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Assembly of whole-chromosome pseudomolecules for polyploid plant genomes using outbred mapping populations

Nature Genetics volume 52, pages 1256–1264 (2020)Cite this article

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Abstract

Despite advances in sequencing technologies, assembly of complex plant genomes remains elusive due to polyploidy and high repeat content. Here we report PolyGembler for grouping and ordering contigs into pseudomolecules by genetic linkage analysis. Our approach also provides an accurate method with which to detect and fix assembly errors. Using simulated data, we demonstrate that our approach is of high accuracy and outperforms three existing state-of-the-art genetic mapping tools. Particularly, our approach is more robust to the presence of missing genotype data and genotyping errors. We used our method to construct pseudomolecules for allotetraploid lawn grass utilizing PacBio long reads in combination with restriction site-associated DNA sequencing, and for diploid Ipomoea trifida and autotetraploid potato utilizing contigs assembled from Illumina reads in combination with genotype data generated by single-nucleotide polymorphism arrays and genotyping by sequencing, respectively. We resolved 13 assembly errors for a published I. trifida genome assembly and anchored eight unplaced scaffolds in the published potato genome.

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Fig. 1: PolyGembler framework.
Fig. 2: Pseudomolecule construction for simulated datasets.
Fig. 3: The ITR_r1.0 scaffold Itr_sc000015 is a misassembly.
Fig. 4: Pseudomolecule construction for M9 × M19 and B2721 mapping populations.
Fig. 5: Collinear plots between the Z. japonica pseudomolecules constructed from PolyGembler and the O. sativa chromosomes.

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Data availability

Data for the simulation studies, including comparisons with other methods and studies of M9 × M19 I. trifida and the B2721 potato, are available from http://data.genomicsresearch.org/Projects/polyGembler. Data for the 12601ab1 × Stirling potato mapping population were provided by C. Hackett. Data for the Z. japonica mapping population Carrizo × El Toro are available from the NCBI repository under the accession number SRP055007. The whole-genome PacBio sequence data for the Z. japonica cultivar Yaji are available from the NCBI repository under the accession number SRP110561. Data related to the PGSC version 4.03 pseudomolecules are available from http://solanaceae.plantbiology.msu.edu. The I. trifida de novo genome assembly ITR_r1.0 is available from http://sweetpotato-garden.kazusa.or.jp. The I. trifida de novo genome assembly NCNSP0306 is available from http://sweetpotato.plantbiology.msu.edu. Release 7 of the O. sativa reference genome is available from http://phytozome.jgi.doe.gov. The genome assembly of the Z. japonica accession Nagirizaki is available from http://zoysia.kazusa.or.jp. Source data are provided with this paper.

Code availability

The software PolyGembler, presented in this article, and its documentation are publicly available at GitHub (https://github.com/c-zhou/polyGembler).

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Acknowledgements

We thank F. Diaz for developing the M9 × M19 I. trifida mapping population and M. David for extracting and quantifying DNA from the M9 × M19 cross. The 12601ab1 × Stirling Infinium 8303 potato array data were provided by C. A. Hackett. This research was supported by grants from the Bill & Melinda Gates Foundation (OPP1052983) and Australian Research Council (DP170102626 awarded to L.J.M.C.). The work at the International Potato Center (CIP) was carried out as part of the Consultative Group for International Agricultural Research (CGIAR) Research Program on Roots, Tubers and Bananas, which is supported by CGIAR Fund Donors (http://www.cgiar.org/about-us/our-funders/). This research was also supported by use of the NeCTAR Research Cloud, by QCIF and by the University of Queensland’s Research Computing Centre. The NeCTAR Research Cloud is a collaborative Australian research platform supported by the National Collaborative Research Infrastructure Strategy.

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Authors and Affiliations

  1. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

    Chenxi Zhou, Minh Duc Cao & Lachlan J. M. Coin

  2. Department of Clinical Pathology, University of Melbourne, Melbourne, Victoria, Australia

    Chenxi Zhou & Lachlan J. M. Coin

  3. Department of Horticultural Science, North Carolina State University, Raleigh, NC, USA

    Bode Olukolu & G. Craig Yencho

  4. Department of Entomology and Plant Pathology, University of Tennessee, Knoxville, TN, USA

    Bode Olukolu

  5. International Potato Center, Lima, Peru

    Dorcus C. Gemenet, Wolfgang Gruneberg & Awais Khan

  6. CGIAR Excellence in Breeding Platform, International Maize and Wheat Improvement Center, Nairobi, Kenya

    Dorcus C. Gemenet

  7. Boyce Thompson Institute, Cornell University, Ithaca, NY, USA

    Shan Wu & Zhangjun Fei

  8. Department of Statistics, North Carolina State University, Raleigh, NC, USA

    Zhao-Bang Zeng

  9. Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA

    Zhao-Bang Zeng

  10. Data61, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Queensland, Australia

    Andrew W. George

  11. Department of Plant Pathology and Plant–Microbe Biology, Cornell University, Geneva, NY, USA

    Awais Khan

  12. The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria, Australia

    Lachlan J. M. Coin

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Contributions

C.Z. and L.J.M.C. designed the study and wrote the software. A.K., D.C.G. and W.G. developed and provided the I. trifida mapping population materials. B.O., D.C.G., S.W. and W.G. generated data for the M9 × M19 I. trifida mapping population. C.Z. performed the analysis. C.Z. and L.J.M.C. wrote the manuscript. L.J.M.C., G.C.Y., A.K., M.D.C., A.W.G., Z.-B.Z. and Z.F. supervised the project. All authors contributed to editing the final manuscript.

