Theoretical and Applied Genetics

, Volume 121, Issue 6, pp 1171–1185 | Cite as

Variability of grain quality in sorghum: association with polymorphism in Sh2, Bt2, SssI, Ae1, Wx and O2

  • L. F. de Alencar Figueiredo
  • B. Sine
  • J. Chantereau
  • C. Mestres
  • G. Fliedel
  • J.-F. Rami
  • J.-C. Glaszmann
  • M. Deu
  • B. CourtoisEmail author
Original Paper


To ensure food security in Africa and Asia, developing sorghum varieties with grain quality that matches consumer demand is a major breeding objective that requires a better understanding of the genetic control of grain quality traits. The objective of this targeted association study was to assess whether the polymorphism detected in six genes involved in synthesis pathways of starch (Sh2, Bt2, SssI, Ae1, and Wx) or grain storage proteins (O2) could explain the phenotypic variability of six grain quality traits [amylose content (AM), protein content (PR), lipid content (LI), hardness (HD), endosperm texture (ET), peak gelatinization temperature (PGT)], two yield component traits [thousand grain weight (TGW) and number of grains per panicle (NBG)], and yield itself (YLD). We used a core collection of 195 accessions which had been previously phenotyped and for which polymorphic sites had been identified in sequenced segments of the six genes. The associations between gene polymorphism and phenotypic traits were analyzed with Tassel. The percentages of admixture of each accession, estimated using 60 RFLP probes, were used as cofactors in the analyses, decreasing the proportion of false-positive tests (70%) due to population structure. The significant associations observed matched generally well the role of the enzymes encoded by the genes known to determine starch amount or type. Sh2, Bt2, Ae1, and Wx were associated with TGW. SssI and Ae1 were associated with PGT, a trait influenced by amylopectin amount. Sh2 was associated with AM while Wx was not, possibly because of the absence of waxy accessions in our collection. O2 and Wx were associated with HD and ET. No association was found between O2 and PR. These results were consistent with QTL or association data in sorghum and in orthologous zones of maize. This study represents the first targeted association mapping study for grain quality in sorghum and paves the way for marker-aided selection.


Linkage Disequilibrium Sorghum Amylose Quality Trait Association Mapping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors gratefully acknowledge the financial support from the GABI-Génoplante project “Bridging genomics and genetic diversity: associations between gene polymorphism and trait variation in cereals” for the sequencing of O2, and from the CNPq and from the Universidade Católica de Brasília through a grant to L.F. de A.F.

Supplementary material

122_2010_1380_MOESM1_ESM.doc (742 kb)
Supplementary material 1 (DOC 742 kb)


