Molecular Breeding

, Volume 30, Issue 2, pp 951–966

Population structure revealed by different marker types (SSR or DArT) has an impact on the results of genome-wide association mapping in European barley cultivars

  • Inge E. Matthies
  • Theo van Hintum
  • Stephan Weise
  • Marion S. Röder
Article

Abstract

Diversity arrays technology (DArT) and simple sequence repeat (SSR) markers were applied to investigate population structure, extent of linkage disequilibrium and genetic diversity (kinship) on a genome-wide level in European barley (Hordeum vulgare L.) cultivars. A set of 183 varieties could be clearly distinguished into spring and winter types and was classified into five subgroups based on 253 DArT or 22 SSR markers. Despite the fact, that the same number of groups was revealed by both marker types, it could be shown that this grouping was more distinct for the SSRs than the DArTs, when assigned to a Q-matrix by STRUCTURE. This was supported by the findings from principal coordinate analysis, where the SSRs showed a better resolution according to seasonal habit and row number than the DArTs. A considerable influence on the rate of significant associations with malting and kernel quality parameters was revealed by different marker types in this genome-wide association study using general and mixed linear models considering population structure. Fewer spurious associations were observed when population structure was based on SSR rather than on DArT markers. We therefore conclude that it is advisable to use independent marker datasets for calculating population structure and for performing the association analysis.

Keywords

Barley Population structure Genome-wide association studies DArT SSR 

Supplementary material

11032_2011_9678_MOESM1_ESM.doc (40 kb)
Electronic Supplementary Material Table 1Number of mapped DArT and SSR markers used for the analysis of population structure, linkage disequilibrium (LD), kinship and association studies in a set of 183 barley cultivars and their average PIC values. The number of DArT-markers which were used in association mapping studies after removal of 5% minor allele frequency (MAF) is also shown. (DOC 40 kb)
11032_2011_9678_MOESM2_ESM.xls (120 kb)
Electronic Supplementary Material Table 2Population structure and assignment (>50% probability) of each cultivar to the five groups (Q1 to Q5) revealed by STRUCTURE analysis with both marker types (sheet a). Population structure and assignment (in % probability) of every cultivar to one of the five groups (Q1 to Q5) revealed by STRUCTURE analysis with the 22 SSRs (sheet b). Population structure and assignment (in % probability) of every cultivar to one of the five groups (Q1 to Q5) revealed by STRUCTURE analysis with the 253 DArTs (sheet c). Origin of cultivars and breeders (sheet d). (XLS 120 kb)
11032_2011_9678_MOESM3_ESM.xlsx (217 kb)
Electronic Supplementary Material Table 3GWAS with the kernel quality parameter glume fineness (sheet a), GWAS with the kernel quality parameter TGW (sheet b), GWAS with the malting quality parameter extract (sheet c), GWAS with the malting quality parameter friability (sheet d) considering the GLM. (XLSX 218 kb)
