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http://dx.doi.org/10.5352/JLS.2022.32.12.947

Identification of Domesticated Silkworm Varieties Using a Whole Genome Single Nucleotide Polymorphisms-based Decision Tree  

Park, Jong Woo (Department of Agricultural Biology, National Academy of Agricultural Science, Rural Development Administration)
Park, Jeong Sun (Department of Applied Biology, College of Agriculture & Life Sciences, Chonnam National University)
Jeong, Chan Young (Department of Agricultural Biology, National Academy of Agricultural Science, Rural Development Administration)
Kwon, Hyeok Gyu (Department of Agricultural Biology, National Academy of Agricultural Science, Rural Development Administration)
Kang, Sang Kuk (Department of Agricultural Biology, National Academy of Agricultural Science, Rural Development Administration)
Kim, Seong-Wan (Department of Agricultural Biology, National Academy of Agricultural Science, Rural Development Administration)
Kim, Nam-Suk (Department of Agricultural Biology, National Academy of Agricultural Science, Rural Development Administration)
Kim, Kee Young (Department of Agricultural Biology, National Academy of Agricultural Science, Rural Development Administration)
Kim, Iksoo (Department of Applied Biology, College of Agriculture & Life Sciences, Chonnam National University)
Publication Information
Journal of Life Science / v.32, no.12, 2022 , pp. 947-955 More about this Journal
Abstract
Silkworms, which have recently shown promise as functional health foods, show functional differences between varieties; therefore, the need for variety identification is emerging. In this study, we analyzed the whole silkworm genome to identify 10 unique silkworm varieties (Baekhwang, Baekok, Daebaek, Daebak, Daehwang, Goldensilk, Hansaeng, Joohwang, Kumkang, and Kumok) using single nucleotide polymorphisms (SNP) present in the genome as biomarkers. In addition, nine SNPs were selected to discriminate between varieties by selecting SNPs specific to each variety. We subsequently created a decision tree capable of cross-verifying each variety and classifying the varieties through sequential analysis. Restriction fragment length polymorphism (RFLP) was used for SNP867 and SNP9183 to differentiate between the varieties of Daehwang and Goldensilk and between Kumkang and Daebak, respectively. A tetra-primer amplification refractory (T-ARMS) mutation was used to analyze the remaining SNPs. As a result, we could isolate the same group or select an individual variety using the nine unique SNPs from SNP780 to SNP9183. Furthermore, nucleotide sequence analysis for the region confirmed that the alleles were identical. In conclusion, our results show that combining SNP analysis of the whole silkworm genome with the decision tree is of high value as a discriminative marker for classifying silkworm varieties.
Keywords
Markers; silkworm varieties; SNP; T-ARMS;
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1 Kang, P. D., Kim, K. M., Sohn, B. H., Woo, S. O. and Ryu, K. S. 2003. Breeding of a new silkworm variety, Chugangjam, with a sex-limited larval marking and high silk yielding for summer-autumn rearing season. Int. J. Indust. Entomol. 6, 57-61.
2 Kang, P. D., Lee, S. U., Jung, I. H., Shon, B. H., Kim, Y. S., Kim, K. Y., Kim, M. J., Hong, I. P., Lee, K. G. and Park, K. Y. 2007. Breeding of new silkworm variety golden silk, a yellow cocoon color for spring rearing season. Kor. J. Seric. Sci. 49, 14-17.
3 Kim, S. W., Kim, K. Y., Kim, S. R., Kim, S. B., Ji, S. D., Kim, N. S., Kweon, H. Y., Jo, Y. Y. and Kim, J. G. 2018. Breeding of new silkworm variety, 'Chilseongjam' with peculiar larval mark. Int. J. Indust. Entomol. 37, 69-72.   DOI
4 Kim, J. S., Kim, M. J., Kim, H. K., Vung, N. N. and Kim, I. 2019. Development of single nucleotide polymorphism markers specific to Apis mellifera (Hymenoptera: Apidae) line displaying high hygienic behavior against Varroa destructor, an ectoparasitic mite. J. Asia Pac. Entomol. 22, 1031-1039.   DOI
5 Kim, M. J., Park, J. S., Kim, H., Kim, S. R., Kim, S. W., Kim, K. Y., Kwak, W. and Kim, I. 2022. Phylogeographic relationships among Bombyx mandarina (Lepidoptera: Bombycidae) populations and their relationships to B. mori inferred from mitochondrial genomes. Biology 11, 68.   DOI
6 Kim, S. W., Kim, M. J., Kim, S. R., Park, J. S., Kim, K. Y., Kim, K. H., Kwak, W. and Kim, I. 2022. Whole-genome sequences of 37 breeding line Bombyx mori strains and their phenotypes established since 1960s. Sci. Data. 9, 189.   DOI
7 Kowalczyk, M., Staniszewski, A., Kaminska, K., Domaradzki, P. and Horecka, B. 2021. Advantages, possibilities, and limitations of mitochondrial DNA analysis in molecular identification. Folia Biologica 69, 101-111.
