Advertisement

MutMap: a versatile tool for identification of mutant loci and mapping of genes

  • Kishor U. Tribhuvan
  • Sandhya
  • Kuldeep Kumar
  • Amitha Mithra Sevanthi
  • Kishor Gaikwad
Review Article
  • 32 Downloads

Abstract

With the advancement of sequencing technologies and improvement in data analysis tools, draft genomes of many organisms are readily available. Accessibility to such draft genome sequences assists researchers, especially, plant breeders, to rapidly identify genomic regions contributing to the observed phenotypic variation leading to identification of candidate genes for a particular trait. Traditionally gene mapping is a complex, time-consuming and costly affair requiring large mapping populations and abundant molecular markers spread across the entire linkage groups. With the emergence of re-sequencing techniques, quick mapping of genes has become possible with reduced time and cost by using approaches like SHOREmap, NGM and MutMap methodologies. Among these, MutMap is widely used because it is more focused on causal SNPs. This is made possible by generating a backcross population of the mutant genotype with the parent (wild type), thereby removing the false SNPs and retaining only the SNPs linked to the mutant phenotype. Improved and specialized methods of MutMap like MutMap+, MutMap-Gap, and QTL-Seq have also emerged to expand the horizon of application of MutMap approach. The Mutmap+ methodology is specially designed for capturing those traits where the homozygous mutant leads to either lethality or sterility. MutMap-Gap methodology identifies the mutation site present in the gap regions of the reference genome, whereas QTL-Seq is an improved version of MutMap, specially designed for mapping of quantitative trait loci (QTLs). All these methods are akin to bulked segregant analysis popularly employed for mapping simply inherited traits. These methods escape the requirement of genotyping all the individuals of the mapping population and generation of high-density linkage maps for mapping of the gene for the trait of interest. This article reviews various Next Generation Sequencing-based gene mapping technologies with more emphasis on MutMap and its modifications, and discusses their advantages and proven applications for gene mapping for subsequent crop improvement.

Keywords

MutMap EMS Bulked segregant analysis QTL-Seq NGS techniques 

Notes

Compliance with ethical standards

Conflict of interest

The author declares that they have no conflict of interest.

