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Molecular Breeding

, 35:143 | Cite as

Association mapping of seed quality traits in Brassica napus L. using GWAS and candidate QTL approaches

  • Humberto A. Gajardo
  • Benjamin Wittkop
  • Braulio Soto-Cerda
  • Erin E. Higgins
  • Isobel A. P. Parkin
  • Rod J. Snowdon
  • Maria L. Federico
  • Federico L. Iniguez-LuyEmail author
Article

Abstract

Single nucleotide polymorphisms (SNPs) have rapidly become the molecular marker of choice in plant and animal association mapping (AM) studies. In this work, a genome-wide association study (GWAS) and candidate quantitative trait loci (cQTL) approaches were used to identify SNP markers associated with seed quality traits, in a Brassica napus L. association panel composed of 89 adapted winter oilseed rape accessions. Six seed quality traits (oil and protein content, linolenic acid, total glucosinolates, hemicellulose and cellulose content) were evaluated in two different locations for two seasons. For GWAS, 4025 SNP markers evenly distributed along the B. napus genome were genotyped using a 6K Illumina array platform. For cQTL, 100 SNP markers previously discovered in genomic regions underlying seed quality QTL were genotyped using a competitive allele-specific PCR (KASPar). Analysis of the population structure revealed the presence of two weakly differentiated subpopulations (F ST  = 0.037), with 82 % of the pairwise kinship comparisons ranging from 0 to 0.1. The GWAS approach resulted in the identification of 17 and 5 significant associations for seed glucosinolate content and seed hemicellulose content, respectively. The cQTL approach identified 4 significant associations for seed glucosinolate content and 6 significant associations for seed hemicellulose content. The associated SNPs were consistently identified across environments and were mapped to previously reported QTL. These results illustrate the suitability of AM to identify SNP markers associated with seed quality traits in B. napus.

Keywords

Association mapping Brassica napus Seed quality SNP Glucosinolate Hemicellulose 

Notes

Acknowledgments

The authors would like to thank Katherine Andara for her technical assistance. We acknowledge Fondecyt 1100732, Proyecto Fortalecimiento R13F1001, Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) Regional Program and the Araucania Regional Government/CGNA/R10C1001 and INIA for its support providing laboratory infrastructure. HG was supported by Becas de Magíster Nacional—CONICYT No: 22121770.

