Genotyping by Sequencing for Crop Improvement. Группа авторов

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СКАЧАТЬ of the sequenced genome (Chun‐Chao et al. 2019). The RFGB database contains five major modules including Phenotype, Haplotype, SNP & InDel, Restore sequence, and Germplasm. Users can search variations (SNPs, InDel) using gene locus ID (ex. Os05g0187500, LOC_Os09g26999), chromosome region (ex. Chr1, 200001–500000), and keywords (ex. GW5, heading date). Further, haplotype analysis can be done using gene locus ID, chromosome region, and SNP sites (ex. Chr2‐8120893) (Chun‐Chao et al. 2019).

      3.6.3 Genome Variation Map

      The genome Variation Map (www.ngdc.cncb.ac.cn/gvm/home) is a data repository and retrieval system of genome variations in BIG Data Center (www.ngdc.cncb.ac.cn). It provides free open access to search for SNPs and short InDels from approximately 609 million genome variants for 25 plants, 13 animals, and 3 virus species. The current version of the Genome Var Map includes a total of 41 species, 202 projects, 64819 samples, 9 60 409 451 variants, 2 60 393 associations, and 95 submissions (Li et al. 2021). Users can retrieve variants by variation IDs, genomic coordinates, gene names, gene functions, and variant effects. The results can be downloaded directly in FASTA or VCF file formats.

3.7 Applications and Successful Examples of Whole‐Genome Resequencing

One of the important applications of whole‐genome resequencing is to explore the genetic diversity at the sequence level. Rice has known to be species that shows genetic diversity not at species level but within the genus as well. The 3000 rice genome sequencing has led to the capture of the sequence variation in the genome which aids to decipher population differentiation (local and global population), construct new reference genome, pan‐genome of varieties, haplotyping, and SNP discovery (Li et al. 2014b). Further, previously characterized genes have also been analyzed for haplotype diversity which can be employed in haplotype‐based breeding (Abbai et al. 2019). Another example includes the study of evolutionary history by genome resequencing in peach fruit (Yu et al. 2018).

For the identification of genetic traits associated with complex traits, genome wide association study (GWAS) has been employed. It helps in the recognition of many SNPs involved with the target trait either by correlation or comparative analysis. Abbai et al. (2019) selected 664 cultivated rice accessions for GWAS from the 3K rice genome. In the case of salt‐tolerant traits, they identified the possible candidate gene and causal polymorphism by performing genetic variation, haplotyping, integrated gene information, and homology analysis. As the precise superior haplotype identification will have a great impact on the molecular breeding outcome. Similarly, Lin et al. (2021) explored weediness‐related traits from the 3K rice genome project in the cultivated pool. They identified weediness‐associated markers by performing GWAS within each subpopulation. One of the examples involved GWAS in rice for the salt‐tolerant trait as shown in Figure 3.2 (Yuan et al. 2020). With the help of WGR, it has been possible to construct a high‐quality HapMaps in maize and rice which help to perform GWAS and find unidentified genetic variations of agronomically important traits. The applications of WGR have led to a decrease in the cost of WGS as well as enabled the generation of large data of sequences.

      The online resources available for rice, soybean, canola, maize, and wheat are widely being used for the marker development, allele mining, haplotypic evaluation as well as simple applications like gene family characterization (Deshmukh et al. 2016; André et al. 2017; Rasoolizadeh et al. 2018; Chaudhary et al. 2019c; de Ronne et al. 2020; Singh et al. 2019). The free access of resources more particularly in rice and soybean has accelerated basic as well as applied research in these crops.

      Apart from the high‐throughput applications, WGR is helpful for specific applications like characterization of varieties developed with marker‐assisted breeding, characterization of introgression lines, and evaluation of transgenic events (Patil et al. 2018; Tayade et al. 2018; Shivaraj et al. 2019). Similarly, resequencing also helps for bulk evaluation‐based approaches like Mut‐Map and QTLseq (Bansal et al. 2019; Chaudhary et al. 2019a; Kumawat et al. 2019). Very recent advancements like genome editing also have implication of WGR (Mushtaq et al. 2019; Vats et al. 2019; Ansari et al. 2020). Mutations at target sites as well as at off‐target can be easily verified with whole‐genome resequencing.

3.8 Challenges for Whole‐Genome Resequencing Studies

Although, various platforms such as Illumina, Roche 454, and SoLiD^™ have significantly increased the throughput with a decreased error rate but many plant genomes represent unique challenges because of their repetitive nature which leads to a challenge for reliable genome assembly. This might be due to high copy number and transposable elements amplifying nature in a large number of plants leading to consequences in assembly. Another major constraint is that not all species of plant are homozygous inbred diploids. Currently, there is no WGR technique that meets all conservation geneticists' needs. However, with the advancement in next‐generation sequencing technologies, these challenges can be overcome.

Figure 3.2 Genome‐wide association studies (GWAS) in rice seedling for salt‐tolerant trait. For GWAS, quantile–quantile plots and Manhattan plots are represented for (a) indica, (b) japonica (c) represents population with the help of CMLM. In quantile–quantile plot, black and red points represent GLM and CMLM, respectively. In Manhattan plots, genes represented by red lines were previously cloned while QTLs were represented by black lines, which were considered to be important. The suggestive threshold (P = 1.0 × 10⁻⁴) for each population is indicated by dashed horizontal line.

      The figure is reproduced from Yuan et al. (2020) which is available under a Creative Commons Attribution 4.0 (CC‐By 4.0) International License, which permits reproduction.

3.9 Summary

      A new era has been brought in plant genetics with the rapid advancement in NGS technologies. With the help of this, a large amount of data is generated and used in the scientific community. These generated genomic sequences of diverse lines, especially the whole‐genome resequencing aids in the identification of haplotypic/allelic variation. These techniques help in the identification of novel genes and alleles associated with the target genes or traits deployed in the improvement of crops. WGR‐collected genomic data can aid with planning conservation and management of exploited species used commercially by assisting in the delimitation and monitoring of evolutionary units, as well as the prioritizing of endangered populations.

References

      1 Abbai, R., Singh, V.K., Nachimuthu, V.V. et al. (2019). Plant Biotechnology Journal 17: 1612–1622.

      2 Alexandrov, N., Tai, S., Wang, W. et al. (2015). SNP‐Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Research 43: D1023–D1027.

      3 André, L.L., Sonah, H., Dias, W.P. et al. (2017). Genome‐wide association study for resistance to the southern root‐knot nematode (Meloidogyne incognita) in soybean. Molecular Breeding 37: 148.

      4 Ansari, W.A., Chandanshive, S.U., Bhatt, V. et al. (2020). Genome editing in cereals: approaches, applications and challenges. International Journal of Molecular Sciences 21: 4040.

      5 Bansal, R., Rana, N., Kumawat, S. et al. (2019). Advances in induced mutagenesis and mutation mapping approaches in rice. Oryza 59: 106–114.

      6 Bastide, СКАЧАТЬ