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Euphytica

, Volume 186, Issue 3, pp 919–931 | Cite as

Genome-wide association analysis detecting significant single nucleotide polymorphisms for chlorophyll and chlorophyll fluorescence parameters in soybean (Glycine max) landraces

  • Derong Hao
  • Maoni Chao
  • Zhitong Yin
  • Deyue YuEmail author
Article

Abstract

Chlorophyll fluorescence parameters are generally used to characterize the intrinsic action of photosystem II (PSII), which is interrelated with the photosynthetic capacity. Mapping of quantitative trait loci for chlorophyll fluorescence parameters and associated traits is important for genetic improvement in soybean. In this study, a genome-wide association analysis was conducted to detect key single-nucleotide polymorphisms (SNPs) associated with chlorophyll content (chl) and chlorophyll fluorescence using 1,536 SNPs in a soybean landraces panel. The analysis revealed significant correlations among chl and five chlorophyll fluorescence parameters, including maximum quantum yield of PSII primary photochemistry in the dark-adapted state (Fv/Fm), light energy absorbed per reaction center (ABS/RC), quantum yield for electron transport (ETo/ABS), probability that a trapped exciton moves an electron into the electron transport chain beyond QA (ETo/TRo), and performance index on absorption basis (PIABS). Genome-wide association analysis using a mixed linear model detected 51 SNPs associated with chl and chlorophyll fluorescence parameters. Among these identified SNPs, 14 SNPs were co-associated with two or more different traits in this study, and 8 SNPs were co-associated with soybean yield and yield components in our previous study. These significant SNPs will help to better understand the genetic basis of photosynthesis-related physiological traits, and facilitate the pyramiding of favorable alleles for photosynthetic traits in soybean marker assisted selection schemes for high photosynthetic efficiency.

Keywords

Genome-wide association SNP Chlorophyll Chlorophyll fluorescence parameters Soybean 

Notes

Acknowledgments

This work was supported in part by the National Basic Research Program of China (973 Program) (2010CB125906, 2009CB118400), and the National Natural Science Foundation of China (30800692, 31000718, 31171573).

