GWAS of agronomic traits in soybean collection included in breeding pool in Kazakhstan
Tóm tắt
In recent years soybean is becoming one of the most important oilseed crops in Kazakhstan. Only within the last ten years (2006–2016), the area under soybean is expanded from 45 thousand hectares (ha) in 2006 to 120 thousand ha in 2016. The general trend of soybean expansion is from south-eastern to eastern and northern regions of the country, where average temperatures are lower and growing seasons are shorter. These new soybean growing territories were poorly examined in terms of general effects on productivity level among the diverse sample of soybean accessions. In this study, phenotypic data were collected in three separate regions of Kazakhstan and entire soybean sample was genotyped for identification of marker-trait associations (MTA). In this study, the collection of 113 accessions representing five different regions of the World was planted in 2015–2016 in northern, eastern, and south-eastern regions of Kazakhstan. It was observed that North American accessions showed the highest yield in four out of six trials especially in Northern Kazakhstan in both years. The entire sample was genotyped with 6 K SNP Illumina array. 4442 SNPs found to be polymorphic and were used for whole genome genotyping purposes. Obtained SNP markers data and field data were used for GWAS (genome-wide association study). 30 SNPs appear to be very significant in 42 MTAs in six studied environments. The study confirms the efficiency of GWAS for the identification of molecular markers which tag important agronomic traits. Overall thirty SNP markers associated with time to flowering and maturation, plant height, number of fertile nodes, seeds per plant and yield were identified. Physical locations of 32 identified out of 42 total MTAs coincide well with positions of known analogous QTLs. This result indicates importance of revealed MTAs for soybean growing regions in Kazakhstan. Obtained results would serve as required prerequisite for forming and realization of specific breeding programs towards effective adaptation and increased productivity of soybean in three different regions of Kazakhstan.
Tài liệu tham khảo
Abugalieva S, Didorenko S, Anuarbek S, Volkova L, Gerasimova Y, Sidorik I, Turuspekov Y. Assessment of soybean flowering and seed maturation time in different latitude regions of Kazakhstan. PLoS One. 2016;11(12):1–11.
Didorenko SV, Zakieva AA, Sidorik IV, Abugalieva AI, Kudaibergenov MS, Iskakov AR. Diversification of crop production be means of spreading soybean to the northern regions of the Republic of Kazakhstan. Biosciences biotechnology research. Asia. 2016;13(1):23–30.
Abugalieva S, Didorenko S, Anuarbek S, Volkova L, Gerasimova Y, Sidorik I, Turuspekov Y. Genotype-environment interaction patterns of soybean growing in Kazakhstan. The 1st international workshop “plant genetics nd genomics for food security” (PGGFS-2016), Novosibirsk, Russia. 2016;1.
David Grant D, Nelson RT, Cannon SB, Shoemaker RC. SoyBase, the USDA-ARS soybean genetics and genomics database. Nucleic Acids Res. 2010;38:843–6.
Song Q, Hyten DL, Jia G, Quigley CV, Fickus EW, Nelson RL, et al. Development and Evaluation of SoySNP50K, a High-Density Genotyping Array for Soybean. PLoS ONE. 2013; 8(1); e54985 doi:org/https://doi.org/10.1371/journal.pone.0054985.
Kan G, Zhang W, Yang W, Ma D, Zhang D, Hao D, Hu Z, Yu D. Association. Mapping of soybean seed germination under salt stress. Mol Gen Genomics. 2015;290:2147–62.
Zeng A, Chen P, Korth K, Hancock F, Pereira A, Brye K, Wu C, Shi A. Genome-wide association study (GWAS) of salt tolerance in worldwide soybean germplasm lines. Mol Breeding. 2017;37:30. https://doi.org/10.1007/s11032-017-0634-8.
Hao D, Cheng H, Yin Z, Cui S, Zhang D, Wang H, et al. Identification of single nucleotide polymorphisms and haplotypes associated with yield and yield components in soybean (Glycine max) landraces across multiple environments. Theor Appl Genet. 2012;124:447–58.