Supplementary material

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References

  1. Amar S, Ecke W, Becker HC, Möllers C (2008) QTL for phytosterol and sinapate ester content in Brassica napus L. collocate with the two erucic acid genes. Theor Appl Genet 116:1051–1061PubMedCentralPubMedCrossRefGoogle Scholar
  2. Barchi L, Lanteri S, Portis E, Acquardo A, Vale G, Toppino L, Rotino GL (2011) Identification of SNP and SSR markers in eggplant using RAD tag sequencing. BMC Genom 12:304CrossRefGoogle Scholar
  3. Basunanda P, Radoev M, Ecke W, Friedt W, Becker H, Snowdon RJ (2010) Comparative mapping of quantitative trait loci involved in heterosis for seedling and yield traits in oilseed rape (Brassica napus L.). Theor Appl Genet 120:271–281PubMedCentralPubMedCrossRefGoogle Scholar
  4. Becker HC, Engqvist GM, Karlsson B (1995) Comparison of rapeseed cultivars and resynthesized lines based on allozyme and RFLP markers. Theor Appl Genet 91:62–67PubMedCrossRefGoogle 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. Bradbury PJ, Zhang Z, Kroon DE, Casstevens RM, Ramdoss Y, Buckler ES (2007) TASSELL software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635PubMedCrossRefGoogle Scholar
  7. Brader G, Tas E, Palva ET (2001) Jasmonate-dependent induction of indol glucosinolates in Arabidopsis by culture filtrates of the nonspecific pathogen Erwinia carotovora. Plant Physiol 126:849–860PubMedCentralPubMedCrossRefGoogle Scholar
  8. Breseghello F, Sorrells M (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177PubMedCentralPubMedCrossRefGoogle Scholar
  9. Bus A, Körber N, Snowdon RJ, Stich B (2011) Patterns of molecular variation in a species-wide germplasm set of Brassica napus. Theor Appl Genet 123:1413–1423PubMedCrossRefGoogle Scholar
  10. Bus A, Hecht J, Huettel B, Reinhardt R, Stich B (2012) High-throughput polymorphism detection and genotyping in Brassica napus using next-generation RAD sequencing. BMC Genom 13:281CrossRefGoogle Scholar
  11. Chalhoub et al (2014) Early allopolyploid evolution in the post-Neolithic Brassica napus oilseed genome. Science 345:950–953PubMedCrossRefGoogle Scholar
  12. Cheng F, Liu S, Wu J, Fang L, Sun S, Liu B, Li P, Hua W, Wang X (2011) BRAD, the genetics and genomics database for Brassica plants. BMC Plant Biol 11:136PubMedCentralPubMedCrossRefGoogle Scholar
  13. Clarke WE, Parkin IA, Gajardo HA, Gerhardt DJ, Higgins E, Sidebottom C, Sharpe AG, Snowdon RJ, Federico ML, Iniguez-Luy FL (2013) Genomic DNA enrichment using sequence capture microarrays: a novel approach to discover sequence nucleotide polymorphisms (SNP) in Brassica napus L. PLoS ONE 8(12):e81992PubMedCentralPubMedCrossRefGoogle Scholar
  14. Collins DW, Jukes TH (1994) Rates of transition and transversion in coding sequence since the human-rodent divergence. Genomics 20:386–396PubMedCrossRefGoogle Scholar
  15. Cuppen E (2007) Genotyping by Allele-Specific Amplification (KASPar). Protoc, Cold Spring Harb. doi: 10.1101/pdb.prot4841 Google Scholar
  16. Delourme R, Falentin C, Huteau V, Clouet V, Horvais R, Gandon B, Specel S, Hanneton L, Dheu JE, Deschamps M, Margale E, Vincourt P, Renard M (2006) Genetic control of oil content in oilseed rape (Brassica napus L.). Theor Appl Genet 113:1331–1345PubMedCrossRefGoogle Scholar
  17. Delourme R, Falentin C, Fopa Fomeju B, Boillot M, Lassalle G, André I, Duarte J, Gauthier V, Lucante N, Marty A, Pauchon M, Pichon J-P, Ribière N, Trotoux G, Blanchard P, Rivière N, Martinant J-P, Pauquet J (2013) High-density SNP-based genetic map development and linkage disequilibrium assessment in Brassica napus L. BMC Genom 14:120CrossRefGoogle Scholar
  18. Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissue. Focus 12:13–15Google Scholar