11032_2011_9678_MOESM4_ESM.xlsx (593 kb)
Electronic Supplementary Material Table 4Results of GWAS considering the MLM with four different combinations of marker types used for the estimation the Q-matrix and kinship (MLM_1 = Q5-DArT + K-DArT, MLM_2 = Q5-SSR + K-DArT, MLM_3 = Q5-DArT + K-SSR, MLM_4 = Q5-SSR + K-SSR). Two kernel quality parameters, such as glume fineness (sheet a), and TGW (sheet b), and two malting quality parameters like extract (sheet c), and friability (sheet d) were assessed. (XLSX 593 kb)
11032_2011_9678_MOESM5_ESM.ppt (153 kb)
Electronic Supplementary Material Fig. 1Fig. 1 Estimated probability of number of subgroups k (goodness of fit computed as lnPr (X|K) for the investigated set of 183 cultivars studied with two marker types (a) 22 SSRs, and (b) 253 DArTs. The ln likelihood L(K) mean values determined by STRUCTURE are plotted against the assumed number of subgroups (k1 to k20). (PPT 153 kb)
11032_2011_9678_MOESM6_ESM.ppt (147 kb)
Electronic Supplementary Material Fig. 2Scatterplot showing the distribution of the intrachromosomal LD-decay parameter r2 in 183 European barley cultivars and plotted against the genetic distance in cM. The horizontal line indicates the 95% percentile of the distribution of unlinked r2, which gives the critical value of r2. Second degree LOESS curve fitted to the plot (black bottom line). (PPT 147 kb)
11032_2011_9678_MOESM7_ESM.pdf (9 kb)
Electronic Supplementary Material Fig. 3Proportion of marker pairwise r2 intrachromosomal measurements above and below background linkage disequilibrium with a critical r2 of 0.21 plotted as a logarithmic function of the genetic distance (in classes) of the entire set of 183 European barley cultivars investigated with 862 mapped DArT markers considering 5 % MAF. (PDF 10 kb)
11032_2011_9678_MOESM8_ESM.pptx (189 kb)
Electronic Supplementary Material Fig. 4Cumulative distribution of the observed p values assessed for different variantsof the MLM_QK with respect to the marker type used for assessing population structure and kinshipinformation (a) MLM_1 = Q5-DArT + K-DArT, (b) MLM_2 = Q5-SSR + K-DArT, (c) MLM_3 = Q5-DArT + K-SSR, (d) MLM_4 = Q5-SSR + K-SSR). Following traits were considered: Glume fineness,thousand grain weight (TGW), extract, and friability. (PPTX 190 kb)
11032_2011_9678_MOESM9_ESM.pptx (319 kb)
Electronic Supplementary Material Fig. 5Genomewide association studies of 183 barley cultivars considering the MLM_QK for four traits (a) extract, and (b) thousand grain weight. Population structure (Q) and kinship (K) was taking into account by using the Q5 matrix calculated either with SSR- or DArT markers. This information was used in different combinations on the MLM in order to assess the effect of the marker type on the rate of signifianct association results (MLM_1 = Q5-DArT + K-DArT, MLM_2 = Q5-SSR + K-DArT, MLM_3 = Q5-DArT + K-SSR, MLM_4 = Q5-SSR + K-SSR). The calculated p-values were converted into –log10(p). The significance thresholds p < 0.05, and p < 0.001 are indicated by dashed lines. (PPTX 320 kb)

