Transcriptome sequencing assisted discovery and computational analysis of novel SNPs associated with flowering in Raphanus sativus in-bred lines for marker-assisted backcross breeding

Jinhee Kim , Abinaya Manivannan , Do-Sun Kim , Eun-Su Lee , Hye-Eun Lee

Horticulture Research ›› 2019, Vol. 6 ›› Issue (1) : 120

PDF (1772KB)
Horticulture Research ›› 2019, Vol. 6 ›› Issue (1) :120 DOI: 10.1038/s41438-019-0200-0
Article
research-article
Transcriptome sequencing assisted discovery and computational analysis of novel SNPs associated with flowering in Raphanus sativus in-bred lines for marker-assisted backcross breeding
Author information +
History +
PDF (1772KB)

Abstract

The sequencing of radish genome aids in the better understanding and tailoring of traits associated with economic importance. In order to accelerate the genomics assisted breeding and genetic selection, transcriptomes of 33 radish inbred lines with diverse traits were sequenced for the development of single nucleotide polymorphic (SNP) markers. The sequence reads ranged from 2,560,543,741 bp to 20,039,688,139 bp with the GC (%) of 47.80–49.34 and phred quality score (Q30) of 96.47–97.54%. A total of 4951 polymorphic SNPs were identified among the accessions after stringent filtering and 298 SNPs with efficient marker assisted backcross breeding (MAB) markers were generated from the polymorphic SNPs. Further, functional annotations of SNPs revealed the effects and importance of the SNPs identified in the flowering process. The SNPs were predominantly associated with the four major flowering related transcription factors such as MYB, MADS box (AG), AP2/EREB, and bHLH. In addition, SNPs in the vital flowering integrator gene (FT) and floral repressors (EMBRYONIC FLOWER 1, 2, and FRIGIDA) were identified among the radish inbred lines. Further, 50 SNPs were randomly selected from 298 SNPs and validated using Kompetitive Allele Specific PCR genotyping system (KASP) in 102 radish inbred lines. The homozygosity of the inbred lines varied from 56 to 96% and the phylogenetic analysis resulted in the clustering of inbred lines into three subgroups. Taken together, the SNP markers identified in the present study can be utilized for the discrimination, seed purity test, and adjusting parental combinations for breeding in radish.

Cite this article

Download citation ▾
Jinhee Kim, Abinaya Manivannan, Do-Sun Kim, Eun-Su Lee, Hye-Eun Lee. Transcriptome sequencing assisted discovery and computational analysis of novel SNPs associated with flowering in Raphanus sativus in-bred lines for marker-assisted backcross breeding. Horticulture Research, 2019, 6 (1) : 120 DOI:10.1038/s41438-019-0200-0

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Manivannan, A., Kim, J. H., Kim, D. S., Lee, E. S. & Lee, H. E. Deciphering the nutraceutical potential of Raphanus sativus-a comprehensive overview. Nutrients 11, 1-15 (2019).

[2]

Yi, G. et al. MYB1 transcription factor is a candidate responsible for red root skin in radish (Raphanus sativus L.). PLoS ONE https://doi.org/10.1371/journal.pone.0204241 (2018).

[3]

Wang, Y. et al. Development of SNP markers based on transcriptome sequences and their application in germplasm identification in radish (Raphanus sativus L.). Mol. Breed. 37, 26 (2017).

[4]

Budahn, H. et al. Molecular mapping in oil radish (Raphanus sativus L.) and QTL analysis of resistance against beet cyst nematode (Heterodera schachtii). Theor. Appl. Genet. 118, 775-782 (2009).

[5]

Shirasawa, K. et al. An EST-SSR linkage map of Raphanus sativus and comparative genomics of the Brassicaceae. DNA Res. 18, 221-232 (2011).

[6]

Tsuro, M., Suwabe, K., Kubo, N., Matsumoto, S. & Hirai, M. Construction of a molecular linkage map of radish (Raphanus sativus L.), based on AFLP and Brassica-SSR markers. Breed. Sci. 55, 107-111 (2005).

[7]

Ashrafi, H. et al. De novo assembly of the pepper transcriptome (Capsicum annuum): a benchmark for in silico discovery of SNPs, SSRs and candidate genes. BMC Genomics 13, 1-15 (2012).

[8]

Blanca, J. et al. Transcriptome characterization and high throughput SSRs and SNPs discovery in Cucurbita pepo (Cucurbitaceae). BMC Genomics 12, 1-15 (2011).

[9]

Gramazio, P. et al. Transcriptome analysis and molecular marker discovery in Solanum incanum and S. aethiopicum, two close relatives of the common eggplant (Solanum melongena) with interest for breeding. BMC Genomics 17, 300 (2016).

[10]

Pingault, L. et al. Deep transcriptome sequencing provides new insights into the structural and functional organization of the wheat genome. Genome Biol. 16, 29 (2015).

[11]

Nie, S. et al. De novo transcriptome analysis in radish (Raphanus sativus L.) and identification of critical genes involved in bolting and flowering. BMC Genomics https://doi.org/10.1186/s12864-016-2633-2 (2016).

[12]

Jo, I. H. et al. De novo transcriptome assembly and the identification of gene-associated single-nucleotide polymorphism markers in Asian and American ginseng roots. Mol. Genet. Genomics 290, 1055-1065 (2015).

[13]

Varshney, R. K. in Molecular Techniques in Crop Improvement (eds Jain, S. M. & Brar, D. S.) 119-142 (Springer, Dordrecht, 2010).

[14]

Li, F. et al. Extensive chromosome homoeology among Brassiceae species were revealed by comparative genetic mapping with high-density EST-based SNP markers in radish (Raphanus sativus L.). DNA Res. 18, 401-411 (2011).