Hwang EY, Song Q, Jia G, Specht JE, Hyten DL, Costa JM, et al. A genome-wide association study of seed protein and oil content in soybean. BMC Genomics. 2014;15(1) https://doi.org/10.1186/1471-2164-15-1.
Zhang J, Song Q, Cregan PB, Jiang GL. Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max). Theor Appl Genet. 2016;129:117–30.
Contreras-Soto RI, Mora F, de Oliveira MAR, Higashi W, Scapim CA, Schuster I. A genome-wide association study for agronomic traits in soybean using SNP markers and SNP based haplotype analysis. PLoS One. 2017;12(2) https://doi.org/10.1371/journal.pone.0171105.
Wallace GJ, Zhang X, Beyene Y, Semagn K, Olsen M, Prasanna BM, Buckler ES. Genome-wide Association for Plant Height and Flowering Time across 15 tropical maize populations under managed drought stress and well-watered conditions in sub-Saharan Africa. Crop Sci. 2016;56(5):2365–78.
Bandillo N, Jarquin D, Song Q, Nelson R, Cregan P, Specht J, Lorenz AA. Population structure and genome-wide association analysis on the USDA soybean Germplasm collection. The Plant Genome. 2015;8(3)
Niu Y, Xu Y, Liu XF, Yang SX, Wei SP, Xie FT, Zhang YM. Association mapping for seed size and shape traits in soybean cultivars. Mol Breeding. 2013;31:785–94.
Wen Z, Boyse JF, Song Q, Cregan PB, Wang D. Genomic consequences of selection and genome-wide association mapping in soybean. BMC Genomics. 2015;16:671. https://doi.org/10.1186/s12864–015–1872-y.
Sonah H, O’Donoughue L, Cober E, Rajcan I, Belzile F. Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean. Plant Biotechnol J. 2015;13:211–21.
Wang H, Smith KP, Combs E, Blake T, Horsley RD, Muehlbauer GJ. Effect of population size and unbalanced data sets on QTL detection using genome-wide association mapping in barley breeding germplasm. Theor Appl Genet 2012;124:111–124.
Wang J, Chu S, Zhang H, Zhu Y, Cheng H, Deyue Y. Development and application of a novel genome-wide SNP array reveals domestication history in soybean. Sci Rep. 2016;6(20728):1–10.
Myles S, Peiffer J, Brown P, Ersoz E, Zhang Z, Costich D, Buckler E. Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell. 2009;21(8):2194–202.
Turner MK, Kolmer JA, Pumphrey MO, Bulli P, Chao S, Anderson JA. Association mapping of leaf rust resistance loci in a spring wheat core collection. Theor Appl Genet. 2016;doi https://doi.org/10.1007/s00122-016-2815-y.
Tsubokura Y, Watanabe S, Xia Z, Kanamori H. Yamagata, Kaga a, et al. natural variation in the genes responsible for maturity loci E1, E2, E3 and E4 in soybean. Ann Bot. 2014;113:429–41.
Watanabe S, Hideshima R, Xia Z, Tsubokura Y, Sato S, Nakamoto Y, et al. Map-based cloning of the gene associated with the soybean locus E3. Genetics. 2009;182:1251–62.
Watanabe S, Xia Z, Hideshima R, Tsubokura Y, Sato S, Yamanaka N, et al. A map-based cloning strategy employing a residual heterozygous line reveals that the GIGANTEA gene is involved in soybean maturity and flowering. Genetics. 2011;188(2):395–407.
Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, Nelson W, et al. Genome sequence of the palaeopolyploid soybean. Nature. 2010;463:178–83.
Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in excel. Population genetic software for teaching and research – an update. Bioinformatics. 2012;28:2537–9.
Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14:2611–20.
Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ESTASSEL. Software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23:2633–5.