  19. Earl Dent A, von Holdt Bridgett M (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361CrossRefGoogle Scholar
  20. Ebersberger I, Metzler D, Scwarz C, Paabo S (2002) Genomewide comparison of DNA between humans and chimpanzees. Am J Hum Genet 70:1490–1497PubMedCentralPubMedCrossRefGoogle Scholar
  21. Ecke W, Uzunova M, Weißleder K (1995) Mapping the genome of rapeseed (Brassica napus L.). II. Localization of genes controlling erucic acid synthesis and seed oil content. Theor Appl Genet 91:972–977PubMedGoogle Scholar
  22. Ecke W, Clemens R, Honsdorf N, Becker HC (2010) Extent and structure of linkage disequilibrium in canola quality winter rapeseed (Brassica napus L.). Theor Appl Genet 120:921–931PubMedCentralPubMedCrossRefGoogle Scholar
  23. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620PubMedCrossRefGoogle Scholar
  24. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587PubMedCentralPubMedGoogle Scholar
  25. Feng J, Long Y, Shi L, Shi J, Barker G, Meng J (2012) Characterization of metabolite quantitative trait loci and metabolic networks that control glucosinolate concentration in the seeds and leaves of Brassica napus. New Phytol 193:96–108PubMedCrossRefGoogle Scholar
  26. Fritsche S, Wang X, Li J, Stich B, Kopisch-Obuch FJ, Endrigkeit J, Leckband G, Dreyer F, Friedt W, Meng J, Jung C (2012) A candidate gene-based association study of tocopherol content and composition in rapeseed (Brassica napus). Front Plant Sci 3:129PubMedCentralPubMedCrossRefGoogle Scholar
  27. Galeano C, Cortés A, Fernández A, Soler A, Franco-Herrera N, Makunde G, Vanderleyden J, Blair M (2012) Gene-based single nucleotide polymorphism markers for genetic and association mapping in common bean. BMC Genet 13:48PubMedCentralPubMedCrossRefGoogle Scholar
  28. Gruber MY, Xu N, Grenkow LF, Onyilagha J, Soroka JJ, Westcott ND, Hugedus DD (2009) Responses of the crucifer flea beetle to Brassica volatiles in an olfactometer. Environ Entomol 38:1467–1479PubMedCrossRefGoogle Scholar
  29. Gupta P, Rustgi S, Kulwal P (2005) Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Mol Biol 57:461–485PubMedCrossRefGoogle Scholar
  30. Gyawali S, Hegedus DD, Parkin I, Poon J, Higgins E, Horner K, Bekkaoui D, Coutu C, Buchwaldt L (2013) Genetic diversity and population structure in a world collection of Brassica napus accessions with emphasis on South Korea, Japan, and Pakistan. Crop Sci 53:1537–1545CrossRefGoogle Scholar
  31. Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer program to analyze spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620CrossRefGoogle Scholar
  32. Harper AL, Trick M, Higgins J, Fraser F, Clissold L, Wells R, Hattori C, Werner P, Bancroft I (2012) Associative transcriptomics of traits in the polyploid crop species Brassica napus. Nat Biotech 30:798–802CrossRefGoogle Scholar
  33. Hasan M, Seyis F, Badani AG, Pons-Kühnemann J, Friedt W, Lühs W, Snowdon RJ (2006) Analysis of genetic diversity in the Brassica napus L. gene pool using SSR markers. Genet Resour Crop Evol 53:793–802CrossRefGoogle Scholar
  34. Hasan M, Friedt W, Pons-Kühnemann J, Freitag NM, Link K, Snowdon RJ (2008) Association of gene-linked SSR markers to seed glucosinolate content in oilseed rape (Brassica napus ssp. napus). Theor Appl Genet 116:1035–1049PubMedCrossRefGoogle Scholar
  35. Honsdorf N, Becker HC, Ecke W (2010) Association mapping for phenological, morphological, and quality traits in canola quality winter rapeseed (Brassica napus L.). Genome 53:899–907PubMedCrossRefGoogle Scholar
  36. Howell PM, Sharpe AG, Lydiate DJ (2003) Homoeologous loci control the accumulation of seed glucosinolates in oilseed rape (Brassica napus). Genome 46:454–460PubMedCrossRefGoogle Scholar