References

  1. Ayoub M, Symons SJ, Edney MJ, Mather DE (2002) QTLs affecting kernel size and shape in a two-rowed by six-rowed barley cross. Theor Appl Genet 105:237–247PubMedCrossRefGoogle Scholar
  2. Barr AR, Jeffries SP, Broughton S, Chalmers KJ, Kretschmer JM, Boyd WJR, Collins HM, Roumeliotis S, Logue SJ, Coventry SJ, Moody DB, Read BJ, Poulsen D, Lance RCM, Platz GJ, Park RF, Panozzo JF, Karakousis A, Lim P, Verbyla AP, Eckermann PJ (2003a) Mapping and QTL analysis of the barley population Alexis × Sloop. Aust J Agric Res 54:1117–1123CrossRefGoogle Scholar
  3. Barr AR, Karakousis A, Lance RCM, Logue SJ, Manning S, Chalmers KJ, Kretschmer JM, Boyd WJR, Collins HM, Roumeliotis S, Coventry SJ, Moody DB, Read BJ, Poulsen D, Li CD, Platz GJ, Inkerman PA, Panozzo JF, Cullis BR, Smith AB, Lim P, Langridge P (2003b) Mapping and QTL analysis of the barley population Chebec × Harrington. Aust J Agric Res 54:1125–1130CrossRefGoogle Scholar
  4. Beattie AD, Edney MJ, Scoles GJ, Rossnagel BG (2010) Association mapping of malting quality data from Western Canadian two-row barley cooperative trials. Crop Sci 50:1649–1663CrossRefGoogle Scholar
  5. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300Google Scholar
  6. Bezant J, Laurie D, Pratchett N, Chojecki J, Kearsey M (1997) Mapping QTL controlling yield and yield components in a spring barley (Hordeum vulgare L.) cross using marker regression. Mol Breed 3:29–38CrossRefGoogle Scholar
  7. Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32:314–331PubMedGoogle Scholar
  8. Bradbury PJ, Zhang Z, Kroon DE, Casstevens RM, Ramdoss Y, Buckler ES (2007) TASSEL software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635PubMedCrossRefGoogle Scholar
  9. Breseghello F, Sorrells ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177PubMedCrossRefGoogle Scholar
  10. Camus-Kulandaivelu L, Veyrieras J, Gouesnard B, Charcosset A, Manicacci D (2007) Evaluating the reliability of structure outputs case of relatedness between individuals. Crop Sci 47:887–890CrossRefGoogle Scholar
  11. Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74:829–836CrossRefGoogle Scholar
  12. Comadran J, Thomas WTB, van Eeuwijk FA, Ceccarelli S, Grando S, Stanca AM, Pecchioni N, Akar T, Al-Yassin A, Benbelkacem A, Ouabbou H, Bort J, Romagosa I, Hackett CA, Russell JR (2009) Patterns of genetic diversity and linkage disequilibrium in a highly structured Hordeum vulgare association-mapping population for the Mediterranean basin. Theor Appl Genet 119:175–187PubMedCrossRefGoogle Scholar
  13. Comadran J, Ramsay L, MacKenzie K, Hayes P, Close TJ, Muehlbauer G, Stein N, Waugh R (2011) Patterns of polymorphism and linkage disequilibrium in cultivated barley. Theor Appl Genet. doi:10.1007/s00122-010-1466-7
  14. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure: extensions to linked loci and correlated allele frequencies. Genetics 164:1567–1587PubMedGoogle Scholar
  15. Flint-Garcia SA, Thornsberry JM, Buckler ES IV (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374Google Scholar
  16. Hamblin MT, Warburton ML, Buckler ES (2007) Empirical comparison of simple sequence repeats and single nucleotide polymorphisms in assessment of maize diversity and relatedness. PLoS One 2(12):e1367PubMedCrossRefGoogle Scholar
  17. Han F, Romagosa I, Ullrich SE, Jones BL, Hayes PM, Wesenberg DM (1997a) Molecular marker-assisted selection for malting quality traits in barley. Mol Breed 3:427–437CrossRefGoogle Scholar
  18. Han F, Ullrich SE, Kleinhofs A, Jones BL, Hayes PM, Wesenberg DM (1997b) Fine structure mapping of the barley chromosome-1 centromere region containing malting-quality QTLs. Theor Appl Genet 95:903–910CrossRefGoogle Scholar
  19. Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620CrossRefGoogle Scholar
