Unravelling the complex trait of harvest index in rapeseed (Brassica napus L.) with association mapping

BMC Genomics

Volumn 16, Issue 1, 2015, Pages

  Luo, Xiang ^a   Ma, Chaozhi ^a   Yue, Yao ^a   Hu, Kaining ^a   Li, Yaya ^a   Duan, Zhiqiang ^a   Wu, Ming ^a   Tu, Jinxing ^a   Shen, Jinxiong ^a   Yi, Bin ^a   Fu, Tingdong ^a  

^a HUAZHONG AGRICULTURAL UNIVERSITY   (China)

Author keywords

Association mapping; Brassica napus; Complex traits; Correlation; Harvest index

Indexed keywords

ARTICLE; BIOMASS PRODUCTION; BRANCH NUMBER; CORRELATIONAL STUDY; ENVIRONMENTAL FACTOR; GENE MAPPING; GENETIC ASSOCIATION; GENETIC TRAIT; GENETIC VARIABILITY; GENOTYPE; HARVEST INDEX; NONHUMAN; PLANT GENOME; PLANT HEIGHT; PLANT PARAMETERS; POPULATION STRUCTURE; RAPESEED; SEED YIELD; SINGLE NUCLEOTIDE POLYMORPHISM; BIOMASS; CHROMOSOMAL MAPPING; GENETICS; GROWTH, DEVELOPMENT AND AGING; PHENOTYPE; PLANT BREEDING;

BRASSICA NAPUS;

BIOMASS; BRASSICA NAPUS; CHROMOSOME MAPPING; PHENOTYPE; PLANT BREEDING; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84929176018     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/s12864-015-1607-0     Document Type: Article

References (51)

