Theoretical and Applied Genetics

, Volume 126, Issue 11, pp 2699–2716 | Cite as

Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations

  • Jennifer Spindel
  • Mark Wright
  • Charles Chen
  • Joshua Cobb
  • Joseph Gage
  • Sandra Harrington
  • Mathias Lorieux
  • Nourollah Ahmadi
  • Susan McCouchEmail author
Original Paper


Genotyping by sequencing (GBS) is the latest application of next-generation sequencing protocols for the purposes of discovering and genotyping SNPs in a variety of crop species and populations. Unlike other high-density genotyping technologies which have mainly been applied to general interest “reference” genomes, the low cost of GBS makes it an attractive means of saturating mapping and breeding populations with a high density of SNP markers. One barrier to the widespread use of GBS has been the difficulty of the bioinformatics analysis as the approach is accompanied by a high number of erroneous SNP calls which are not easily diagnosed or corrected. In this study, we use a 384-plex GBS protocol to add 30,984 markers to an indica (IR64) × japonica (Azucena) mapping population consisting of 176 recombinant inbred lines of rice (Oryza sativa) and we release our imputation and error correction pipeline to address initial GBS data sparsity and error, and streamline the process of adding SNPs to RIL populations. Using the final imputed and corrected dataset of 30,984 markers, we were able to map recombination hot and cold spots and regions of segregation distortion across the genome with a high degree of accuracy, thus identifying regions of the genome containing putative sterility loci. We mapped QTL for leaf width and aluminum tolerance, and were able to identify additional QTL for both phenotypes when using the full set of 30,984 SNPs that were not identified using a subset of only 1,464 SNPs, including a previously unreported QTL for aluminum tolerance located directly within a recombination hotspot on chromosome 1. These results suggest that adding a high density of SNP markers to a mapping or breeding population through GBS has a great value for numerous applications in rice breeding and genetics research.


Segregation Distortion Leaf Width Imputation Accuracy Impute Genotype Aluminum Tolerance 
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.



We thank Sharon Mitchell, Charlotte Acharya, and Wenyan Zhu with the Cornell Institute of Genomic Diversity for assistance with GBS library prep, Ed Buckler, Jeff Glaubitz, Rob Elshire, Peter Bradbury, and James Harriman at Cornell University for assistance and advice on GBS data analysis and using the TASSEL GBS pipeline, Gen Onishi for greenhouse support, Cheryl Utter for helping format the manuscript, Francisco Agosto-Perez, Genevieve DeClerck, and Chih-Wei Tung for bioinformatics support, and Mike Spindel for Python consulting and troubleshooting support.

Supplementary material

122_2013_2166_MOESM1_ESM.docx (10.3 mb)
Supplementary material 1 (DOCX 10531 kb) (1.9 mb)
Supplementary material 2 (ZIP 1930 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jennifer Spindel
    • 1
  • Mark Wright
    • 1
  • Charles Chen
    • 1
  • Joshua Cobb
    • 1
  • Joseph Gage
    • 1
  • Sandra Harrington
    • 1
  • Mathias Lorieux
    • 3
    • 4
  • Nourollah Ahmadi
    • 2
  • Susan McCouch
    • 1
    Email author
  1. 1.Department of Plant Breeding and GeneticsCornell UniversityIthacaUSA
  2. 2.Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD)Montpellier Cedex 05France
  3. 3.UMR DIADEInstitut de Recherche pour le Développement (IRD)Montpellier Cedex 5France
  4. 4.Rice Genetics and Genomics LaboratoryInternational Center for Tropical Agriculture (CIAT)CaliColombia

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