Euphytica

, Volume 201, Issue 1, pp 67–78 | Cite as

Development of a SNP genotyping panel for detecting polymorphisms in Oryza glaberrima/O. sativa interspecific crosses

  • Juan Pariasca-Tanaka
  • Mathias Lorieux
  • Chunlin He
  • Susan McCouch
  • Michael J. Thomson
  • Matthias Wissuwa
Article

Abstract

Oryza glaberrima accessions harbor genes for tolerance to abiotic stresses such as mineral deficiency in problem soils. This genetic potential could be exploited in interspecific crosses with Oryza sativa, as in the case of the ‘New Rice for Africa’ (NERICA) varieties; however, to attain this goal it would be desirable to develop a high-throughput marker system to specifically detect O. glaberrima introgressions in an O. sativa background. Therefore, a single nucleotide polymorphism (SNP) genotyping analysis of an O. glaberrima accession (CG14) with two O. sativa lines (WAB56-104 and WAB181-18) was performed on a genome-wide basis. Comparison of CG14 and the WAB lines resulted in a set of 9,523 polymorphic SNPs which would be suitable to detect O. glaberrima introgressions in upland NERICAs. In addition, a set of 1,540 polymorphic SNPs between O. glaberrima versus O. sativa was identified. A subset of SNPs which were evenly distributed in the genome was then used to design a flexible and cost-effective SNP genotyping panel using the Competitive Allele-Specific PCR technology (KASP). This SNP genotyping panel consists of 2,015 SNPs successfully converted into KASP markers, providing 745 polymorphic SNPs for the parents O. glaberrima CG14/O. sativa WAB56-104 (upland NERICA), and 752 for O. glaberrima TOG5681/O. sativa IR64 (lowland NERICA). KASP markers were successfully validated by mapping O. glaberrima introgressions in NERICA-derived breeding lines. This new SNP genotyping panel will be useful in modern breeding applications such as QTL mapping and/or marker-assisted selection.

Keywords

O. glaberrima SNP Competitive Allele-Specific PCR markers Genotyping panel NERICA 

Supplementary material

10681_2014_1183_MOESM1_ESM.xls (15.3 mb)
Online resource 1–Table S1. The complete list of 44 K SNP genotyping data. A SNP genotyping analysis of O. glaberrima (CG14) and O. sativa (WAB56-104, WAB181-18, IR74) was performed on a genome-wide basis using the 44 K SNP Affymetrix genotyping array. (XLS 15683 kb)
10681_2014_1183_MOESM2_ESM.doc (56 kb)
Online resource 2–Fig. S1. Distribution of polymorphic SNPs between O. glaberrima CG14 and six O. sativa accessions. (DOC 55 kb)
10681_2014_1183_MOESM3_ESM.xls (444 kb)
Online resource 3–Table S2. The complete list of SNP–KASP markers. A set of selected SNPs were successfully converted into KASP markers. The genotyping data for the parents of upland and lowland NERICAs (O. glaberrima and O. sativa) is presented. (XLS 443 kb)
10681_2014_1183_MOESM4_ESM.xlsx (220 kb)
Online resource 4–Table S3. KASP data for N10W3/4 lines.The N10W3 and N10W4 are two BC1F4 sister lines derived from a NERICA10 × WAB56-104 backcross used for mapping O. glaberrima introgressions. (XLSX 220 kb)
10681_2014_1183_MOESM5_ESM.xlsx (181 kb)
Online resource 5–Table S4. Flanking region for each SNP–KASP marker. The physical position and the flanking region of each KASP marker is provided. The SNP is represented as [x/y], where x and y indicate the allele. (XLSX 180 kb)