  1. Aboubacar A, Hamaker BR (1999) Physicochemical properties of flours that relate to sorghum couscous quality. Cereal Chem 76:308–313CrossRefGoogle Scholar
  2. Agrama HA, Eizenga GC, Yan W (2007) Association mapping of yield and its components in rice cultivars. Mol Breed 19:341–356CrossRefGoogle Scholar
  3. Andersen JR, Schrag T, Melchinger AE, Zien I, Lübberstedt T (2005) Validation of Dwarf8 polymorphisms associated with flowering time in elite European inbred lines of maize. Theor Appl Genet 111:206–217CrossRefPubMedGoogle Scholar
  4. Ayres NM, McClung AM, Larkin PD, Bligh HFJ, Jones CA, Park WD (1997) Microsatellites and a single-nucleotide polymorphism differentiate apparent amylose classes in an extended rice pedigree of US rice germplasm. Theor Appl Genet 94:773–781CrossRefGoogle Scholar
  5. Belton PS, Taylor JRN (2004) Sorghum and millets: protein sources for Africa. Trends Food Sci Technol 15:94–98CrossRefGoogle Scholar
  6. 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 23:2633–2635CrossRefPubMedGoogle Scholar
  7. Breseghello F, Sorrells ME (2006) Association mapping of kernel size and milling quality in wheat. Genetics 172:1165–1177CrossRefPubMedGoogle Scholar
  8. Brown PJ, Rooney WL, Franks C, Kresovich S (2008) Efficient mapping of plant height quantitative trait loci in a sorghum association population with introgressed dwarfism genes. Genetics 180:629–637CrossRefPubMedGoogle Scholar
  9. Buckler ES, Thornsberry JM (2002) Plant molecular diversity and applications to genomics. Curr Opin Plant Biol 5:107–111CrossRefPubMedGoogle Scholar
  10. Buntjer JB, Sorensen AP, Peleman JD (2005) Haplotype diversity: the link between statistical and biological association. Trends Plant Sci 10:466–471CrossRefPubMedGoogle Scholar
  11. Camus-Kulandaivelu L, Veyrieras JB, Madur D, Combes V, Fourmann M, Barraud S, Dubreuil P, Gouesnard B, Manicacci D, Charcosset A (2006) Maize adaptation to temperate climate: relationship between population structure and polymorphism in the Dwarf8 gene. Genetics 172:2449–2463CrossRefPubMedGoogle Scholar
  12. Chantereau J, Trouche G, Luce C, Deu M, Hamon P (1997) Le Sorgho. In: Charrier A, Jacquot M, Hamon S, Nicolas D (eds) L’amélioration des plantes tropicales. Cirad and Orstom, Montpellier, pp 565–590Google Scholar
  13. Cockram J, White J, Leigh FJ, Lea VJ, Chiapparino E, Laurie DA, Mackay IJ, Powell W, O’Sullivan DM (2008) Association mapping of partitioning loci in barley. BMC Genetics 9:1–14CrossRefGoogle Scholar
  14. de Alencar Figueiredo LF, Davrieux F, Fliedel G, Rami J-F, Chantereau J, Deu M, Courtois B, Mestres C (2006) Development of NIRS equations based on a core collection to predict quality traits in sorghum grain. J Agric Food Chem 54:8501–8509CrossRefPubMedGoogle Scholar
  15. de Alencar Figueiredo LF, Calatayud C, Dupuis C, Billot C, Rami JF, Brunel D, Perrier X, Courtois B, Deu M, Glaszmann J-C (2008) Phylogeographic evidence of crop neo-diversity in sorghum. Genetics 179:997–1008CrossRefPubMedGoogle Scholar
  16. Deu M, Gonzàlez-de-Leòn D, Glaszmann J-C, Dégremont I, Chantereau J, Lanaud C, Hamon P (1994) RFLP diversity in cultivated sorghum in relation to racial differentiation. Theor Appl Genet 88:838–844CrossRefGoogle Scholar
  17. Deu M, Rattunde F, Chantereau J (2006) A global view of genetic diversity in cultivated sorghums using a core collection. Genome 49:168–180PubMedGoogle Scholar
  18. Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997–1004CrossRefPubMedGoogle Scholar
  19. Doebley J, Bacigalupo A, Stec A (1994) Inheritance of kernel weight in two maize–teosinte hybrid populations: implications for crop evolution. J Hered 85:191–195Google Scholar
  20. Doggett H (1988) Sorghum, 2nd edn. Longman, New York (512 pp)Google Scholar
  21. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software Structure: a simulation study. Mol Ecol 14:2611–2620CrossRefPubMedGoogle Scholar
  22. Fliedel G (1994) Evaluation de la qualité du sorgho pour la fabrication du tô. Agriculture et développement 34:12–21Google Scholar
  23. Fliedel G, Marti A, Thiebaut S (1996) Caractérisation et valorisation du sorgho. Cirad Montpellier, 404 ppGoogle Scholar
  24. Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 53:357–374CrossRefGoogle Scholar
  25. Gao H, Williamson S, Bustamante C (2007) An MCMC approach for joint inference of population structure and inbreeding rates from multi-locus genotype data. Genetics 176:1635–1651CrossRefPubMedGoogle Scholar
