SNP marker association for incrementing soybean seed protein content
Abstract
Soybean seed protein content (SPC) has been decreasing throughout last decades and DNA marker association has shown its usefulness to improve this trait even in soybean breeding programs that focus primarily on soybean yield and seed oil content (SOC). Aiming to elucidate the association of two SNP markers (ss715630650 and ss715636852) to the SPC, a soybean population of 264 F5-derived recombinant inbred lines (RILs) from a bi-parental cross was tested in four environments. Through the single-marker analysis, the additive effect () and the portion of SPC variation due to the SNPs () for single and multi-environment data were assessed, and transgressive RILs for SPC were observed. The estimates revealed the association of both markers to SPC in most of environments. The marker ss715636852 was more frequently associated to SPC, including multi-environment data, and contributed up to = 1.30% for overall SPC, whereas ss715630650 had significant association just in two locations, with contributions of = 0.76% and = 0.74% to overall SPC in Vic1 and Cap1, respectively. The RIL 84-13 was classified as an elite genotype due to its favorable alleles and high SPC means, which reached 53.78% in Cap1, and 46.33% in MET analysis. Thus, these results confirm the usefulness of the SNP marker ss715636852 in a soybean breeding program for SPC.
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Bandillo, N., Jarquin, D., Song, Q., Nelson, R., Cregan, P., Specht, J., & Lorenz, A. (2015). A Population Structure and Genome-Wide Association Analysis on the USDA Soybean Germplasm Collection. The Plant Genome, 8(3), plantgenome2015.04.0024. https://doi.org/10.3835/plantgenome2015.04.0024
Bolon, Y. T., Joseph, B., Cannon, S. B., Graham, M. A., Diers, B. W., Farmer, A. D., �?� Vance, C. P. (2010). Complementary genetic and genomic approaches help characterize the linkage group I seed protein QTL in soybean. BMC Plant Biology, 10(41), 4�??24. https://doi.org/10.1186/1471-2229-10-41
Brummer, E. C., Graef, G. L., Orf, J., Wilcox, J. R., & Shoemaker, R. C. (1997). Mapping QTL for seed protein and oil content in eight soybean populations. Crop Science, 37(2), 370�??378. https://doi.org/10.2135/cropsci1997.0011183X003700020011x
Cruz, C. D. (2013). GENES - Software para análise de dados em estatística experimental e em genética quantitativa. Acta Scientiarum - Agronomy, 35(3), 271�??276. https://doi.org/10.4025/actasciagron.v35i3.21251
Dellaporta, S. L., Wood, J., & Hicks, J. B. (1983). A plant DNA minipreparation: Version II. Plant Molecular Biology Reporter, 1(4), 19�??21. https://doi.org/10.1007/BF02712670
Hwang, E. Y., Song, Q., Jia, G., Specht, J. E., Hyten, D. L., Costa, J., & Cregan, P. B. (2014). A genome-wide association study of seed protein and oil content in soybean. BMC Genomics, 15(1), 1�??12. https://doi.org/10.1186/1471-2164-15-1
Jun, T. H., Van, K., Kim, M. Y., Lee, S. H., & Walker, D. R. (2008). Association analysis using SSR markers to find QTL for seed protein content in soybean. Euphytica, 162(2), 179�??191. https://doi.org/10.1007/s10681-007-9491-6
Kang, M. S. (1997). Using Genotype-by-Environment Interaction for Crop Cultivar Development. Advances in Agronomy, 62(C), 199�??252. https://doi.org/10.1016/S0065-2113(08)60569-6
Kwon, S. H., & Torrie, J. H. (1964). Heritability of and Interrelationships Among Traits of Two Soybean Populations 1 . Crop Science, 4(2), 196�??198. https://doi.org/10.2135/cropsci1964.0011183x000400020023x
Li, Z., Stewart-Brown, B., Steketee, C., Vaughn, J. (2017). Impact of Genomic Research on Soybean Breeding. In M. K. Nguyen, H. T., Bhattacharyya (Ed.), The Soybean Genome (pp. 111�??129). Springer. https://doi.org/https://doi.org/10.1007/978-3-319-64198-0
Mahmoud, A. A., Natarajan, S. S., Bennett, J. O., Mawhinney, T. P., Wiebold, W. J., & Krishnan, H. B. (2006). Effect of six decades of selective breeding on soybean protein composition and quality: A biochemical and molecular analysis. Journal of Agricultural and Food Chemistry, 54(11), 3916�??3922. https://doi.org/10.1021/jf060391m
Patil, G., Mian, R., Vuong, T., Pantalone, V., Song, Q., Chen, P., �?� Nguyen, H. T. (2017). Molecular mapping and genomics of soybean seed protein: a review and perspective for the future. Theoretical and Applied Genetics, 130(10), 1975�??1991. https://doi.org/10.1007/s00122-017-2955-8
Patil, G., Vuong, T. D., Kale, S., Valliyodan, B., Deshmukh, R., Zhu, C., �?� Nguyen, H. T. (2018). Dissecting genomic hotspots underlying seed protein, oil, and sucrose content in an interspecific mapping population of soybean using high-density linkage mapping. Plant Biotechnology Journal, 16(11), 1939�??1953. https://doi.org/10.1111/pbi.12929
