Genome-wide association study identifies favorable SNP alleles and candidate genes for waterlogging tolerance in chrysanthemums

Abstract

Chrysanthemums are sensitive to waterlogging stress, and the development of screening methods for tolerant germplasms or genes and the breeding of tolerant new varieties are of great importance in chrysanthemum breeding. To understand the genetic basis of waterlogging tolerance (WT) in chrysanthemums, we performed a genome-wide association study (GWAS) using 92,811 single nucleotide polymorphisms (SNPs) in a panel of 88 chrysanthemum accessions, including 64 spray cut and 24 disbud chrysanthemums. The results showed that the average MFVW (membership function value of waterlogging) of the disbud type (0.65) was significantly higher than that of the spray type (0.55) at P < 0.05, and the MFVW of the Asian accessions (0.65) was significantly higher than that of the European accessions (0.48) at P < 0.01. The GWAS performed using the general linear model (GLM) and mixed linear model (MLM) identified 137 and 14 SNP loci related to WT, respectively, and 11 associations were commonly predicted. By calculating the phenotypic effect values for 11 common SNP loci, six highly favorable SNP alleles that explained 12.85–21.85% of the phenotypic variations were identified. Furthermore, the dosage-pyramiding effects of the favorable alleles and the significant linear correlations between the numbers of highly favorable alleles and phenotypic values were identified (r² = 0.45; P < 0.01). A major SNP locus (Marker6619-75) was converted into a derived cleaved amplified polymorphic sequence (dCAPS) marker that cosegregated with WT with an average efficiency of 78.9%. Finally, four putative candidate genes in the WT were identified via quantitative real-time PCR (qRT-PCR). The results presented in this study provide insights for further research on WT mechanisms and the application of molecular marker-assisted selection (MAS) in chrysanthemum WT breeding programs.

References

[1] Teixeira da Silva, J. A. et al. Chrysanthemum biotechnology: Quo vadis?. Crit. Rev. Plant Sci. 32, 21-52 (2013).

[2] Yin, D. M., Chen, S. M., Chen, F., Guan, Z. Y. & Fang, W. M. Morphological and physiological responses of two chrysanthemum cultivars differing in their tolerance to waterlogging. Environ. Exp. Bot. 67, 87-93 (2009).

[3] Zhang, X. et al. Identification of major QTL for waterlogging tolerance using genome-wide association and linkage mapping of maize seedlings. Plant. Mol. Biol. Rep. 31, 594-606 (2013).

[4] Valliyodan, B. et al. Genetic diversity and genomic strategies for improving drought and waterlogging tolerance in soybeans. J. Exp. Bot. 68, 1835-1849 (2017).

[5] Zhang, X. et al. A new major-effect QTL for waterlogging tolerance in wild barley (H. spontaneum). Theor. Appl. Genet. 130, 1559-1568 (2017).

[6] Soltani, A. et al. Genetic architecture of flooding tolerance in the dry bean middle-american diversity panel. Front. Plant Sci. 8, 1183 (2017).

[7] Su, J. et al. Genetic variation and association mapping of waterlogging tolerance in chrysanthemum. Planta 244, 1241-1252 (2016).

[8] Mccouch, S. R. et al. Development of genome-wide SNP assays for rice. Breed. Sci. 60, 524-535 (2010).

[9] Baird, N. A. et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE 3, e3376 (2008).

[10] Elshire, R. J. et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6, e19379 (2011).

[11] Sun, X. et al. SLAF-seq: an efficient method of large-scale de novo SNP discovery and genotyping using high-throughput sequencing. PLoS ONE 8, e58700 (2013).

[12] Dacosta, J. M. & Sorenson, M. D. Amplification biases and consistent recovery of loci in a double-digest RAD-seq protocol. PLoS ONE 9, e106713 (2014).

[13] Agarwal, M., Shrivastava, N. & Padh, H. Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep. 27, 617-631 (2008).

[14] Huang, X. et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet. 42, 961-967 (2010).

[15] Horton, M. W. et al. Genome-wide association study of Arabidopsis thaliana leaf microbial community. Nat. Commun. 5, 5320 (2014).