  37. 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 E, Qian Q, Zhang Q, Li J, Han B (2010) Genome-wide studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967PubMedCrossRefGoogle Scholar
  38. Iniguez-Luy FL, Federico ML (2011) The genetics of Brassica napus L. In: Schmidt R, Bancroft I (ed) Genetic and Genomics of the Brassicaceae, New York, pp 291–322Google Scholar
  39. Jestin C, Lodé M, Vallée P, Domin C, Falentin C, Horvais R, Coedel S, Manzanares-Dauleux MJ, Delourme R (2011) Association mapping of quantitative resistance for Leptosphaeria maculans in oilseed rape (Brassica napus L.). Mol Breed 27:271–287CrossRefGoogle Scholar
  40. Khajali F, Slominski BA (2012) Factors that affect the nutritive value of canola meal for poultry. Poult Sci 91:2564–2575PubMedCrossRefGoogle Scholar
  41. Kimber DS, McGregor DI (1995) The species and their origin, cultivation and world production. In: Kimber D, McGregor DI (eds) Brassica oilseeds: production and utilization. CABI Publishing, Wallingford, pp 1–9Google Scholar
  42. Kump K, Bradbury P, Wisser R, Buckler E, Belcher A, Oropeza-Rosas M, Zwonitzer J, Kresovich S, McMullen M, Ware D, Balint-Kurti P, Holland J (2011) Genome wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet 43:163–168PubMedCrossRefGoogle Scholar
  43. Li F, Chen B, Xu K, Wu J, Song W, Bancroft I, Harper A, Trick M, Liu S, Gao G, Wang N, Yan G, Qiao J, Li J, Li H, Xiao X, Zhang T and Wu X (2014) Genome-wide association study dissects the genetic architecture of seed weight and seed quality in rapeseed (Brassica napus L.). DNA Research, pp 1–13Google Scholar
  44. Liu K, Muse S (2005) PowerMarker: an integrative analysis environment for genetic marker analysis. Bioinformatics 21:2128–2129PubMedCrossRefGoogle Scholar
  45. Liu L, Qu C, Witttkop B, Yi B, Xiao Y, He Y, Snowdon R, Li J (2013) A high-density SNP map for accurate mapping of seed fiber QTL in Brassica napus L. PLoS ONE 8(12):e83052PubMedCentralPubMedCrossRefGoogle Scholar
  46. Liu S et al (2014) The Brassica oleracea genome reveals the asymmetrical evolution of polyploidy genomes. Nat Commun 5:3930PubMedCentralPubMedGoogle Scholar
  47. Loiselle BA, Sork VL, Nason J, Graham C (1995) Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). Am J Bot 82:1420–1425CrossRefGoogle Scholar
  48. Ma ZZ, Li JN, Wittkop B, Frauen M, Yan XY, Liu LZ, Xiao Y (2013) QTL mapping for oil, protein, cellulose and hemicellulose content in seeds of Brassica napus L. Acta Agron Sin 39:1214–1222CrossRefGoogle Scholar
  49. Marwede V, Gül MK, Becker HC, Ecke W (2005) Mapping of QTL controlling tocopherol contents in winter oilseed rape. Plant Breed 124:20–26CrossRefGoogle Scholar
  50. Park S, Yu HJ, Mun JH, Lee SC (2010) Genome-wide discovery of DNA polymorphism in Brassica rapa. Mol Genet Genomics 283:135–145PubMedCrossRefGoogle Scholar
  51. Pasam R, Sharma R, Malosetti M, van Eeuwijk F, Haseneyer G, Kilian B, Graner A (2012) Genome-wide association studies for agronomical traits in a worldwide spring barley collection. BMC Plant Biol 12:16PubMedCentralPubMedCrossRefGoogle Scholar
  52. Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6:288–295CrossRefGoogle Scholar
  53. Poland J, Bradbury P, Buckler E, Nelson R (2011) Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. PNAS 108:6893–6898PubMedCentralPubMedCrossRefGoogle Scholar
  54. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedCentralPubMedGoogle Scholar
  55. Qiu D, Morgan C, Shi J, Long Y, Liu J, Li R, Zhuang X, Wang Y, Tan X, Dietrich E, Weihmann T, Everett C, Vanstraelen S, Beckett P, Fraser F, Trick M, Barnes S, Wilmer J, Schmidt R, Li J, Li D, Meng J, Brancroft I (2006) A comparative linkage map of oilseed rape and its use for QTL analysis of seed oil and erucic acid content. Theor Appl Genet 114:67–80PubMedCrossRefGoogle Scholar