References

  1. Agnoun Y, Sié M, Djedatin G, Dramé KN, Toulou B, Ogunbayo SA, Sanni KA, Tia D, Ahanchédé A, Vodouhé RS, Ndjiondjop MN (2012) Molecular profiling of interspecific lowland rice progenies resulting from crosses between TOG5681 and TOG5674 (Oryza glaberrima) and IR64 (Oryza sativa). Int J Biol. doi:10.5539/ijb.v4n3p19 Google Scholar
  2. Fukuta Y, Konisho K, Senoo-Namai S, Yanagihara S, Tsunematsu H, Fukuo A, Kumashiro T (2012) Genetic characterization of rainfed upland New Rice for Africa (NERICA) varieties. Breed Sci 62:27–37PubMedCentralPubMedCrossRefGoogle Scholar
  3. Futakuchi K, Sié M (2009) Better exploitation of African rice (Oryza glaberrima Steud.) in varietal development for resource-poor farmers in West and Central Africa. Agric J 4:96–102Google Scholar
  4. Futakuchi K, Sié M, Saito K (2012) Yield potential and physiological and morphological characteristics related to yield performance in Oryza glaberrima Steud. Plant Prod Sci 15:151–163CrossRefGoogle Scholar
  5. Gutiérrez AG, Carabali JS, Giraldo OX, Martinez CP, Correa F, Prado G, Tohme J, Lorieux M (2010) Identification of a rice stripe necrosis virus resistance locus and yield component QTLs using Oryza sativa x O. glaberrima introgression lines. BMC Plant Biol 10:6PubMedCentralPubMedCrossRefGoogle Scholar
  6. Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967PubMedCrossRefGoogle Scholar
  7. Jones MP, Dingkuhn M, Aluko GK, Semon M (1997a) Interspecific Oryza sativa L. x O. glaberrima Steud. progenies in upland rice improvements. Euphytica 92:237–246CrossRefGoogle Scholar
  8. Jones MP, Mande S, Aluko K (1997b) Diversity and potential of Oryza glaberrima Steud in upland rice breeding. Breed Sci 47:395–398Google Scholar
  9. KBioscience—KASP version 4.0 SNP genotyping manual, p 8Google Scholar
  10. Koide Y, Pariasca-Tanaka J, Rose T, Fukuo A, Konisho K, Yanagihara S, Fukuta Y, Wissuwa M (2013) QTLs for phosphorus-deficiency tolerance detected in upland NERICA varieties. Plant Breed. doi:10.1111/pbr.12052 Google Scholar
  11. Lorieux M, Ndjiondjop MN, Ghesquiere A (2000) A first interspecific Oryza sativa x Oryza glaberrima microsatellite-based genetic linkage map. Theor Appl Genet 100:593–601. doi:10.1534/genetics.108.089367 Google Scholar
  12. Lorieux M, Reversat G, Garcia Diaz S, Denance C, Jouvenet N, Orieux Y, Bourger N, Pando-Bahuon A, Ghesquière A (2003) Linkage mapping of Hsa-1og, a resistance gene of African rice to the cyst nematode, Heterodera sacchari. Theor Appl Genet 107:691–696PubMedCrossRefGoogle Scholar
  13. McCouch SR, Zhao K, Wright M, Tung CW, Ebana K, Thomson M, Reynolds A, Wang D, DeClerck G, Ali ML, McClung A, Eizenga G, Bustamante C (2010) Development of genome-wide SNP assays for rice. Breed Sci 60:524–535CrossRefGoogle Scholar
  14. McNally KL, Childs KL, Bohert R, Davidson RM, Zhao K, Ulat BJ, Zeller GG, Clark RM, Hoen DR, Bureau TE, Stokowski R, Ballinger DG, Frazer K, Cox D, Padhukasahasram B, Bustamante CD, Weigel D, Mackill D, Bruskiewich R, Rätsch G, Buell CR, Leung H, Leach JE (2009) Genome-wide SNP variation reveals relationships among land- races and modern varieties of rice. Proc Natl Acad Sci USA 106:12273–12278PubMedCentralPubMedCrossRefGoogle Scholar
