QTL mapping of root and aboveground biomass in the Brassica C genome using a B. napus population carrying genome content introgressed from B. oleracea
- 44 Downloads
Root and aboveground biomass are important traits for resource acquisition and play an important role in the plant’s competitive ability and contribute to seed yield. A linkage map of the C genome of Brassica napus was constructed using a doubled haploid population, derived from cross between a B. napus line RIL144 carrying genome content introgressed from B. oleracea and a B. napus cultivar Hi-Q, and using SNP and SSR markers. The mapping population was evaluated for these two traits in a growth chamber set at 18/8 °C and 16 h photoperiod. Variation for both traits including transgressive segregation for root biomass was found. Ten QTL on chromosomes C1, C2, C6, C7 and C9 affecting root biomass and seven QTL on C1, C2, C4 and C8 affecting aboveground biomass were detected; among these, QTL allele of C2 and C6 of RIL-144 increased root biomass. Two additive × additive epistatic interactions were detected for aboveground biomass; the epistatic effects were 2–3 folds greater than the main effect of the QTL implying that gene interaction plays as an important role for this trait. BLASTn search of the C1, C2, C6 and C9 QTL regions showed homoeology with Arabidopsis thaliana chromosome At1, C9 QTL showed homoeology with At4 and At5, and C7 QTL showed homoeology with At2; these A. thaliana chromosome regions found to carry genes regulating root characteristics. Thus, the molecular markers identified and the knowledge of the genomic regions gained from this research can be used to improve the root and aboveground biomass traits of B. napus.
KeywordsBrassica C genome QTL Root biomass Aboveground biomass Composite interval mapping Association mapping
We thanks the personnel’s from the Canola Program of the University of Alberta including summer students for assistance in different routine operations.
B.K. carried out the research, analyzed data and prepared the first draft. H.R. designed the project, supervised B.K., secured funding, and finalized the paper. Both authors read and approved the final manuscript.
H.R. thanks the Natural Sciences and Engineering Research Council (NSERC), Alberta Innovates Bio Solutions (AI Bio), Alberta Crop Industry Development Fund (ACIDF), Alberta Canola Producers Commission (ACPC), and the industry partner Crop Production Services (CPS) for providing financial support to this project. Financial support from the Canada Foundation of Innovation (CFI) for infrastructure development in the Canola Program, and infrastructure provided by University of Alberta is also gratefully acknowledged.
Compliance with ethical standards
Conflict of interests
The authors declare that they have no competing interests.
- Clarke WE, Higgins EE, Plieske J, Wieseke R, Sidebottom C, Khedikar Y, Batley J, Edwards D, Meng J, Li R, Lawley CT, Pauquet J, Laga B, Cheung W, Iniguez-Luy F, Dyrszka E, Rae S, Stich B, Snowdon RJ, Sharpe AG, Ganal MW, Parkin IA (2016) A high-density SNP genotyping array for Brassica napus and its ancestral diploid species based on optimised selection of single-locus markers in the allotetraploid genome. Theor Appl Genet 129:1887–1899. https://doi.org/10.1007/s00122-016-2746-7 CrossRefPubMedPubMedCentralGoogle Scholar
- Hill J, Becker HC, Tigerstedt PMA (1998) Quantitative and ecological aspects of plant breeding. p. 119–120. Chapman & Hall, LondonGoogle Scholar
- Jordan WR, Dugas WA, Shouse PJ (1983) Strategies for crop improvement drought-prone region (Sorghum bicolor, Triticum aestivum, wheat plant breeding). J.F. Stone and W.O. Willis, editors, Agricultural water management. Elsevier, Amsterdam, the Netherlands, pp 281–299Google Scholar
- Körber N, Bus A, Li J, Higgins J, Bancroft I, Higgins EE, Parkin IAP, Salazar-Colqui B, Snowdon RJ, Stich B (2015) Seedling development traits in Brassica napus examined by gene expression analysis and association mapping. BMC Plant biol 15:136. https://doi.org/10.1186/s12870-015-0496-3