Piper, E. L., & Boote, K. I. (1999). Temperature and cultivar effects on soybean seed oil and protein concentrations. Journal of the American Oil Chemists�?? Society, 76(10), 1233�??1241. https://doi.org/10.1007/s11746-999-0099-y
Rao, C. R. (1973). Linear statistical inference and its applications. In Zeitschrift Angewandte Mathematik und Mechanik (XX, Vol. 57). John Wiley & Sons. https://doi.org/10.1002/zamm.19770570832
R Core Team. (2019). R: A Language and Environment for Statistical Computing Version 3.5.2, R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/index.html
Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21�??33. Retrieved from http://instatmy.org.my/downloads/e-jurnal 2/3.pdf%0Ahttps://www.nrc.gov/docs/ML1714/ML17143A100.pdf
Reinprecht, Y., Poysa, V. W., Yu, K., Rajcan, I., Ablett, G. R., & Pauls, K. P. (2006). Seed and agronomic QTL in low linolenic acid, lipoxygenase-free soybean (Glycine max (L.) Merrill) germplasm. Genome, 49(12), 1510�??1527. https://doi.org/10.1139/G06-112
Rodrigues, J. I. S., de Miranda, F. D., Ferreira, A., Borges, L. L., Ferreira, M. F. da S., Good-God, P. I. V., �?� Moreira, M. A. (2010). Mapeamento de QTL para conteúdos de proteína e óleo em soja. Pesquisa Agropecuaria Brasileira, 45(5), 472�??480. https://doi.org/10.1590/S0100-204X2010000500006
Rodrigues, J. I. S., Arruda, K. M. A., Cruz, C. D., Piovesan, N. D., de Barros, E. G., & Moreira, M. A. (2014). Biometric analysis of protein and oil contents of soybean genotypes in different environments. Pesquisa Agropecuaria Brasileira, 49(6), 475�??482. https://doi.org/10.1590/S0100-204X2014000600009
Santana, D. P., & Moura-Filho, W. (1978). Estudos de solos do Triângulo Mineiro e de Viçosa. I. Mineralogia. Embrapa Milho e Sorgo-Artigo Em Periódico Indexado (ALICE). https://www.embrapa.br/busca-de-publicacoes/-/publicacao/477158/estudos-de-solos-do-triangulo-mineiro-e-de-vicosa-i-mineralogia
Schuster, I., & Cruz, C. D. (2008). Estatística Genômica (2nd ed.). Viçosa: Editora UFV
Singh, R. J. (2017). Botany and Cytogenetics of Soybean. In M. K. Nguyen, H. T., Bhattacharyya (Ed.), The Soybean Genome (pp. 11�??40). Springer. https://doi.org/https://doi.org/10.1007/978-3-319-64198-0
Song, Q., Hyten, D. L., Jia, G., Quigley, C. V., Fickus, E. W., Nelson, R. L., & Cregan, P. B. (2013). Development and Evaluation of SoySNP50K, a High-Density Genotyping Array for Soybean. PLoS ONE, 8(1), 1�??12. https://doi.org/10.1371/journal.pone.0054985
Sebolt, A. M., Shoemaker, R. C., & Diers, B. W. (2000). Analysis of a quantitative trait locus allele from wild soybean that increases seed protein concentration in soybean. Crop Science, 40(5), 1438�??1444. https://doi.org/10.2135/cropsci2000.4051438x
Sediyama, T., Silva, F., & Borém, A. (2015). Soja: do plantio à colheita. Viçosa: Editora UFV
Vaughn, J. N., Nelson, R. L., Song, Q., Cregan, P. B., & Li, Z. (2014). The genetic architecture of seed composition in soybean is refined by genome-wide association scans across multiple populations. G3: Genes, Genomes, Genetics, 4(11), 2283�??2294. https://doi.org/10.1534/g3.114.013433
Wang, X., Jiang, G. L., Green, M., Scott, R. A., Song, Q., Hyten, D. L., & Cregan, P. B. (2014). Identification and validation of quantitative trait loci for seed yield, oil and protein contents in two recombinant inbred line populations of soybean. Molecular Genetics and Genomics : MGG, 289(5), 935�??949. https://doi.org/10.1007/s00438-014-0865-x
Warrington, C. V., Abdel-Haleem, H., Hyten, D. L., Cregan, P. B., Orf, J. H., Killam, A. S., �?� Boerma, H. R. (2015). QTL for seed protein and amino acids in the Benning �? Danbaekkong soybean population. Theoretical and Applied Genetics, 128(5), 839�??850. https://doi.org/10.1007/s00122-015-2474-4
Yesudas, C. R., Bashir, R., Geisler, M. B., & Lightfoot, D. A. (2013). Identification of germplasm with stacked QTL underlying seed traits in an inbred soybean population from cultivars Essex and Forrest. Molecular Breeding, 31(3), 693�??703. https://doi.org/10.1007/s11032-012-9827-3
Zhang, J., Wang, X., Lu, Y., Bhusal, S. J., Song, Q., Cregan, P. B., �?� Jiang, G. L. (2018). Genome-wide Scan for Seed Composition Provides Insights into Soybean Quality Improvement and the Impacts of Domestication and Breeding. Molecular Plant, 11(3), 460�??472. https://doi.org/10.1016/j.molp.2017.12.016
Zhang, Y. H., Liu, M. F., He, J. B., Wang, Y. F., Xing, G. N., Li, Y., �?� Gai, J. Y. (2015). Marker-assisted breeding for transgressive seed protein content in soybean [Glycine max (L.) Merr.]. Theoretical and Applied Genetics, 128(6), 1061�??1072. https://doi.org/10.1007/s00122-015-2490-4
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