[16] Revilla, P. et al. Association mapping for cold tolerance in two large maize inbred panels. BMC Plant Biol. 16, 127 (2016).

[17] Zanke, C. D. et al. Analysis of main effect QTL for thousand grain weight in European winter wheat (Triticum aestivum L.) by genome-wide association mapping. Front. Plant Sci. 6, 644 (2015).

[18] Schulz, D. F. et al. Genome-wide association analysis of the anthocyanin and carotenoid contents of rose petals. Front. Plant Sci. 7, 1798 (2016).

[19] Klie, M., Menz, I., Linde, M. & Debener, T. Strigolactone pathway genes and plant architecture: association analysis and QTL detection for horticultural traits in chrysanthemum. Mol. Genet. Genom. 291, 957-969 (2016).

[20] Li, P. et al. Genetic diversity, population structure and association analysis in cut chrysanthemum (Chrysanthemum morifolium Ramat.). Mol. Genet. Genom. 291, 1117-1125 (2016).

[21] Chong, X. et al. A SNP-enabled assessment of genetic diversity, evolutionary relationships and the identification of candidate genes in chrysanthemum. Genome Biol. Evol. 8, 3661-3671 (2016).

[22] Bertin, I., Zhu, J. H. & Gale, M. D. SSCP-SNP in pearl millet-a new marker system for comparative genetics. Theor. Appl. Genet. 110, 1467-1472 (2005).

[23] Lehmensiek, A., Sutherland, M. W. & Mcnamara, R. B. The use of high resolution melting (HRM) to map single nucleotide polymorphism markers linked to a covered smut resistance gene in barley. Theor. Appl. Genet. 117, 721-728 (2008).

[24] Lei, T. G. et al. Development of CAPS markers and allele-specific PCR primers in citrus. Acta Hortic. Sin. 39, 1027-1034 (2012).

[25] Lestari, P. & Koh, H. J. Development of new CAPS/dCAPS and SNAP markers for rice eating quality. Hayati J. Biosci. 20, 15-23 (2013).

[26] Semagn, K., Babu, R., Hearne, S. & Olsen, M. Single nucleotide polymorphism genotyping using kompetitive allele specific PCR (KASP): overview of the technology and its application in crop improvement. Mol. Breed. 33, 1-14 (2014).

[27] Neff, M. M., Turk, E. & Kalishman, M. Web-based primer design for single nucleotide polymorphism analysis. Trends Genet. 18, 613-615 (2002).

[28] Yanagisawa, T., Kiribuchi-Otobe, C., Hirano, H., Suzuki, Y. & Fujita, M. Detection of single nucleotide polymorphism (SNP) controlling the waxy character in wheat by using a derived cleaved amplified polymorphic sequence (dCAPS) marker. Theor. Appl. Genet. 107, 84-88 (2003).

[29] De Castro, A. P., Blanca, J. M., Díez, M. J. & Vinals, F. N. Identification of a CAPS marker tightly linked to the Tomato yellow leaf curl disease resistance gene Ty-1 in tomato. Eur. J. Plant Pathol. 117, 347-356 (2007).

[30] Di, H. et al. Development of SNP-based dCAPS markers linked to major head smut resistance quantitative trait locus qHS2.09 in maize. Euphytica 202, 69-79 (2015).

[31] Kushanov, F. N. et al. Development, genetic mapping and QTL association of cotton PHYA, PHYB, and HY5-specific CAPS and dCAPS markers. BMC Genet. 17, 141 (2016).

[32] Saitou, N. & Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406-425 (1987).

[33] Tamura, K. et al. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol. Biol. Evol. 28, 2731-2739 (2011).

[34] Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655-1664 (2009).

[35] Hardy, O. J. & Vekemans, X. SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Resour. 2, 618-620 (2002).

[36] de Hoon, M. J., Imoto, S., Nolan, J. & Miyano, S. Open source clustering software. Bioinformatics 20, 1453-1454 (2004).

[37] Bradbury, P. J. et al. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633-2635 (2007).

[38] Kan, G. et al. Association mapping of soybean seed germination under salt stress. Mol. Genet. Genom. 290, 2147-2162 (2015).

[39] Su, J. et al. Dynamic and epistatic QTL mapping reveals the complex genetic architecture of waterlogging tolerance in chrysanthemum. Planta 247, 899-924 (2018).