- Kumar M (2016) Impact of climate change on crop yield and role of model for achieving food security. Environ Monit Assess 188:465. https://doi.org/10.1007/s10661-016-5472-3
- Lou P, Zhao J, Kim JS, Shen S, Del Carpio DP, Song X, Jin M, Vreugdenhil D, Wang X, Koornneef M, Bonnema G (2007) Quantitative trait loci for flowering time and morphological traits in multiple populations of Brassica rapa. J Exp Bot 58:4005–4016. https://doi.org/10.1093/jxb/erm255 CrossRefPubMedGoogle Scholar
- MacMillan K, Emrich K, Piepho HP, Mullins CE, Price AH (2006) Assessing the importance of genotype × environment interaction for root traits in rice using a mapping population II: conventional QTL analysis. Theor Appl Genet 113:953–964. https://doi.org/10.1007/s00122-006-0357-4 CrossRefPubMedGoogle Scholar
- Petricka JJ, Winter CM, Benfey PN (2012) Control of Arabidopsis root development. Ann Rev Plant Biol 63:563–590. https://doi.org/10.1146/annurev-arplant-042811-105501 CrossRefGoogle Scholar
- Renard M (2014) Breeding for seed yield and seed quality in oilseed Brassicas: main past successes and main challenges for the future? 19th crucifer genetic workshop. Wuhan, ChinaGoogle Scholar
- Rahman H, Kebede B (2012) Improvement of spring canola Brassica napus (L.) by use of winter canola. J Oilseed Brassica 3:1–17Google Scholar
- Rahman H, Bennett R, Kebede B (2017) Mapping of days to flower and seed yield in spring oilseed Brassica napus carrying genome content introgressed from B. oleracea. Mol breed 37:5. https://doi.org/10.1007/s11032-016-0608-2
- SAS Institute (2012). SAS/Stat User’s Guide, Version 9.4. SAS Institute Inc., Cary, NCGoogle Scholar
- Shi L, Shi T, Broadley MR, White PJ, Long Y, Meng J, Xu F, Hammond JP (2013) High throughput root phenotyping screens identify genetic loci associated with root architectural traits in Brassica napus under contrasting phosphate availabilities. Ann Bot 112:381–389. https://doi.org/10.1093/aob/mcs245 CrossRefPubMedGoogle Scholar
- Thomas CL, Graham NS, Hayden R, Meacham MC, Neugebauer K, Nightingale M, et. al. (2016) High throughput phenotyping (HTP) identifies seedling root traits linked to variation in seed yield and nutrient capture in field-grown oilseed rape (Brassica napus L.). Ann Bot 118:655–665. doi: https://doi.org/10.1093/aob/mcw046 CrossRefGoogle Scholar
- Van Ooijen J, Voorrips R (2006) JoinMap 4.0. Software for the calculation of genetic linkage maps in experimental populations. Kyazma B.V, Wageningen, NetherlandsGoogle Scholar
- Wang J, Dun X, Shi J, Wang X, Liu G, Wang H (2017a) Genetic dissection of root morphological traits related to nitrogen use efficiency in Brassica napus L. under two contrasting nitrogen conditions. Front plant Sci 8:1709. https://doi.org/10.3389/fpls.2017.01709
- Wang X, Chen Y, Thomas CL, Ding G, Xu P, Shi D, Grandke F, Jin K, Cai H, Xu F, Yi B, Broadley MR, Shi L (2017b) Genetic variants associated with the root system architecture of oilseed rape (Brassica napus L.) under contrasting phosphate supply. DNA Res 24:407–417. https://doi.org/10.1093/dnares/dsx013 CrossRefPubMedPubMedCentralGoogle Scholar
- Wang SC, Bastern J, Zeng ZB (2006) Windows QTL cartographer 2.5. Department of statistics, North Carolina state university, Raleigh, NC. http://statgen.ncsu.edu/qtlcart/ WQTLCart.Htm
- Wu J, Yuan Y-X, Zhang X-W, Zhao J, Song X, Li Y, Li X, Sun R, Koornneef M, Aarts MGM, Wang X-W (2008) Mapping QTLs for mineral accumulation and shoot dry biomass under different Zn nutritional conditions in Chinese cabbage (Brassica rapa L. ssp. pekinensis). Plant Soil 310:25–40CrossRefGoogle Scholar
- Zhang Y, Thomas CL, Xiang J, Long Y, Wang X, Zou J, Luo Z, Ding G, Cai H, Graham NS, Hammond JP, King GJ, White PJ, Xu F, Broadley MR, Shi L, Meng J (2016) QTL meta-analysis of root traits in Brassica napus under contrasting phosphorus supply in two growth systems. Sci Rep 6:33113. https://doi.org/10.1038/srep33113 CrossRefPubMedPubMedCentralGoogle Scholar