  20. Haseneyer G, Stracke S, Paul C, Einfeldt C, Broda A, Piepho HP, Graner A, Geiger HH (2009) Population structure and phenotypic variation of a spring barley world collection set up for association studies. Plant Breed 129(3):271–279CrossRefGoogle Scholar
  21. Hayes PM, Liu BH, Knapp SJ, Chen F, Jones B, Blake T, Franckowiak J, Rasmusson D, Sorrells M, Ulrich SE, Wesenberg D, Kleinhofs A (1993) Quantitative trait locus effects and environmental interaction in a sample of North American barley germplasm. Theor Appl Genet 87:392–401CrossRefGoogle Scholar
  22. Herz MP (2000) Kartierung quantitativer vererbter Eigenschaften einschließlich Brauqualität und Resistenz gegen Krankheiten mit molekularen Markern in Gerste. PhD-Thesis. Technische Universität München (TUM), Lehrstuhl für PflanzenzüchtungGoogle Scholar
  23. Hill WG, Robertson A (1968) Linkage disequilibrium in finite populations. Theor Appl Genet 38:226–231CrossRefGoogle Scholar
  24. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70Google Scholar
  25. Jaccard P (1908) Nouvelles recherches sur la distribution florale. Bull Soc Vaud Sci Nat 44:223–270Google Scholar
  26. Jaccoud D, Peng K, Feinstein D, Kilian A (2001) Diversity arrays: a solid state technology for sequence information independent genotyping. Nucleic Acids Res 29:e25PubMedCrossRefGoogle Scholar
  27. Kennedy BW, Quinton M, Vanarendonk JAM (1992) Estimation of effects of single genes on quantitative traits. J Anim Sci 70:2000–2012PubMedGoogle Scholar
  28. Kjaer B, Jensen J (1996) Quantitative trait loci for grain yield and yield components in a cross between a six-rowed and a two-rowed barley. Euphytica 90:39–48Google Scholar
  29. Krumnacker K (2009) Untersuchung der funktionellen Assoziation von Kandidatengenen in Zusammenhang mit der Malzqualität der Gerste durch Transkriptomkartierung. PhD-Thesis. Technische Universität München (TUM), Lehrstuhl für PflanzenzüchtungGoogle Scholar
  30. Larson SR, Habernicht DK, Blake TK, Adamson M (1997) Backcross gains for six-rowed grain and malt qualities with introgression of a feed barley yield QTL. J Am Soc Brew Chem 55:52–57Google Scholar
  31. Li JZ, Huang XQ, Heinrichs F, Ganal MW, Röder MS (2005) Analysis of QTLs for yield, yield components, and malting quality in a BC3-DH population of spring barley. Theor Appl Genet 110:356–363PubMedCrossRefGoogle Scholar
  32. Liu K, Muse SV (2005) PowerMarker: integrated analysis environment for genetic marker data. Bioinformatics 21(9):2128–2129PubMedCrossRefGoogle Scholar
  33. Malysheva-Otto LV, Ganal MW, Röder MS (2006) Analysis of molecular diversity, population structure and linkage disequilibrium in worldwide cultivated barley germplasm (Hordeum vulgare L.). BMC Genet 7:6PubMedCrossRefGoogle Scholar
  34. Marquez-Cedillo LA, Hayes PM, Jones BL, Kleinhofs A, Legge WG, Rossnagel BG, Sato K, Ullric E, Wesenberg DM (2000) QTL analysis of malting quality in barley based on doubled-haploid progeny of two elite North American varieties representing different germplasm pools. Theor Appl Genet 101:173–184CrossRefGoogle Scholar
  35. Marquez-Cedillo LA, Hayes PM, Kleinhofs A, Legge WG, Rossnagel BG, Sato K, Ullrich SE, Wesenberg DM (2001) QTL analysis of agronomic traits in barley based on the doubled haploid progeny of two elite North American varieties representing different germplasm groups. Theor Appl Genet 103:625–637CrossRefGoogle Scholar
  36. Mather DE, Tinker NA, LaBerge DE, Edney M, Jones BL, Rossnagel BG, Legge WG, Briggs KG, Irvine RB, Falk DE, Kasha KJ (1997) Regions of the genome that affect grain and malt quality in a North American two-row barley cross. Crop Sci 37:544–554CrossRefGoogle Scholar
  37. Matthies IE, Weise S, Röder MS (2009a) Association of haplotype diversity in the α-amylase gene amy1 with malting quality parameters in barley. Mol Breed 23:139–152CrossRefGoogle Scholar