References

  1. Ainsworth EA, Yendrek CR, Skoneczka JA, Long SP (2011) Accelerating yield potential in soybean: potential targets for biotechnological improvement. Plant Cell Environ. doi: 10.1111/j.1365-3040.2011.02378.x Google Scholar
  2. Baker N (2008) Chlorophyll fluorescence: a probe of photosynthesis in vivo. Plant Biol 59(1):89–113CrossRefGoogle Scholar
  3. Bergelson J, Roux F (2010) Towards identifying genes underlying ecologically relevant traits in Arabidopsis thaliana. Nat Rev Genet 11(12):867–879PubMedCrossRefGoogle Scholar
  4. Bradbury P, Zhang Z, Kroon D, Casstevens T, Ramdoss Y, Buckler E (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23(19):2633PubMedCrossRefGoogle Scholar
  5. Chan EKF, Rowe HC, Corwin JA, Joseph B, Kliebenstein DJ (2011) Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana. PLoS Biol 9(8):e1001125PubMedCrossRefGoogle Scholar
  6. Chapman A, Pantalone VR, Ustun A, Allen FL, Landau-Ellis D, Trigiano RN, Gresshoff PM (2003) Quantitative trait loci for agronomic and seed quality traits in an F2 and F4:6 soybean population. Euphytica 129(3):387–393CrossRefGoogle Scholar
  7. Cui S, He X, Fu S, Meng Q, Gai J, Yu D (2008) Genetic dissection of the relationship of apparent biological yield and apparent harvest index with seed yield and yield related traits in soybean. Aust J Agric Res 59(1):86–93CrossRefGoogle Scholar
  8. Dau H (1994) Molecular mechanisms and quantitative models of variable photosystem II fluorescence. Photochem Photobiol 60(1):1–23CrossRefGoogle Scholar
  9. Debabrata P, Kumar SR (2011) Improvement of photosynthesis by Sub1 QTL in rice under submergence: probed by chlorophyll fluorescence OJIP transients. J Stress Physiol Biochem 7(3):250–259Google Scholar
  10. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14(8):2611–2620PubMedCrossRefGoogle Scholar
  11. Excoffier L, Laval G, Schneider S (2005) ARLEQUIN version 3.01: an integrated software package for population genetics data analysis. Evol Bioinform Online 1:47–50Google Scholar
  12. Force L, Critchley C, van Rensen JJS (2003) New fluorescence parameters for monitoring photosynthesis in plants. Photosynth Res 78(1):17–33PubMedCrossRefGoogle Scholar
  13. Fracheboud Y, Ribaut J-M, Vargas M, Messmer R, Stamp P (2002) Identification of quantitative trait loci for cold-tolerance of photosynthesis in maize (Zea mays L.). J Exp Bot 53(376):1967–1977PubMedCrossRefGoogle Scholar
  14. Fu S, Zhan Y, Zhi H, Gai J, Yu D (2006) Mapping of SMV resistance gene Rsc-7 by SSR markers in soybean. Genetica 128(1):63–69PubMedCrossRefGoogle Scholar
  15. Guo P, Li M (1996) Studies on photosynthetic characteristics in rice hybrid progenies and their parents I. Chlorophyll content, chlorophyll-protein complex and chlorophyll fluorescence kinetics. J Tropic Subtropic Bot 4(4):60–65Google Scholar
  16. Guo P, Baum M, Varshney RK, Graner A, Grando S, Ceccarelli S (2008) QTLs for chlorophyll and chlorophyll fluorescence parameters in barley under post-flowering drought. Euphytica 163(2):203–214CrossRefGoogle Scholar
  17. 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 124:447–458PubMedCrossRefGoogle Scholar
  18. Hardy O, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2(4):618–620CrossRefGoogle Scholar
  19. Hemler ME (2001) Specific tetraspanin functions. J Cell Biol 155(7):1103–1108PubMedCrossRefGoogle Scholar
  20. Hervé D, Fabre F, Berrios EF, Leroux N, Chaarani GA, Planchon C, Sarrafi A, Gentzbittel L (2001) QTL analysis of photosynthesis and water status traits in sunflower (Helianthus annuus L.) under greenhouse conditions. J Exp Bot 52(362):1857PubMedCrossRefGoogle Scholar