[40] Murray, M. G. & Thompson, W. F. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 8, 4321-4326 (1980).

[41] Cheng, P. et al. A transcriptomic analysis targeting genes involved in the floral transition of winter-flowering chrysanthemum. J. Plant. Growth Regul. 37, 220-232 (2018).

[42] Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2^−ΔΔCT method . Methods 25, 402-408 (2012).

[43] Zhang, F., Chen, S., Chen, F., Fang, W. & Li, F. A preliminary genetic linkage map of chrysanthemum (Chrysanthemum morifolium) cultivars using RAPD, ISSR and AFLP markers. Sci. Hortic. 125, 422-428 (2010).

[44] Zhang, F. et al. SRAP-based mapping and QTL detection for inflorescence-related traits in chrysanthemum (Dendranthema morifolium). Mol. Breed. 27, 11-23 (2011).

[45] Wang, C. et al. Inheritance and molecular markers for aphid (Macrosiphoniella sanbourni) resistance in chrysanthemum (Chrysanthemum morifolium Ramat.). Sci. Hortic. 180, 220-226 (2014).

[46] van Geest, G. et al. An ultra-dense integrated linkage map for hexaploid chrysanthemum enables multi-allelic QTL analysis. Theor. Appl. Genet. 130, 2527-2541 (2017).

[47] van Geest, G. et al. Conclusive evidence for hexasomic inheritance in chrysanthemum based on analysis of a 183 k SNP array. BMC Genom. 18, 585 (2017).

[48] Zhang, J. et al. Identification of putative candidate genes for water stress tolerance in canola (Brassica napus). Front. Plant Sci. 6, 1058 (2015).

[49] Long, A. D. & Langley, C. H. The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits. Genome Res. 9, 720-731 (1999).

[50] Zhu, C., Gore, M., Buckler, E. S. & Yu, J. Status and prospects of association mapping in plants. Plant Genome 1, 5-20 (2008).

[51] Upadhyaya, H. D., Wang, Y. H., Gowda, C. L. L. & Sharma, S. Association mapping of maturity and plant height using SNP markers with the sorghum mini core collection. Theor. Appl. Genet. 126, 2003-2015 (2013).

[52] Dang, X. et al. QTL detection and elite alleles mining for stigma traits in Oryza sativa by association mapping. Front. Plant Sci. 7, 1188 (2016).

[53] Zhou, Q. et al. Genome-wide SNP markers based on SLAF-Seq uncover breeding traces in rapeseed (Brassica napus L.). Front. Plant Sci. 8, 648 (2017).

[54] Zhang, J. et al. Genome-wide association mapping for tomato volatiles positively contributing to tomato flavor. Front. Plant Sci. 6, 1042 (2015).

[55] Su, J. et al. Detection of favorable QTL alleles and candidate genes for lint percentage by GWAS in Chinese upland cotton. Front. Plant Sci. 7, 1576 (2016).

[56] Wu, J. et al. Genome-wide association study identifies new loci for resistance to sclerotinia stem rot in Brassica napus. Front. Plant Sci. 7, 1418 (2016).

[57] Su, J. et al. Identification of favorable SNP alleles and candidate genes for traits related to early maturity via GWAS in upland cotton. BMC Genom. 17, 687 (2016).

[58] Li, L. et al. A genome-wide association study reveals new loci for resistance to clubroot disease in Brassica napus. Front. Plant Sci. 7, 1483 (2016).

[59] Su, J. et al. Combining ability, heterosis, genetic distance and their inter-correlations for waterlogging tolerance traits in chrysanthemum. Euphytica 213, 42 (2017).

[60] Zhang, F., Jiang, J., Chen, S., Chen, F. & Fang, W. Detection of quantitative trait loci for leaf traits in chrysanthemum. J. Hortic. Sci. Biotech. 87, 613-618 (2012).

[61] Zhang, F., Jiang, J., Chen, S., Chen, F. & Fang, W. Mapping single-locus and epistatic quantitative trait loci for plant architectural traits in chrysanthemum. Mol. Breed. 30, 1027-1036 (2012).