  38. Matthies IE, Weise S, Förster J, Röder MS (2009b) Association mapping and marker development of the candidate genes (1→3),(1→4)-β-d-Glucan-4-glucanohydrolase and (1→4)-β-Xylan-endohydrolase 1 for malting quality in barley. Euphytica 170:109–122CrossRefGoogle Scholar
  39. Mezmouk S, Dubreuil P, Bosio M, Décousset L, Charcosset A, Praud S, Mangin B (2011) Effect of population structure corrections on the results of association mapping tests in complex maize diversity panels. Theor Appl Genet 122:1149–1160PubMedCrossRefGoogle Scholar
  40. Nei M, Li WH (1979) Mathematical models for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci USA 76:5269–5273PubMedCrossRefGoogle Scholar
  41. Oziel A, Hayes PM, Chen FQ, Jones B (1996) Application of quantitative trait locus mapping to the development of winter-habit malting barley. Plant Breed 115:43–51CrossRefGoogle Scholar
  42. Plaschke J, Ganal MW, Röder MS (1995) Detection of genetic diversity in closely related bread wheat using microsatellite markers. Theor Appl Genet 91:1001–1007CrossRefGoogle Scholar
  43. Pourkheirandish M, Komatsuda T (2007) The importance of barley genetics and domestication in a global perspective. Ann Bot 100:999–1008PubMedCrossRefGoogle Scholar
  44. Pritchard JK, Rosenberg NA (1999) Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 65:220–228PubMedCrossRefGoogle Scholar
  45. Pritchard JK, Stephens M, Donnelly PJ (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedGoogle Scholar
  46. Pswarayi A, van Eeuwijk FA, Ceccarelli S, Grando S, Comadran J, Russell JR, Pecchioni N, Tondelli A, Akar T, Al-Yassin A, Benbelkacem A, Ouabbou H, Thomas WTB, Romagosa I (2008) Changes in allele frequencies in landraces, old and modern barley cultivars of marker loci close to QTL for grain yield under high and low input conditions. Euphytica 163:435–447CrossRefGoogle Scholar
  47. Remington DL, Thornsberry JM, Matsuoka Y, Wilson LM, Whitt SR, Doebley J, Kresovich S, Goodman MM, Buckler ES (2001) Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc Natl Acad Sci USA 98:11479–11484PubMedCrossRefGoogle Scholar
  48. Ritland K (1996) Estimators for pairwise relatedness and individual inbreeding coefficients. Genet Res 67:175–186CrossRefGoogle Scholar
  49. Rostoks N, Ramsay L, MacKenzie K, Cardle L, Bhat PR, Roose ML, Svensson JT, Stein N, Varshney RK, Marshall DF, Graner A, Close TJ, Waugh R (2006) Recent history of artificial outcrossing facilitates whole-genome association mapping in elite inbred crop varieties. Proc Natl Acad Sci USA 103:18656–18661PubMedCrossRefGoogle Scholar
  50. Russell JR, Ellis RP, Thomas WTB, Waugh R, Provan J, Booth A, Fuller J, Lawrence P, Young G, Powell W (2000) A retrospective analysis of spring barley germplasm development from ‘foundation genotypes’ to currently successful cultivars. Mol Breed 6:553–568CrossRefGoogle Scholar
  51. Russell J, Booth A, Fuller J, Harrower B, Hedley P, Machray G, Powell W (2004) A comparison of sequence-based polymorphism and haplotype content in transcribed and anonymous regions of the barley genome. Genome 47:389–398PubMedCrossRefGoogle Scholar
  52. Schmalenbach I, Léon J, Pillen K (2009) Identifcation and verification of QTLs for agronomic traits using wild barley introgression lines. Theor Appl Genet 118:483–497PubMedCrossRefGoogle Scholar
  53. Searle SR (1987) Linear models for unbalanced data. Wiley, New YorkGoogle Scholar
  54. Smith JSC, Kresovich S, Hopkins MS, Mitchell SE, Dean RE, Woodman WL, Lee M, Porter K (2000) Genetic diversity among elite Sorghum inbred lines assessed with simple sequence repeats. Crop Sci 40:226–232CrossRefGoogle Scholar
  55. Sneath PHA (1957) Some thoughts on bacterial classification. J Gen Microbiol 17:184–200PubMedCrossRefGoogle Scholar
  56. Stracke S, Haseneyer G, Veyrieras JB, Geiger HH, Sauer S, Graner A, Piepho HP (2009) Association mapping reveals gene action and interactions in the determination of flowering time in barley. Theor Appl Genet 118(2):259–273PubMedCrossRefGoogle Scholar