  26. Gebhardt C, Ballvora A, Walkemeier B, Oberhagemann P, Schüler K (2004) Assessing genetic potential in germplasm collections of crop plants by marker–trait association: a case study for potatoes with quantitative variation of resistance to late blight and maturity type. Mol Breed 13:93–102CrossRefGoogle Scholar
  27. Goldman LL, Rocheford TR, Dudley JW (1993) Quantitative trait loci influencing protein and starch concentration in Illinois long term selection maize strains. Theor Appl Genet 87:217–224CrossRefGoogle Scholar
  28. Hamblin MT, Salas Fernandez MG, Casa AM, Mitchell SE, Paterson AH, Kresovich S (2005) Equilibrium processes cannot explain high levels of short- and medium-range linkage disequilibrium in the domesticated grass Sorghum bicolor. Genetics 171:1247–1256CrossRefPubMedGoogle Scholar
  29. Hamblin MT, Salas Fernandez MG, Tuinstra MR, Rooney WL, Kresovich S (2007) Sequence variation at candidate loci in the starch metabolism pathway in sorghum: prospects for linkage disequilibrium mapping. Plant Genome 2:125–134Google Scholar
  30. Harlan JR, de Wet JMJ (1972) A simplified classification of cultivated sorghum. Crop Sci 12:172–176CrossRefGoogle Scholar
  31. Henry AM, Damerval C (1997) High rates of polymorphism and recombination at the Opaque-2 locus in cultivated maize. Mol Gen Genet 256:147–157CrossRefPubMedGoogle Scholar
  32. Ihaka R, Gentleman R (1996) R: a language for data analysis and graphics. J Comput Graph Stat 5:299–314CrossRefGoogle Scholar
  33. James MG, Denyer K, Myers AM (2003) Starch synthesis in the cereal endosperm. Curr Opin Plant Biol 6:215–222CrossRefPubMedGoogle Scholar
  34. Kraakman ATW, Niks RE, Van den Berg PMMM, Stam P, VanEuuwijk FA (2004) Linkage disequilibrium mapping of yield and yield stability in modern spring barley cultivars. Genetics 168:435–446CrossRefPubMedGoogle Scholar
  35. Larkin PD, McClung AM, Ayres NM, Park WD (2003) The effect of the Waxy locus (Granule Bound Starch Synthase) on pasting curve characteristics in specialty rices. Euphytica 131:243–253CrossRefGoogle Scholar
  36. Lopes MA, Larkins BA (1991) Gamma-zein content is related to endosperm modification in quality protein maize. Crop Sci 31:1655–1662CrossRefGoogle Scholar
  37. Maddaloni M, Donini G, Balconi C, Rizzi E, Gallusci P, Forlani F, Lohmer S, Thompson R, Salamini F, Motto M (1996) The transcriptional activator Opaque-2 controls the expression of a cytosolic form of pyruvate orthophosphate dikinase-1 in maize endosperms. Mol Gen Genet 250:647–654PubMedGoogle Scholar
  38. Manicacci D, Falque M, Le Guillou S, Piegu B, Henry A-M, Le Guilloux M, Damerval C, De Vienne D (2007) Maize Sh2 gene is constrained by natural selection but escaped domestication. J Evol Biol 20:503–516CrossRefPubMedGoogle Scholar
  39. McIntyre CL, Drenth J, Gonzalez N, Henzell RG, Jordan DR (2008) Molecular characterization of the waxy locus in sorghum. Genome 51:524–533CrossRefPubMedGoogle Scholar
  40. Motto M, Hartings H, Maddaloni M, Lohmer S, Salamini F, Thompson R (1996) Genetic manipulation of protein quality in maize grains. Field Crop Res 45:37–48CrossRefGoogle Scholar
  41. Murray SC, Sharma A, Rooney WL, Klein PE, Mullet JE, Mitchell SE, Kresovich S (2008) Genetic improvement of sorghum as a biofuel feedstock: I. QTL for stem sugar and grain non-structural carbohydrates. Crop Sci 48:2165–2179CrossRefGoogle Scholar
  42. Murray SC, Rooney WL, Hamblin MT, Mitchell SE, Kresovich S (2009) Sweet sorghum genetic diversity and association mapping for brix and height. Plant Genome 2:48–62CrossRefGoogle Scholar
  43. Myers AM, Morell MK, James MG, Ball SG (2000) Recent progress toward understanding biosynthesis of the amylopectin crystal. Plant Physiol 122:989–998CrossRefPubMedGoogle Scholar
  44. Nielsen R (2005) Molecular signatures of natural selection. Annu Rev Genet 39:197–218CrossRefPubMedGoogle Scholar
  45. Nishi A, Nakamura Y, Tanaka N, Satoh H (2001) Biochemical and genetic analysis of the effects of amylose-extender mutation in rice endosperm. Plant Physiol 127:459–472CrossRefPubMedGoogle Scholar
  46. Ollitrault P (1987) Evaluation génétique des sorghos cultivés (Sorghum bicolor L. Moench) par l’analyse conjointe des diversités enzymatique et morphophysiologique. Relations avec les sorghos sauvages. PhD Thesis, Université Paris XI Orsay, 187 ppGoogle Scholar
  47. Olsen KM, Purugganan MD (2002) Molecular evidence on the origin and evolution of glutinous rice. Genetics 162:941–950PubMedGoogle Scholar
  48. Perrier X, Jacquemoud-Collet JP (2006) DARwin software. Available at
  49. Pirovano L, Lanzini S, Hartings H, Lazzaroni N, Rossi V, Joshi R, Thompson RD, Salamini F, Motto M (1994) Structural and functional analysis of an Opaque-2-related gene from sorghum. Plant Mol Biol 24:515–523CrossRefPubMedGoogle Scholar