  15. Pariasca-Tanaka J, Satoh K, Rose T, Mauleon R, Wissuwa M (2009) Stress response versus stress tolerance: a transcriptome analysis of two rice lines contrasting in tolerance to phosphorus deficiency. Rice 2:167–185CrossRefGoogle Scholar
  16. Sano Y, Sano R, Morishima H (1984) Neighbor effects between two co-occurring rice species, Oryza sativa and O. glaberrima. J Appl Ecol 21:245–254CrossRefGoogle Scholar
  17. Semagn K, Ndjiondjop MN, Lorieux M, Cissoko M, Jones M, McCouch S (2007) Molecular profiling of an interspecific rice population derived from a cross between WAB56-104 (Oryza sativa) and CG14 (Oryza glaberrima). Afr J Biotechnol 6:2014–2022Google Scholar
  18. Sie MS, Dogbe Y, Coulibaly M (2005) Selection of interspecific hybrids (O. sativa x O. glaberrima or lowland NERICAs) and intraspecifics adapted to rained lowland growing conditions. Int Rice Comm Newsl 54:47–51Google Scholar
  19. Thomson M, Zhao K, Wright M, McNally K, Rey J, Tung C, Reynolds A, Scheffler B, Eizenga G, McClung A, Kim H, Ismail A, Ocampo M, Mojica C, Reveche MA, Dilla-Ermita C, Mauleon R, Leung H, Bustamante C, McCouch SR (2012) High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform. Mol Breed 29:875–886CrossRefGoogle Scholar
  20. Tobita S, Saharawat KL, Diatta S, Jones MP (2003) Response of African rice (Oryza glaberrima Steud.) to phosphate application in the upland of a P-deficient soil in the humid forest zone of West Africa. In: Proceedings for the 2nd international symposium on phosphorus dynamics in the soil-plant continuum, Perth, Western Australia, pp. 70–71Google Scholar
  21. Tung CW, Zhao K, Wright MH, Ali ML, Jung J, Kimball J, Tyagi W, Thomson M, McNally K, Leung H, Kim H, Ahn SN, Reynolds A, Scheffler B, Eizenga G, McClung A, Bustamante C, McCouch SR (2010) Development of a research platform for dissecting phenotype–genotype associations in rice (Oryza spp). Rice 3:205–217CrossRefGoogle Scholar
  22. Wright MH, Tung CW, Zhao K, Reynolds A, McCouch SR, Bustamante C (2010) ALCHEMY: a reliable method for automated SNP genotype calling for small batch sizes and highly homozygous populations. Bioinformatics 26:2952–2960PubMedCentralPubMedCrossRefGoogle Scholar
  23. Zhao K, Wright M, Kimball J, Eizenga G, McClung A, Kovach M, Tyagi W, Ali ML, Tung CW, Reynolds A, Bustamante CD, McCouch SR (2010) Genomic diversity and introgression in O. sativa reveal the impact of domestication and breeding on the rice genome. PLoS One 5:e10780. doi:10.1371/journal.pone.0010780 PubMedCentralPubMedCrossRefGoogle Scholar
  24. Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2. doi: 10.1038/ncomms1467

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Juan Pariasca-Tanaka
    • 1
  • Mathias Lorieux
    • 2
  • Chunlin He
    • 3
  • Susan McCouch
    • 4
  • Michael J. Thomson
    • 5
  • Matthias Wissuwa
    • 1
  1. 1.Japan International Research Center for Agricultural Sciences (JIRCAS)TsukubaJapan
  2. 2.DIADE Research Unit, Institut de Recherche pour le DéveloppementInternational Center for Tropical Agriculture (CIAT)CaliColombia
  3. 3.Generation Challenge ProgrammeCIMMYTMexicoMexico
  4. 4.Department of Plant Breeding and GeneticsCornell UniversityIthacaUSA
  5. 5.International Rice Research Institute (IRRI)Los BañosPhilippines

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