  56. Quijada PA, Udall JA, Lambert B, Osborn TC (2006) Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 1. Identification of genomic regions from winter germplasm. Theor Appl Genet 113:549–561PubMedCrossRefGoogle Scholar
  57. Radoev M (2007) Genetic analysis of heterosis in rapeseed (B. napus L.) by QTL mapping [online]. Ph.D. thesis, Faculty of Agriculture, University Göttingen. http://webdoc.sub.gwdg.de/diss/2007/radoev/radoev.pdf
  58. Rashid U, Anwar F (2008) Production of biodiesel through optimized alkaline-catalyzed transesterification of rapeseed oil. Fuel 87:265–273CrossRefGoogle Scholar
  59. Rezaeizad A, Wittkop B, Snowdon R, Hasan M, Mohammadi V, Zali A, Friedt W (2011) Identification of QTLs for phenolic compounds in oilseed rape (Brassica napus L.) by association mapping using SSR markers. Euphytica 177:335–342CrossRefGoogle Scholar
  60. Rousset M, Bonnin I, Remoué C, Falque M, Rhoné B, Veyrieras J, Madur D, Murigneux A, Balfourier F, Le Gouis J, Santoni S, Goldringer I (2011) Deciphering the genetics of flowering time by an association study on candidate genes in bread wheat (Triticum aestivum L.). Theor Appl Genet 123:907–926PubMedCrossRefGoogle Scholar
  61. Snowdon RJ, Iniguez-Luy FL (2012) Potential to improve oilseed rape and canola breeding in the genomics era. Plant Breed 131:351–360CrossRefGoogle Scholar
  62. Snowdon RJ, Luhs W, Friedt W (2007) Oilseed rape. In: Kole C (ed) Genome mapping and molecular breeding in plants, vol 2. Springer, Heidelberg, pp 55–114Google Scholar
  63. Sorkheh K, Malysheva-Otto LV, Wirthensohn MG, Tarkesh-Esfahani S, Martínez-Gómez P (2008) Linkage disequilibrium, genetic association mapping and gene localization in crop plants. Genet Mol Biol 31:805–814CrossRefGoogle Scholar
  64. Soto-Cerda BJ, Cloutier S (2012) Association mapping in plant genomes. In: Caliskan M (ed) Genetic diversity in plants. InTech, Rijeka, pp 29–54Google Scholar
  65. Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. PNAS 100:9440–9445PubMedCentralPubMedCrossRefGoogle Scholar
  66. Thormann CE, Romero J, Mantet J, Osborn TC (1996) Mapping loci controlling the concentrations of erucic and linolenic acids in seed oil of Brassica napus L. Theor Appl Genet 93:282–286PubMedCrossRefGoogle Scholar
  67. Tian F, Bradbury P, Brown P, Hung H, Sun Q, Flint-Garcia S, Rocheford T, McMullen M, Holland J, Buckler E (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43:159–162PubMedCrossRefGoogle Scholar
  68. Tierens KF, Thomma BP, Brouwer M, Schmidt J, Kistner K, Porzel A, Mauch-Mani B, Cammue BP, Broekaert WF (2001) Study of the role of antimicrobial glucosinolate-derived isothiocyanates in resistance of Arabidopsis to microbial pathogens. Plant Physiol 125:1688–1699PubMedCentralPubMedCrossRefGoogle Scholar
  69. Toroser D, Thormann CE, Osborn TC, Mithen R (1995) RFLP mapping of quantitative trait loci controlling seed aliphatic-glucosinolate content in oilseed rape (Brassica napus L.). Theor Appl Genet 91:802–880PubMedCrossRefGoogle Scholar
  70. Udall JA, Quijada PA, Lambert B, Osborn TC (2006) Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 2. identification of alleles from unadapted germplasm. Theor Appl Genet 113:597–609PubMedCrossRefGoogle Scholar
  71. Upadhyaya HD, Wang YH, Gowda CLL, Sharma S (2013) Association mapping of maturity and plant height using SNP markers with the sorghum mini core collection. Theor Appl Genet 126:2003–2015PubMedCrossRefGoogle Scholar
  72. Uzunova M, Ecke W, Weißleder K, Röbbelen G (1995) Mapping the genome of rapeseed (Brassica napus L.) I. construction of an RFLP linkage map and localization of QTLs for seed glucosinolate content. Theor Appl Genet 90:194–204PubMedCrossRefGoogle Scholar
