRefPubMedGoogle Scholar
  15. Green JM, Owen MDK (2011) Herbicide-resistant crops: utilities and limitations for herbicide resistant weed management. J Agric Food Chem 59:5819–5829CrossRefPubMedGoogle Scholar
  16. 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 124:447–458CrossRefPubMedGoogle Scholar
  17. 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 15:1Google Scholar
  18. Jannink JL, Orf JH, Jordan NR, Shaw RG (2000) Index selection for weed suppressive ability in soybean. Crop Sci 40:1087–1094CrossRefGoogle Scholar
  19. Jannink JL, Jordan NR, Orf JH (2001) Feasibility of selection for high weed suppressive ability in soybean: absence of tradeoffs between rapid initial growth and sustained later growth. Euphytica 120:291–300CrossRefGoogle Scholar
  20. Kaler AS, Dhanapal AP, Ray JD, King CA, Fritsch FB, Purcell LC (2017a) Genome-wide association mapping of carbon isotope and oxygen isotope ratios in diverse soybean genotypes. Crop Sci 57:3085–3100CrossRefGoogle Scholar
  21. Kaler AS, Ray JD, King CA, Schapaugh WT, Purcell LC (2017b) Genome-wide association mapping of canopy wilting in diverse soybean genotypes. Theor Appl Genet 130:1–15CrossRefGoogle Scholar
  22. Karcher DE, Richardson MD (2005) Batch analysis of digital images to evaluate turfgrass characteristics. Crop Sci 45:1536–1539CrossRefGoogle Scholar
  23. Liebisch F, Kirchgessner N, Schneider D, Walter A, Hund A (2015) Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 11:9CrossRefPubMedPubMedCentralGoogle Scholar
  24. 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):e1005767CrossRefPubMedPubMedCentralGoogle Scholar
  25. Manandhar A, Sinclair TR, Rufty TW, Ghanem ME (2017) Leaf emergence (phyllochron index) and leaf expansion response to soil drying in cowpea genotypes. Physiol Plantarum 160:201–208CrossRefGoogle Scholar
  26. 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):356–369CrossRefPubMedGoogle Scholar
  27. 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):23383–22390CrossRefGoogle Scholar
  28. Nordborg M, Tavaré S (2002) Linkage disequilibrium: what history has to tell us. Trends Genet 18(2):83–90CrossRefPubMedGoogle Scholar
  29. Place GT, Reberg-Horton SC, Dickey DA, Carter TE (2011a) Identifying soybean traits of interest for weed competition. Crop Sci 51:2642–2654CrossRefGoogle Scholar
  30. Place GT, Reberg-Horton SC, Carter TE, Smith AN (2011b) Effects of soybean seed size on weed competition. Agron J 103:175–181CrossRefGoogle Scholar
  31. Purcell LC (2000) Soybean canopy coverage and light interception measurements using digital imagery. Crop Sci 40:834–837CrossRefGoogle Scholar
  32. Purcell LC, Specht JE (2004) Physiological traits for ameliorating drought stress. In: Boema HR, Specht JE (eds) Soybeans: improvement, production, and uses. 3rd ed. Madison, WI: American Society of Agronomy. Pp, pp 569–620Google Scholar
  33. Purcell LC, Edwards JT, Brye KR (2007) Soybean yield and biomass responses to cumulative transpiration: questioning widely held beliefs. Field Crop Res 101:10–18CrossRefGoogle Scholar
  34. 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):2391–2403CrossRefPubMedPubMedCentralGoogle Scholar
  35. Rebetzke GJ, Ellis MH, Bonnett DG, Richards RA (2007) Molecular mapping of genes for coleoptile growth in bread wheat (Triticum aestivum L.) Theor Appl Genet 114:1173–1183CrossRefPubMedGoogle Scholar
  36. Richards RA, Watt M, Rebetzke GJ (2007) Physiological traits and cereal germplasm for sustainable agricultural systems. Euphytica 154:409–425CrossRefGoogle Scholar
  37. Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273(5281):1516–1517CrossRefPubMedGoogle Scholar
  38. SAS Institute (2013) The SAS system for Windows. Version 9.3. SAS Inst. Inc., Cary, NCGoogle Scholar
  39. Shaner DL (1995) Herbicide resistance: where are we? How did we get here? Where are we going? Weed Technol 9:850–856CrossRefGoogle Scholar
  40. Slafer GA, Araus JL, Royo C, Garcia Del Moral LF (2005) Promising eco-physiological traits for genetic improvement of cereal yields in Mediterranean environments. Ann Appl Biol 146:61–70CrossRefGoogle Scholar
  41. 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):e54985CrossRefPubMedPubMedCentralGoogle Scholar
  42. Tardieu F, Tuberosa R (2010) Dissection and modelling of abiotic stress tolerance in plants. Curr Opin Plant Biol 13:206–212CrossRefPubMedGoogle Scholar
  43. 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 47:120–141CrossRefGoogle Scholar
  44. 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 Genomics 15:809CrossRefPubMedPubMedCentralGoogle Scholar
  45. Xavier A, Hall B, Hearst AA, Cherkauer KA, Rainey KM (2017) Genetic architecture of phenomic-enabled canopy coverage in Glycine max. Genetics 206:1081–1089. CrossRefPubMedPubMedCentralGoogle Scholar
  46. Yu J, Pressoir G, Briggs WH, Vroh BI, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen HJB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208CrossRefPubMedGoogle Scholar
  47. 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 Genomics 16:217. CrossRefPubMedPubMedCentralGoogle Scholar
  48. Zhu C, Gore MA, Buckler ES, Yu J (2008) Status and prospects of association mapping in plants. Plant Genome 1:5–20CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Avjinder S. Kaler
    • 1
  • Jeffery D. Ray
    • 2
  • William T. Schapaugh
    • 3
  • Marilynn K. Davies
    • 1
  • C. Andy King
    • 1
  • Larry C. Purcell
    • 1
  1. 1.Department of Crop, Soil, and Environmental SciencesUniversity of ArkansasFayettevilleUSA
  2. 2.Crop Genetics Research Unit, USDA-ARS141 Experimental Station RoadStonevilleUSA
  3. 3.Department of AgronomyKansas State UniversityManhattanUSA

Personalised recommendations