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Correspondence to Lachlan J. M. Coin.

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Extended data

Extended Data Fig. 1 Pseudomolecule construction for 20× tetraploid simulated GBS data.

A total of 42,715 SNPs located on 678 scaffolds were used for linkage analysis. These scaffolds of ~482Mb covered approximately 99.6% of the genome. a, Dot plot for the RF estimations for scaffold pairs mapped to the same reference chromosome. The x- and y-axis represents the physical distances and the estimated RFs, respectively. b, Histogram of the RF estimations for scaffold pairs mapped to different reference chromosomes. c, Collinear plots of pseudomolecules mapped to reference chromosomes. The x- and y-axis represents physical positions (Mb) on the reference chromosomes and pseudomolecules, respectively. Each line represents a collinear block between the reference chromosome and the pseudomolecule. The diagonal line in each plot indicates a high correlation between the reference chromosome and the pseudomolecule constructed from scaffolds.

Extended Data Fig. 2 Collinear plots between the Ipomoea nil reference chromosomes and pseudomolecules constructed from the Ipomoea trifida genotype data.

The x- and y-axis represents the physical positions (Mb) on the reference chromosomes and pseudomolecules, respectively. Each line represents a collinear block between the Ipomoea nil reference chromosome and the pseudomolecules.

Extended Data Fig. 3 Genetic linkage map construction from the Infinium 8303 SNP array data of the Stirling×12601ab1 mapping population.

a, Dot plot for RF estimations between scaffold pairs mapped to the same PGSC v4.03 chromosomes. The x- and y-axis represents the physical distances and the estimated RFs, respectively. b, Histogram of the RF estimations for scaffold pairs mapped to different PGSC v4.03 pseudomolecules. c, Comparison between the genetic linkage map constructed by the proposed method and the PGSC v4.03 pseudomolecules. Twelve genetic linkage groups corresponding to 12 pseudomolecules were constructed. In each plot, the x-axis represents the positions (Mb) on the PGSC v4.03 pseudomolecules, and the y-axis represents the positions (cM) on the genetic linkage map.

Extended Data Fig. 4 Genetic linkage map constructed from the Infinium 8303 SNP array data of the B2721 mapping population with TetraploidSNPMap.

Each dot represents a SNP. The x-axis represents the positions (Mb) on the PGSC v4.03 pseudomolecules, and the y-axis represents the positions (cM) on the genetic linkage map. The genetic linkage map comprises a total of 4,745 SNPs including 56 SNPs located on the unplaced PGSC v4.03 scaffolds (red) and 76 SNPs placed in incorrect PGSC v4.03 pseudomolecules (blue). Since the physical positions of the red and blue dots cannot be determined, they were set to zero in the plots.

Extended Data Fig. 5 Genetic linkage map constructed from the Infinium 8303 SNP array data of the Stirling×12601ab1 mapping population with TetraploidSNPMap.

Each dot represents a SNP. The x-axis represents the positions (Mb) on the PGSC v4.03 pseudomolecules, and the y-axis represents the positions (cM) on the genetic linkage map. The genetic linkage map comprises a total of 3,593 SNPs including 54 SNPs located on the unplaced PGSC v4.03 scaffolds (red) and 35 SNPs placed in incorrect PGSC v4.03 pseudomolecules (blue). Since the physical positions of the red and blue dots cannot be determined, they were set to zero in the plots.

Extended Data Fig. 6 Collinear plots between the pseudomolecules of Zoysia japonica accession Yaji and Nagirizaki.

The x- and y-axis represent the positions (Mb) on the pseudomolecules. Each line represents a collinear block between the pseudomolecules.

Extended Data Fig. 7 Relationship between the number of genetic markers and computational resources required for the haplotype phasing algorithm.

The x- and y-axis (in logarithm scale) represents the number of genetic markers and the consumption of resources, respectively. a, CPU time and b, Memory. Each point in the plot was averaged over 30 independent experiments (Intel® Xeon® Processor E5-2667 v3 CPU, 3.20GHz). The error bar for one standard deviation was included at each point.

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Zhou, C., Olukolu, B., Gemenet, D.C. et al. Assembly of whole-chromosome pseudomolecules for polyploid plant genomes using outbred mapping populations. Nat Genet 52, 1256–1264 (2020). https://doi.org/10.1038/s41588-020-00717-7

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