  57. Szücs P, Blake VC, Bhat PR, Chao S, Close TJ, Cuesta-Marcos A, Muehlbauer GJ, Ramsay L, Waugh R, Hayes PM (2009) An integrated resource for barley linkage map and malting quality QTL alignment. Plant Genome 2:134–140CrossRefGoogle Scholar
  58. Tinker NA, Mather DE, Rossnagel BG, Kasha KJ, Kleinhofs A, Hayes PM, Falk DE, Ferguson T, Shugar LP, Legge WG, Irvine RB, Choo TM, Briggs KG, Ullrich SE, Franckowiak JD, Blake TK, Graf RJ, Dofing SM, Saghai Maroof MA, Scoles GJ, Hoffman D, Dahleen LS, Kilian A, Chen F, Biyashev RM, Kudrna DA, Steffenson BJ (1996) Regions of the genome that affect agronomic performance in two-row barley. Crop Sci 36:1053–1062CrossRefGoogle Scholar
  59. Ullrich SE, Han F (1997) Genetic complexity of the malt extract trait in barley suggested by QTL analysis. J Am Soc Brew Chem 55(1):1–4Google Scholar
  60. van Hintum TJL (2007) Data resolution: a jackknife procedure for determining the consistency of molecular marker datasets. Theor Appl Genet 115:343–349PubMedCrossRefGoogle Scholar
  61. van Inghelandt D, Melchinger AE, Lebreton C, Stich B (2010) Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers. Theor Appl Genet 120:1289–1299PubMedCrossRefGoogle Scholar
  62. Varshney RK, Marcel TC, Ramsay L, Russell J, Röder MS, Stein N, Waugh R, Langridge P, Niks RE, Graner A (2007) A high density barley microsatellite consensus map with 775 SSR loci. Theor Appl Genet 114:1091–1103PubMedCrossRefGoogle Scholar
  63. Waugh R, Jannink JL, Muehlbauer GL, Ramsay L (2009) The emergence of whole genome association scans in barley. Curr Opin Plant Biol 12:1–5CrossRefGoogle Scholar
  64. Weir BS (1996) Genetic data analysis II. Sinauer, MassachusettsGoogle Scholar
  65. Weise S, Scholz U, Röder MS, Matthies IE (2009) A comprehensive database of malting quality traits in brewing barley. Barley Genet Newsl 39:1–4Google Scholar
  66. Wenzl P, Carling J, Kudrna D, Jaccoud D, Huttner E, Kleinhofs A, Kilian A (2004) Diversity arrays technology (DArT) for whole-genome profiling of barley. Proc Natl Acad Sci USA 101(26):9915–9920PubMedCrossRefGoogle Scholar
  67. Wenzl P, Li H, Carling J, Zhou M, Raman H, Paul E, Hearnden P, Maier C, Xia L, Caig V, Ovesná J, Cakir M, Poulsen D, Wang J, Raman R, Smith KP, Muehlbauer GJ, Chalmers KJ, Kleinhofs A, Huttner E, Kilian A (2006) A high-density consensus map of barley linking DArT markers to SSR, RFLP and STS loci and agricultural traits. BMC Genomics 7:206PubMedCrossRefGoogle Scholar
  68. Wenzl P, Raman H, Wang J, Zhou M, Huttner E, Kilian A (2007) A DArT platform for quantitative bulked segregant analysis. BMC Genomics 8:196PubMedCrossRefGoogle Scholar
  69. Worch S, Kalladan R, Harshavardhan VT, Pietsch C, Korzun V, Kuntze L, Börner A, Wobus U, Röder MS, Sreenivasulu N (2011) Haplotyping, linkage mapping and expression analysis of barley genes regulated by terminal drought stress influencing seed quality. BMC Plant Biol 11:1PubMedCrossRefGoogle Scholar
  70. Yu J, Buckler ES (2006) Genetic association mapping and genome organization of maize. Curr Opin Biotechnol 17:155–160PubMedCrossRefGoogle Scholar
  71. 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 38:203–208PubMedCrossRefGoogle Scholar
  72. 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(1):63–77CrossRefGoogle Scholar
  73. Zhang LY, Marchand S, Tinker NA, Belzile F (2009) Population structure and linkage disequilibrium in barley assessed by DArT markers. Theor Appl Genet 119(1):43–52PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Inge E. Matthies
    • 1
  • Theo van Hintum
    • 2
  • Stephan Weise
    • 1
  • Marion S. Röder
    • 1
  1. 1.Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)GaterslebenGermany
  2. 2.Centre for Genetic Resources, The NetherlandsWageningen University and Research CentreWageningenThe Netherlands

Personalised recommendations