8 Park, J. W., Lee, C. H., Jeong, C. Y., Kang, S. K., Ju, W. T., Kim, S. W., Kim, N. S., Kweon, H. Y. and Kim, K. Y. 2021. Comparison of antioxidant activity according to silkworm cultivars. J. Life Sci. 31, 1010-1018.   DOI
9 Park, J. S., Kim, M. J., Kim, S. W., Kim, K. Y., Kim, S. R. and Kim, I. 2022 Molecular identification of the strains of the domestic silkworm, Bombyx mori (Lepidoptera: Bombycidae), which are endemic to Korea, based on single nucleotide polymorphisms in mitochondrial genome sequences. J. Asia-Pac. Entomol. 25, 101922.   DOI
10 Partis, L., Croan, D., Guo, Z., Clark, R., Coldham, T. and Murby, J. 2000. Evaluation of a DNA fingerprinting method for determining the species origin of meats. Meat. Sci. 54, 369-376.   DOI
11 Salzberg, S. L. 1994. C4.5: programs for machine learning. Machine Learning 16, 235-240.   DOI
12 Ryu, K. S., Lee, H. S., Chung, S. H. and Kang, P. D. 1997. An activity of lowering blood-glucose levels according to preparative conditions of silkworm powder. Kor. J. Seric. Sci. 39, 79-85.
13 Son, J. H., Cheong, Y. K., Park, J. C., Kim, Y. K., Kim, K. H., Park, T. I., Kim, B. K. and Kang, C. S. 2017. Construction of complete DNA marker set for 32 Korean wheat cultivar identification. J. Kor. Int. Agric. 29, 150-154.   DOI
14 Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. H. 2009. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11, 10-18.   DOI
15 Harada, C. 1961. Heterosis of the quantitative characters in the silkworm. Bull. Seric. Exp. Sta. 17, 1-52.
16 Hong, K. W., Ryu, K. S., Hwang, S. J., Sohn, B. H., Kang, P. D. and Choi, S. R., et al. 1996. Breeding of Kumokjam, an artificial diet adaptable silkworm variety for spring rearing season. RDA J. Agric. Sci. 38, 801-805.
17 Vasimuddin, M., Misra, S., Li, H. and Aluru, S. 2019. Efficient architecture-aware acceleration of BWA-MEM for multicore systems. In 2019 IEEE International Parallel and Distributed Processing Symposium pp. 314-324.
18 Bosco, D., Loria, A., Sartor, C. and Cenis, J. L. 2006. PCR-RFLP identification of Bemisia tabaci biotypes in the Mediterranean Basin. Phytoparasitica 34, 243-251.   DOI
19 Kang, P. D., Jung, I. Y., Kim, K. Y., Kim, M. J., Sohn, B. H. and Lee, K. G. 2010. Breeding of new silkworm variety with peculiar laval mark "Eolrukmal, Hukpyobeom". Int. J. Indust. Entomol. 20, 115-116.
20 Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G. and Durbin, R. 2009. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078-2079.   DOI
21 Ahn, H. Y., Cha, J. Y., Park, K. R., Kim, Y. R. and Cho, Y. S. 2013. Improvement effect of fermented silkworm (Bombyx mori L.) power against orotic acid-induced fatty liver in rats. J. Life Sci. 23, 789-795.   DOI
22 Bolger, A. M., Lohse, M. and Usadel, B. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120.   DOI
23 Ji, S. D., Kim, N. S., Kweon, H. Y., Choi, B. H., Kim, K. Y. and Koh, Y. H. 2016. Nutrition composition differences among steamed and freeze-dried mature silkworm larval powders made from 3 Bombyx mori varieties weaving different colored cocoons. Int. J. Indust. Entomol. 33, 6-14.   DOI
24 Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banksn, E., DePristo, M. A., Handsaker, R. E., Lunter, G., Marth, G. T., Sherry, S. T., McVean, G. and Durbin, R. 2011. The variant call format and VCFtools. Bioinformatics 27, 2156-2158.   DOI
25 Groeneveld, L. F., Lenstra, J. A., Eding, H., Toro, M. A., Scherf, B., Pilling, D., Negrini, R., Finlay, E. K., Jianlin, H., Groeneveld, E., Weigend, S. and Globaldiv, C. 2010. Genetic diversity in farm animals-a review. Anim. Genet. 41, 6-31.   DOI
26 Medrano, R. F. V. and Oliveira, C. A. 2014. Guidelines for the tetra-primer ARMS-PCR technique development. Mol. Biotechnol. 56, 599-608.   DOI