References

  1. Abe, A., Kosugi, S., Yoshida, K., et al. (2012). Genome sequencing reveals agronomically important loci in rice using MutMap. Nature Biotechnology, 30, 174–178.  https://doi.org/10.1038/nbt.2095.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Austin, R. S., Vidaurre, D., Stamatiou, G., et al. (2011). Next-generation mapping of Arabidopsis genes. Plant Journal, 67(4), 715–725.  https://doi.org/10.1111/j.1365-313X.2011.04619.x.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Chen, Z., Yan, W., Wang, N., et al. (2014). Cloning of a rice male sterility gene by a modified MutMap method. Chinese, 36(1), 85–93.Google Scholar
  4. Clark, A. (1976). Naturally occurring mutagens. Mutation Research, 32, 361–374.CrossRefPubMedCentralGoogle Scholar
  5. Deng, L., Qin, P., Liu, Z., et al. (2017). Characterization and fine-mapping of a novel premature leaf senescence mutant yellow leaf and dwarf-1 in rice. Plant Physiology and Biochemistry, 111, 50–58.  https://doi.org/10.1016/j.plaphy.2016.11.012.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Etherington, G. J., Monaghan, J., Zipfel, C., & MacLean, D. (2014). Mapping mutations in plant genomes with the user-friendly web application CandiSNP. Plant Methods, 10, 1–11.  https://doi.org/10.1186/s13007-014-0041-7.CrossRefGoogle Scholar
  7. Fekih, R., Takagi, H., Tamiru, M., et al. (2013). MutMap+: genetic mapping and mutant identification without crossing in rice. PLoS ONE, 8, 1–10.  https://doi.org/10.1371/journal.pone.0068529.CrossRefGoogle Scholar
  8. GenBank, and WGS Statistics. (2018). https://www.ncbi.nlm.nih.gov/genbank/statistics/. Accessed 18 Jul 2018.
  9. Guo, L., Gao, Z., & Qian, Q. (2014). Application of re-sequencing to rice genomics, functional genomics and evolutionary analysis. Rice, 7, 1–10.  https://doi.org/10.1186/s12284-014-0004-7.CrossRefGoogle Scholar
  10. Han, X., Xu, R., Duan, P., et al. (2017). Genetic analysis and identification of candidate genes for two spotted-leaf mutants (spl101 and spl102) in rice. Chinese, 39, 346–353.  https://doi.org/10.16288/j.yczz.16-416.CrossRefGoogle Scholar
  11. Hao, N., Du, Y., Li, H., et al. (2018). CsMYB36 is involved in the formation of yellow-green peel in cucumber (Cucumis sativus L.). Theoretical and Applied Genetics, 131, 1659–1669.  https://doi.org/10.1007/s00122-018-3105-7.CrossRefGoogle Scholar
  12. Henson, J., Tischler, G., & Ning, Z. (2014). Next-generation sequencing and large genome assemblies. Pharmacogenomics, 13, 901–915.  https://doi.org/10.2217/pgs.12.72.CrossRefGoogle Scholar
  13. Hu, Y., Guo, L., Yang, G., et al. (2016). Genetic analysis of dense and erect panicle-2 allele DEP2-1388 and its application in hybrid rice breeding. Chinese, 38, 72–81.  https://doi.org/10.16288/j.yczz.15-158.CrossRefGoogle Scholar
  14. Imamura, T., Takagi, H., Miyazato, A., et al. (2018). Isolation and characterization of the betalain biosynthesis gene involved in hypocotyl pigmentation of the allotetraploid Chenopodium quinoa. Biochemical and Biophysical Research Communications, 496, 280–286.  https://doi.org/10.1016/j.bbrc.2018.01.041.CrossRefPubMedPubMedCentralGoogle Scholar
  15. Jiao, Y., Burow, G., Gladman, N., et al. (2018). Efficient identification of causal mutations through sequencing of bulked F2 from two allelic bloomless mutants of Sorghum bicolor. Frontiers in Plant Science, 8, 1–11.  https://doi.org/10.3389/fpls.2017.02267.CrossRefGoogle Scholar
  16. Lawson, N. D., & Wolfe, S. A. (2011). Forward and reverse genetic approaches for the analysis of vertebrate development in the Zebrafish. Developmental Cell, 21, 48–64.  https://doi.org/10.1016/j.devcel.2011.06.007.CrossRefPubMedPubMedCentralGoogle Scholar
  17. Liang, D., Chen, M., Qi, X., et al. (2016). QTL mapping by SLAF-seq and expression analysis of candidate genes for aphid resistance in cucumber. Frontiers in Plant Science, 7, 1–8.  https://doi.org/10.3389/fpls.2016.01000.CrossRefGoogle Scholar
  18. Michelmore, R. W., Paran, I., & Kesseli, R. V. (1991). Identification of markers linked to disease-resistance genes by bulked segregant analysis: A rapid method to detect markers in specific genomic regions by using segregating populations. Proceedings of the National Academy of Sciences, 88, 9828–9832.CrossRefGoogle Scholar
  19. Nakata, M., Miyashita, T., Kimura, R., et al. (2018). MutMapPlus identified novel mutant alleles of a rice starch branching enzyme IIb gene for fine-tuning of cooked rice texture. Plant Biotechnology Journal, 16, 111–123.  https://doi.org/10.1111/pbi.12753.CrossRefPubMedGoogle Scholar
  20. Oladosu, Y., Rafii, M. Y., Abdullah, N., et al. (2016). Principle and application of plant mutagenesis in crop improvement: A review. Biotechnology and Biotechnological Equipment, 30, 1–16.  https://doi.org/10.1080/13102818.2015.1087333.CrossRefGoogle Scholar