  50. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal component analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909CrossRefPubMedGoogle Scholar
  51. Pritchard JK, Stephens M, Donnelly P (2000a) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedGoogle Scholar
  52. Pritchard JK, Stephens M, Rosenberg NA, Donnelly P (2000b) Association mapping in structured populations. Am J Hum Genet 67:170–181CrossRefPubMedGoogle Scholar
  53. Rami JF (1999) Etude des facteurs génétiques impliqués dans la qualité technologique du grain chez le maïs et le sorgho. PhD thesis, Orsay University, 96 ppGoogle Scholar
  54. Rami JF, Dufour P, Trouche G, Fliedel G, Mestres C, Davrieux F, Blanchard P, Hamon P (1998) Quantitative trait loci for grain quality, productivity, morphological and agronomical traits in sorghum (Sorghum bicolor L. Moench). Theor Appl Genet 97:605–616CrossRefGoogle Scholar
  55. Schultz JA, Juvik JA (2004) Current models for starch synthesis and the sugary enhancer1 (se1) mutation in Zea mays. Plant Physiol Biochem 42:457–464CrossRefPubMedGoogle Scholar
  56. Séne M, Causse M, Damerval C, Thévenot C, Prioul J-L (2000) Quantitative trait loci affecting amylose, amylopectin and starch content in maize recombinant inbred lines. Plant Physiol Biochem 38:459–472CrossRefGoogle Scholar
  57. Séne M, Thévenot C, Hoffmann D, Bénétrix F, Causse M, Prioul J-L (2001) QTLs for grain dry milling properties, composition and vitreousness in maize recombinant inbred lines. Theor Appl Genet 102:591–599CrossRefGoogle Scholar
  58. Sine B (2003) Evaluation d’une core collection de sorgho en conditions de stress hydrique pré-floral. Master University Cheikh Anta Diop, Dakar, Sénégal, 67 ppGoogle Scholar
  59. Skot L, Humphreys MO, Armstead I, Heywood S, Skot KP, Sanderson R, Thomas ID, Chorlton KH, Sackville Hamilton NR (2005) An association mapping approach to identify flowering time genes in natural populations of Lolium perenne. Mol Breed 15:233–245CrossRefGoogle Scholar
  60. Thornsberry JM, Goodman MJ, Doebley J, Kresovich S, Nielsen D, Buckler ED (2001) Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet 28:286–289CrossRefPubMedGoogle Scholar
  61. Tian Z, Qian Q, Liu Q, Yan M, Liu X, Yan C, Liu G, Gao Z, Tang S, Zeng D, Wang Y, Yu J, Gu M, Li J (2009) Allelic diversities in rice starch biosynthesis lead to a diverse array of rice eating and cooking qualities. Proc Natl Acad Sci USA 106:21760–21765CrossRefPubMedGoogle Scholar
  62. Vasemägi A, Primmer RC (2005) Challenges for identifying functionally important genetic variation: the promise of combining complementary research strategies. Mol Ecol 14:3623–3642CrossRefPubMedGoogle Scholar
  63. Vigouroux Y, McMullen M, Hittinger CT, Houchins K, Schulz L, Kresovich S, Matsuoka Y, Doebley J (2002) Identifying genes of agronomic importance in maize by screening microsatellites for evidence of selection during domestication. Proc Natl Acad Sci USA 99:9650–9655CrossRefPubMedGoogle Scholar
  64. Wendorf F, Close AE, Schild R, Wasylikowa K, Housley RA, Harlan JR, Krolik H (1992) Saharan exploitation of plants 8000 years BP. Nature 359:721–724CrossRefGoogle Scholar
  65. Wilson LM, Whitt SR, Ibanez AM, Rocheford TR, Goodman MM (2004) Dissection of maize kernel composition and starch production by candidate gene association. Plant Cell 16:2719–2733CrossRefPubMedGoogle Scholar
  66. Yetneberk S, de Kock HL, Rooney LW, Taylor JRN (2004) Effects of sorghum cultivar on injera quality. Cereal Chem 81:314–321CrossRefGoogle Scholar
  67. Yu J, Pressoir G, Briggs WH, Vroh BI, 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 38:203–208CrossRefPubMedGoogle Scholar
  68. Yu J, Zhang Z, Zhu C, Tabanao DA, Pressoir G, Tuinstra MR, Kresovich S, Todhunter RJ, Buckler ES (2009) Simulation appraisal of the adequacy of number of background markers for relationship estimation in association mapping. Plant Genome 2:63–77CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • L. F. de Alencar Figueiredo
    • 1
    • 2
  • B. Sine
    • 3
  • J. Chantereau
    • 4
  • C. Mestres
    • 5
  • G. Fliedel
    • 5
  • J.-F. Rami
    • 1
  • J.-C. Glaszmann
    • 1
  • M. Deu
    • 1
  • B. Courtois
    • 1
    Email author
  1. 1.Cirad, UMR DAPMontpellierFrance
  2. 2.Dept. Biologia Celular, Instituto de Ciências BiológicasUniversidade de BrasíliaBrasíliaBrazil
  3. 3.CERAASThièsSenegal
  4. 4.Cirad, UPR AIVAMontpellierFrance
  5. 5.Cirad, UPR QualiSudMontpellierFrance

Personalised recommendations