[15]

Fornara, F., de Montaigu, A. & Coupland, G. SnapShot: control of flowering in Arabidopsis. Cell 141, 550- 550 (2010).

[16]

Wang, J. et al. Genome-wide identification, characterization, and evolutionary analysis of flowering genes in radish (Raphanus sativus L.). BMC Genomics 18, 981 (2017).

[17]

Jeong, Y. M. et al. Elucidating the triplicated ancestral genome structure of radish based on chromosome-level comparison with the Brassica genomes. Theor. Appl. Genet. 129, 1357-1372 (2016).

[18]

Ahn, Y. K. et al. Whole genome resequencing of Capsicum baccatum and Capsicum annuum to discover single nucleotide polymorphism related to powdery mildew resistance. Sci. Rep. 8, 1-11 (2018).

[19]

Hiremath, P. J. et al. Large-scale development of cost-effective SNP marker assays for diversity assessment and genetic mapping in chickpea and comparative mapping in legumes. Plant Biotechnol. J. 10, 716-732 (2012).

[20]

Jian, H. et al. Joint QTL mapping and transcriptome sequencing analysis reveal candidate flowering time genes in Brassica napus L.. BMC Genomics 20, 1-14 (2019).

[21]

Jeon, J. et al. Transcriptome analysis and metabolic profiling of green and red kale (Brassica oleracea var. acephala) seedlings. Food Chem. 241, 7-13 (2018).

[22]

Kong, X. M. et al. Transcriptome analysis of harvested bell peppers (Capsicum annuum L.) in response to cold stress. Plant Physiol. Biotech. 139, 314-324 (2019).

[23]

Wang, Y. et al. Transcriptome profiles reveal new regulatory factors of anthocyanin accumulation in a novel purple-colored cherry tomato cultivar Jinling Moyu. Plant Growth Regul. 87, 9-18 (2019).

[24]

Kim, J. et al. Development of a high-throughput SNP marker set by transcriptome sequencing to accelerate genetic background selection in Brassica rapa. Hortic. Environ. Biotech. 57, 280-290 (2016).

[25]

Manivannan, A. et al. Next-generation sequencing approaches in genome-wide discovery of single nucleotide polymorphism markers associated with pungency and disease resistance in pepper. BioMed. Res. Int. https://doi.org/10.1155/2018/5646213 (2018).

[26]

Wang, B. et al. Developing single nucleotide polymorphism (SNP) markers from transcriptome sequences for identification of longan (Dimocarpus longan) germplasm. Hortic. Res. 2, 14065 (2015).

[27]

Lee, J. H. et al. SNP discovery of Korean short day onion inbred lines using double digest restriction site-associated DNA sequencing. PLoS ONE https://doi.org/10.1371/journal.pone.0201229 (2018).

[28]

Bhardwaj, A., Dhar, Y. V., Asif, M. H. & Bag, S. K. In silico identification of SNP diversity in cultivated and wild tomato species: insight from molecular simulations. Sci. Rep. 6, 1-13 (2016).

[29]

Schiessl, S., Iniguez-Luy, F., Qian, W. & Snowdon, R. J. Diverse regulatory factors associate with flowering time and yield responses in winter-type Brassica napus. BMC Genomics 16, 737 (2015).

[30]

Bluemel, M., Dally, N. & Jung, C. Flowering time regulation in crops-what did we learn from Arabidopsis?. Curr. Opin. Biotech. 32, 121-129 (2015).

[31]

Henderson, I. R. & Dean, C. Control of Arabidopsis flowering: the chill before the bloom. Development 131, 3829-3838 (2004).

[32]

Pico, S., Ortiz-Marchena, M. I., Merini, W. & Calonje, M. Deciphering the role of POLYCOMB REPRESSIVE COMPLEX1 variants in regulating the acquisition of flowering competence in Arabidopsis. Plant Physiol. 168, 1286-1297 (2015).

[33]

Li, C. et al. Genome-wide characterization of the MADS-Box gene family in Radish (Raphanus sativus L.) and assessment of its roles in flowering and floral organogenesis. Front. Plant Sci. https://doi.org/10.3389/fpls.2016.01390 (2016).

[34]

Ertiro, B. T. et al. Comparison of Kompetitive Allele Specific PCR (KASP) and genotyping by sequencing (GBS) for quality control analysis in maize. BMC Genomics 16, 908 (2015).

[35]

Steele, K. A. et al. Accelerating public sector rice breeding with high-density KASP markers derived from whole genome sequencing of indica rice. Mol. Breed. 38, 38 (2018).

[36]

Devran, Z., Goknur, A. & Mesci, L. Development of molecular markers for the Mi-1 gene in tomato using the KASP genotyping assay. Hortic. Environ. Biotechnol. 57, 156-160 (2016).

[37]

Cheon, K. S. et al. Single nucleotide polymorphism (SNP) discovery and Kompetitive allele-specific PCR (KASP) marker development with Korean Japonica rice varieties. Plant Breed. Biotech. 6, 391-403 (2018).

[38]

Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120 (2014).

[39]

Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105-1111 (2009).

[40]

Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078-2079 (2009).

[41]

Voorrips, R. E. MapChart: software for the graphical presentation of linkage maps and QTLs. J. Hered. 93, 77-78 (2002).

[42]

Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80-92 (2012).

[43]

Perrier, X. & Flori, A. in Genetic Diversity of Cultivated Tropical Plants. (eds Hamon, P., Seguin, M., Perrier, X. & Glaszmann J. C.) 47-80 (CRC Press: 2003).

PDF (1772KB)

0

Accesses

0

Citation

Detail

Sections
Recommended

/