  21. Holland J, Nyquist W, Cervantes-Martínez C (2003) Estimating and interpreting heritability for plant breeding: an update. Plant Breed Rev 22:9–112Google Scholar
  22. Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Li W, Lin Z, Buckler ES, Qian Q, Zhang Q-F, Li J, Han B (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42(11):961–967PubMedCrossRefGoogle Scholar
  23. Hyten DL, Pantalone VR, Sams CE, Saxton AM, Landau-Ellis D, Stefaniak TR, Schmidt ME (2004) Seed quality QTL in a prominent soybean population. Theor Appl Genet 109(3):552–561PubMedCrossRefGoogle Scholar
  24. Hyten DL, Choi I-Y, Song Q, Shoemaker RC, Nelson RL, Costa JM, Specht JE, Cregan PB (2007) Highly variable patterns of linkage disequilibrium in multiple soybean populations. Genetics 175(4):1937–1944PubMedCrossRefGoogle Scholar
  25. Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23(14):1801–1806PubMedCrossRefGoogle Scholar
  26. Jiang C, Gao H, Zou Q (2003) Changes of donor and acceptor side in photosystem 2 complex induced by iron deficiency in attached soybean and maize leaves. Photosynthetica 41(2):267–271CrossRefGoogle Scholar
  27. Jun T-H, Van K, Kim M, Lee S-H, Walker D (2008) Association analysis using SSR markers to find QTL for seed protein content in soybean. Euphytica 162(2):179–191CrossRefGoogle Scholar
  28. Kabelka E, Diers B, Fehr W, LeRoy A, Baianu I, You T, Neece D, Nelson R (2004) Putative alleles for increased yield from soybean plant introductions 44(3):784–791Google Scholar
  29. Kassem M, Shultz J, Meksem K, Cho Y, Wood A, Iqbal M, Lightfoot D (2006) An updated ‘Essex’ by ‘Forrest’ linkage map and first composite interval map of QTL underlying six soybean traits. Theor Appl Genet 113(6):1015–1026PubMedCrossRefGoogle Scholar
  30. Kautsky H, Hirsch A (1931) Neue versuche zur kohlensäureassimilation. Naturwissenschaften 19(48):964–964CrossRefGoogle Scholar
  31. Keim P, Diers BW, Olson TC, Shoemaker RC (1990) RFLP mapping in soybean: association between marker loci and variation in quantitative traits. Genetics 126(3):735–742PubMedGoogle Scholar
  32. Krause G, Weis E (1991) Chlorophyll fluorescence and photosynthesis: the basics. Ann Rev Plant Biol 42(1):313–349CrossRefGoogle Scholar
  33. Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeza-Rosas MA, Zwonitzer JC, Kresovich S, McMullen MD, Ware D (2011) Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet 43(2):163–168PubMedCrossRefGoogle Scholar
  34. Lee S, Park K, Lee H, Park E, Boerma H (2001) Genetic mapping of QTLs conditioning soybean sprout yield and quality. Theor Appl Genet 103(5):702–709CrossRefGoogle Scholar
  35. Li Y, Guan R, Liu Z, Ma Y, Wang L, Li L, Lin F, Luan W, Chen P, Yan Z (2008) Genetic structure and diversity of cultivated soybean (Glycine max (L.) Merr.) landraces in China. Theor Appl Genet 117(6):857–871PubMedCrossRefGoogle Scholar
  36. Li X, Yan W, Agrama H, Jia L, Shen X, Jackson A, Moldenhauer K, Yeater K, McClung A, Wu D (2011a) Mapping QTLs for improving grain yield using the USDA rice mini-core collection. Planta 1–15. doi: 10.1007/s00425-011-1405-0
  37. Li YH, Smulders MJM, Chang RZ, Qiu LJ (2011b) Genetic diversity and association mapping in a collection of selected Chinese soybean accessions based on SSR marker analysis. Conserv Genet 12:1145–1157CrossRefGoogle Scholar
  38. Liang Q, Cheng X, Mei M, Yan X, Liao H (2010a) QTL analysis of root traits as related to phosphorus efficiency in soybean. Ann Bot 106(1):223–234PubMedCrossRefGoogle Scholar
  39. Liang Y, Zhang K, Zhao L, Liu B, Meng Q, Tian J, Zhao S (2010b) Identification of chromosome regions conferring dry matter accumulation and photosynthesis in wheat (Triticum aestivum L.). Euphytica 171(1):145–156CrossRefGoogle Scholar