  73. Van Ooijen JW (2006) Join Map ® 4, Software for the calculation of genetic linkage maps in experimental populations. Kyazma B.V., Wageningen, Netherlands.Google Scholar
  74. Wang N, Wang Y, Tian F, King GJ, Zhang C, Long Y, Shi L, Meng J (2008) A functional genomics resource for Brassica napus: development of an EMS mutagenized population and discovery of FAE1 point mutations by TILLING. New Phytol 180:751–765PubMedCrossRefGoogle Scholar
  75. Wang X et al (2011) The genome of the mesopolyploi crop species Brassica rapa. Nat Genet 43:1035–1039Google Scholar
  76. Wittkop B, Snowdon R, Friedt W (2009) Status and perspectives of breeding for enhanced yield and quality of oilseed crops for Europe. Euphytica 170:131–140CrossRefGoogle Scholar
  77. Xiao Y, Cai D, Yang W, Ye W, Younas M, Wu J, Liu K (2012) Genetic structure and linkage disequilibrium pattern of a rapeseed (Brassica napus L.) association mapping panel revealed by microsatellites. Theor Appl Genet 125:437–447PubMedCrossRefGoogle Scholar
  78. Yang X, Yan J, Shah T, Warburton ML, Li Q, Li L, Gao Y, Chai Y, Fu Z, Zhou Y, Xu S, Bai G, Meng Y, Zheng Y, Li J (2010) Genetic analysis and characterization of a new maize association mapping panel for quantitative trait loci dissection. Theor Appl Genet 121:417–431PubMedCrossRefGoogle Scholar
  79. Yu J, Pressoir G, Briggs W, Bi IV, Yamasaki M, Doebley J, McMullen M, Gaut B, Nielsen D, Holland J, Kresovich S, Buckler E (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208Google Scholar
  80. Zhang GQ, He Y, Xu L, Tang GX, Zhou WJ (2006) Genetic analyses of agronomic and seed quality traits of doubled haploid population in Brassica napus through microspore culture. Euphytica 149:169–177CrossRefGoogle Scholar
  81. Zhao J, Meng J (2003) Detection of loci controlling seed glucosinolate content and their association with Sclerotinia resistance in Brassica napus. Plant Breed 122:19–23CrossRefGoogle Scholar
  82. Zhao JY, Becker HC, Zhang DQ, Zhang YF, Ecke W (2005) Oil content in a European Chinese rapeseed population: QTL with additive and epistatic effects and their genotype-environment interactions. Crop Sci 45:51–59CrossRefGoogle Scholar
  83. Zhao J, Becker HC, Zhang D, Zhang Y, Ecke W (2006) Conditional QTL mapping of oil content in rapeseed with respect to protein content and traits related to plant development and grain yield. Theor Appl Genet 113:33–38PubMedCrossRefGoogle Scholar
  84. Zhao J, Dimov Z, Becker HC, Ecke W, Möllers C (2008) Mapping QTL controlling fatty acid composition in a doubled haploid rapeseed population segregating for oil content. Mol Breed 21:115–125CrossRefGoogle Scholar
  85. Zhu Ch, Gore M, Buckler E, Yu J (2008) Status and prospects of association mapping in plants. Plant Genome 1:5–20CrossRefGoogle Scholar
  86. Zou J, Jiang C, Cao Z, Li R, Long Y, Chen S, Meng J (2010) Association mapping of seed oil content in Brassica napus and comparison with quantitative trait loci identified from linkage mapping. Genome 53:908–916PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Humberto A. Gajardo
    • 1
    • 2
  • Benjamin Wittkop
    • 3
  • Braulio Soto-Cerda
    • 1
  • Erin E. Higgins
    • 4
  • Isobel A. P. Parkin
    • 4
  • Rod J. Snowdon
    • 3
  • Maria L. Federico
    • 1
  • Federico L. Iniguez-Luy
    • 1
    Email author
  1. 1.Genomics and Bioinformatics UnitAgriaquaculture Nutritional Genomic Center (CGNA)TemucoChile
  2. 2.Faculty of Agricultural Science, Graduate SchoolUniversidad Austral de ChileValdiviaChile
  3. 3.Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and NutritionJustus Liebig UniversityGiessenGermany
  4. 4.Agriculture and Agri-Food CanadaSaskatoonCanada

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