  21. Pandey, M. K., Khan, A. W., Singh, V. K., et al. (2017). QTL-Seq approach identified genomic regions and diagnostic markers for rust and late leaf spot resistance in groundnut (Arachis hypogaea L.). Plant Biotechnology Journal, 15, 927–941.  https://doi.org/10.1111/pbi.12686.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Pettersson, E., Lundeberg, J., & Ahmadian, A. (2009). Genomics generations of sequencing technologies. Genomics, 93, 105–111.  https://doi.org/10.1016/j.ygeno.2008.10.003.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Sanger, F., Nicklen, S., & Coulson, A. R. (1977). DNA sequencing with chain-terminating inhibitors. Proceedings of the National Academy of Sciences, 74, 5463–5467.  https://doi.org/10.1073/pnas.74.12.5463.CrossRefGoogle Scholar
  24. Sathya, B., Parvathy, A., & Ramesh, G. (2015). NGS meta data analysis for identification of SNP and INDEL patterns in human airway transcriptome: A preliminary indicator for lung cancer. Applied and Translational Genomics, 4, 4–9.  https://doi.org/10.1016/j.atg.2014.12.003.CrossRefGoogle Scholar
  25. Schneeberger, K., Ossowski, S., Lanz, C., et al. (2009). SHOREmap: Simultaneous mapping and mutation identification by deep sequencing. Nature Methods, 6, 550–551.  https://doi.org/10.1038/nmeth0809-550.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Sevanthi, A. M. V., Kandwal, P., Kale, P. B., Prakash, C., Ramkumar, M. K., Yadav, N., et al. (2018). Whole genome characterization of a few EMS-induced mutants of upland rice variety Nagina 22 reveals a staggeringly high frequency of SNPs which show high phenotypic plasticity towards the wild-type. Front Plant Sci., 9, 1179.  https://doi.org/10.3389/fpls.2018.01179.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Smykal, P. (2014). Pea (Pisum sativum L.) in biology prior and after Mendel’s discovery. Czech Journal of Genetics and Plant Breeding, 50(2), 52–64.  https://doi.org/10.17221/2/2014-cjgpb.CrossRefGoogle Scholar
  28. Song, J., Li, Z., Liu, Z., et al. (2017). Next-generation sequencing from bulked-segregant analysis accelerates the simultaneous identification of two qualitative genes in soybean. Frontiers in Plant Science, 8, 1–11.  https://doi.org/10.3389/fpls.2017.00919.CrossRefGoogle Scholar
  29. Takagi, H., Abe, A., Yoshida, K., et al. (2013a). QTL-Seq: Rapid mapping of quantitative trait loci in rice by whole-genome resequencing of DNA from two bulked populations. Plant Journal, 74, 174–183.  https://doi.org/10.1111/tpj.12105.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Takagi, H., Uemura, A., Yaegashi, H., et al. (2013b). MutMap-Gap: Whole-genome re-sequencing of mutant F2 progeny bulk combined with de novo assembly of gap regions identifies the rice blast resistance gene Pii. New Phytologist, 200, 276–283.  https://doi.org/10.1111/nph.12369.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Till, B. J., Cooper, J., Tai, T. H., Colowit, P., Greene, E. A., Henikoff, S., et al. (2007). Discovery of chemically induced mutation in rice by TILLING. BMC Plant Biology, 7, 19.  https://doi.org/10.1186/1471-2229-7-19.CrossRefPubMedCentralGoogle Scholar
  32. Wadapurkar, R. M., & Vyas, R. (2018). Computational analysis of next-generation sequencing data and its applications in clinical oncology. Informatics in Medicine Unlocked, 11, 75–82.  https://doi.org/10.1016/j.imu.2018.05.003.CrossRefGoogle Scholar
  33. Wang, H., Cheng, H., Wang, W., et al. (2016a). Identification of BnaYUCCA6 as a candidate gene for branch angle in Brassica napus by QTL-Seq. Scientific Reports, 6, 1–10.  https://doi.org/10.1038/srep38493.CrossRefGoogle Scholar
  34. Wang, H., Li, W., Qin, Y., et al. (2017). The Cytochrome P450 Gene CsCYP85A1 is a putative candidate for super compact-1 (Scp-1) plant architecture mutation in cucumber (Cucumis sativus L.). Frontiers. Plant Science, 8, 266.  https://doi.org/10.3389/fpls.2017.00266.CrossRefGoogle Scholar
  35. Wang, Y., Xiao, L., Guo, S., et al. (2016b). Fine mapping and whole-genome re-sequencing identify the seed coat color gene in Brassica rapa. PLoS ONE, 11, 1–14.  https://doi.org/10.1371/journal.pone.0166464.CrossRefGoogle Scholar
  36. Xu, L., Wang, C., Cao, W., et al. (2018). CLAVATA1-type receptor-like kinase CsCLAVATA1 is a putative candidate gene for dwarf mutation in cucumber. Molecular Genetics and Genomics.  https://doi.org/10.1007/s00438-018-1467-9.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Yuan, H., Fan, S., Huang, J., et al. (2017). 08SG2/OsBAK1 regulates grain size and number, and functions differently in Indica and Japonica backgrounds in rice. Rice, 10, 25.  https://doi.org/10.1186/s12284-017-0165-2.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Zhang, J., Chiodini, R., Badr, A., & Zhang, G. (2011). The impact of next-generation sequencing on genomics. Journal of Genetics and Genomics, 38, 95–109.  https://doi.org/10.1016/j.jgg.2011.02.003.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Zou, T., Xiao, Q., Li, W., et al. (2017). OsLAP6/OsPKS1, an orthologue of Arabidopsis PKSA/LAP6, is critical for proper pollen exine formation. Rice, 10, 53.  https://doi.org/10.1186/s12284-017-0191-0.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Indian Society for Plant Physiology 2018

Authors and Affiliations

  • Kishor U. Tribhuvan
    • 1
  • Sandhya
    • 1
  • Kuldeep Kumar
    • 1
  • Amitha Mithra Sevanthi
    • 1
  • Kishor Gaikwad
    • 1
  1. 1.ICAR-National Research Centre on Plant BiotechnologyNew DelhiIndia

Personalised recommendations