1. Fu TD, Zhou YM. Progress and future development of hybrid rapeseed in China. Engineering Science. 2013;5(11):13-8.
2. Chhabra ML. Translocation pattern of assimilates in India mustard. Proc 7th Int Rapeseed Cong, May 11-14, Poznan, Poland. 1987;3:804-12.
3. Shen JX, Fu TD, Yang GS. Genetic analysis of rapeseed self-incompatibility lines reveals significant heterosis of different patterns for yield and oil content traits. Plant Breed. 2005;124(2):111-6.
4. Li ZB, Xiao XH, Zhu YG. Studies and practice of Hybrid Rice. Shanghai: Shanghai Science and Technology Press; 1982 (in Chinese).
5. Li P, Zhu LH, Zhu YG. Characterization of QTLs for Harvest Index and Source-sink Characters in a DH Population of Rice (Oryza sativa L.). Acta Genetica Sinica. 2003;30(12):1118-26.
6. Sinclair TR. Historical changes in harvest index and crop nitrogen accumulation. Crop Sci. 1998;38(3):638-43.
7. Marri PR, Sarla N, Reddy LV, Siddiq EA. Identification and mapping of yield and yield related QTLs from an Indian accession of Oryza rufipogon. BMC Genet. 2005;6(1):33.
8. Sabouri H, Sabouri A, Dadras AR. Genetic dissection of biomass production, harvest index and panicle characteristics in indica-indica crosses of Iranian rice (Oryza sativa L.) cultivars. Aust J Crop Sci. 2009;3(3):155-66.
9. Can ND, Yoshida T. Genotypic and phenotypic variances and covariances in early maturing grain sorghum in a double cropping. Plant Prod Sci. 1999;2(1):67-70.
10. Mohammad D, Cox PB, Posler GL, Kirkham MB, Hussain A, Khan S. Correlation of characters contributing to grain and forage yield and forage quality in sorghum (sorghum bicolor). Indian J Agr Sci. 1993;63:92-5.
11. Soltani A, Rezai AM, Khajeh Pour MR. Genetic variability for some physiological and agronomic traits in grain Sorghum (Sorghum bicolor L.). JWSS-Isfahan University of Technology. 2001;5(1):127-37.
12. Hay RKM. Harvest index: a review of its use in plant breeding and crop physiology. Ann Appl Biol. 1995;126(1):197-216.
13. Nordborg M, Weigel D. Next-generation genetics in plants. Nature. 2008;456:720-3.
14. Nordborg M, Tavare S. Linkage disequilibrium: what history has to tell us. Trends Genet. 2002;18:83-90.
15. Tian F, Bradbury PJ, Brown PJ. Genome-wide association study of maize identifies genes affecting leaf architecture. Nat Genet. 2011;43:159-62.
16. Kump KL, Bradbury PJ, Wisser RJ. Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet. 2011;43(2):163-8.
17. Poland JA, Bradbury PJ, Buckler ES. Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proc Natl Acad Sci. 2011;108(17):6893-8.
18. Li H, Peng Z, Yang X. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet. 2013;45(1):43-50.
19. Chen ZJ. Genomic and epigenetic insights into the molecular bases of heterosis. Nat Rev Genet. 2013;14:471-82.
20. Atwell S, Huang YS, Vilhjálmsson BJ. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature. 2010;465(7298):627-31.
21. Li F, Chen B, Xu K, Wu J, Song W, Bancroft I, Harper AL, Trick M, Liu S, Gao G, Wang N, Yan G, Qiao J, Li J, Li H, Xiao X, Zhang T, Wu X. Genome-wide association study dissects the genetic architecture of seed weight and seed quality in rapeseed (Brassica napus L.). DNA research. 2014; 21(4): 355-67.
22. Cai D, Xiao Y, Yang W. Association mapping of six yield-related traits in rapeseed (Brassica napus L.). Theor Appl Genet. 2014;127(1):85-96.
23. Li N, Shi J, Wang X. A combined linkage and regional association mapping validation and fine mapping of two major pleiotropic QTLs for seed weight and silique length in rapeseed (Brassica napus L.). BMC Plant Biol. 2014;14(1):114.
24. Huang X, Wei X, Sang T. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet. 2010;42(11):961-7.
25. Chen W, Gao Y, Xie W. Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet. 2014;46(7):714-21.
26. Aranzana MJ, Kim S, Zhao K. Genome-wide association mapping in Arabidopsis identifies previously known flowering time and pathogen resistance genes. PLoS Genet. 2005;1(5), e60.
27. Yang N, Lu Y, Yang X, Huang J, Zhou Y. Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet. 2014;10(9), e1004573.
28. Korir PC, Zhang J, Wu K, Zhao T, Gai J. Association mapping combined with linkage analysis for aluminum tolerance among soybean cultivars released in Yellow and Changjiang River Valleys in China. Theor Appl Genet. 2013;126(6):1659-75.
29. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945-59.
30. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14(8):2611-20.
31. Jakobsson M, Rosenberg NA. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics. 2007;23(14):1801-4.
32. Rosenberg N. DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes. 2004;4:137-8.
33. Nei M, Takezaki N. Estimation of Genetic Distances and Phylogenetic Trees from DNA Analysis. In: Proceedings of the Fifth World Congress on Genetics Applied to Livestock Production. Guelph, Ontario, Canada: University of Guelph; 1983. p. 405-12.
34. Rohlf FJ. Multivariate Analysis System, Version 2.10 e. Applied Biostatistics. 2000; Inc, New York.
35. Yu J, Pressoir G, Briggs WH. A unified mixedmodel method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006;38:203-8.
36. Yang X, Yan J, Shah T. Genetic analysis and characterization of a new maize association mapping panel for quantitative trait loci dissection. Theor Appl Genet. 2010;121:417-31.
37. Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Comput Graph Stat. 1996;5(3):299-314.
38. Hasan M, Friedt W, Pons-Kühnemann J, Freitag NM, Link K, Snowdon RJ. Association of gene-linked SSR markers to seed glucosinolate content in oilseed rape (Brassica napus ssp. napus). Theor Appl Genet. 2008;116(8):1035-49.
39. Yang J, Zhang J. Crop management techniques to enhance harvest index in rice. J Exp Bot. 2010;61:3177-89.
40. Board JE, Modali H. Dry matter accumulation predictors for optimal yield in soybean. Crop Sci. 2005;45(5):1790-9.
41. Yazdani F, Allahdadi I, Akbari GA. Impact of superabsorbent polymer on yield and growth analysis of soybean (Glycine max L.) under drought stress condition. Pak J Biol Sci. 2007;10(23):4190-6.
42. Zhu XG, Long SP, Ort DR. Improving photosynthetic efficiency for greater yield. Annu Rev Plant Biol. 2010;61:235-61.
43. Yuan W, Guan C. Harvest index in rapeseed affected by a few physiological factors. Zuo Wu Xue Bao. 1996;23(5):580-6.
44. Yuan WZ, Guan CY, Liao AL. Contribution of harvest index to seed yield of rapeseed. J Nat Hunan Norm Univ. 1999;22(1):65-9.
45. D'Andrea KE, Otegui ME, de la Vega AJ. Multi-attribute responses of maize inbred lines across managed environments. Euphytica. 2008;162(3):381-94.
46. Li X, Yan W, Agrama H. Unraveling the complex trait of harvest index with association mapping in rice (Oryza sativa L.). PLoS ONE. 2012;7(1):e29350.
47. Peng S, Cassman KG, Virmani SS. Yield potential trends of tropical rice since the release of IR8 and the challenge of increasing rice yield potential. 1999. p. 1552-9.
48. Zhang Q. Strategies for developing green super rice. Proc Natl Acad Sci. 2007;104(42):16402-9.
49. Li YY, Shen JX, Wang TH, Chen QF, Zhang XG, Fu TD, et al. QTL analysis of yield-related traits and their association with functional markers in Brassica napus.L. Aust J Agr Res. 2007;58(8):759-66.
50. Li F, Ma C, Wang X. Characterization of sucrose transporter alleles and their association with seed yield-related traits in Brassica napus L. BMC Plant Biol. 2011;11(1):168.
51. Qu Z, Li L, Luo J. QTL mapping of combining ability and heterosis of agronomic traits in rice backcross recombinant inbred lines and hybrid crosses. PLoS ONE. 2012;7(1), e28463.