  40. Lin S, Cianzio S, Shoemaker R (1997) Mapping genetic loci for iron deficiency chlorosis in soybean. Mol Breed 3(3):219–229CrossRefGoogle Scholar
  41. Lu Y, Zhang S, Shah T, Xie C, Hao Z, Li X, Farkhari M, Ribaut J, Cao M, Rong T (2010) Joint linkage–linkage disequilibrium mapping is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize. Proc Nat Acad Sci USA 107(45):19585–19590PubMedCrossRefGoogle Scholar
  42. Mian MAR, Bailey MA, Tamulonis JP, Shipe ER, Carter TE, Parrott WA, Ashley DA, Hussey RS, Boerma HR (1996) Molecular markers associated with seed weight in two soybean populations. Theor Appl Genet 93(7):1011–1016CrossRefGoogle Scholar
  43. Morrison MJ, Voldeng HD, Cober ER (1999) Physiological changes from 58 years of genetic improvement of short-season soybean cultivars in Canada. Agron J 91(4):685–689CrossRefGoogle Scholar
  44. Murray M, Thompson W (1980) Rapid isolation of high molecular weight plant DNA. Nucl Acid Res 8(19):4321–4326CrossRefGoogle Scholar
  45. Palomeque L, Li-Jun L, Li W, Hedges B, Cober E, Rajcan I (2009) QTL in mega-environments: I. Universal and specific seed yield QTL detected in a population derived from a cross of high-yielding adapted × high-yielding exotic soybean lines. Theor Appl Genet 119(3):417–427PubMedCrossRefGoogle Scholar
  46. Palomeque L, Liu L, Li W, Hedges B, Cober E, Smid M, Lukens L, Rajcan I (2010) Validation of mega-environment universal and specific QTL associated with seed yield and agronomic traits in soybeans. Theor Appl Genet 120(5):997–1003PubMedCrossRefGoogle Scholar
  47. Papageorgiou GC (2004) Chlorophyll a fluorescence: a signature of photosynthesis, vol 19. Kluwer Academic Publishers, pp 321–362Google Scholar
  48. Pritchard J, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155(2):945PubMedGoogle Scholar
  49. Raines CA (2011) Increasing photosynthetic carbon assimilation in C3 plants to improve crop yield: current and future strategies. Plant Physiol 155(1):36PubMedCrossRefGoogle Scholar
  50. Rawson H, Constable G, Howe G (1980) Carbon production of sunflower cultivars in field and controlled environment II. Leaf growth. Funct Plant Biol 7(5):575–586Google Scholar
  51. Sarkar RK, Panda D (2009) Distinction and characterisation of submergence tolerant and sensitive rice cultivars, probed by the fluorescence OJIP rise kinetics. Funct Plant Biol 36(3):222–233CrossRefGoogle Scholar
  52. Scoles G, Reinprecht Y, Poysa V, Yu K, Rajcan I, Ablett G, Pauls K (2006) Seed and agronomic QTL in low linolenic acid, lipoxygenase-free soybean (Glycine max (L.) Merrill) germplasm. Genome 49(12):1510–1527CrossRefGoogle Scholar
  53. Shen R, Fan J, Campbell D, Chang W, Chen J, Doucet D, Yeakley J, Bibikova M, Wickham Garcia E, McBride C (2005) High-throughput SNP genotyping on universal bead arrays. Mutat Res Fund Mol Mech 573(1–2):70–82CrossRefGoogle Scholar
  54. Sinclair TR, Purcell LC, Sneller CH (2004) Crop transformation and the challenge to increase yield potential. Trends Plant Sci 9(2):70–75PubMedCrossRefGoogle Scholar
  55. Strasser RJ, Tsimilli-Michael M, Srivastava A (2004) Analysis of the chlorophyll a fluorescence transient. Chlorophyll a fluorescence: a signature of photosynthesis, Springer, The Netherlands, pp 321–362Google Scholar
  56. Sun J, Leakey A, Markelz C, Ort D (2008) Stimulated photosynthesis alters sugar and amino-acid profiles, lowers osmotic potential and improves water status of soybean leaves under free-air CO2. American Society of Plant Biologists Annual Meeting Paper No P11003 Available: http://abstractsaspborg/pb2008/public/P11/P11003html