[62] Fu, X. et al. Genetic variation and association mapping of aphid (Macrosiphoniella sanbourni) resistance in chrysanthemum (Chrysanthemum morifolium Ramat.). Euphytica 214, 21 (2018).

[63] Zhao, J., Chen, S. & Chen, F. Conversion of RAPD marker linked to creep plant type in ground-cover chrysanthemum to SCAR marker. Sci. Silvae Sin. 45, 147-150 (2009).

[64] Shi, X. H. et al. Development and utilization of CAPS/dCAPS markers based on the SNPs lying in soybean cyst nematode resistant genes Rhg4. Acta Agron. Sin. 41, 1463-1471 (2015).

[65] Zhu, W. W. et al. Development and verification of a CAPS marker linked to tuber shape gene in potato. Acta Agron. Sin. 41, 1529-1536 (2015).

[66] Stone, J. M. & Walker, J. C. Plant protein kinase families and signal transduction. Plant Physiol. 108, 451-457 (1995).

[67] Murata, N., Mohanty, P. S., Hayashi, H. & Papageorgiou, G. C. Glycinebetaine stabilizes the association of extrinsic proteins with the photosynthetic oxygen-evolving complex. FEBS Lett. 296, 187-189 (1992).

[68] Robinson, S. P. & Jones, G. P. Accumulation of glycinebetaine in chloroplasts provides osmotic adjustment during salt stress. Funct. Plant Biol. 13, 659-668 (1986).

[69] Yan, J. P., Liang, Y. & Tan, X. L. Expression of BADH in young root of wheat (Triticum aestivum) under waterlog and low temperature stress. Hubei Agr. Sci. 50, 4804-4806 (2011).

[70] Yan, J. P., Liang, Y. & Tan, X. L. Expression of ADHa and BADH in young root of cotton (Gossypium hirsutum) under waterlogged stress. China Cotton 39, 153-154 (2011).

[71] Hirayama, T. & Oka, A. Novel protein kinase of Arabidopsis thaliana (APK₁) that phosphorylates tyrosine, serine and threonine . Plant Mol. Biol. 20, 653-662 (1992).

[72] Elhaddad, N. S., Hunt, L., Sloan, J. & Gray, J. E. Light-induced stomatal opening is affected by the guard cell protein kinase APK1b. PLoS ONE 9, e97161 (2014).

[73] Hrabak, E. M. et al. The Arabidopsis CDPK-SnRK superfamily of protein kinases. Plant Physiol. 132, 666-680 (2003).

[74] Yoshida, R. et al. The regulatory domain of SRK2E/OST1/SnRK2.6 interacts with ABI1 and integrates abscisic acid (ABA) and osmotic stress signals controlling stomatal closure in Arabidopsis. J. Biol. Chem. 281, 5310-5318 (2006).

[75] Hord, C. L., Chen, C., Deyoung, B. J., Clark, S. E. & Ma, H. The BAM₁/BAM₂ receptor-like kinases are important regulators of Arabidopsis early anther development . Plant Cell 18, 1667-1680 (2006).

[76] Zanella, M. et al. β-amylase 1 (BAM1) degrades transitory starch to sustain proline biosynthesis during drought stress. J. Exp. Bot. 67, 1819-1826 (2016).

[77] Maruyama, K. et al. Metabolic pathways involved in cold acclimation identified by integrated analysis of metabolites and transcripts regulated by DREB1A and DREB2A. Plant Physiol. 150, 1972 (2009).

[78] Jha, A., Saxena, J. & Sharma, V. Investigation on phosphate solubilization potential of agricultural soil bacteria as affected by different phosphorus sources, temperature, salt, and pH. Commun. Soil Sci. Plan. 44, 2443-2458 (2013).

[79] Valerio, C. et al. Thioredoxin-regulated β-amylase (BAM1) triggers diurnal starch degradation in guard cells, and in mesophyll cells under osmotic stress. J. Exp. Bot. 62, 545-555 (2011).

[80] Monroe, J. D. et al. β-Amylase1 and β-Amylase3 are plastidic starch hydrolases in Arabidopsis that seem to be adapted for different thermal, pH, and stress conditions. Plant Physiol. 166, 1748-1763 (2014).