  57. Wang J, McClean P, Lee R, Goos R, Helms T (2008) Association mapping of iron deficiency chlorosis loci in soybean (Glycine max L. Merr.) advanced breeding lines. Theor Appl Genet 116(6):777–787PubMedCrossRefGoogle Scholar
  58. Whitney SM, Houtz RL, Alonso H (2011) Advancing our understanding and capacity to engineer nature’s CO2-sequestering enzyme Rubisco. Plant Physiol 155(1):27–35PubMedCrossRefGoogle Scholar
  59. Xu Y, Crouch J (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48(2):391–407CrossRefGoogle Scholar
  60. Yan J, Shah T, Warburton M, Buckler E, McMullen M, Crouch J (2009) Genetic characterization and linkage disequilibrium estimation of a global maize collection using SNP markers. PLoS ONE 4(12):e8451PubMedCrossRefGoogle Scholar
  61. Yan J, Yang X, Shah T, Sánchez-Villeda H, Li J, Warburton M, Zhou Y, Crouch JH, Xu Y (2010) High-throughput SNP genotyping with the GoldenGate assay in maize. Mol Breed 25(3):441–451CrossRefGoogle Scholar
  62. Yang DL, Jing RL, Chang XP, Li W (2007) Quantitative trait loci mapping for chlorophyll fluorescence and associated traits in wheat (Triticum aestivum). J Int Plant Biol 49(5):646–654CrossRefGoogle Scholar
  63. Yang X, Yan J, Shah T, Warburton M, Li Q, Li L, Gao Y, Chai Y, Fu Z, Zhou Y (2010) Genetic analysis and characterization of a new maize association mapping panel for quantitative trait loci dissection. Theor Appl Genet 121:417–431PubMedCrossRefGoogle Scholar
  64. Yan J, Warburton M, Crouch J (2011) Association mapping for Yan J, Warburton M, Crouch J (2011) Association mapping for enhancing maize (Zea mays L.) genetic improvement. Crop Sci 51:433–449 Google Scholar
  65. Yin Z, Meng F, Song H, He X, Xu X, Yu D (2010) Mapping quantitative trait loci associated with chlorophyll a fluorescence parameters in soybean (Glycine max (L.) Merr.). Planta 231(4):875–885PubMedCrossRefGoogle Scholar
  66. Yin Z, Meng F, Song H, Wang X, Chao M, Zhang G, Xu X, Deng D, Yu D (2011) GmFtsH9 expression correlates with in vivo photosystem II function: chlorophyll a fluorescence transient analysis and eQTL mapping in soybean. Planta 234:815–827PubMedCrossRefGoogle Scholar
  67. Yu J, Buckler E (2006) Genetic association mapping and genome organization of maize. Curr Opin Biotech 17(2):155–160PubMedCrossRefGoogle Scholar
  68. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, 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(2):203–208PubMedCrossRefGoogle Scholar
  69. Zhang WK, Wang YJ, Luo GZ, Zhang JS, He CY, Wu XL, Gai JY, Chen SY (2004) QTL mapping of ten agronomic traits on the soybean (Glycine max L. Merr.) genetic map and their association with EST markers. Theor Appl Genet 108(6):1131–1139PubMedCrossRefGoogle Scholar
  70. Zhang D, Cheng H, Geng L, Kan G, Cui S, Meng Q, Gai J, Yu D (2009) Detection of quantitative trait loci for phosphorus deficiency tolerance at soybean seedling stage. Euphytica 167(3):313–322CrossRefGoogle Scholar
  71. Zhang D, Cheng H, Wang H, Zhang H, Liu C, Yu D (2010) Identification of genomic regions determining flower and pod numbers development in soybean (Glycine max L.). J Genet Genomics 37(8):545–556PubMedCrossRefGoogle Scholar
  72. Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2:467PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Derong Hao
    • 1
    • 2
  • Maoni Chao
    • 1
  • Zhitong Yin
    • 3
  • Deyue Yu
    • 1
    Email author
  1. 1.National Center for Soybean ImprovementNational Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural UniversityNanjingChina
  2. 2.Jiangsu Yanjiang Institute of Agricultural SciencesNantongChina
  3. 3.Jiangsu Provincial Key Laboratory of Crop Genetics and PhysiologyYangzhou UniversityYangzhouChina

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